3 Geeks and a Law Blog https://www.geeklawblog.com/ Where legal technology, innovation, and creativity is discussed. Mon, 28 Sep 2026 11:08:23 +0000 en-US hourly 1 https://wordpress.org/?v=7.1.1&lxb_maple_bar_source=lxb_maple_bar_source https://geeklawblog.lexblogplatform.com/wp-content/uploads/sites/528/2018/02/cropped-geeks-icon-32x32.png 3 Geeks and a Law Blog https://www.geeklawblog.com/ 32 32 no(c) 20253 Geeks, TGIR, Geek in Review, Legal Technology, Legal Tech, Legal AIGreg Lambert and Marlene Gebauer discuss technology, innovation, and creativity in the legal industry.Where Innovation Meets the Legal IndustryGreg Lambertxlambert@gmail.comGreg Lambert Legal AI Strategy Beyond the Prompt: Maryam Salehijam on Adoption, ROI, and Building What Works https://www.geeklawblog.com/2026/09/legal-ai-strategy-beyond-the-prompt-maryam-salehijam-on-adoption-roi-and-building-what-works.html Mon, 28 Sep 2026 11:08:23 +0000 https://www.geeklawblog.com/?p=19564

Law firms and legal departments face pressure to adopt AI, yet buying another tool does not answer the hardest question: Which problem needs solving? Dr. Maryam Salehijam, founder and CEO of Andarzi, joins TGIR hosts, Greg Lambert and Marlene Gebauer, to discuss how legal teams choose between existing systems, new vendors, and tools built for their own workflows. Her starting point is to observe the work, identify a useful outcome, and then decide what technology belongs in the process.

Salehijam describes legal teams paying for AI platforms while struggling to get consistent use from them. She urges organizations to examine tools they already own and ask vendors for more training before adding another subscription. A legal department’s use of ServiceNow for intake provides one example. The conversation also tackles shifting model capabilities, consumption pricing, and the cost of assigning an expensive model to a routine task.

What counts as a return on legal AI investment? Time and money matter, but Salehijam argues for measuring employee satisfaction as well. Repetitive contract review and document work consume attention lawyers would prefer to spend on judgment, business problems, and client relationships. She describes using anonymous feedback before and after a workflow change to learn whether people’s work has improved, while Greg raises the tension between hours saved and the billable hour.

The discussion turns to local AI models, data security, and the growing interest in AI-native law firms. Salehijam argues for decisions based on the task and the client’s requirements, including a direct conversation with clients about how their information should be handled. Greg presses her on whether smaller firms and large firms face different constraints. Marlene asks whether the emerging “legal engineer” role will endure, prompting a pointed exchange about technical skill, credentials, and what lawyers need to learn.

For teams putting AI into practice, Salehijam favors hands-on learning with peers, repeated use, and work product over a quick certificate. She also warns leaders to account for the time and effort a new workflow demands before promising immediate gains. In the closing questions, she reflects on a legal AI market with fewer new entrants than she expected and predicts more law firms and legal departments will ask whether owning a workflow serves them better than renewing another set of software seats.

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Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

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Transcript

Marlene Gebauer (00:07)
Welcome to the Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (00:14)
And I’m Greg Lambert.

Marlene Gebauer (00:16)
This week we’re joined by Maryam Salehijam, founder and CEO of Andarzi. Maryam started on the technology side of legal way back when AI mostly meant e-discovery and moved to the services side when the technology wasn’t ready and came back when generative AI arrived. Along the way, she held roles at LexisNexis, Axiom, Robin AI, and Junior. Through Andarzi, she helps law firms and legal departments figure out what problem they actually have and what to do about it.

Greg Lambert (00:46)
Yeah, I like the good old days of when AI meant e-discovery. So, Maryam, welcome

Marlene Gebauer (00:51)
Ha ha ha.

Greg Lambert (00:52)
to the Geek in Review. Good to have you here.

Dr. Maryam Salehijam (00:53)
Thank you, Greg. Thank you, Marlene. It’s my pleasure to be here today to speak about my new initiative, Andarzi.

Marlene Gebauer (00:59)
So Maryam, we mentioned the places you’ve been in where most of us know you from, but you recently launched, as you said, your new company, Andarzi. So what inspired you to go out and build it?

Dr. Maryam Salehijam (01:09)
That’s a great question. Well, it honestly came from encouragement from my family and friends. They all said we don’t know why you don’t start your own company. If there’s one person to have her own company it’d be you. That’s literally what it was.

Greg Lambert (01:19)
Stop making other people money and make yourself money, right?

Marlene Gebauer (01:23)
And I’m sorry, like if your family is already like involved in AI and they’re talking about that too.

Dr. Maryam Salehijam (01:30)
Yeah, they said go for it. but really the reason I jumped in is because I kept getting calls from my friends in legal departments and law firms saying, “Hey, you’re in AI, right? And I said, “Yes.” And they said, “We don’t need what you’re selling because we do this other thing, but can you help us with our strategy? We’re trying to figure this out. There’s all of this pressure. If you’re at a legal department from your management at the company, the C-suite. If you’re at a law firm, it’s from your clients. We don’t know what to do with it and we don’t have the time to figure it out on our own. We need somebody to come in who we can trust to tell us what’s going on, where does AI make sense, where does AI not make sense and what should be our strategic plan for the next six months or year, right? What’s gonna happen to us as an industry and that’s where Andarzi was born from. And the name actually comes from the Persian word “andarz”, which means to give advice.

Greg Lambert (02:21)
Very, very nice. And so when they reach out to you with initially, what’s some of the common problems that they’re having that is causing them to reach out to you?

Dr. Maryam Salehijam (02:33)
That’s a great question. So let’s talk about, for example, a decent sized legal department where they don’t have a huge internal engineering team inside of the legal department. Their main issue is they’re getting this mandate to become more efficient, spend less on law firms, right? Hire less. But they don’t really know what tools to choose from. Let’s say they wanna deploy a tool for contract intelligence. There’s at least two hundred tools out there. They all say the same thing.

Greg Lambert (02:59)
Sure.

Marlene Gebauer (03:00)
Ha ha ha.

Dr. Maryam Salehijam (03:01)
They all tell you they’re the best and the pricing varies. How do you choose the right one for you? Right? The second issue is if you’re buying multiple tools, how do you keep on top of everything? How do you keep on top of the training? How do you get your people to use it? I have so many friends inside legal departments at companies that bought from the likes of Harvey and Legora, and they say, “Maryam, our utilization is not where we need ito be for this to make sense. Right? We’re really worried. We’re paying a lot of money, but not really seeing the savings or the efficiency we would hope to see when we went into this. And then, for the law firms who don’t have an internal chief innovation officer or really a team dedicated to figuring it out, they’re going, Well, if I try to buy a tool for every problem I have, I’m spending north of $300,000 a year. And that just doesn’t make sense for me so could I potentially build some of this myself? And I see a lot of conversation coming in of is this something we can build? And so that’s where I go in and say, okay, wait a minute. Before we make a decision, let’s actually figure out, let’s observe your workflows, where is there a quick win for you that you can have with your existing systems? Sometimes they really are underutilizing what they already have. And then where it makes sense we can make the determination between build versus buy. And guess what? If you want to buy I can help you With all the vendor selection, I can help you negotiate those contracts. And if you want to build, I do partner with two agencies that can come in and do the AI build for you.

Greg Lambert (04:23)
Very interesting.

Marlene Gebauer (04:25)
Mm-hmm.

Greg Lambert (04:26)
one of the things that when you and I were talking in prep for this, we were talking about how some of the firms are still focusing in on the prompt engineering that it’s still around the prompt around the chatbot. So what are you seeing, and what are you advising firms and other legal organizations to build and training on that’s beyond the prompt itself. What are you telling them to start focusing in on?

Dr. Maryam Salehijam (05:01)
What I’m telling them is to throw everything they know about technology out the door because the speed at which Gen AI is moving is beyond comprehension. So maybe four months ago we could have said learn how to prompt that’s important, that is no longer a valid skill to have because the systems can prompt themselves. So the way I think about it is think about where cars started where you had to hand crank to get it going and it was really unsafe and there were so many things that could go wrong. To now we have self-driving cars in the shape of Waymos, not even a human in the seat. I mean, they’re also really sketchy if you ask me, but that transition took what 200 years? Gen AI is doing that kind of transformation within less than 12 months. So when it comes to skill set for law firms and legal departments, I really encourage all of my clients to think like they’ve never done before. It’s not like law school where you go to law school and you’ve learned. It’s very much every day there’s a new thing that’s coming out that’s gonna scrap everything you’ve learned about how you know how to do things. And you have to have that mentality to really stay on top of everything.

Marlene Gebauer (06:08)
Yeah, it’s really challenging because, you know, you’re talking about we’re talking about prompt engineering, right? And at one point, like maybe last year, that was actually a job. And now that is not a job anymore. And so for firms trying to plan for, you know, hiring, you know, allied professionals or even trying to determine what kind of skills you know, attorneys should have in their organization. It’s it’s really rather challenging. what kind of feedback are you getting?

Dr. Maryam Salehijam (06:39)
exactly that they feel like they’re constantly behind. Everyone feels behind at all times, which is what I tell them is like actually you’re in the same boat with everyone else, so you’re not behind on anything everyone feels behind. So don’t stress it too much, but also don’t jump. I always tell people don’t jump into hiring people if you’ve never had someone do this for you. Start with a consultant, start with a contractor. If you are not a massive organization, if you are not a massive law firm, if you are not a massive legal department, you do not need a full Time person doing this kind of work for you. And guess what? A lot of your vendors will do most of the heavy lifting. So you just have to ask them. So many people don’t actually talk to their vendors. They buy the product and then they go and they start trying to figure out how to use it internally. And I go, no, the vendor wants to work with you. You’re just afraid of working with them because you think they’re gonna sell you more stuff. Just learn to say no and use your vendors for what they’re good at, which is teaching you how to use their tooling. so that’s kind of the advice I go in with. Again, it’s super practical, right? I’m like, don’t spend any money. try to use what you have try to go to the people learn to say no right yeah don’t spend

Greg Lambert (07:39)
You’re speaking the right language.

Marlene Gebauer (07:41)
Okay.

Dr. Maryam Salehijam (07:42)
anything and that’s like one of the legal departments I love the most because they used to be my client over at Axiom Nutanix that’s their mentality as well they said before we buy a legal intake tool for the front door of our legal department well we have ServiceNow can we use ServiceNow for this and they engineered ServiceNow to do exactly what they needed to do and they did not have to spend hundreds of thousands of dollars to buy a tool to do it. Right? And it’s that kind of mindset that I think is gonna make legal leave the stone ages where we were always seen as not technical people to come into, hey, we’re actually at the frontier of this general AI movement because it makes sense for our business model.

Greg Lambert (08:24)
that’s let me let me kind of read back what I think I heard here. because I think a lot of us are thinking, you know, AI yeah, it’s been around three and you know, three and a half years, going on fourth year now. but it’s still pretty new and we think that we need to jump into whatever the newest and latest and you know the startups are. But from what I’m hearing, you’re saying we’re probably not leveraging our existing relationships enough. Is that is that what I’m hearing?

Dr. Maryam Salehijam (08:54)
One hundred percent. I’ll give you another example. I work with a big pharma company where they simply need to cross-check three data points across fifteen different types of document. just project at a time. They tried to use their own internal tooling and they found it wasn’t working well. But it’s because they were simply prompting it. They didn’t know how to set up some sort of vault within their systems, how to build a skill set, how to build the citation checker, how to do the charting. But their existing Copilot tooling could do all of that. They just didn’t know it could, right? So it’s an education piece that all I had to bridge is the education. Hey, this is how you can do it. They did it. Voila. Didn’t have to buy a tool with a vault function at thousands of dollars just to do something Gen AI on its own if you already have access to Copilot or Google Gemini can do for you.

Marlene Gebauer (09:47)
Yeah, it’s amazing that the you like sometimes we have so many products that we don’t know what we actually have and what it can actually do. interesting. So all right, I’m I’m very interested in this question here because all we’re hearing about is consumption-based pricing. Consumption-based pricing. Consumption-based pricing. now you’re saying tokens are becoming a commodity. So Okay, that goes a little bit against the grain of what we’re hearing and what not so much what we’re hearing, but what seems to be a big concern among law firms and like how are we gonna pay for this. So what happens, you know, to the vendors and their pricing when that all plays out?

Dr. Maryam Salehijam (10:31)
That is a great question. And I actually had a like a mini debate with someone at the bar at running legal like the business because they were

Marlene Gebauer (10:36)
I bet you did.

Greg Lambert (10:37)
Yeah.

Dr. Maryam Salehijam (10:39)
at one of the vendors that does token based pricing and they were talking about how great that is. And I said, That is a terrible business model for your clients and he said, Why? I’m like, There’s no predictability on their bills. This is one of the reasons people get mad at law firms, right? You just send them a bill and they go, oh my God. What is c

Marlene Gebauer (10:55)
Right, like w one time it’s super expensive, then the next time it’s gonna be not as expensive.

Dr. Maryam Salehijam (11:00)
Right.

Marlene Gebauer (11:01)
And we’ve done this before, you know, we’ve done this consumption based pricing before and nobody

Dr. Maryam Salehijam (11:06)
Yes.

Marlene Gebauer (11:06)
liked it.

Dr. Maryam Salehijam (11:07)
Nobody liked it. And I also really dislike this whole thing in legal that I see of like, oh my God, Claude for Legal is the winner. now it’s OpenAI. Let’s write articles about it. Let’s

Greg Lambert (11:15)
Ha ha ha.

Dr. Maryam Salehijam (11:16)
move our skill set. Unlike everybody calm down.

Marlene Gebauer (11:19)
Mm-hmm.

Dr. Maryam Salehijam (11:20)
There’s no winners or losers yet. Let’s just not constantly make deterministic decisions. Again, that you d this is like never before. You cannot apply your same mentality. It’s either Lexis or TR. It’s either this or it’s that. It’s not like that. It’s not black and white. It’s very much gray. Even for the providers, it’s gray. They don’t even know what’s happening after time, right? So when it comes to tokens, we know they’re very resource intensive, right? From data centers, from the cost of compute, from the cloud. It’s very, very expensive. And certain tasks consume a lot more tokens than others. Certain models consume a lot more tokens than others. I also see this fallacy constantly where people are like, why doesn’t Lexis have the latest version of Claude for this? I’m like, because it doesn’t need to. You don’t need the latest models for everything, right? It’s that education piece where we need to really think about what is the task, how simple is this task, right? Or how complex is this task, and what is the cost of utilizing AI on this task for each project? Before we try to,

Marlene Gebauer (12:21)
And is it worth it?

Dr. Maryam Salehijam (12:23)
is it worth it before we try to throw AI on everything? And then my friends who actually work at the Frontier Labs and the actual tech companies, so many of them are moving to local compute power where there’s basically no cost to the token. And that’s the other conversation we need to start having of okay, why not just do it locally? It’s actually more secure a lot of the times. It’s cheaper. and from a technical perspective, also not that hard. So how do we also in think about that as law firms and legal teams if our one of our biggest concerns is secure? Right? Before we just go spend more money buying stuff. So it’s a lot of those conversations of let’s first actually understand what we have available to us before we make a decision on what we’re gonna do with it.

Marlene Gebauer (13:01)
Have you heard any discussion about, you know, some of the some of the vendors are using you know their training on models that are like foreign models, like Chinese models. Like have you heard?

Greg Lambert (13:14)
Yeah.

Marlene Gebauer (13:16)
oh my God, we have to get the eye roll. That was beautiful. You missed the eye roll.

Greg Lambert (13:18)
I was gonna say for the for the for the people that are only listening, you missed probably the best part of the show.

Dr. Maryam Salehijam (13:25)
oh my God.

Marlene Gebauer (13:26)
So wha you know, what are your thoughts on that or what are or what are you hearing from your clients about vendors that are doing that?

Dr. Maryam Salehijam (13:34)
Yeah, great question. So I think everybody wants to sound like they’re going against the grain because it’s cool, right? oh my God, we’re doing it this differently. It’s cheaper, it’s

Marlene Gebauer (13:43)
It’s like it’s cheaper. Okay, it is cheaper.

Dr. Maryam Salehijam (13:45)
this, that. But actually, like this is the other thing where again some of the vendors are gonna hate me after this because I just say what I really think. Why are you trying to build your own LLM? You think you have more resources and knowledge and money than OpenAI or Anthropic? Are you kidding me? It’s never gonna be better. Like, it’s just those things where I’m like, so you’re gonna go trust a Model that hasn’t been vetted at all for our industry for these things. You’re the person vetting it, which doesn’t matter. I don’t care about your personal benchmarks. That’s like me being like, I’m the greatest. Doesn’t matter. my mom thinks I’m great. Doesn’t matter. Against literally companies that have so much money and resources behind them that they can surpass the GDP of a country. Like, let’s get real here. This is this is not logical, right? Why would you do that? So it’s a lot of those things where I go, oh my God, here we go again. Somebody’s like saying something that makes no sense. Like nobody has more resources than OpenAI, Anthropic, or Google. Maybe Microsoft, right? Maybe IBM, but right now, if you’re not plugging in OpenAI or Anthropic, I don’t really think you’re that serious about understanding Gen AI.

Greg Lambert (14:55)
I wanna dig a little deeper on something that you mentioned earlier and that was using local models. are when can you define that for me? Is that something that’s running like literally on your laptop or is it running in your cloud? Well can you define

Dr. Maryam Salehijam (15:13)
Yeah.

Greg Lambert (15:13)
that a little bit more?

Dr. Maryam Salehijam (15:15)
Is running on your machine, or even your phone has compute power, or you can plug in a GPU from NVIDIA or Apple. And So what happens when we need to process data, right? We need compute power. Now you can have compute power via the cloud, but actually the cloud ultimately connects to the data center, or you can have a mini version of your own data center connected to your device. Now, a lot of smaller law firms are already doing that actually. the one-person shops, right? Especially in The IP space because it’s super secure. Data’s going nowhere. Nobody’s seeing the data. But that only works if you actually know what models to select, what the task is, you know how to quality check. It’s not gonna be something you can easily use for general purpose things, like it’s not gonna replace your OpenAI or you know Anthropic subscription. But let’s say you’re constantly reviewing a specific type of contract where you know exactly what to do, you’re writing a specific type of document, it’s perfect for that kind of work because you know what the task. is you know what the model weight needs to be, how complex it needs to be, and you know what the deliverable looks like. So then it’s quite actually easy to do it yourself. The same is true for anything that contains like patient information, right? Medical records, timelines, easy to do it locally, safer. You don’t have to worry about HIPAA compliance.

Greg Lambert (16:30)
And are you seeing like smaller companies, departments that are experimenting with the with the local GPUs to run this?

Dr. Maryam Salehijam (16:40)
Yeah, not and not just the small, right? Latham, I think just announced they bought a whole GPU farm to bring in house. So it’s happening at all levels and I will suspect that’s gonna happen more and more as we struggle with this concept of when do we wanna use AI and not only when, but how do we wanna use that AI. Are we getting our water in a bottle? Are we getting it through the tap? Are we getting it delivered via a truck? How are we gonna use it? What makes sense? for us and what makes sense for the specific task. So I really think people are gonna start thinking more efficiently also about where they’re gonna get their AI from and why.

Greg Lambert (17:20)
So I think if I look back at my predictions for 2026, I think one of the predictions was that law firms and legal departments were going to have to come up with the return on investment and the metrics of you know, we’re we’ve spent so much money in experimenting with AI, but at some point we’re going to be asked about how do we measure the success on that. So what we’d love to measure, you know, it’s like hours saved, money saved, I guess, but what do you think and what are you advising your firms and legal departments on what they should be measuring to show the return on the investment that they’re making?

Dr. Maryam Salehijam (18:06)
I love that question. First of all, let’s talk about the elephant in the room. Law firms measuring time saved.

Greg Lambert (18:13)
What could go wrong with that?

Dr. Maryam Salehijam (18:13)
What?

Marlene Gebauer (18:14)
Yeah.

Dr. Maryam Salehijam (18:15)
Absolutely like logical, everybody wants to do that at a law firm. Don’t worry about billables. Like again, it’s just those things where I’m like, what are we talking about here? but I actually have something that I say which is so against the grain. I talk about happiness. One of the biggest problems in our profession is not how many hours it does take, is that depression, anxiety, burnout. It’s insane and legal. Why are we so miserable? I’m pretty sure none of us went to law school to be miserable. and we have one of the highest rate of suicide.

Greg Lambert (18:43)
Mar Marlene did. She went to law school in Cleveland. So

Marlene Gebauer (18:48)
E everybody in Cleveland I want you to know that Greg said it, not me.

Dr. Maryam Salehijam (18:52)
That’s so funny. No, Cleveland has a good law school. Come on now. I’m sure it was fun. Yeah.

Marlene Gebauer (18:57)
At Cleveland? Yeah, it does.

Dr. Maryam Salehijam (18:59)
but that’s like can we focus on what we really wanna do in legal, not the work we don’t wanna do? Who wants to review an NDA? Come on now. Who wants to do this? Who wants to redline the same provision in the contracts over and over again? Nobody wants to do that. Nobody wants to do doc review. At some point you just lose where you’re at. So it’s just all of this work that I actually consider a poor use of the human brain. It’s not critical tasks. Let’s have these systems do these things for us so we can focus on what really brings us fulfillment. And by being fulfilled, we actually start to get more creative. And we start to solve the business problems and we starto be better partners, not just to our clients as law firms, but also to the clients of the legal department at a company, right? Because at the end of the day, it’s the business that’s your client. And we can become more business people than legal people, because so much of law right now doesn’t consider business, technology. And I think that transformation is gonna be good for our industry and yes it might threaten the billable hour but that doesn’t mean it’s gonna wipe out law firms they just have to rethink how they generate their money, right? And potentially the split of their revenue and where it’s gonna come from and maybe law firms start offering more of the managed service like Eversheds does or Flex by Fenwick does, right? Where they have the actually multiple streams that they can offer their clients outside of just billables for specific projects.

Greg Lambert (20:25)
Yeah. How I know this is a tough question to ask you. but you know it’s easy to measure dollars saved, it’s easy to measure time saved. when you talk about job satisfaction, if you talk you know, those sorts of things, how do you measure that and are there other industries or examples that we could look toward that would help us kind of wrap our minds around how you measure that and what it looks like.

Dr. Maryam Salehijam (20:56)
Absolutely. So you can do the most basic old school way, which is employee satisfaction surveys, right? They’re anonymous. You can survey them before, then a year later. Or actually the company I partner with, what we do before we deploy on a project is we send out voice agents that are conversational, you talk to them. But it’s super anonymous. And people just talk to them about their workflows, what they’re frustrated with, what they like, what they wish they could do, what they wish they couldn’t do. And then we aggregate all that data and build a couple of solutions for them that we think would address the main things people are annoyed about. And then we deploy and then do the voice agents again to see are the problems solved or people happier, what’s going on. and data, you know, even though happiness is hard to measure, data can help you measure that, right? How do people feel? Do they feel fulfilled? What’s going on? I can tell you for in-house teams, the biggest pain point is often their workload. It’s way more work than they anticipated. They don’t have the headcount, right? And so sometimes they end up leaving their company and then that’s so expensive for the company to try to replace them. It’s hundreds of thousands of dollars, right? That retention issue is huge for companies. And with all the layoffs, oh my God, additional mental load. So it’s about creating that safe space for people to do their work, but do their work enthusiastically and as efficiently as they can.

Marlene Gebauer (22:12)
I just think it requires sort of extraordinary leadership and courage and creativity to sort of implement that kind of change management on a large scale because it’s so much easier to just say, Okay, we’re just gonna raise our rates and we’ll just give you a discount and you know, that’s kind of what we’ve been seeing this model for years and years and years, rather than actually fix the model.

Dr. Maryam Salehijam (22:38)
Yeah.

Marlene Gebauer (22:38)
And you know, I know that we have always said, well, maybe this is the time and maybe AI is gonna be what does it but I mean what are you hearing in terms of you know from about this like from clients and like how they’re how they’re trying to trying to do this?

Dr. Maryam Salehijam (22:58)
Yeah, that’s a great question. I think for a long time, businesses, right, they didn’t really know how to get legal work done without lawyers. Right? We were the gatekeepers, we had the knowledge, we had the know how. But now these AI models can go and give advice and I know people lawyers are gonna get mad at me, but a lot of the advice is pretty good. So a lot of the stuff it says and does is hmm, it’s pretty good. And so that grace that law firms used to have by their clients of well you’re the expert, you’re the professional, that’s going out the door really fast. And I think the reason the change might come now is because it’s no longer in our control how people get their legal work done. Companies are starting to do a lot of what they used to send to law firms in house. And so and I know law firms are still growing ’cause they’re increasing their rates, but is there actual work growing? I don’t know. and that’s where I feel like there might be a reckoning. Now, not for the work streams that you have to have a law firm, not for compliance, not for litigation, right? Certain work streams are safe. But if you’re a transactional attorney and the bulk of your client work comes from product counseling, privacy, commercial, labor and employment, just doc review, and not actual in court or anywhere else, you’re at risk.

Marlene Gebauer (24:15)
I wanna sort of go back to what you were saying before about kind of throwing AI at everything and that maybe we don’t need to do that. you know, maybe the fix is, you know, sometimes it’s AI, you know, sometimes it’s other software, a flexible legal team, kind of new staffing, or you know, no new technology. So how do you decide, which one a client needs or a multi you know, multiple of what they need based, you know, based on their workflow.

Dr. Maryam Salehijam (24:47)
Yeah. That’s a great question. So I’ll give you an example of a client problem I’m dealing with. They have no matter management system. It’s an Excel sheet. And they don’t know who’s doing what work. I mean, that one you might be like, let’s throw AI at this. But before we do that, we have to actually see what are they tracking in their Excel? What do they want to track? Where does the data live, right? Does any of the AI tools they want to use have those connectors? What is their budget? How much time do they have for implementation and training? How easy does the workflow need to be? And then based on all of that, then we can say, actually, all you need is a project management tool, and you can go buy a tool like Notion. And Notion has its own built-in AI workflows. Easy peasy, everybody uses for project management. Or you might need something like Clio because you need to do more complex stuff, but that’s pretty expensive. Or maybe we can vibe code this for you. It’ll be yours. AI is sometimes inaccurate, so you need to be careful around that, right? So it’s very much about showing them the end results and how many steps it takes to get there and letting them decide for themselves what do they want to do. Because when you make the choice of what to do versus what I tell you to do, you’re gonna like that choice a lot more. Now I can guide you with it. but the other thing I always tell people is if you’re gonna build it, you’re gonna like it more. And they go, What? I’m like, yeah, because it’s your baby. And nobody calls their own baby ugly. So you’re probably gonna use it more. So

Greg Lambert (26:17)
Yeah, yeah.

Marlene Gebauer (26:17)
Ha ha ha.

Dr. Maryam Salehijam (26:18)
it’s like I don’t wanna tell you to build. But if you build it, you’re probably gonna think it’s great. So that’s the other kind of like it’s just psychology at the end of the day.

Greg Lambert (26:27)
Yeah. Maryam, I want to kind of take what you were just talking about with things like you know, use Notion in order to do your project management and all. Are you dealing with any of the firms that consider themselves to be what’s called an AI native firm?

Dr. Maryam Salehijam (26:48)
Yeah.

Greg Lambert (26:48)
Cause when you when you talk about using products off the shelf like a Notion or a Cursor or other

Dr. Maryam Salehijam (26:54)
Yeah.

Greg Lambert (26:54)
tools, if I were to try and use that at a large firm, I would get pushback from my security ops people saying, you know, those are not secure, you know, the information that you’re using is not ours, it’s our clients’ information, and we have to be very careful with that. So One, are you finding that the firms that are calling themselves AI native or AI native adjacent, I guess, are able to use these types of off-the-shelf tools effectively, securely? And do you think that maybe the larger firms are overly sensitive when it comes to using some of these tools?

Dr. Maryam Salehijam (27:38)
Yes and yes. So overly sensitive

Greg Lambert (27:39)
Yeah.

Dr. Maryam Salehijam (27:40)
for sure. Some of these law firms, I’m like, what do you think you’re dealing with? National security? Like, this is crazy.

Marlene Gebauer (27:46)
Yeah.

Dr. Maryam Salehijam (27:47)
Like this is like the it’s like but also my question to law firms is have you asked your clients what they would prefer? Because guess what? Your client is throwing this the same stuff you’re holding near and dear everywhere. It’s on seven different Slack channels. It was copied and pasted somewhere. It’s on in some random drive. Like before we make such black and white decisions, let’s actually understand which clients need that crazy level of security. So for them we don’t put the stuff in the efficiency tools. And which clients would love for us to l use the efficiency tools. And guess what? If it’s a general counsel at a decent sized company that’s tech forward, they might want to get added to that Notion tab or any project management tool you use so they don’t have to email the partner and ask for an update and they can actually see how things are progressing, right? So there’s so much value we can add to our clients actually by having more visibility utilizing more tech, but we hide behind a security layer as if security’s so hard to figure out. At the end of the day, like outside of a patent litigation or really it like, you know, super confidential stuff, everything else we’re doing was emailed somewhere else at our client. So I don’t really know what we’re worried about. And like you can make things secure, but you don’t have to pretend like it’s the nuclear codes. It’s it’s a contract. Like no one’s like scraping the web trying to find that one contract. So again, it’s just sometimes I think we’re like scaring ourselves into staying in the stone ages for no reason.

Marlene Gebauer (29:18)
I wanna go back again. So we were talking about prompt engineers, right? And how that’s kind of gone by the wayside. Now sort of the title du jour is sort of legal engineer. And I

Dr. Maryam Salehijam (29:30)
Yeah.

Marlene Gebauer (29:31)
I think it’s a little different in that with you know, with a legal engineer, this is somebody who, you know, does understand the technology and can use the technology and you know, build agents and workflows and things like that. But They also understand the law. you know, they’re generally they’re lawyers that are doing this. And but I’m wondering, I mean, is this something that maybe is also a temporary type of job? Or is this something that’s always going to be, you know, part of the fabric and in a support role, or is this something that, you know, a legal engineer is essentially going to become what, you know, like But a lawyer is, like that’s that’s what everybody does. you know, what should what

Dr. Maryam Salehijam (30:13)
Yeah.

Marlene Gebauer (30:14)
should we should we be

Dr. Maryam Salehijam (30:16)
Yeah.

Marlene Gebauer (30:16)
looking for? ‘Cause I imagine they have to know law, they have to know data and what’s

Dr. Maryam Salehijam (30:21)
Yeah.

Marlene Gebauer (30:21)
important and technology as well.

Dr. Maryam Salehijam (30:24)
Okay, I love that question. So my gut reaction to the term legal engineer is ugh. ‘Cause you’re not an engineer. Like, why are you calling yourself that? What is going on? I did not know false

Marlene Gebauer (30:34)
damn.

Dr. Maryam Salehijam (30:36)
marketing was like on the table. “I’m a legal engineer.”. I’m like, and I took the course by Harvey, it was a joke. I’m like, this is the certificate. Like, this is not testing anything. This is what? and then people are like, I took it justo be like, what is this? It’s so easy. If anything, you should go through an official AI lab to get certified. But again, it’s wrong to call ourselves engineers when we are not engineers. We are not engineers. Engineers, we are not. I’m not an engineer. I’m a lawyer who understands technology, right? So you can call yourself a tech consultant. You can call it what it is. You are not an engineer, right? So that

Greg Lambert (31:13)
Yeah, but I can add another hundred thousand dollars to my paycheck if I call myself an engineer.

Dr. Maryam Salehijam (31:18)
an engineer. It’s very false marketing. so again I really care about like keep it to your core but also this concept of well it’s a lawyer so they understand a lot. Do they? I’m sorry most lawyers are specialized in their practice. Try to get an employment lawyer to talk to an M&A lawyer about private equity deals. They don’t know what the other person’s talking about. The terminology is different everything is different. The mindset is different. So that’s also weird to say. I think what you need right now in legal, and this might be just because I’m biased towards my own company is people who can ask good questions, understand your problems, who then can go ask good questions to other people, understand their solutions, and then come to you and say these are the ways you might be able to solve your problems. Decide what problems you want to solve. By the way, any problem you choose to solve is gonna suck at first because you have to spend time fixing it. Right? Anything that’s broken doesn’t get fixed easily. So that’s what we really need, but I think the title is just getting marketed around to make it sound fancy for some reason. What we should focus on as law firms and legal departments is ensure that every single person on our team is actually certified to use AI tools. And again, an actual certification that is hands-on and results in a work product where they’ve actively built something. Not just something I can take on Harvey in fifteen minutes. That’s not a certificate. That’s marketing.

Marlene Gebauer (32:40)
You raise a really good point about it takes time to solve these problems because I think that is also in itself a problem that these are not easy things to solve for a lot of times. And so but I think that the demand is that these are solved very quickly and immediately, and immediacy is really demanded. So again you know, talking to your clients, you know, how do you kinda prepare them for the fact that like, look, this could take six months to figure out and if you know, we gotta stay

Dr. Maryam Salehijam (33:18)
Yeah, I yeah, I’m

Marlene Gebauer (33:20)
the course.

Dr. Maryam Salehijam (33:20)
I’m super blunt. I tell them, I’m like, okay, you sound like you wanna Yeah. I’m like okay, sounds like

Marlene Gebauer (33:24)
No, you’re not.

Greg Lambert (33:25)
What? No.

Dr. Maryam Salehijam (33:27)
you wanna solve your matter management problem. How much time and money do you wanna spend on solving that problem? And is that actually a problem? Sounds like Excel’s working for you. Like, what would happen if you solved that problem? You’ll have more time for business development? Okay, cool. So it sounds like it’s worth it. So by the way it’s gonna cost sixty thousand and probably seven months of engineering if you wanna build yourself. So it’s really

Marlene Gebauer (33:53)
Right, right, right. And you’re the SME and so you have to be in it through the whole thing and you know, are you willing to do that?

Dr. Maryam Salehijam (33:59)
Because you want to build it. And then if you wanna buy it, guess what? You have to be willing to pay. You have to be willing to accept that the product sometimes sucks. You have to complain about your vendor like we all do, you know, but it’s more quick and after like a year or two you’re gonna say this solution didn’t work, we’re gonna replace it. I mean so typical. Everybody does this. Like nothing ever lasts, like other than LexisNexis or TR case law search. But like most things get pulled out and replaced and technology gets better and better product comes out. So again have to have that mindset of this is just the solution I’m using right now, and I might change my mind in six months to twelve months and do something else.

Greg Lambert (34:35)
Well what about and I found this description to be surprisingly effective when it comes to lawyers. when we talk about personal skill sets that you run into what’s called the J-curve, where when you implement a new technology or a change management that typically your skill set actually drops initially. before recovering and then going beyond where you were so it makes that J shape when it comes to improvement. do you use a similar argument to that to when you’re bringing in a new product, the change in the structure that it’s not going to be often up and to the right immediately. How do you how do you explain that to your customers?

Dr. Maryam Salehijam (35:25)
Yeah. Okay, this is again, I feel like I’m gonna get blacklisted because of all my opinions. I tell people, I’m like, if your people don’t know how to use a computer, I can’t teach them how to use AI. So it’s like talking about a J-curve, but I’m like

Marlene Gebauer (35:42)
Ha

Dr. Maryam Salehijam (35:43)
Actually like you want your people to get more efficient, just sign them up for a typing class. They go what? I’m like I just saw them share their screen and type. Literally a typing class is all they need. And they go what? I’m like, yep, three months typing class. That’s their typing is what’s slowing them down. And they go, Are you kidding me? I’m like, no. This is not an AI. It’s like they can’t copy paste. They can’t use the short like it’s literally sometimes that bad. And so I always walk it back to like, hey, let’s actually see what’s happening visibly. Look at it. And then we’ll know, The reason you’re spending 10 hours on a contract is because you don’t know how to use Control-F. Like this is And it hurts to hear it, right? Because everybody doesn’t want to like people don’t want to hear that they’re worse at something than other people. But literally I’ve seen lawyers not know how to use a computer. And it is concerning. And then they wanna go use AI, which is even more concerning because then they really don’t know what’s happening. And so that’s when we run into they submitted stuff that doesn’t make sense. They used it wrong. Because they don’t even understand how systems work. So that’s where I go again, wait a minute. Like, who’s going to be touching this? And who’s going to be approving this? Because you’re about to set yourself up for failure. If you give people access to AI engines who don’t know how systems and softwares and API calls work. So it’s again, education is a big part of my company to make sure people don’t put themselves in the hot seat.

Greg Lambert (37:11)
All right, well let’s say that you found the magic firm that has people that understands computers, can type, can use Control-F to find things on a on a

Dr. Maryam Salehijam (37:22)
Ha ha ha.

Greg Lambert (37:22)
document. but there’s still even an the next step. So we’re looking at bringing in AI tools and I think we’re still thinking of them almost as magical, you know, because we don’t quite understand them. But how do you get your get people to stop thinking of them as a as a like a separate tool and just start seeing it as part of just the infrastructure and the way that things get done going forward? How do you go to that level?

Dr. Maryam Salehijam (37:55)
Mm. That is a great question. And there’s like a lot of conversations in tech around unified interfaces. Basically you just have one system and the back end has all access points, right? And then there’s the conversations around B where people get their work done, which is why we see so many Word add-ins with lawyers. But to change the way somebody does work, like the way their brain goes and does the work, is really, really hard. And the only way you can actually do that is by repetition. So it’s about every day we’re gonna Have this many hours where we’re gonna all work together, right? Whether it’s sharing screens, being on one call, and we’re gonna have a pod lead that says, What are you working on? And they say this and this, and they go, Okay, I need you to try it this way, but they’re doing it in front of their peers. Why? Because peer pressure works. I know it sounds like everyone’s I don’t want to do that, but peer pressure works like magic. That’s why classroom settings work, that’s why asking people questions in front of the whole class works. So we need to bring back that methodology when we’re teaching people how to use some. something. Otherwise they’re gonna kinda listen, do something else, not really take it in. But if they have to prove themselves in front of their peers and we’re lawyers and we’re perfectionists, they’re gonna actually listen and do it and try to be good at it ’cause we all love to compete and show that we’re capable. So it brings back that law school setting into the AI department setting.

Greg Lambert (39:11)
I say we have to bring back the Socratic method to training.

Marlene Gebauer (39:13)
Socratic method I was saying the same thing.

Dr. Maryam Salehijam (39:16)
It works! Like why reinvent the wheel? We know how people learn, so why are we trying to do it differently?

Marlene Gebauer (39:23)
So Maryam, we’re going to ask sort of a question of looking at the past and then Greg’s gonna ask our crystal ball question about what’s happening for the future. So tell me what is true today that wasn’t true a year ago.

Dr. Maryam Salehijam (39:41)
Great question. What is true today that’s not true a year ago was that we thought we’re gonna see a lot more AI companies pop up in the legal space. A year ago we said that. I’m not seeing that. I’m not seeing too many new players. And I’m not sure if it’s because the venture capital firms don’t want to fund them or everyone’s watching the market. But I really thought there was gonna be an explosion of tools. But

Marlene Gebauer (40:03)
It’s interesting ’cause it’s like I see like a little but l little tiny like point solution types of things, but nothing big.

Dr. Maryam Salehijam (40:08)
Yeah. Nothing major. Nothing big, nothing major. and so that was surprising to me, ’cause I thought we would see a lot more of it. And maybe everyone’s in the build stage right now and we’re gonna hear about it in 2027. That’s true, yeah.

Marlene Gebauer (40:20)
And then there’s a lot of buying. There’s a lot of there’s a lot of acquisitions too. Yeah.

Dr. Maryam Salehijam (40:24)
So I think funding is drying up, actually instead of going up. And maybe it’s ’cause the frontier labs moved into legal. Maybe that’s what’s scaring the investors, but

Greg Lambert (40:33)
I was gonna say, ChatGPT just launched, what is it, Astra for Law? Yeah.

Marlene Gebauer (40:37)
Mm-hmm.

Dr. Maryam Salehijam (40:37)
Astra, yes. So that was surprising to me. I did not see funding dry up so quickly. For new players. Now, the crystal ball moment, what will happen? As a good lawyer, I’ll say it depends. It depends on the market, it depends on the environment, depends on global warfare. But if I was just to look six months ahead of from now, I would say we’re gonna see a lot of legal departments and law firms ask the question of can we build something we own that can do what the vendors do, but cheaper or better? We don’t have to renew it every year, right? We can maintain it ourselves, it’ll be our workflows, and we buy limited licenses to the vendors for the people that really need it. Because right now the way legal tech is getting sold is traditional SaaS, right? Seat-based pricing, minimum a year. Often they’re pushing three years. They want multiple licenses. But if you look at the usage data, it doesn’t even make sense. So I think we’re gonna see more of that shift coming because right now the spend on technology is not, back to the ROI conversation, supported by the ROI versus their price point.

Marlene Gebauer (41:53)
Well, Dr. Maryam Salehijam, founder and CEO of Andarzi, thank you for joining us on the Geek in Review.

Dr. Maryam Salehijam (41:59)
Thank you so much, Marlene. Thank you, Greg.

Greg Lambert (42:02)
And thanks everyone for listening and if you enjoy the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Marlene Gebauer (42:12)
And Maryam, what is the best place for listeners to learn more about Andarzi and follow your work?

Dr. Maryam Salehijam (42:18)
If they follow me on LinkedIn, I’ll post links to my website, andarzy.com, and also my courses on AI fluency via Maven.

Greg Lambert (42:26)
I feel like we’ve flipped the script here at the end, Marlene. So I’m taking your part. And

Marlene Gebauer (42:30)
We did. Like I noticed.

Greg Lambert (42:34)
and as always, the music you hear is from our good friend Jerry David DeCicca Thank you, Jerry.

Marlene Gebauer (42:39)
Thanks, Jerry.

Greg Lambert (42:40)
All right, bye everyone.

Marlene Gebauer (42:42)
Bye.

]]> Law firms and legal departments face pressure to adopt AI, yet buying another tool does not answer the hardest question: Which problem needs solving? Dr. Maryam Salehijam, founder and CEO of Andarzi, joins TGIR hosts, Greg Lambert and Marlene Gebauer, to discuss how legal teams choose between existing systems, new vendors, and tools built for their own workflows. Her starting point is to observe the work, identify a useful outcome, and then decide what technology belongs in the process.

Salehijam describes legal teams paying for AI platforms while struggling to get consistent use from them. She urges organizations to examine tools they already own and ask vendors for more training before adding another subscription. A legal department’s use of ServiceNow for intake provides one example. The conversation also tackles shifting model capabilities, consumption pricing, and the cost of assigning an expensive model to a routine task.

What counts as a return on legal AI investment? Time and money matter, but Salehijam argues for measuring employee satisfaction as well. Repetitive contract review and document work consume attention lawyers would prefer to spend on judgment, business problems, and client relationships. She describes using anonymous feedback before and after a workflow change to learn whether people’s work has improved, while Greg raises the tension between hours saved and the billable hour.

The discussion turns to local AI models, data security, and the growing interest in AI-native law firms. Salehijam argues for decisions based on the task and the client’s requirements, including a direct conversation with clients about how their information should be handled. Greg presses her on whether smaller firms and large firms face different constraints. Marlene asks whether the emerging “legal engineer” role will endure, prompting a pointed exchange about technical skill, credentials, and what lawyers need to learn.

For teams putting AI into practice, Salehijam favors hands-on learning with peers, repeated use, and work product over a quick certificate. She also warns leaders to account for the time and effort a new workflow demands before promising immediate gains. In the closing questions, she reflects on a legal AI market with fewer new entrants than she expected and predicts more law firms and legal departments will ask whether owning a workflow serves them better than renewing another set of software seats.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

LINKS

Transcript

Marlene Gebauer (00:07)
Welcome to the Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (00:14)
And I’m Greg Lambert.

Marlene Gebauer (00:16)
This week we’re joined by Maryam Salehijam, founder and CEO of Andarzi. Maryam started on the technology side of legal way back when AI mostly meant e-discovery and moved to the services side when the technology wasn’t ready and came back when generative AI arrived. Along the way, she held roles at LexisNexis, Axiom, Robin AI, and Junior. Through Andarzi, she helps law firms and legal departments figure out what problem they actually have and what to do about it.

Greg Lambert (00:46)
Yeah, I like the good old days of when AI meant e-discovery. So, Maryam, welcome

Marlene Gebauer (00:51)
Ha ha ha.

Greg Lambert (00:52)
to the Geek in Review. Good to have you here.

Dr. Maryam Salehijam (00:53)
Thank you, Greg. Thank you, Marlene. It’s my pleasure to be here today to speak about my new initiative, Andarzi.

Marlene Gebauer (00:59)
So Maryam, we mentioned the places you’ve been in where most of us know you from, but you recently launched, as you said, your new company, Andarzi. So what inspired you to go out and build it?

Dr. Maryam Salehijam (01:09)
That’s a great question. Well, it honestly came from encouragement from my family and friends. They all said we don’t know why you don’t start your own company. If there’s one person to have her own company it’d be you. That’s literally what it was.

Greg Lambert (01:19)
Stop making other people money and make yourself money, right?

Marlene Gebauer (01:23)
And I’m sorry, like if your family is already like involved in AI and they’re talking about that too.

Dr. Maryam Salehijam (01:30)
Yeah, they said go for it. but really the reason I jumped in is because I kept getting calls from my friends in legal departments and law firms saying, “Hey, you’re in AI, right? And I said, “Yes.” And they said, “We don’t need what you’re selling because we do this other thing, but can you help us with our strategy? We’re trying to figure this out. There’s all of this pressure. If you’re at a legal department from your management at the company, the C-suite. If you’re at a law firm, it’s from your clients. We don’t know what to do with it and we don’t have the time to figure it out on our own. We need somebody to come in who we can trust to tell us what’s going on, where does AI make sense, where does AI not make sense and what should be our strategic plan for the next six months or year, right? What’s gonna happen to us as an industry and that’s where Andarzi was born from. And the name actually comes from the Persian word “andarz”, which means to give advice.

Greg Lambert (02:21)
Very, very nice. And so when they reach out to you with initially, what’s some of the common problems that they’re having that is causing them to reach out to you?

Dr. Maryam Salehijam (02:33)
That’s a great question. So let’s talk about, for example, a decent sized legal department where they don’t have a huge internal engineering team inside of the legal department. Their main issue is they’re getting this mandate to become more efficient, spend less on law firms, right? Hire less. But they don’t really know what tools to choose from. Let’s say they wanna deploy a tool for contract intelligence. There’s at least two hundred tools out there. They all say the same thing.

Greg Lambert (02:59)
Sure.

Marlene Gebauer (03:00)
Ha ha ha.

Dr. Maryam Salehijam (03:01)
They all tell you they’re the best and the pricing varies. How do you choose the right one for you? Right? The second issue is if you’re buying multiple tools, how do you keep on top of everything? How do you keep on top of the training? How do you get your people to use it? I have so many friends inside legal departments at companies that bought from the likes of Harvey and Legora, and they say, “Maryam, our utilization is not where we need ito be for this to make sense. Right? We’re really worried. We’re paying a lot of money, but not really seeing the savings or the efficiency we would hope to see when we went into this. And then, for the law firms who don’t have an internal chief innovation officer or really a team dedicated to figuring it out, they’re going, Well, if I try to buy a tool for every problem I have, I’m spending north of $300,000 a year. And that just doesn’t make sense for me so could I potentially build some of this myself? And I see a lot of conversation coming in of is this something we can build? And so that’s where I go in and say, okay, wait a minute. Before we make a decision, let’s actually figure out, let’s observe your workflows, where is there a quick win for you that you can have with your existing systems? Sometimes they really are underutilizing what they already have. And then where it makes sense we can make the determination between build versus buy. And guess what? If you want to buy I can help you With all the vendor selection, I can help you negotiate those contracts. And if you want to build, I do partner with two agencies that can come in and do the AI build for you.

Greg Lambert (04:23)
Very interesting.

Marlene Gebauer (04:25)
Mm-hmm.

Greg Lambert (04:26)
one of the things that when you and I were talking in prep for this, we were talking about how some of the firms are still focusing in on the prompt engineering that it’s still around the prompt around the chatbot. So what are you seeing, and what are you advising firms and other legal organizations to build and training on that’s beyond the prompt itself. What are you telling them to start focusing in on?

Dr. Maryam Salehijam (05:01)
What I’m telling them is to throw everything they know about technology out the door because the speed at which Gen AI is moving is beyond comprehension. So maybe four months ago we could have said learn how to prompt that’s important, that is no longer a valid skill to have because the systems can prompt themselves. So the way I think about it is think about where cars started where you had to hand crank to get it going and it was really unsafe and there were so many things that could go wrong. To now we have self-driving cars in the shape of Waymos, not even a human in the seat. I mean, they’re also really sketchy if you ask me, but that transition took what 200 years? Gen AI is doing that kind of transformation within less than 12 months. So when it comes to skill set for law firms and legal departments, I really encourage all of my clients to think like they’ve never done before. It’s not like law school where you go to law school and you’ve learned. It’s very much every day there’s a new thing that’s coming out that’s gonna scrap everything you’ve learned about how you know how to do things. And you have to have that mentality to really stay on top of everything.

Marlene Gebauer (06:08)
Yeah, it’s really challenging because, you know, you’re talking about we’re talking about prompt engineering, right? And at one point, like maybe last year, that was actually a job. And now that is not a job anymore. And so for firms trying to plan for, you know, hiring, you know, allied professionals or even trying to determine what kind of skills you know, attorneys should have in their organization. It’s it’s really rather challenging. what kind of feedback are you getting?

Dr. Maryam Salehijam (06:39)
exactly that they feel like they’re constantly behind. Everyone feels behind at all times, which is what I tell them is like actually you’re in the same boat with everyone else, so you’re not behind on anything everyone feels behind. So don’t stress it too much, but also don’t jump. I always tell people don’t jump into hiring people if you’ve never had someone do this for you. Start with a consultant, start with a contractor. If you are not a massive organization, if you are not a massive law firm, if you are not a massive legal department, you do not need a full Time person doing this kind of work for you. And guess what? A lot of your vendors will do most of the heavy lifting. So you just have to ask them. So many people don’t actually talk to their vendors. They buy the product and then they go and they start trying to figure out how to use it internally. And I go, no, the vendor wants to work with you. You’re just afraid of working with them because you think they’re gonna sell you more stuff. Just learn to say no and use your vendors for what they’re good at, which is teaching you how to use their tooling. so that’s kind of the advice I go in with. Again, it’s super practical, right? I’m like, don’t spend any money. try to use what you have try to go to the people learn to say no right yeah don’t spend

Greg Lambert (07:39)
You’re speaking the right language.

Marlene Gebauer (07:41)
Okay.

Dr. Maryam Salehijam (07:42)
anything and that’s like one of the legal departments I love the most because they used to be my client over at Axiom Nutanix that’s their mentality as well they said before we buy a legal intake tool for the front door of our legal department well we have ServiceNow can we use ServiceNow for this and they engineered ServiceNow to do exactly what they needed to do and they did not have to spend hundreds of thousands of dollars to buy a tool to do it. Right? And it’s that kind of mindset that I think is gonna make legal leave the stone ages where we were always seen as not technical people to come into, hey, we’re actually at the frontier of this general AI movement because it makes sense for our business model.

Greg Lambert (08:24)
that’s let me let me kind of read back what I think I heard here. because I think a lot of us are thinking, you know, AI yeah, it’s been around three and you know, three and a half years, going on fourth year now. but it’s still pretty new and we think that we need to jump into whatever the newest and latest and you know the startups are. But from what I’m hearing, you’re saying we’re probably not leveraging our existing relationships enough. Is that is that what I’m hearing?

Dr. Maryam Salehijam (08:54)
One hundred percent. I’ll give you another example. I work with a big pharma company where they simply need to cross-check three data points across fifteen different types of document. just project at a time. They tried to use their own internal tooling and they found it wasn’t working well. But it’s because they were simply prompting it. They didn’t know how to set up some sort of vault within their systems, how to build a skill set, how to build the citation checker, how to do the charting. But their existing Copilot tooling could do all of that. They just didn’t know it could, right? So it’s an education piece that all I had to bridge is the education. Hey, this is how you can do it. They did it. Voila. Didn’t have to buy a tool with a vault function at thousands of dollars just to do something Gen AI on its own if you already have access to Copilot or Google Gemini can do for you.

Marlene Gebauer (09:47)
Yeah, it’s amazing that the you like sometimes we have so many products that we don’t know what we actually have and what it can actually do. interesting. So all right, I’m I’m very interested in this question here because all we’re hearing about is consumption-based pricing. Consumption-based pricing. Consumption-based pricing. now you’re saying tokens are becoming a commodity. So Okay, that goes a little bit against the grain of what we’re hearing and what not so much what we’re hearing, but what seems to be a big concern among law firms and like how are we gonna pay for this. So what happens, you know, to the vendors and their pricing when that all plays out?

Dr. Maryam Salehijam (10:31)
That is a great question. And I actually had a like a mini debate with someone at the bar at running legal like the business because they were

Marlene Gebauer (10:36)
I bet you did.

Greg Lambert (10:37)
Yeah.

Dr. Maryam Salehijam (10:39)
at one of the vendors that does token based pricing and they were talking about how great that is. And I said, That is a terrible business model for your clients and he said, Why? I’m like, There’s no predictability on their bills. This is one of the reasons people get mad at law firms, right? You just send them a bill and they go, oh my God. What is c

Marlene Gebauer (10:55)
Right, like w one time it’s super expensive, then the next time it’s gonna be not as expensive.

Dr. Maryam Salehijam (11:00)
Right.

Marlene Gebauer (11:01)
And we’ve done this before, you know, we’ve done this consumption based pricing before and nobody

Dr. Maryam Salehijam (11:06)
Yes.

Marlene Gebauer (11:06)
liked it.

Dr. Maryam Salehijam (11:07)
Nobody liked it. And I also really dislike this whole thing in legal that I see of like, oh my God, Claude for Legal is the winner. now it’s OpenAI. Let’s write articles about it. Let’s

Greg Lambert (11:15)
Ha ha ha.

Dr. Maryam Salehijam (11:16)
move our skill set. Unlike everybody calm down.

Marlene Gebauer (11:19)
Mm-hmm.

Dr. Maryam Salehijam (11:20)
There’s no winners or losers yet. Let’s just not constantly make deterministic decisions. Again, that you d this is like never before. You cannot apply your same mentality. It’s either Lexis or TR. It’s either this or it’s that. It’s not like that. It’s not black and white. It’s very much gray. Even for the providers, it’s gray. They don’t even know what’s happening after time, right? So when it comes to tokens, we know they’re very resource intensive, right? From data centers, from the cost of compute, from the cloud. It’s very, very expensive. And certain tasks consume a lot more tokens than others. Certain models consume a lot more tokens than others. I also see this fallacy constantly where people are like, why doesn’t Lexis have the latest version of Claude for this? I’m like, because it doesn’t need to. You don’t need the latest models for everything, right? It’s that education piece where we need to really think about what is the task, how simple is this task, right? Or how complex is this task, and what is the cost of utilizing AI on this task for each project? Before we try to,

Marlene Gebauer (12:21)
And is it worth it?

Dr. Maryam Salehijam (12:23)
is it worth it before we try to throw AI on everything? And then my friends who actually work at the Frontier Labs and the actual tech companies, so many of them are moving to local compute power where there’s basically no cost to the token. And that’s the other conversation we need to start having of okay, why not just do it locally? It’s actually more secure a lot of the times. It’s cheaper. and from a technical perspective, also not that hard. So how do we also in think about that as law firms and legal teams if our one of our biggest concerns is secure? Right? Before we just go spend more money buying stuff. So it’s a lot of those conversations of let’s first actually understand what we have available to us before we make a decision on what we’re gonna do with it.

Marlene Gebauer (13:01)
Have you heard any discussion about, you know, some of the some of the vendors are using you know their training on models that are like foreign models, like Chinese models. Like have you heard?

Greg Lambert (13:14)
Yeah.

Marlene Gebauer (13:16)
oh my God, we have to get the eye roll. That was beautiful. You missed the eye roll.

Greg Lambert (13:18)
I was gonna say for the for the for the people that are only listening, you missed probably the best part of the show.

Dr. Maryam Salehijam (13:25)
oh my God.

Marlene Gebauer (13:26)
So wha you know, what are your thoughts on that or what are or what are you hearing from your clients about vendors that are doing that?

Dr. Maryam Salehijam (13:34)
Yeah, great question. So I think everybody wants to sound like they’re going against the grain because it’s cool, right? oh my God, we’re doing it this differently. It’s cheaper, it’s

Marlene Gebauer (13:43)
It’s like it’s cheaper. Okay, it is cheaper.

Dr. Maryam Salehijam (13:45)
this, that. But actually, like this is the other thing where again some of the vendors are gonna hate me after this because I just say what I really think. Why are you trying to build your own LLM? You think you have more resources and knowledge and money than OpenAI or Anthropic? Are you kidding me? It’s never gonna be better. Like, it’s just those things where I’m like, so you’re gonna go trust a Model that hasn’t been vetted at all for our industry for these things. You’re the person vetting it, which doesn’t matter. I don’t care about your personal benchmarks. That’s like me being like, I’m the greatest. Doesn’t matter. my mom thinks I’m great. Doesn’t matter. Against literally companies that have so much money and resources behind them that they can surpass the GDP of a country. Like, let’s get real here. This is this is not logical, right? Why would you do that? So it’s a lot of those things where I go, oh my God, here we go again. Somebody’s like saying something that makes no sense. Like nobody has more resources than OpenAI, Anthropic, or Google. Maybe Microsoft, right? Maybe IBM, but right now, if you’re not plugging in OpenAI or Anthropic, I don’t really think you’re that serious about understanding Gen AI.

Greg Lambert (14:55)
I wanna dig a little deeper on something that you mentioned earlier and that was using local models. are when can you define that for me? Is that something that’s running like literally on your laptop or is it running in your cloud? Well can you define

Dr. Maryam Salehijam (15:13)
Yeah.

Greg Lambert (15:13)
that a little bit more?

Dr. Maryam Salehijam (15:15)
Is running on your machine, or even your phone has compute power, or you can plug in a GPU from NVIDIA or Apple. And So what happens when we need to process data, right? We need compute power. Now you can have compute power via the cloud, but actually the cloud ultimately connects to the data center, or you can have a mini version of your own data center connected to your device. Now, a lot of smaller law firms are already doing that actually. the one-person shops, right? Especially in The IP space because it’s super secure. Data’s going nowhere. Nobody’s seeing the data. But that only works if you actually know what models to select, what the task is, you know how to quality check. It’s not gonna be something you can easily use for general purpose things, like it’s not gonna replace your OpenAI or you know Anthropic subscription. But let’s say you’re constantly reviewing a specific type of contract where you know exactly what to do, you’re writing a specific type of document, it’s perfect for that kind of work because you know what the task. is you know what the model weight needs to be, how complex it needs to be, and you know what the deliverable looks like. So then it’s quite actually easy to do it yourself. The same is true for anything that contains like patient information, right? Medical records, timelines, easy to do it locally, safer. You don’t have to worry about HIPAA compliance.

Greg Lambert (16:30)
And are you seeing like smaller companies, departments that are experimenting with the with the local GPUs to run this?

Dr. Maryam Salehijam (16:40)
Yeah, not and not just the small, right? Latham, I think just announced they bought a whole GPU farm to bring in house. So it’s happening at all levels and I will suspect that’s gonna happen more and more as we struggle with this concept of when do we wanna use AI and not only when, but how do we wanna use that AI. Are we getting our water in a bottle? Are we getting it through the tap? Are we getting it delivered via a truck? How are we gonna use it? What makes sense? for us and what makes sense for the specific task. So I really think people are gonna start thinking more efficiently also about where they’re gonna get their AI from and why.

Greg Lambert (17:20)
So I think if I look back at my predictions for 2026, I think one of the predictions was that law firms and legal departments were going to have to come up with the return on investment and the metrics of you know, we’re we’ve spent so much money in experimenting with AI, but at some point we’re going to be asked about how do we measure the success on that. So what we’d love to measure, you know, it’s like hours saved, money saved, I guess, but what do you think and what are you advising your firms and legal departments on what they should be measuring to show the return on the investment that they’re making?

Dr. Maryam Salehijam (18:06)
I love that question. First of all, let’s talk about the elephant in the room. Law firms measuring time saved.

Greg Lambert (18:13)
What could go wrong with that?

Dr. Maryam Salehijam (18:13)
What?

Marlene Gebauer (18:14)
Yeah.

Dr. Maryam Salehijam (18:15)
Absolutely like logical, everybody wants to do that at a law firm. Don’t worry about billables. Like again, it’s just those things where I’m like, what are we talking about here? but I actually have something that I say which is so against the grain. I talk about happiness. One of the biggest problems in our profession is not how many hours it does take, is that depression, anxiety, burnout. It’s insane and legal. Why are we so miserable? I’m pretty sure none of us went to law school to be miserable. and we have one of the highest rate of suicide.

Greg Lambert (18:43)
Mar Marlene did. She went to law school in Cleveland. So

Marlene Gebauer (18:48)
E everybody in Cleveland I want you to know that Greg said it, not me.

Dr. Maryam Salehijam (18:52)
That’s so funny. No, Cleveland has a good law school. Come on now. I’m sure it was fun. Yeah.

Marlene Gebauer (18:57)
At Cleveland? Yeah, it does.

Dr. Maryam Salehijam (18:59)
but that’s like can we focus on what we really wanna do in legal, not the work we don’t wanna do? Who wants to review an NDA? Come on now. Who wants to do this? Who wants to redline the same provision in the contracts over and over again? Nobody wants to do that. Nobody wants to do doc review. At some point you just lose where you’re at. So it’s just all of this work that I actually consider a poor use of the human brain. It’s not critical tasks. Let’s have these systems do these things for us so we can focus on what really brings us fulfillment. And by being fulfilled, we actually start to get more creative. And we start to solve the business problems and we starto be better partners, not just to our clients as law firms, but also to the clients of the legal department at a company, right? Because at the end of the day, it’s the business that’s your client. And we can become more business people than legal people, because so much of law right now doesn’t consider business, technology. And I think that transformation is gonna be good for our industry and yes it might threaten the billable hour but that doesn’t mean it’s gonna wipe out law firms they just have to rethink how they generate their money, right? And potentially the split of their revenue and where it’s gonna come from and maybe law firms start offering more of the managed service like Eversheds does or Flex by Fenwick does, right? Where they have the actually multiple streams that they can offer their clients outside of just billables for specific projects.

Greg Lambert (20:25)
Yeah. How I know this is a tough question to ask you. but you know it’s easy to measure dollars saved, it’s easy to measure time saved. when you talk about job satisfaction, if you talk you know, those sorts of things, how do you measure that and are there other industries or examples that we could look toward that would help us kind of wrap our minds around how you measure that and what it looks like.

Dr. Maryam Salehijam (20:56)
Absolutely. So you can do the most basic old school way, which is employee satisfaction surveys, right? They’re anonymous. You can survey them before, then a year later. Or actually the company I partner with, what we do before we deploy on a project is we send out voice agents that are conversational, you talk to them. But it’s super anonymous. And people just talk to them about their workflows, what they’re frustrated with, what they like, what they wish they could do, what they wish they couldn’t do. And then we aggregate all that data and build a couple of solutions for them that we think would address the main things people are annoyed about. And then we deploy and then do the voice agents again to see are the problems solved or people happier, what’s going on. and data, you know, even though happiness is hard to measure, data can help you measure that, right? How do people feel? Do they feel fulfilled? What’s going on? I can tell you for in-house teams, the biggest pain point is often their workload. It’s way more work than they anticipated. They don’t have the headcount, right? And so sometimes they end up leaving their company and then that’s so expensive for the company to try to replace them. It’s hundreds of thousands of dollars, right? That retention issue is huge for companies. And with all the layoffs, oh my God, additional mental load. So it’s about creating that safe space for people to do their work, but do their work enthusiastically and as efficiently as they can.

Marlene Gebauer (22:12)
I just think it requires sort of extraordinary leadership and courage and creativity to sort of implement that kind of change management on a large scale because it’s so much easier to just say, Okay, we’re just gonna raise our rates and we’ll just give you a discount and you know, that’s kind of what we’ve been seeing this model for years and years and years, rather than actually fix the model.

Dr. Maryam Salehijam (22:38)
Yeah.

Marlene Gebauer (22:38)
And you know, I know that we have always said, well, maybe this is the time and maybe AI is gonna be what does it but I mean what are you hearing in terms of you know from about this like from clients and like how they’re how they’re trying to trying to do this?

Dr. Maryam Salehijam (22:58)
Yeah, that’s a great question. I think for a long time, businesses, right, they didn’t really know how to get legal work done without lawyers. Right? We were the gatekeepers, we had the knowledge, we had the know how. But now these AI models can go and give advice and I know people lawyers are gonna get mad at me, but a lot of the advice is pretty good. So a lot of the stuff it says and does is hmm, it’s pretty good. And so that grace that law firms used to have by their clients of well you’re the expert, you’re the professional, that’s going out the door really fast. And I think the reason the change might come now is because it’s no longer in our control how people get their legal work done. Companies are starting to do a lot of what they used to send to law firms in house. And so and I know law firms are still growing ’cause they’re increasing their rates, but is there actual work growing? I don’t know. and that’s where I feel like there might be a reckoning. Now, not for the work streams that you have to have a law firm, not for compliance, not for litigation, right? Certain work streams are safe. But if you’re a transactional attorney and the bulk of your client work comes from product counseling, privacy, commercial, labor and employment, just doc review, and not actual in court or anywhere else, you’re at risk.

Marlene Gebauer (24:15)
I wanna sort of go back to what you were saying before about kind of throwing AI at everything and that maybe we don’t need to do that. you know, maybe the fix is, you know, sometimes it’s AI, you know, sometimes it’s other software, a flexible legal team, kind of new staffing, or you know, no new technology. So how do you decide, which one a client needs or a multi you know, multiple of what they need based, you know, based on their workflow.

Dr. Maryam Salehijam (24:47)
Yeah. That’s a great question. So I’ll give you an example of a client problem I’m dealing with. They have no matter management system. It’s an Excel sheet. And they don’t know who’s doing what work. I mean, that one you might be like, let’s throw AI at this. But before we do that, we have to actually see what are they tracking in their Excel? What do they want to track? Where does the data live, right? Does any of the AI tools they want to use have those connectors? What is their budget? How much time do they have for implementation and training? How easy does the workflow need to be? And then based on all of that, then we can say, actually, all you need is a project management tool, and you can go buy a tool like Notion. And Notion has its own built-in AI workflows. Easy peasy, everybody uses for project management. Or you might need something like Clio because you need to do more complex stuff, but that’s pretty expensive. Or maybe we can vibe code this for you. It’ll be yours. AI is sometimes inaccurate, so you need to be careful around that, right? So it’s very much about showing them the end results and how many steps it takes to get there and letting them decide for themselves what do they want to do. Because when you make the choice of what to do versus what I tell you to do, you’re gonna like that choice a lot more. Now I can guide you with it. but the other thing I always tell people is if you’re gonna build it, you’re gonna like it more. And they go, What? I’m like, yeah, because it’s your baby. And nobody calls their own baby ugly. So you’re probably gonna use it more. So

Greg Lambert (26:17)
Yeah, yeah.

Marlene Gebauer (26:17)
Ha ha ha.

Dr. Maryam Salehijam (26:18)
it’s like I don’t wanna tell you to build. But if you build it, you’re probably gonna think it’s great. So that’s the other kind of like it’s just psychology at the end of the day.

Greg Lambert (26:27)
Yeah. Maryam, I want to kind of take what you were just talking about with things like you know, use Notion in order to do your project management and all. Are you dealing with any of the firms that consider themselves to be what’s called an AI native firm?

Dr. Maryam Salehijam (26:48)
Yeah.

Greg Lambert (26:48)
Cause when you when you talk about using products off the shelf like a Notion or a Cursor or other

Dr. Maryam Salehijam (26:54)
Yeah.

Greg Lambert (26:54)
tools, if I were to try and use that at a large firm, I would get pushback from my security ops people saying, you know, those are not secure, you know, the information that you’re using is not ours, it’s our clients’ information, and we have to be very careful with that. So One, are you finding that the firms that are calling themselves AI native or AI native adjacent, I guess, are able to use these types of off-the-shelf tools effectively, securely? And do you think that maybe the larger firms are overly sensitive when it comes to using some of these tools?

Dr. Maryam Salehijam (27:38)
Yes and yes. So overly sensitive

Greg Lambert (27:39)
Yeah.

Dr. Maryam Salehijam (27:40)
for sure. Some of these law firms, I’m like, what do you think you’re dealing with? National security? Like, this is crazy.

Marlene Gebauer (27:46)
Yeah.

Dr. Maryam Salehijam (27:47)
Like this is like the it’s like but also my question to law firms is have you asked your clients what they would prefer? Because guess what? Your client is throwing this the same stuff you’re holding near and dear everywhere. It’s on seven different Slack channels. It was copied and pasted somewhere. It’s on in some random drive. Like before we make such black and white decisions, let’s actually understand which clients need that crazy level of security. So for them we don’t put the stuff in the efficiency tools. And which clients would love for us to l use the efficiency tools. And guess what? If it’s a general counsel at a decent sized company that’s tech forward, they might want to get added to that Notion tab or any project management tool you use so they don’t have to email the partner and ask for an update and they can actually see how things are progressing, right? So there’s so much value we can add to our clients actually by having more visibility utilizing more tech, but we hide behind a security layer as if security’s so hard to figure out. At the end of the day, like outside of a patent litigation or really it like, you know, super confidential stuff, everything else we’re doing was emailed somewhere else at our client. So I don’t really know what we’re worried about. And like you can make things secure, but you don’t have to pretend like it’s the nuclear codes. It’s it’s a contract. Like no one’s like scraping the web trying to find that one contract. So again, it’s just sometimes I think we’re like scaring ourselves into staying in the stone ages for no reason.

Marlene Gebauer (29:18)
I wanna go back again. So we were talking about prompt engineers, right? And how that’s kind of gone by the wayside. Now sort of the title du jour is sort of legal engineer. And I

Dr. Maryam Salehijam (29:30)
Yeah.

Marlene Gebauer (29:31)
I think it’s a little different in that with you know, with a legal engineer, this is somebody who, you know, does understand the technology and can use the technology and you know, build agents and workflows and things like that. But They also understand the law. you know, they’re generally they’re lawyers that are doing this. And but I’m wondering, I mean, is this something that maybe is also a temporary type of job? Or is this something that’s always going to be, you know, part of the fabric and in a support role, or is this something that, you know, a legal engineer is essentially going to become what, you know, like But a lawyer is, like that’s that’s what everybody does. you know, what should what

Dr. Maryam Salehijam (30:13)
Yeah.

Marlene Gebauer (30:14)
should we should we be

Dr. Maryam Salehijam (30:16)
Yeah.

Marlene Gebauer (30:16)
looking for? ‘Cause I imagine they have to know law, they have to know data and what’s

Dr. Maryam Salehijam (30:21)
Yeah.

Marlene Gebauer (30:21)
important and technology as well.

Dr. Maryam Salehijam (30:24)
Okay, I love that question. So my gut reaction to the term legal engineer is ugh. ‘Cause you’re not an engineer. Like, why are you calling yourself that? What is going on? I did not know false

Marlene Gebauer (30:34)
damn.

Dr. Maryam Salehijam (30:36)
marketing was like on the table. “I’m a legal engineer.”. I’m like, and I took the course by Harvey, it was a joke. I’m like, this is the certificate. Like, this is not testing anything. This is what? and then people are like, I took it justo be like, what is this? It’s so easy. If anything, you should go through an official AI lab to get certified. But again, it’s wrong to call ourselves engineers when we are not engineers. We are not engineers. Engineers, we are not. I’m not an engineer. I’m a lawyer who understands technology, right? So you can call yourself a tech consultant. You can call it what it is. You are not an engineer, right? So that

Greg Lambert (31:13)
Yeah, but I can add another hundred thousand dollars to my paycheck if I call myself an engineer.

Dr. Maryam Salehijam (31:18)
an engineer. It’s very false marketing. so again I really care about like keep it to your core but also this concept of well it’s a lawyer so they understand a lot. Do they? I’m sorry most lawyers are specialized in their practice. Try to get an employment lawyer to talk to an M&A lawyer about private equity deals. They don’t know what the other person’s talking about. The terminology is different everything is different. The mindset is different. So that’s also weird to say. I think what you need right now in legal, and this might be just because I’m biased towards my own company is people who can ask good questions, understand your problems, who then can go ask good questions to other people, understand their solutions, and then come to you and say these are the ways you might be able to solve your problems. Decide what problems you want to solve. By the way, any problem you choose to solve is gonna suck at first because you have to spend time fixing it. Right? Anything that’s broken doesn’t get fixed easily. So that’s what we really need, but I think the title is just getting marketed around to make it sound fancy for some reason. What we should focus on as law firms and legal departments is ensure that every single person on our team is actually certified to use AI tools. And again, an actual certification that is hands-on and results in a work product where they’ve actively built something. Not just something I can take on Harvey in fifteen minutes. That’s not a certificate. That’s marketing.

Marlene Gebauer (32:40)
You raise a really good point about it takes time to solve these problems because I think that is also in itself a problem that these are not easy things to solve for a lot of times. And so but I think that the demand is that these are solved very quickly and immediately, and immediacy is really demanded. So again you know, talking to your clients, you know, how do you kinda prepare them for the fact that like, look, this could take six months to figure out and if you know, we gotta stay

Dr. Maryam Salehijam (33:18)
Yeah, I yeah, I’m

Marlene Gebauer (33:20)
the course.

Dr. Maryam Salehijam (33:20)
I’m super blunt. I tell them, I’m like, okay, you sound like you wanna Yeah. I’m like okay, sounds like

Marlene Gebauer (33:24)
No, you’re not.

Greg Lambert (33:25)
What? No.

Dr. Maryam Salehijam (33:27)
you wanna solve your matter management problem. How much time and money do you wanna spend on solving that problem? And is that actually a problem? Sounds like Excel’s working for you. Like, what would happen if you solved that problem? You’ll have more time for business development? Okay, cool. So it sounds like it’s worth it. So by the way it’s gonna cost sixty thousand and probably seven months of engineering if you wanna build yourself. So it’s really

Marlene Gebauer (33:53)
Right, right, right. And you’re the SME and so you have to be in it through the whole thing and you know, are you willing to do that?

Dr. Maryam Salehijam (33:59)
Because you want to build it. And then if you wanna buy it, guess what? You have to be willing to pay. You have to be willing to accept that the product sometimes sucks. You have to complain about your vendor like we all do, you know, but it’s more quick and after like a year or two you’re gonna say this solution didn’t work, we’re gonna replace it. I mean so typical. Everybody does this. Like nothing ever lasts, like other than LexisNexis or TR case law search. But like most things get pulled out and replaced and technology gets better and better product comes out. So again have to have that mindset of this is just the solution I’m using right now, and I might change my mind in six months to twelve months and do something else.

Greg Lambert (34:35)
Well what about and I found this description to be surprisingly effective when it comes to lawyers. when we talk about personal skill sets that you run into what’s called the J-curve, where when you implement a new technology or a change management that typically your skill set actually drops initially. before recovering and then going beyond where you were so it makes that J shape when it comes to improvement. do you use a similar argument to that to when you’re bringing in a new product, the change in the structure that it’s not going to be often up and to the right immediately. How do you how do you explain that to your customers?

Dr. Maryam Salehijam (35:25)
Yeah. Okay, this is again, I feel like I’m gonna get blacklisted because of all my opinions. I tell people, I’m like, if your people don’t know how to use a computer, I can’t teach them how to use AI. So it’s like talking about a J-curve, but I’m like

Marlene Gebauer (35:42)
Ha

Dr. Maryam Salehijam (35:43)
Actually like you want your people to get more efficient, just sign them up for a typing class. They go what? I’m like I just saw them share their screen and type. Literally a typing class is all they need. And they go what? I’m like, yep, three months typing class. That’s their typing is what’s slowing them down. And they go, Are you kidding me? I’m like, no. This is not an AI. It’s like they can’t copy paste. They can’t use the short like it’s literally sometimes that bad. And so I always walk it back to like, hey, let’s actually see what’s happening visibly. Look at it. And then we’ll know, The reason you’re spending 10 hours on a contract is because you don’t know how to use Control-F. Like this is And it hurts to hear it, right? Because everybody doesn’t want to like people don’t want to hear that they’re worse at something than other people. But literally I’ve seen lawyers not know how to use a computer. And it is concerning. And then they wanna go use AI, which is even more concerning because then they really don’t know what’s happening. And so that’s when we run into they submitted stuff that doesn’t make sense. They used it wrong. Because they don’t even understand how systems work. So that’s where I go again, wait a minute. Like, who’s going to be touching this? And who’s going to be approving this? Because you’re about to set yourself up for failure. If you give people access to AI engines who don’t know how systems and softwares and API calls work. So it’s again, education is a big part of my company to make sure people don’t put themselves in the hot seat.

Greg Lambert (37:11)
All right, well let’s say that you found the magic firm that has people that understands computers, can type, can use Control-F to find things on a on a

Dr. Maryam Salehijam (37:22)
Ha ha ha.

Greg Lambert (37:22)
document. but there’s still even an the next step. So we’re looking at bringing in AI tools and I think we’re still thinking of them almost as magical, you know, because we don’t quite understand them. But how do you get your get people to stop thinking of them as a as a like a separate tool and just start seeing it as part of just the infrastructure and the way that things get done going forward? How do you go to that level?

Dr. Maryam Salehijam (37:55)
Mm. That is a great question. And there’s like a lot of conversations in tech around unified interfaces. Basically you just have one system and the back end has all access points, right? And then there’s the conversations around B where people get their work done, which is why we see so many Word add-ins with lawyers. But to change the way somebody does work, like the way their brain goes and does the work, is really, really hard. And the only way you can actually do that is by repetition. So it’s about every day we’re gonna Have this many hours where we’re gonna all work together, right? Whether it’s sharing screens, being on one call, and we’re gonna have a pod lead that says, What are you working on? And they say this and this, and they go, Okay, I need you to try it this way, but they’re doing it in front of their peers. Why? Because peer pressure works. I know it sounds like everyone’s I don’t want to do that, but peer pressure works like magic. That’s why classroom settings work, that’s why asking people questions in front of the whole class works. So we need to bring back that methodology when we’re teaching people how to use some. something. Otherwise they’re gonna kinda listen, do something else, not really take it in. But if they have to prove themselves in front of their peers and we’re lawyers and we’re perfectionists, they’re gonna actually listen and do it and try to be good at it ’cause we all love to compete and show that we’re capable. So it brings back that law school setting into the AI department setting.

Greg Lambert (39:11)
I say we have to bring back the Socratic method to training.

Marlene Gebauer (39:13)
Socratic method I was saying the same thing.

Dr. Maryam Salehijam (39:16)
It works! Like why reinvent the wheel? We know how people learn, so why are we trying to do it differently?

Marlene Gebauer (39:23)
So Maryam, we’re going to ask sort of a question of looking at the past and then Greg’s gonna ask our crystal ball question about what’s happening for the future. So tell me what is true today that wasn’t true a year ago.

Dr. Maryam Salehijam (39:41)
Great question. What is true today that’s not true a year ago was that we thought we’re gonna see a lot more AI companies pop up in the legal space. A year ago we said that. I’m not seeing that. I’m not seeing too many new players. And I’m not sure if it’s because the venture capital firms don’t want to fund them or everyone’s watching the market. But I really thought there was gonna be an explosion of tools. But

Marlene Gebauer (40:03)
It’s interesting ’cause it’s like I see like a little but l little tiny like point solution types of things, but nothing big.

Dr. Maryam Salehijam (40:08)
Yeah. Nothing major. Nothing big, nothing major. and so that was surprising to me, ’cause I thought we would see a lot more of it. And maybe everyone’s in the build stage right now and we’re gonna hear about it in 2027. That’s true, yeah.

Marlene Gebauer (40:20)
And then there’s a lot of buying. There’s a lot of there’s a lot of acquisitions too. Yeah.

Dr. Maryam Salehijam (40:24)
So I think funding is drying up, actually instead of going up. And maybe it’s ’cause the frontier labs moved into legal. Maybe that’s what’s scaring the investors, but

Greg Lambert (40:33)
I was gonna say, ChatGPT just launched, what is it, Astra for Law? Yeah.

Marlene Gebauer (40:37)
Mm-hmm.

Dr. Maryam Salehijam (40:37)
Astra, yes. So that was surprising to me. I did not see funding dry up so quickly. For new players. Now, the crystal ball moment, what will happen? As a good lawyer, I’ll say it depends. It depends on the market, it depends on the environment, depends on global warfare. But if I was just to look six months ahead of from now, I would say we’re gonna see a lot of legal departments and law firms ask the question of can we build something we own that can do what the vendors do, but cheaper or better? We don’t have to renew it every year, right? We can maintain it ourselves, it’ll be our workflows, and we buy limited licenses to the vendors for the people that really need it. Because right now the way legal tech is getting sold is traditional SaaS, right? Seat-based pricing, minimum a year. Often they’re pushing three years. They want multiple licenses. But if you look at the usage data, it doesn’t even make sense. So I think we’re gonna see more of that shift coming because right now the spend on technology is not, back to the ROI conversation, supported by the ROI versus their price point.

Marlene Gebauer (41:53)
Well, Dr. Maryam Salehijam, founder and CEO of Andarzi, thank you for joining us on the Geek in Review.

Dr. Maryam Salehijam (41:59)
Thank you so much, Marlene. Thank you, Greg.

Greg Lambert (42:02)
And thanks everyone for listening and if you enjoy the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Marlene Gebauer (42:12)
And Maryam, what is the best place for listeners to learn more about Andarzi and follow your work?

Dr. Maryam Salehijam (42:18)
If they follow me on LinkedIn, I’ll post links to my website, andarzy.com, and also my courses on AI fluency via Maven.

Greg Lambert (42:26)
I feel like we’ve flipped the script here at the end, Marlene. So I’m taking your part. And

Marlene Gebauer (42:30)
We did. Like I noticed.

Greg Lambert (42:34)
and as always, the music you hear is from our good friend Jerry David DeCicca Thank you, Jerry.

Marlene Gebauer (42:39)
Thanks, Jerry.

Greg Lambert (42:40)
All right, bye everyone.

Marlene Gebauer (42:42)
Bye.

]]> xlambert@gmail.com (Greg Lambert) Teaching a Multiple-Choice Machine to Read 18 Years of 3 Geeks https://www.geeklawblog.com/2026/09/teaching-a-multiple-choice-machine-to-read-18-years-of-3-geeks.html Fri, 25 Sep 2026 03:47:00 +0000 https://www.geeklawblog.com/?p=19561

What happened when I pointed Jev, SALI, and CLOC’s Core 12 at every post we’ve ever written

I read Nate Jones’s post on Jev, TypeSafe’s new “System One” model, and he described it as an LLM that can only talk in multiple choice. It reads what you give it, you hand it the possible answers, and it picks one and tells you how sure it is. It can’t write you a sentence. My first reaction was, “What good is that?” My second reaction, about ten minutes later, was, “Wait... that’s what a cataloger does all day.”

So I did what any recovering law librarian would do with a new classification toy. I grabbed the biggest pile of legal-industry text I have the rights to, which is every post on 3 Geeks and a Law Blog going back to July 2008 (2,269 posts, about 3.9 million words, including 84 Geek in Review episodes with full transcripts), and I tried to catalog all of it twice. First against the SALI Legal Matter Specification Standard, then against CLOC’s Core 12 legal operations functions.

Here’s a link to the spreadsheet with the SALI and CLOC tags.

Let me walk you through how it went, warts and all.

The Setup

The division of labor is the whole trick, so let me explain it before the numbers.

Jev did the first pass. It’s cheap (about four cents per million input tokens, with output free) and fast (about a quarter of a second per call in this run). I called it through OpenRouter, since TypeSafe paused new signups the day I tried to get a key. Every call asks Jev a batch of yes/no questions about one post (“Is Knowledge Management a substantial topic of this post?”), and Jev returns a probability for each one.

Claude did the second pass, and it did it inside my regular Cowork session, which meant no per-call API charges. Any tag where Jev was unsure went into a review queue. Claude read the post and kept a tag only if it could quote a sentence, word for word, that supported it. A script then went back and checked every one of those quotes against the actual post text.

Code did everything else. It enforced the rules that a child tag can’t survive without its parent, capped each branch at five tags per post, and merged everything into one spreadsheet.

Before touching the blog, I also ran a warm-up on the podcast RSS feed (370 episodes). That’s where most of the early mistakes happened, and I’ll get to those.

Run 1: SALI

SALI’s standard is a family tree of 18,324 tags, published as an open ontology file on GitHub. I used four branches: Area of Law, Service, Document / Artifact, and Legal Use Cases (about 1,200 tags in all). Jev can only answer the questions it’s handed, so the script walks the tree like a game of 20 Questions. It asks about the 31 top-level Areas of Law in one call, then goes one level down only where Jev said yes, then one more level.

The calibration step. Before running everything, we pulled a 100-post sample spread across the years, including 25 transcript episodes, and had Claude review every tag Jev scored 0.30 or higher. That’s 1,610 judgments, made without Claude seeing Jev’s score. This is the part that saved the project, because it showed Jev’s confidence scores meant something different than I assumed:

Jev’s score How often it was right
0.30 to 0.49 9%
0.50 to 0.69 20%
0.70 to 0.89 41%
0.90 and up 84%

On the podcast warm-up, I’d been letting Jev auto-keep anything at 0.70 or higher. On blog posts, that rule would have been wrong about half the time. The Document / Artifact branch was the worst of it. Below 0.80, Jev was almost always wrong about what kinds of documents a post discussed.

So we set a per-branch rule we called Policy A. Anything Jev scored at 0.90 or higher was kept automatically. A middle band went to Claude for review, and that band was different for each branch (0.50 to 0.89 for Legal Use Cases, 0.60 to 0.89 for Service, 0.80 to 0.89 for Documents, 0.30 to 0.89 for Area of Law). Everything else was dropped. The sample said that would give us about 94% precision while losing about 18% of the tags a full review would have found.

The full run, by the numbers:

  • Jev made 241,498 yes/no decisions across 14,657 calls on all 2,269 posts. Total cost: about $3.35.
  • Jev settled about 92% of those decisions on its own: 219,970 answers under 0.30 were dropped, and 1,536 answers at 0.90 or higher were kept (before the parent and cap rules trimmed that to 1,174).
  • 6,336 decisions (about 2.6%) went to Claude for review, spread over 1,347 posts. Claude kept 2,118 of them, and 1,910 survived the parent and cap rules.
  • Counting the calibration sample, Claude made 7,946 review calls on the SALI run, handled by 34 review agents running in batches.
  • Final result: 3,431 SALI tags on 1,151 posts. Legal Use Cases carried the load with 2,569 tags, followed by Document / Artifact (443), Service (220), and Area of Law (199).

The most common tags were Business of Law, Legal Research, Pricing Management, Knowledge Management, and Legal Process Improvement, which sounds about right for a blog that has spent eighteen years arguing about billable hours and law libraries.

Run 2: CLOC Core 12

CLOC’s Core 12 is a flat list of twelve legal operations functions (Financial Management, Knowledge Management, Technology, Training & Development, and so on). With no tree to walk, each post got exactly one Jev call with twelve questions.

The calibration step used the same 100 posts, with 318 judgments this time. Jev behaved much better on the flat list:

Jev’s score How often it was right
0.30 to 0.49 16%
0.50 to 0.69 41%
0.70 to 0.89 69%
0.90 and up 100% (47 of 47)

We auto-kept everything at 0.90 or higher and reviewed 0.50 to 0.89. For Information Governance and Business Intelligence we reviewed down to 0.30, because the sample showed real tags hiding lower down. The expected loss was about 7%.

The full run, by the numbers:

  • Jev made 27,228 yes/no decisions in 2,269 calls, in about two minutes, for about $0.39.
  • 450 decisions were 0.90 or higher and kept automatically (403 after the sample posts were swapped for their reviewed versions). 22,682 were under 0.30 and dropped.
  • 2,104 decisions (about 7.7%) went to Claude across 1,166 posts. Claude kept 1,298.
  • Counting calibration, Claude made 2,422 review calls on the CLOC run, handled by 13 review agents.
  • Final result: 1,849 CLOC tags on 1,190 posts. Technology led with 542, then Financial Management (214), Training & Development (149), and Knowledge Management (145).

What Worked

Letting Jev do the boring part. Across both runs, Jev answered 268,726 yes/no questions for under four dollars. Most of those answers were obvious “no” calls (a 2009 post about Twitter search tips is not about Bankruptcy Law), and Jev knocked them out at a quarter-second a call. That’s exactly the kind of job Nate described in his post, and on this project it held up well.

The quote rule. Requiring a verbatim quote for every kept tag turned out to be the single best quality control in the project. A script checked all of those quotes against the source text, and zero failed. It also means that every reviewed tag in the spreadsheet comes with its own receipt, which the librarian in me loves.

Calibrating before scaling. The 100-post sample cost thirty cents in Jev calls and one afternoon of review, and it kept me from shipping a dataset where a third of the tags were wrong.

Running Claude review inside Cowork. Moving the review out of the paid API and into my regular session changed the cost picture completely. The API version would have been something like $70 to $110 for the SALI run alone.

CLOC fit the content. A flat, twelve-item list turned out to be much easier for Jev than a deep tree, and it maps better to what this blog is actually about. Knowledge Management shows up on 145 posts under CLOC, compared with 83 under SALI.

Transcripts held up. I expected the long, rambling podcast conversations to confuse things. They didn’t. Transcript posts and regular posts scored about the same (55% and 53% precision at 0.70 and up in the SALI sample).

What Was Questionable

SALI is the wrong shape for this corpus. SALI was built to describe legal matters. Area of Law barely registered (199 tags across 18 years), and most of the work landed in Legal Use Cases. That’s a reasonable outcome for a business-of-law blog, but it tells me SALI is a better fit for matter data and client documents than for commentary about the industry.

Nearly half the posts have no SALI tags. 1,118 posts came out with no SALI tags, and 805 have no tags from either taxonomy. Plenty of those deserve to be empty (I’m not sure “Merry Christmas!” or “The Leadership and Management Styles of Dolly Parton” needs a SALI tag), but some are real misses. The AALL 2026 Annual Meeting preview should have picked up Legal Research, and it didn’t.

The reviewers haven’t been reviewed. All 10,368 review calls were made by Claude. Nobody has graded Claude against a human yet, and that includes me. The quote rule tells me the evidence really is in the post, and I still need a person to tell me whether Claude read it the right way.

The 84% auto-keep rate on SALI. Tags Jev scored 0.90 or higher went straight in without review. On the sample that was right 84% of the time, so something like one in six of those 1,174 auto-kept SALI tags is probably wrong.

Jev’s confident mistakes. Jev said a confident “yes” to Expert Opinion and Template on the Best Lawyers episode, and to Opinion Memo Practice on the LexisNexis CTO interview. That’s why the per-branch thresholds exist. Nate made this point in his post as well: calibration is measured across thousands of answers, so a 0.95 on any single post is still just a strong hunch.

What happened below 0.30. We never reviewed anything Jev scored under 0.30, so whatever real tags are hiding down there are unmeasured.

What Failed

The first dollar. The first pilot sent Jev’s close calls to Claude through the paid API. Claude kept all 85 of the borderline tags it reviewed, which makes a review step pretty pointless. Then 17 episodes failed after Claude had already answered and been billed. Fifteen of those failures happened because a fix I thought had been saved to my computer never actually landed, so the old script kept running. To top it off, the script didn’t count the cost of failed episodes, so my $1 key limit was gone before the report said it should be. We fixed the logging, tightened the prompt, started checking every saved file with a checksum, and eventually moved the review into Cowork.

The RSS feed was the wrong source. The podcast feed cuts show notes off at about 4,000 characters, right where the “Transcript” heading starts. Everything on the first podcast run was tagged from summaries only. The blog feed has the full text, which is why we switched.

I knocked over my own blog. While downloading the archive, about 65 pages in, geeklawblog.com started throwing Cloudflare 520 and 521 errors. My back-to-back requests probably didn’t help. We waited for it to come back and pulled the rest slowly, one page every few seconds.

Sponsor reads. About half the recent transcripts open with a Legal Technology Hub segment on AI governance, which would have tagged every episode with AI governance. The first attempt to strip it out missed most of them. A second pass that looked for the sponsor’s guest speaker did better, but it’s still a rough heuristic.

Where This Leaves Me

For less than five dollars in Jev and OpenRouter charges, plus about 47 review agents’ worth of Cowork usage, every post on the blog now has two sets of standard tags. Every tag comes with a Jev score, a record of who made the call, and in most cases a quote from the post itself. That’s a catalog I would have killed for back when I was running a law firm library with a card catalog and a budget line for “cataloging backlog.”

The part that sticks with me is how much the calibration step mattered. I went in assuming Jev’s confidence scores could be taken at face value, and on a deep taxonomy like SALI that assumption would have burned me. Thirty cents and 100 posts worth of spot-checking showed me which score ranges needed a second reader.

This was a fun initial test that presented a few areas that need improvement, but there is a lot of potential here to open up a number of decision processes at a very affordable price.

]]> What happened when I pointed Jev, SALI, and CLOC’s Core 12 at every post we’ve ever written

I read Nate Jones’s post on Jev, TypeSafe’s new “System One” model, and he described it as an LLM that can only talk in multiple choice. It reads what you give it, you hand it the possible answers, and it picks one and tells you how sure it is. It can’t write you a sentence. My first reaction was, “What good is that?” My second reaction, about ten minutes later, was, “Wait… that’s what a cataloger does all day.”

So I did what any recovering law librarian would do with a new classification toy. I grabbed the biggest pile of legal-industry text I have the rights to, which is every post on 3 Geeks and a Law Blog going back to July 2008 (2,269 posts, about 3.9 million words, including 84 Geek in Review episodes with full transcripts), and I tried to catalog all of it twice. First against the SALI Legal Matter Specification Standard, then against CLOC’s Core 12 legal operations functions.

Here’s a link to the spreadsheet with the SALI and CLOC tags.

Let me walk you through how it went, warts and all.

The Setup

The division of labor is the whole trick, so let me explain it before the numbers.

Jev did the first pass. It’s cheap (about four cents per million input tokens, with output free) and fast (about a quarter of a second per call in this run). I called it through OpenRouter, since TypeSafe paused new signups the day I tried to get a key. Every call asks Jev a batch of yes/no questions about one post (“Is Knowledge Management a substantial topic of this post?”), and Jev returns a probability for each one.

Claude did the second pass, and it did it inside my regular Cowork session, which meant no per-call API charges. Any tag where Jev was unsure went into a review queue. Claude read the post and kept a tag only if it could quote a sentence, word for word, that supported it. A script then went back and checked every one of those quotes against the actual post text.

Code did everything else. It enforced the rules that a child tag can’t survive without its parent, capped each branch at five tags per post, and merged everything into one spreadsheet.

Before touching the blog, I also ran a warm-up on the podcast RSS feed (370 episodes). That’s where most of the early mistakes happened, and I’ll get to those.

Run 1: SALI

SALI’s standard is a family tree of 18,324 tags, published as an open ontology file on GitHub. I used four branches: Area of Law, Service, Document / Artifact, and Legal Use Cases (about 1,200 tags in all). Jev can only answer the questions it’s handed, so the script walks the tree like a game of 20 Questions. It asks about the 31 top-level Areas of Law in one call, then goes one level down only where Jev said yes, then one more level.

The calibration step. Before running everything, we pulled a 100-post sample spread across the years, including 25 transcript episodes, and had Claude review every tag Jev scored 0.30 or higher. That’s 1,610 judgments, made without Claude seeing Jev’s score. This is the part that saved the project, because it showed Jev’s confidence scores meant something different than I assumed:

Jev’s score How often it was right
0.30 to 0.49 9%
0.50 to 0.69 20%
0.70 to 0.89 41%
0.90 and up 84%

On the podcast warm-up, I’d been letting Jev auto-keep anything at 0.70 or higher. On blog posts, that rule would have been wrong about half the time. The Document / Artifact branch was the worst of it. Below 0.80, Jev was almost always wrong about what kinds of documents a post discussed.

So we set a per-branch rule we called Policy A. Anything Jev scored at 0.90 or higher was kept automatically. A middle band went to Claude for review, and that band was different for each branch (0.50 to 0.89 for Legal Use Cases, 0.60 to 0.89 for Service, 0.80 to 0.89 for Documents, 0.30 to 0.89 for Area of Law). Everything else was dropped. The sample said that would give us about 94% precision while losing about 18% of the tags a full review would have found.

The full run, by the numbers:

  • Jev made 241,498 yes/no decisions across 14,657 calls on all 2,269 posts. Total cost: about $3.35.
  • Jev settled about 92% of those decisions on its own: 219,970 answers under 0.30 were dropped, and 1,536 answers at 0.90 or higher were kept (before the parent and cap rules trimmed that to 1,174).
  • 6,336 decisions (about 2.6%) went to Claude for review, spread over 1,347 posts. Claude kept 2,118 of them, and 1,910 survived the parent and cap rules.
  • Counting the calibration sample, Claude made 7,946 review calls on the SALI run, handled by 34 review agents running in batches.
  • Final result: 3,431 SALI tags on 1,151 posts. Legal Use Cases carried the load with 2,569 tags, followed by Document / Artifact (443), Service (220), and Area of Law (199).

The most common tags were Business of Law, Legal Research, Pricing Management, Knowledge Management, and Legal Process Improvement, which sounds about right for a blog that has spent eighteen years arguing about billable hours and law libraries.

Run 2: CLOC Core 12

CLOC’s Core 12 is a flat list of twelve legal operations functions (Financial Management, Knowledge Management, Technology, Training & Development, and so on). With no tree to walk, each post got exactly one Jev call with twelve questions.

The calibration step used the same 100 posts, with 318 judgments this time. Jev behaved much better on the flat list:

Jev’s score How often it was right
0.30 to 0.49 16%
0.50 to 0.69 41%
0.70 to 0.89 69%
0.90 and up 100% (47 of 47)

We auto-kept everything at 0.90 or higher and reviewed 0.50 to 0.89. For Information Governance and Business Intelligence we reviewed down to 0.30, because the sample showed real tags hiding lower down. The expected loss was about 7%.

The full run, by the numbers:

  • Jev made 27,228 yes/no decisions in 2,269 calls, in about two minutes, for about $0.39.
  • 450 decisions were 0.90 or higher and kept automatically (403 after the sample posts were swapped for their reviewed versions). 22,682 were under 0.30 and dropped.
  • 2,104 decisions (about 7.7%) went to Claude across 1,166 posts. Claude kept 1,298.
  • Counting calibration, Claude made 2,422 review calls on the CLOC run, handled by 13 review agents.
  • Final result: 1,849 CLOC tags on 1,190 posts. Technology led with 542, then Financial Management (214), Training & Development (149), and Knowledge Management (145).

What Worked

Letting Jev do the boring part. Across both runs, Jev answered 268,726 yes/no questions for under four dollars. Most of those answers were obvious “no” calls (a 2009 post about Twitter search tips is not about Bankruptcy Law), and Jev knocked them out at a quarter-second a call. That’s exactly the kind of job Nate described in his post, and on this project it held up well.

The quote rule. Requiring a verbatim quote for every kept tag turned out to be the single best quality control in the project. A script checked all of those quotes against the source text, and zero failed. It also means that every reviewed tag in the spreadsheet comes with its own receipt, which the librarian in me loves.

Calibrating before scaling. The 100-post sample cost thirty cents in Jev calls and one afternoon of review, and it kept me from shipping a dataset where a third of the tags were wrong.

Running Claude review inside Cowork. Moving the review out of the paid API and into my regular session changed the cost picture completely. The API version would have been something like $70 to $110 for the SALI run alone.

CLOC fit the content. A flat, twelve-item list turned out to be much easier for Jev than a deep tree, and it maps better to what this blog is actually about. Knowledge Management shows up on 145 posts under CLOC, compared with 83 under SALI.

Transcripts held up. I expected the long, rambling podcast conversations to confuse things. They didn’t. Transcript posts and regular posts scored about the same (55% and 53% precision at 0.70 and up in the SALI sample).

What Was Questionable

SALI is the wrong shape for this corpus. SALI was built to describe legal matters. Area of Law barely registered (199 tags across 18 years), and most of the work landed in Legal Use Cases. That’s a reasonable outcome for a business-of-law blog, but it tells me SALI is a better fit for matter data and client documents than for commentary about the industry.

Nearly half the posts have no SALI tags. 1,118 posts came out with no SALI tags, and 805 have no tags from either taxonomy. Plenty of those deserve to be empty (I’m not sure “Merry Christmas!” or “The Leadership and Management Styles of Dolly Parton” needs a SALI tag), but some are real misses. The AALL 2026 Annual Meeting preview should have picked up Legal Research, and it didn’t.

The reviewers haven’t been reviewed. All 10,368 review calls were made by Claude. Nobody has graded Claude against a human yet, and that includes me. The quote rule tells me the evidence really is in the post, and I still need a person to tell me whether Claude read it the right way.

The 84% auto-keep rate on SALI. Tags Jev scored 0.90 or higher went straight in without review. On the sample that was right 84% of the time, so something like one in six of those 1,174 auto-kept SALI tags is probably wrong.

Jev’s confident mistakes. Jev said a confident “yes” to Expert Opinion and Template on the Best Lawyers episode, and to Opinion Memo Practice on the LexisNexis CTO interview. That’s why the per-branch thresholds exist. Nate made this point in his post as well: calibration is measured across thousands of answers, so a 0.95 on any single post is still just a strong hunch.

What happened below 0.30. We never reviewed anything Jev scored under 0.30, so whatever real tags are hiding down there are unmeasured.

What Failed

The first dollar. The first pilot sent Jev’s close calls to Claude through the paid API. Claude kept all 85 of the borderline tags it reviewed, which makes a review step pretty pointless. Then 17 episodes failed after Claude had already answered and been billed. Fifteen of those failures happened because a fix I thought had been saved to my computer never actually landed, so the old script kept running. To top it off, the script didn’t count the cost of failed episodes, so my $1 key limit was gone before the report said it should be. We fixed the logging, tightened the prompt, started checking every saved file with a checksum, and eventually moved the review into Cowork.

The RSS feed was the wrong source. The podcast feed cuts show notes off at about 4,000 characters, right where the “Transcript” heading starts. Everything on the first podcast run was tagged from summaries only. The blog feed has the full text, which is why we switched.

I knocked over my own blog. While downloading the archive, about 65 pages in, geeklawblog.com started throwing Cloudflare 520 and 521 errors. My back-to-back requests probably didn’t help. We waited for it to come back and pulled the rest slowly, one page every few seconds.

Sponsor reads. About half the recent transcripts open with a Legal Technology Hub segment on AI governance, which would have tagged every episode with AI governance. The first attempt to strip it out missed most of them. A second pass that looked for the sponsor’s guest speaker did better, but it’s still a rough heuristic.

Where This Leaves Me

For less than five dollars in Jev and OpenRouter charges, plus about 47 review agents’ worth of Cowork usage, every post on the blog now has two sets of standard tags. Every tag comes with a Jev score, a record of who made the call, and in most cases a quote from the post itself. That’s a catalog I would have killed for back when I was running a law firm library with a card catalog and a budget line for “cataloging backlog.”

The part that sticks with me is how much the calibration step mattered. I went in assuming Jev’s confidence scores could be taken at face value, and on a deep taxonomy like SALI that assumption would have burned me. Thirty cents and 100 posts worth of spot-checking showed me which score ranges needed a second reader.

This was a fun initial test that presented a few areas that need improvement, but there is a lot of potential here to open up a number of decision processes at a very affordable price.

]]> xlambert@gmail.com (Greg Lambert) Patlytics and the Patent AI Race: Paul Lee on USPTO Workflows, Human Judgment, and the AI Dividend https://www.geeklawblog.com/2026/09/patlytics-and-the-patent-ai-race-paul-lee-on-uspto-workflows-human-judgment-and-the-ai-dividend.html Mon, 14 Sep 2026 10:38:44 +0000 https://www.geeklawblog.com/?p=19550

Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries.

Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg.

The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice.

Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation.

Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Greg Lambert (00:00)

Hey, everybody. This week we have Paul Lee from Patlytics on the show, and we’ll be talking about patents, AI, the USPTO, and a whole lot more. But first up, here’s a word from our sponsor.

Marlene Gebauer (00:19)

Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:28)

Thank you. Good to see you again.

In the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Most law firms and departments have at least an AI policy of some kind, and there are some broad standards starting to emerge.

A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, toward a more informative approach that defines different risk categories and makes distinctions between routine, low-risk uses of AI and other, more substantial use cases.

I think that’s appropriate for where we are. It’s also what clients are coming to expect from their advisors.

One of the bigger challenges we’re seeing right now is how you express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is likely using AI day to day. They should be able to explain the policy themselves. How else do you know they’re complying with it?

We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies, which are often very much “thou shalt not,” and turn them into more readable, appropriate governance positions that inform the conversation with clients.

If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or visit legaltechnologyhub.com.

Marlene Gebauer (02:09)

Thank you, Sam. It is important to have an AI policy in plain language so people understand it.

Sam Moore (02:16)

Absolutely.

Greg Lambert (02:17)

Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Greg Lambert, flying solo this week. Marlene is out.

This week I am joined by Paul Lee. Paul’s the co-founder of Patlytics. Paul came to legal technology from the venture capital side, and he and his team have built Patlytics specifically around patent workflows, from drafting and prior art analysis to office actions and litigation.

Paul, welcome to The Geek in Review. Good to have you here.

Paul Lee (02:52)

Hey, Greg. Thanks for the invite. Awesome to be here.

Greg Lambert (02:55)

Paul, before we jump into the questions, I wanted to see if you would give us a quick elevator pitch on what Patlytics does and what problems your customers come to you to solve.

Paul Lee (03:09)

Yeah, absolutely.

Patlytics’ mission since day one, and that hasn’t changed since founding the company, has always been to create exceptional value in the IP ecosystem.

The way we do that is to transform and reduce transactional inefficiency, from the creation of IP and patents, from drafting to office actions on the prosecution side, to portfolio management of IP and patents for corporations and the law firms we work with, to litigation readiness and litigation itself.

The whole lifecycle is something I saw as an opportunity where we could have a massive impact. I think we’ve done right by our customers one by one by making sure we help as much as possible.

Greg Lambert (04:12)

Great. Thank you for doing that.

Let’s jump in. Paul, before building Patlytics, you took the time to learn the industry. I think you spoke with more than 100 IP professionals about how patent work gets done.

Having worked with my IP lawyers, I understand it’s highly detailed work. Sometimes it is low-margin work, so efficiencies built into the process are likely welcome by the community.

When you were having those conversations, what did they reveal about patent practice that convinced you Patlytics would be a valuable tool?

Paul Lee (04:58)

Yeah, that’s a great question, Greg.

It’s funny to think about where we are now and where we were when it was only an idea and we were starting up.

Right now, we work with around 55 percent of the Am Law 100 firms. We work with hundreds of corporations, from massive biotech and pharma companies to technology companies across Asia, North America, Europe, and elsewhere.

To understand the momentum we have now, you need to go back to where we were when things were starting.

For background, I do not have a patent attorney background. I was a venture capitalist, as you mentioned. One thing you do learn in venture capital is how to talk with a lot of people and potentially see patterns and insights.

In the early days, while figuring out whether this was an interesting opportunity, I spoke with more than 100 patent attorneys. Confidently, I would say many of them told me at the time that a lot of the processes were quite inefficient.

The fact that you might spend more than 10 hours writing a detailed specification for an application was wild.

Creating a claim chart used for IP litigation, with the level of effort, scrutiny, and back-and-forth involved, was something I saw firsthand. I thought that was pretty wild as well.

When you compound all of these use cases and processes across different parts of the IP lifecycle, I thought there would be tremendous value if we figured out how to create one platform to help with each of those workflows.

Greg Lambert (07:09)

I’m curious, did this idea come to you because you were working around patents, or how did you stumble across patents as a particular industry that needed this?

Paul Lee (07:24)

I first heard about patents back when I was a student. I was in Waterloo, and there was that massive Apple versus Samsung patent litigation battle happening.

At the time, as a student, I was shocked by the dollar value of all that.

Fast forward a couple of years. As a venture capitalist, you tend to invest in a lot of IP companies. Some of those companies spend a lot of money on trade secrets, patents, trademarks, and so on.

From my seat in venture capital, I was curious about how the bill could get so high on certain IP litigation matters. That got me curious.

My co-founder and CTO, Arthur, while he was building Magic, dealt with patent litigation and patent infringement matters.

Then I think the idea took off when my friend Bob Steinberg, who was chair of the IP litigation practice at Latham & Watkins, invited me over to his place and we discussed where he saw the opportunities.

It was a trifecta. It wasn’t one apple-hitting-the-ground kind of moment. The topic kept coming up, and that’s how we got started.

Greg Lambert (09:03)

I know a lot of startups are told to build into their budget the possibility of patent litigation or IP litigation at some point because many of them will face that.

I want to talk about something everyone I’ve talked to about Patlytics mentions, the fact that the USPTO is using Patlytics for its patent examiners.

What changes about the patent system when AI becomes part of the workflow on both the applicant side and the examiner side? What’s AI bringing into the overall workflow?

Paul Lee (09:50)

Greg, I need to preface this with the hundreds of pages I had to sign around confidentiality and the details of what we may or may not do with government agencies.

There are public records out there that people can search, but for this podcast, let me stay pretty high level about what we aim to do.

Greg Lambert (10:19)

Absolutely.

Paul Lee (10:20)

Going back to my original point, the patent and IP ecosystem is highly laborious and transactionally inefficient.

A good chunk of that exists at the corporate and law firm side because there are a lot of use cases, a lot of work, less budget, and the need to do more with less.

From the examination and government side, historically there have been opportunities and room for improvement as well.

We believe the right technology has the opportunity to add a lot of value to the whole flow. It has been rewarding for us to work with Google to make sure we’re on the right footing with everything we’re doing with government agencies.

What I would say is that USPTO examiners do a lot, and there’s a backlog associated with the amount of work involved.

The opportunity we have is to help make sure things remain high quality while helping with the backlog as much as possible.

Greg Lambert (11:56)

I want to talk about the way people are looking at AI through these broad AI platforms, the Harveys, Legoras, Westlaw, Lexis, and others.

I want to talk about being a specialist versus being a platform. What does a specialized patent platform like yours need to do to stand out and earn a permanent place in the law firm’s technology stack? What makes Patlytics shine?

Paul Lee (12:40)

It’s something I think about all the time, and something all of us as a team spend a lot of time thinking about.

The answer is relevance, Greg.

The question is whether we’re earning the right to be 10 times better than the status quo and provide a much better experience than generalized legal tools or the previous way of doing things.

That’s always the benchmark. The benchmark is what people did previously.

For us, we want to make sure the experience and outcomes derived from Patlytics are delightful and 10 times better.

I have a lot of respect for Harvey, Legora, and some of the names you mentioned. There’s always going to be a place for them. Some of the most interesting transformation in law is happening right now because of players like that.

With that said, what I hear over and over again is that the IP practice felt unloved.

Greg Lambert (14:03)

It’s those scientists. They never feel loved.

Paul Lee (14:08)

Exactly.

The IP practice feels the generalized tools, even with the ability to create your own workflows and make things more customized, never fully get there. They get to 80 or 85 percent.

We think of the patent ecosystem like an F1 race. It’s about getting to 99 percent.

When we started the company, I thought getting to 85 or 90 percent would be enough. That’s no longer the benchmark, and the benchmark moves year over year.

From a product perspective, we think about this and act on it constantly.

Compared with the existing flow for creating charts, creating applications, handling office actions, or pruning a portfolio, is the experience we’re putting in front of our customers genuinely 10 times more valuable than the previous way of doing things?

Greg Lambert (15:23)

We joke about scientists not feeling loved, but there are a lot of technical processes involved in setting up a patent filing.

You are moving into work such as patent drafting, office action responses, claim analysis, and even patent figures.

Where do you think technology should deliberately stop and require the patent lawyer to exercise judgment?

Or, even more broadly, where does it need to stop so the USPTO doesn’t look at the work and say AI did so much of this that it’s no longer eligible for a patent? What are you hearing in those areas?

Paul Lee (16:23)

I’m hearing a lot of things, but if I were to pinpoint one or two topics that keep coming up, number one is the demand to make sure human judgment is placed in the right spots.

At the end of the day, the practitioner still signs off, gives approval, and owns the outcome of the work product.

At Patlytics, we fundamentally believe there will always be a human-computer symbiosis. The human part will focus on judgment, while the AI and computer side helps develop the most useful ways to deliver those outcomes.

But the workflow will always require human judgment at the most important points.

That’s why tools in the past that said, “You don’t need to lift a finger. Click a button and the application gets submitted,” never worked out.

There are similar examples where people say, “Type something in, it drafts a claim chart, and you submit it.”

In fact, there has been some recent evidence of people being pulled into malpractice matters because of cases like that in the patent system.

We’re diligent and careful about this. It starts with the philosophy of human-computer symbiosis and making sure there are appropriate checkpoints across workflows, from prosecution to portfolio management to litigation.

I would add that client expectations are becoming higher as well.

Maybe it’s because of AI, or perhaps something else, but the client expectation is, “Use AI, but make sure the result is as good as it would have been if you spent many hours on it. We also expect it to be done in a fraction of the time, with even higher quality.”

That level of expectation puts law firms, and IP attorneys especially, under enormous pressure.

Going back to what you said, in intellectual property and patents specifically, each word matters so much.

Greg Lambert (19:31)

Absolutely.

Paul Lee (19:31)

I know in law each word matters. There are redlines and all of that.

But from patent drafting to patent litigation, this has given me a new sense of respect for the type of work patent attorneys do.

Greg Lambert (19:50)

Pulling on the thread of client demands, one of the biggest topics around AI in legal practice is who gets the AI dividend at the end of the day.

If AI turns work that once cost thousands of dollars in attorney time into something much faster, who ultimately captures the economic value of that AI dividend?

Paul Lee (20:31)

That’s a fascinating point, Greg.

I don’t think the market has figured that piece out yet. We’re moving through a lot of exploration and back-and-forth around this as we speak.

What I would say with certainty is that the transactional inefficiency of the past has come down.

Clients want collaboration. Law firms want faster collaboration done the right way. A lot of that has been achieved through what legal AI has become over the past couple of years.

When it comes to the economics of the value chain, clients want to capture 100 percent of that efficiency. Law firms want to capture as much of those efficiencies as possible as well.

I think the answer is meeting somewhere in the middle.

If the business model of law changes in a way where both sides benefit tremendously, that’s great, and there are examples of that happening right now.

We work with many Am Law 100 firms. One firm we work with is in the top five and historically was known for rarely using flat-rate arrangements.

Greg Lambert (22:10)

Right.

Paul Lee (22:12)

With AI, IP, and the way Patlytics is serving its offices and clients across these practice areas, an interesting business model has emerged.

On certain matters, we’ve seen more flat-rate business models where lawyers benefit because they are better able to predict how long something will take. Margins become more predictable, and lawyers are able to cycle through matters faster when those efficiencies are in place and demand exists.

On the other side, corporations are getting what they have wanted all along, which is greater predictability and lower bills.

I’d call that a win-win situation, and I think we’ll continue to see more of it.

Greg Lambert (23:11)

We talked briefly earlier about Patlytics being used both in-house and by outside counsel.

Where are you seeing that division of labor going? How much more upfront work is the in-house team doing before turning it over, and how has the division of who does what changed when it comes to patents?

Paul Lee (23:47)

For us at Patlytics, everything started with law firms in the first year. That’s where the demand was.

Take the patent prosecution practice as an example.

Historically, it has been one area where costs kept rising over the past 10 years, including inflation, while pricing hasn’t changed much. If anything, pricing has gone down a little.

You have costs going up on one side while the top line comes down. That leaves extremely thin margins. It’s a tough business.

What we’ve been able to accomplish is lowering the cost component in terms of hours and the way work gets done.

Even if the flat rate goes down a little, the margin calculation becomes much better for the prosecution practices we work with.

I’ve had partners at law firms tell me, “I had a record year because of Patlytics and the tooling we’ve put in place.”

I don’t want to get into specific numbers, but this particular partner was extremely happy with the outcome in his practice.

To your question about corporations and law firms and what work gets done where, corporations now have the ability to do more with less. They want to do things they simply didn’t have time for in the past.

I think Patlytics has helped in-house counsel do things such as running infringement analysis before a continuation, looking at the market more quickly before deciding which actions to take in a patent strategy, assessing patent pool M&A opportunities, and having greater confidence about where they’re headed for litigation readiness or potential cross-licensing.

They’re able to get down to claim construction and element-by-element analysis and, eventually, perhaps have greater insight ahead of a Markman hearing.

Greg Lambert (26:30)

Mm-hmm.

Paul Lee (26:31)

Those are more sophisticated outcomes that in-house counsel simply didn’t have much time for before.

We haven’t even talked about R&D, inventions, and engineering.

One of the biggest pain points for patent attorneys is dealing with many engineers submitting material. Attorneys have to parse through a lot of noise to find the signal.

That’s a laborious process.

We saw tremendous value in bringing all of these use cases, or “jobs to be done,” under one umbrella to provide a one-plus-one-equals-three type of experience.

Greg Lambert (27:23)

The more I’ve gotten into patents over the years, the more I realize it isn’t a straight line. There are so many things going back and forth.

Paul Lee (27:35)

Yeah. It’s like drawing many different circles, then drawing 20 zigzag lines, and saying, “Hey, figure it out.”

Greg Lambert (27:45)

An economist’s chart dream right there.

All right. A lot has changed, but is there something true for you, Patlytics, or the industry today that wasn’t true a year ago?

Paul Lee (28:05)

Greg, I’ll talk about patent attorneys and in-house attorneys who deal with IP.

A year ago, the sentiment was perhaps 20 percent optimists and 80 percent still somewhat skeptical.

Now I’d say it’s 95 percent optimists and 5 percent skeptical.

Within one year, we’ve gone from conversations where a firm partner or managing director said, “We’re not going to look into this for the next five or 10 years, if at all,” to many instances where I get a call saying, “Scratch what I said. Could we start something tomorrow?”

This is truly one of the most interesting times in legal. There’s a transformation happening that I think is generational and will leave a lot of second-order and third-order consequences.

Because the whole ecosystem is becoming AI-forward, people are now asking, “What else should we do?”

Greg Lambert (29:29)

Yeah.

Paul Lee (29:30)

There’s much more creativity, more pushing of limits, and more willingness to take bigger shots.

Greg Lambert (29:36)

That leads perfectly into my last question.

We ask all of our guests to pull out their crystal ball and peer into the future for us.

You’ve taken us from last year to this year. What challenges or changes do you think the industry is going to face that we should prepare for over the next two to three years?

Paul Lee (30:05)

That’s a tough one.

Greg Lambert (30:06)

Predictions are hard, especially when they’re about the future.

Paul Lee (30:10)

Yeah. I hope I’m right, because five years from now I want to look back at this.

Greg Lambert (30:15)

We’ll bring you back in a year and see how well you’re doing.

Paul Lee (30:19)

Greg, I’d say there’s a lot of buzz around AI right now and the use of AI, sometimes without enough intention or purpose.

I think in the future its use will become much more intentional.

One thing I deal with right now is creating a layer of transparency so costs don’t go through the roof from an LLM perspective, both for suppliers and for us as a customer of the main suppliers fueling the entire system, the LLMs.

I don’t think the level of growth we’ve seen around LLMs is going to change. People will keep using them and become more intentional about their use, while making sure AI continues to prove value.

But the economics of AI, from the LLM to the end customer, are where I expect to see even more change.

From the venture side, the market has often viewed AI as great because, when you invest in a company, the more people use the product, the more revenue the company generates.

The flaw in that logic is that customers have a budget for this kind of thing. There’s always a budget.

Even if your business model depends on consumption continuing to grow, a budget is a budget. There is only so much a customer will spend.

I think we’re going to deal with that type of correction pretty soon.

It will increasingly come down to proving value and ROI in a meaningful way.

Greg Lambert (32:14)

I think we’ve come to the end of our free ride when it comes to LLMs, and now we’re looking at consumption-based pricing.

You want to make sure you’re not spending $50,000 pursuing a $5,000 matter.

I imagine on the business side you don’t have a “max token” scoreboard anymore. It’s more about being smart about how you’re using the technology.

Paul Lee (32:47)

Yeah. Token-maxing is likely not going to be a thing anymore, unless the LLM economics make sense for the whole value chain.

Greg Lambert (32:57)

I’m waiting for it to go the way of text messaging.

We’ll pay 10 cents a message for a couple of years, then hopefully you buy one plan and we go back to unlimited.

Paul Lee (33:06)

I remember that.

Being a Waterloo guy, that’s why BBM was so awesome. Did you have a BlackBerry back in the day?

Greg Lambert (33:13)

I did not.

Somehow I missed that and went straight from my Palm Pre to an iPhone.

Paul Lee (33:25)

That’s even better.

Greg Lambert (33:29)

Paul Lee from Patlytics, it’s been a pleasure having you on here and geeking out. I appreciate you being here.

Paul Lee (33:38)

Yeah, I had a lot of fun, Greg. Thanks for the invite.

Greg Lambert (33:41)

Absolutely.

And thanks to everyone who’s listening. If you enjoyed the show, please share it with a colleague. I’d love to hear from you on LinkedIn or Substack.

Paul, where’s the best place for people to learn more about Patlytics or the things you’re up to?

Paul Lee (33:59)

Definitely LinkedIn. Feel free to add me.

I’m in conversation with a lot of forward-thinking IP leaders and innovation folks from corporations and law firms, and I’d love to chat.

Also, feel free to give us a shout through the Patlytics homepage at patlytics.ai.

Greg Lambert (34:28)

All right. Thanks again.

And as always, the music you hear is from our friend Jerry David DeCicca.

Thank you, and talk to you later.

Paul Lee (34:37)

See you, Greg.

]]> Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries.

Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg.

The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice.

Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation.

Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Greg Lambert (00:00)

Hey, everybody. This week we have Paul Lee from Patlytics on the show, and we’ll be talking about patents, AI, the USPTO, and a whole lot more. But first up, here’s a word from our sponsor.

Marlene Gebauer (00:19)

Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:28)

Thank you. Good to see you again.

In the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Most law firms and departments have at least an AI policy of some kind, and there are some broad standards starting to emerge.

A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, toward a more informative approach that defines different risk categories and makes distinctions between routine, low-risk uses of AI and other, more substantial use cases.

I think that’s appropriate for where we are. It’s also what clients are coming to expect from their advisors.

One of the bigger challenges we’re seeing right now is how you express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is likely using AI day to day. They should be able to explain the policy themselves. How else do you know they’re complying with it?

We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies, which are often very much “thou shalt not,” and turn them into more readable, appropriate governance positions that inform the conversation with clients.

If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or visit legaltechnologyhub.com.

Marlene Gebauer (02:09)

Thank you, Sam. It is important to have an AI policy in plain language so people understand it.

Sam Moore (02:16)

Absolutely.

Greg Lambert (02:17)

Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Greg Lambert, flying solo this week. Marlene is out.

This week I am joined by Paul Lee. Paul’s the co-founder of Patlytics. Paul came to legal technology from the venture capital side, and he and his team have built Patlytics specifically around patent workflows, from drafting and prior art analysis to office actions and litigation.

Paul, welcome to The Geek in Review. Good to have you here.

Paul Lee (02:52)

Hey, Greg. Thanks for the invite. Awesome to be here.

Greg Lambert (02:55)

Paul, before we jump into the questions, I wanted to see if you would give us a quick elevator pitch on what Patlytics does and what problems your customers come to you to solve.

Paul Lee (03:09)

Yeah, absolutely.

Patlytics’ mission since day one, and that hasn’t changed since founding the company, has always been to create exceptional value in the IP ecosystem.

The way we do that is to transform and reduce transactional inefficiency, from the creation of IP and patents, from drafting to office actions on the prosecution side, to portfolio management of IP and patents for corporations and the law firms we work with, to litigation readiness and litigation itself.

The whole lifecycle is something I saw as an opportunity where we could have a massive impact. I think we’ve done right by our customers one by one by making sure we help as much as possible.

Greg Lambert (04:12)

Great. Thank you for doing that.

Let’s jump in. Paul, before building Patlytics, you took the time to learn the industry. I think you spoke with more than 100 IP professionals about how patent work gets done.

Having worked with my IP lawyers, I understand it’s highly detailed work. Sometimes it is low-margin work, so efficiencies built into the process are likely welcome by the community.

When you were having those conversations, what did they reveal about patent practice that convinced you Patlytics would be a valuable tool?

Paul Lee (04:58)

Yeah, that’s a great question, Greg.

It’s funny to think about where we are now and where we were when it was only an idea and we were starting up.

Right now, we work with around 55 percent of the Am Law 100 firms. We work with hundreds of corporations, from massive biotech and pharma companies to technology companies across Asia, North America, Europe, and elsewhere.

To understand the momentum we have now, you need to go back to where we were when things were starting.

For background, I do not have a patent attorney background. I was a venture capitalist, as you mentioned. One thing you do learn in venture capital is how to talk with a lot of people and potentially see patterns and insights.

In the early days, while figuring out whether this was an interesting opportunity, I spoke with more than 100 patent attorneys. Confidently, I would say many of them told me at the time that a lot of the processes were quite inefficient.

The fact that you might spend more than 10 hours writing a detailed specification for an application was wild.

Creating a claim chart used for IP litigation, with the level of effort, scrutiny, and back-and-forth involved, was something I saw firsthand. I thought that was pretty wild as well.

When you compound all of these use cases and processes across different parts of the IP lifecycle, I thought there would be tremendous value if we figured out how to create one platform to help with each of those workflows.

Greg Lambert (07:09)

I’m curious, did this idea come to you because you were working around patents, or how did you stumble across patents as a particular industry that needed this?

Paul Lee (07:24)

I first heard about patents back when I was a student. I was in Waterloo, and there was that massive Apple versus Samsung patent litigation battle happening.

At the time, as a student, I was shocked by the dollar value of all that.

Fast forward a couple of years. As a venture capitalist, you tend to invest in a lot of IP companies. Some of those companies spend a lot of money on trade secrets, patents, trademarks, and so on.

From my seat in venture capital, I was curious about how the bill could get so high on certain IP litigation matters. That got me curious.

My co-founder and CTO, Arthur, while he was building Magic, dealt with patent litigation and patent infringement matters.

Then I think the idea took off when my friend Bob Steinberg, who was chair of the IP litigation practice at Latham & Watkins, invited me over to his place and we discussed where he saw the opportunities.

It was a trifecta. It wasn’t one apple-hitting-the-ground kind of moment. The topic kept coming up, and that’s how we got started.

Greg Lambert (09:03)

I know a lot of startups are told to build into their budget the possibility of patent litigation or IP litigation at some point because many of them will face that.

I want to talk about something everyone I’ve talked to about Patlytics mentions, the fact that the USPTO is using Patlytics for its patent examiners.

What changes about the patent system when AI becomes part of the workflow on both the applicant side and the examiner side? What’s AI bringing into the overall workflow?

Paul Lee (09:50)

Greg, I need to preface this with the hundreds of pages I had to sign around confidentiality and the details of what we may or may not do with government agencies.

There are public records out there that people can search, but for this podcast, let me stay pretty high level about what we aim to do.

Greg Lambert (10:19)

Absolutely.

Paul Lee (10:20)

Going back to my original point, the patent and IP ecosystem is highly laborious and transactionally inefficient.

A good chunk of that exists at the corporate and law firm side because there are a lot of use cases, a lot of work, less budget, and the need to do more with less.

From the examination and government side, historically there have been opportunities and room for improvement as well.

We believe the right technology has the opportunity to add a lot of value to the whole flow. It has been rewarding for us to work with Google to make sure we’re on the right footing with everything we’re doing with government agencies.

What I would say is that USPTO examiners do a lot, and there’s a backlog associated with the amount of work involved.

The opportunity we have is to help make sure things remain high quality while helping with the backlog as much as possible.

Greg Lambert (11:56)

I want to talk about the way people are looking at AI through these broad AI platforms, the Harveys, Legoras, Westlaw, Lexis, and others.

I want to talk about being a specialist versus being a platform. What does a specialized patent platform like yours need to do to stand out and earn a permanent place in the law firm’s technology stack? What makes Patlytics shine?

Paul Lee (12:40)

It’s something I think about all the time, and something all of us as a team spend a lot of time thinking about.

The answer is relevance, Greg.

The question is whether we’re earning the right to be 10 times better than the status quo and provide a much better experience than generalized legal tools or the previous way of doing things.

That’s always the benchmark. The benchmark is what people did previously.

For us, we want to make sure the experience and outcomes derived from Patlytics are delightful and 10 times better.

I have a lot of respect for Harvey, Legora, and some of the names you mentioned. There’s always going to be a place for them. Some of the most interesting transformation in law is happening right now because of players like that.

With that said, what I hear over and over again is that the IP practice felt unloved.

Greg Lambert (14:03)

It’s those scientists. They never feel loved.

Paul Lee (14:08)

Exactly.

The IP practice feels the generalized tools, even with the ability to create your own workflows and make things more customized, never fully get there. They get to 80 or 85 percent.

We think of the patent ecosystem like an F1 race. It’s about getting to 99 percent.

When we started the company, I thought getting to 85 or 90 percent would be enough. That’s no longer the benchmark, and the benchmark moves year over year.

From a product perspective, we think about this and act on it constantly.

Compared with the existing flow for creating charts, creating applications, handling office actions, or pruning a portfolio, is the experience we’re putting in front of our customers genuinely 10 times more valuable than the previous way of doing things?

Greg Lambert (15:23)

We joke about scientists not feeling loved, but there are a lot of technical processes involved in setting up a patent filing.

You are moving into work such as patent drafting, office action responses, claim analysis, and even patent figures.

Where do you think technology should deliberately stop and require the patent lawyer to exercise judgment?

Or, even more broadly, where does it need to stop so the USPTO doesn’t look at the work and say AI did so much of this that it’s no longer eligible for a patent? What are you hearing in those areas?

Paul Lee (16:23)

I’m hearing a lot of things, but if I were to pinpoint one or two topics that keep coming up, number one is the demand to make sure human judgment is placed in the right spots.

At the end of the day, the practitioner still signs off, gives approval, and owns the outcome of the work product.

At Patlytics, we fundamentally believe there will always be a human-computer symbiosis. The human part will focus on judgment, while the AI and computer side helps develop the most useful ways to deliver those outcomes.

But the workflow will always require human judgment at the most important points.

That’s why tools in the past that said, “You don’t need to lift a finger. Click a button and the application gets submitted,” never worked out.

There are similar examples where people say, “Type something in, it drafts a claim chart, and you submit it.”

In fact, there has been some recent evidence of people being pulled into malpractice matters because of cases like that in the patent system.

We’re diligent and careful about this. It starts with the philosophy of human-computer symbiosis and making sure there are appropriate checkpoints across workflows, from prosecution to portfolio management to litigation.

I would add that client expectations are becoming higher as well.

Maybe it’s because of AI, or perhaps something else, but the client expectation is, “Use AI, but make sure the result is as good as it would have been if you spent many hours on it. We also expect it to be done in a fraction of the time, with even higher quality.”

That level of expectation puts law firms, and IP attorneys especially, under enormous pressure.

Going back to what you said, in intellectual property and patents specifically, each word matters so much.

Greg Lambert (19:31)

Absolutely.

Paul Lee (19:31)

I know in law each word matters. There are redlines and all of that.

But from patent drafting to patent litigation, this has given me a new sense of respect for the type of work patent attorneys do.

Greg Lambert (19:50)

Pulling on the thread of client demands, one of the biggest topics around AI in legal practice is who gets the AI dividend at the end of the day.

If AI turns work that once cost thousands of dollars in attorney time into something much faster, who ultimately captures the economic value of that AI dividend?

Paul Lee (20:31)

That’s a fascinating point, Greg.

I don’t think the market has figured that piece out yet. We’re moving through a lot of exploration and back-and-forth around this as we speak.

What I would say with certainty is that the transactional inefficiency of the past has come down.

Clients want collaboration. Law firms want faster collaboration done the right way. A lot of that has been achieved through what legal AI has become over the past couple of years.

When it comes to the economics of the value chain, clients want to capture 100 percent of that efficiency. Law firms want to capture as much of those efficiencies as possible as well.

I think the answer is meeting somewhere in the middle.

If the business model of law changes in a way where both sides benefit tremendously, that’s great, and there are examples of that happening right now.

We work with many Am Law 100 firms. One firm we work with is in the top five and historically was known for rarely using flat-rate arrangements.

Greg Lambert (22:10)

Right.

Paul Lee (22:12)

With AI, IP, and the way Patlytics is serving its offices and clients across these practice areas, an interesting business model has emerged.

On certain matters, we’ve seen more flat-rate business models where lawyers benefit because they are better able to predict how long something will take. Margins become more predictable, and lawyers are able to cycle through matters faster when those efficiencies are in place and demand exists.

On the other side, corporations are getting what they have wanted all along, which is greater predictability and lower bills.

I’d call that a win-win situation, and I think we’ll continue to see more of it.

Greg Lambert (23:11)

We talked briefly earlier about Patlytics being used both in-house and by outside counsel.

Where are you seeing that division of labor going? How much more upfront work is the in-house team doing before turning it over, and how has the division of who does what changed when it comes to patents?

Paul Lee (23:47)

For us at Patlytics, everything started with law firms in the first year. That’s where the demand was.

Take the patent prosecution practice as an example.

Historically, it has been one area where costs kept rising over the past 10 years, including inflation, while pricing hasn’t changed much. If anything, pricing has gone down a little.

You have costs going up on one side while the top line comes down. That leaves extremely thin margins. It’s a tough business.

What we’ve been able to accomplish is lowering the cost component in terms of hours and the way work gets done.

Even if the flat rate goes down a little, the margin calculation becomes much better for the prosecution practices we work with.

I’ve had partners at law firms tell me, “I had a record year because of Patlytics and the tooling we’ve put in place.”

I don’t want to get into specific numbers, but this particular partner was extremely happy with the outcome in his practice.

To your question about corporations and law firms and what work gets done where, corporations now have the ability to do more with less. They want to do things they simply didn’t have time for in the past.

I think Patlytics has helped in-house counsel do things such as running infringement analysis before a continuation, looking at the market more quickly before deciding which actions to take in a patent strategy, assessing patent pool M&A opportunities, and having greater confidence about where they’re headed for litigation readiness or potential cross-licensing.

They’re able to get down to claim construction and element-by-element analysis and, eventually, perhaps have greater insight ahead of a Markman hearing.

Greg Lambert (26:30)

Mm-hmm.

Paul Lee (26:31)

Those are more sophisticated outcomes that in-house counsel simply didn’t have much time for before.

We haven’t even talked about R&D, inventions, and engineering.

One of the biggest pain points for patent attorneys is dealing with many engineers submitting material. Attorneys have to parse through a lot of noise to find the signal.

That’s a laborious process.

We saw tremendous value in bringing all of these use cases, or “jobs to be done,” under one umbrella to provide a one-plus-one-equals-three type of experience.

Greg Lambert (27:23)

The more I’ve gotten into patents over the years, the more I realize it isn’t a straight line. There are so many things going back and forth.

Paul Lee (27:35)

Yeah. It’s like drawing many different circles, then drawing 20 zigzag lines, and saying, “Hey, figure it out.”

Greg Lambert (27:45)

An economist’s chart dream right there.

All right. A lot has changed, but is there something true for you, Patlytics, or the industry today that wasn’t true a year ago?

Paul Lee (28:05)

Greg, I’ll talk about patent attorneys and in-house attorneys who deal with IP.

A year ago, the sentiment was perhaps 20 percent optimists and 80 percent still somewhat skeptical.

Now I’d say it’s 95 percent optimists and 5 percent skeptical.

Within one year, we’ve gone from conversations where a firm partner or managing director said, “We’re not going to look into this for the next five or 10 years, if at all,” to many instances where I get a call saying, “Scratch what I said. Could we start something tomorrow?”

This is truly one of the most interesting times in legal. There’s a transformation happening that I think is generational and will leave a lot of second-order and third-order consequences.

Because the whole ecosystem is becoming AI-forward, people are now asking, “What else should we do?”

Greg Lambert (29:29)

Yeah.

Paul Lee (29:30)

There’s much more creativity, more pushing of limits, and more willingness to take bigger shots.

Greg Lambert (29:36)

That leads perfectly into my last question.

We ask all of our guests to pull out their crystal ball and peer into the future for us.

You’ve taken us from last year to this year. What challenges or changes do you think the industry is going to face that we should prepare for over the next two to three years?

Paul Lee (30:05)

That’s a tough one.

Greg Lambert (30:06)

Predictions are hard, especially when they’re about the future.

Paul Lee (30:10)

Yeah. I hope I’m right, because five years from now I want to look back at this.

Greg Lambert (30:15)

We’ll bring you back in a year and see how well you’re doing.

Paul Lee (30:19)

Greg, I’d say there’s a lot of buzz around AI right now and the use of AI, sometimes without enough intention or purpose.

I think in the future its use will become much more intentional.

One thing I deal with right now is creating a layer of transparency so costs don’t go through the roof from an LLM perspective, both for suppliers and for us as a customer of the main suppliers fueling the entire system, the LLMs.

I don’t think the level of growth we’ve seen around LLMs is going to change. People will keep using them and become more intentional about their use, while making sure AI continues to prove value.

But the economics of AI, from the LLM to the end customer, are where I expect to see even more change.

From the venture side, the market has often viewed AI as great because, when you invest in a company, the more people use the product, the more revenue the company generates.

The flaw in that logic is that customers have a budget for this kind of thing. There’s always a budget.

Even if your business model depends on consumption continuing to grow, a budget is a budget. There is only so much a customer will spend.

I think we’re going to deal with that type of correction pretty soon.

It will increasingly come down to proving value and ROI in a meaningful way.

Greg Lambert (32:14)

I think we’ve come to the end of our free ride when it comes to LLMs, and now we’re looking at consumption-based pricing.

You want to make sure you’re not spending $50,000 pursuing a $5,000 matter.

I imagine on the business side you don’t have a “max token” scoreboard anymore. It’s more about being smart about how you’re using the technology.

Paul Lee (32:47)

Yeah. Token-maxing is likely not going to be a thing anymore, unless the LLM economics make sense for the whole value chain.

Greg Lambert (32:57)

I’m waiting for it to go the way of text messaging.

We’ll pay 10 cents a message for a couple of years, then hopefully you buy one plan and we go back to unlimited.

Paul Lee (33:06)

I remember that.

Being a Waterloo guy, that’s why BBM was so awesome. Did you have a BlackBerry back in the day?

Greg Lambert (33:13)

I did not.

Somehow I missed that and went straight from my Palm Pre to an iPhone.

Paul Lee (33:25)

That’s even better.

Greg Lambert (33:29)

Paul Lee from Patlytics, it’s been a pleasure having you on here and geeking out. I appreciate you being here.

Paul Lee (33:38)

Yeah, I had a lot of fun, Greg. Thanks for the invite.

Greg Lambert (33:41)

Absolutely.

And thanks to everyone who’s listening. If you enjoyed the show, please share it with a colleague. I’d love to hear from you on LinkedIn or Substack.

Paul, where’s the best place for people to learn more about Patlytics or the things you’re up to?

Paul Lee (33:59)

Definitely LinkedIn. Feel free to add me.

I’m in conversation with a lot of forward-thinking IP leaders and innovation folks from corporations and law firms, and I’d love to chat.

Also, feel free to give us a shout through the Patlytics homepage at patlytics.ai.

Greg Lambert (34:28)

All right. Thanks again.

And as always, the music you hear is from our friend Jerry David DeCicca.

Thank you, and talk to you later.

Paul Lee (34:37)

See you, Greg.

]]> xlambert@gmail.com (Greg Lambert) Beyond the Law Firm Pyramid: Manuel Deó on Ambar, Fractional Legal Talent, and the Future of Legal Delivery https://www.geeklawblog.com/2026/09/beyond-the-law-firm-pyramid-manuel-deo-on-ambar-fractional-legal-talent-and-the-future-of-legal-delivery.html Mon, 07 Sep 2026 12:56:22 +0000 https://www.geeklawblog.com/?p=19543

What happens when you separate elite legal talent from the traditional law firm structure? This week on The Geek in Review, we talk with Manuel Deó, co-founder and co-CEO of Ambar Partners, about a model designed around senior independent lawyers, flexible capacity, enterprise technology, and client choice. Deó explains why he and co-founder Rosa Espín did not set out to replace Big Law, but instead to address a gap between permanent in-house hiring and traditional outside counsel.

At the center of Ambar’s model is a simple idea: legal demand comes in different shapes, and the delivery model should match the problem. Deó describes how Ambar gives lawyers control over the clients, projects, fees, and schedules they take on, while giving clients greater visibility into cost and the individual lawyers doing the work. He also discusses Ambar’s recent Chambers recognition and argues that the term “alternative” is starting to lose some of its usefulness as clients grow more comfortable assembling legal services from a wider range of providers.

Deó walks through what Ambar calls its legal operating system, built around belonging, business, backbone, and badge. The model combines a professional community with business development, compliance, contracting, billing, technology, and institutional credibility for independent lawyers and specialist boutiques. Ambar’s public materials describe a shared technology environment that includes tools such as Harvey, Microsoft Copilot, Legora, and other legal technology products. The goal, according to Deó, is to give independent lawyers access to the infrastructure associated with a large firm without requiring them to give up professional independence.

The conversation also turns to AI, knowledge, and professional judgment. Deó argues that legal knowledge is becoming more abundant while judgment grows more valuable. He describes Ambar’s work on “expert twins,” where approved knowledge, prior work, playbooks, and experience associated with an individual lawyer form a trusted layer for AI-assisted work. That emphasis on institutional knowledge and permissions echoes a broader trend across legal AI, where vendors are increasingly focused on connecting AI systems to trusted internal work product and organizational context.

Finally, Deó offers a broader view of where legal delivery is heading. He sees legal departments assembling teams dynamically from in-house lawyers, traditional firms, independent specialists, boutiques, managed services, and AI agents based on the needs of a particular matter. Instead of asking which firm to hire, clients increasingly have reason to ask what combination of people, technology, expertise, and risk structure best fits the work. For Deó, the future belongs less to a single dominant delivery model and more to legal departments acting as orchestrators of capability.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

LINKS

Transcript

Greg Lambert (00:01)

Hey everyone, I’m Greg Lambert, and this week on The Geek in Review, we have with us Manuel Deó from Ambar Partners. But first, let’s get some insights from our friends at Legal Technology Hub.

Marlene Gebauer (00:14)

Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:23)

Thank you. Good to see you again. Well, in the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Now, most law firms and departments have at least an AI policy of some kind, and there are some broad standards which are starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and towards a more informative approach which defines different risk categories and makes distinctions between routine, low-risk use of AI and other more substantial use cases. I think that’s appropriate for where we are. It’s also what clients are coming to expect from their advisors. But one of the bigger challenges we’re seeing right now is, how do you express an AI use policy in such a way that any member of your legal team could explain it to a client? I think that’s still a big challenge for the industry.

Marlene Gebauer (00:41)

Okay.

Sam Moore (01:21)

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is using AI day-to-day. So they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not” and turn them into more easily readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit legaltechnologyhub.com.

Marlene Gebauer (02:05)

Thank you, Sam. I mean, it is important to have AI policy in plain language so people understand it.

Sam Moore (02:11)

Absolutely.

Marlene Gebauer (02:19)

Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:26)

And I’m Greg Lambert, and today we are examining the complete deconstruction of the traditional law firm. For decades, the legal industry was defined by centralization, kind of bringing the talent, the technology, the overhead, and the clients under one structured, highly leveraged roof and partnership pyramid. But a new model is proving you can unbundle law firms, keeping the elite talent and the enterprise technology while stripping away the rigid hierarchy.

Marlene Gebauer (02:56)

Our guest today is operating at the absolute forefront of this shift in the European and Latin American markets. Manuel Deó is the co-founder and co-CEO of Ambar Partners, which recently secured a Band One ranking in Chambers as a leading alternative legal service provider. Before launching Ambar, Manuel was the youngest Spanish lawyer to make partner at Latham & Watkins, eventually leading their corporate and M&A practice in Spain.

Greg Lambert (03:25)

And Manuel and his co-founder, Rosa Espín, have built what they describe as a legal operating system. It’s a digital platform called Ambar Haus that connects a highly vetted, curated community of over 400 senior independent lawyers with multinational enterprise clients. They are deploying enterprise tools like Harvey AI at scale, across borders, and completely rethinking how legal capacity is consumed and built.

Marlene Gebauer (03:55)

Manuel, welcome to The Geek in Review.

Manuel Deó (03:57)

Thank you, Marlene. Thank you, Greg, for having me. It’s a true pleasure. Thank you.

Marlene Gebauer (04:03)

So Manuel, you went from leading corporate and private equity at Latham & Watkins in Spain to co-founding Ambar. What did you see in the traditional legal market that convinced you to build a different kind of legal business?

Manuel Deó (04:18)

First, I should say that I loved Big Law, right? I always consider myself a son of the old model, and I’m a grateful son for many different reasons. Latham, in particular, where I was a partner, taught me what excellence, intensity, and global execution look like.

I did not build Ambar because I believed traditional law firms were broken. This is something people sometimes ask me. I built it with Rosa because no single institution is optimized for every type of legal need. We kept seeing two false choices when we were back at Big Law.

On one side, you had outstanding lawyers who had to choose between institutional prestige and professional autonomy. On the other side, you had clients who had to choose between adding permanent headcount to their teams or engaging a traditional law firm, usually through a narrow model, an hourly-rate basis.

But a significant part of corporate legal demand is neither permanent nor junior. It is senior, important, urgent, and variable. The market has never been short of talent. I think quite the opposite. What it’s been short of is an architecture capable of deploying that talent differently.

We wanted to separate expertise from real estate in specific offices on specific streets, seniority from hierarchy, and value from time. I think the traditional legal market had two gears. One is hire a lawyer, or hire a law firm. What we built is a gearbox. I think that’s the broader opportunity we’re building here. Not to replace law firms or internal legal departments, but to give clients a more intelligent, flexible way of building legal capacity around the problem they have.

So that’s what we’re doing and why we went from Big Law to building Ambar.

Greg Lambert (06:38)

As you were building this with Rosa, how were you thinking of structuring it in a way that was both enticing for the lawyers you wanted to recruit and also for the clients to understand what the business model was and what you were offering?

Manuel Deó (07:02)

Right. I think for lawyers the most important part of our model is freedom. We give lawyers freedom. Freedom to decide which projects they are engaged on, for which clients, for what fees, on what terms, and at what moment of the year they want to work. They have full control to decide which type of projects they are a part of. That’s the main value proposition for lawyers.

On the other side, for clients, it’s twofold. One is having full control. Full control over the cost, and also over the specific lawyer delivering the legal service. When you’re part of other, more traditional models, you hire a firm, but you don’t always have full control over who’s doing the job, right? Who’s doing the legal work? Because they have a number of people there doing the legal delivery.

So from one specific point, that’s full control over cost and delivery. And second, access to highly vetted lawyers where you have full control over the talent, expertise, and knowledge of the lawyer, who is highly vetted by third parties, not only by us, but by other clients, other colleagues, and, obviously, traditional firms.

Marlene Gebauer (08:17)

See, this is interesting. When we talk about value, we’re usually talking about value to the client, but I think you’re talking about it going both ways. What does the lawyer find valuable to work on, as well as delivering value to the client?

Manuel Deó (08:29)

Yeah, absolutely.

Greg Lambert (08:32)

Marlene mentioned this in the intro, but congratulations on Ambar’s recent Chambers recognition as Band One for ALSPs in Europe. Both you and Rosa were also named individually in Band One as well. The Chambers submissions, I know outside of legal no one knows what those are, but it’s a big deal inside legal. So what does that recognition tell you about the legal market’s acceptance of the type of model you’re providing?

Manuel Deó (09:07)

Right. The recognition is specifically for what they call NewLaw, or alternative legal service firms. What that tells me is that the word “alternative,” which is a buzzword used in the sector for many years, is approaching its expiration date.

We’re grateful for a prestigious institution like Chambers recognizing Ambar as Band One with other global companies, but clients validated the model before Chambers did. Chambers has made visible something that was already happening.

I think the true thing is that sophisticated clients are increasingly indifferent to labels and brands. They care about quality, speed, accountability, commercial understanding, and value. The general counsel, which is the name given to the most senior person in the legal department at big corporations and S&P 500 companies, are sophisticated people with a lot of pressure in their companies. They’re typically trusted advisors to the CEO and have a lot of pressure to deliver more value with less.

This recognition demonstrates that top-tier standards exist outside the traditional partnership pyramid. Independence, which is part of our value for lawyers because they are independent lawyers, does not have to mean fragmentation. Flexibility, which is something we provide to our clients, does not have to mean lower quality. Quite the opposite.

A lawyer does not have to sit inside a large institution for that lawyer’s expertise to be institutional-grade, relevant, and appealing to a big client. We are proud of that recognition. But the ranking is a mirror, it’s not a moat. The question is no longer whether clients will accept these models. I think they already do.

The hard question for me is who will scale the model without diluting trust, quality, or accountability.

Marlene Gebauer (11:33)

So I want to dig in a little bit at how the platform works.

Manuel Deó (11:38)

Sure.

Marlene Gebauer (11:39)

Ambar describes itself as a global platform of independent senior legal talent and as an operating system for flexible, borderless legal work. So walk us through what that operating system provides to lawyers and clients.

Manuel Deó (11:55)

Sure. I sometimes regret using the word “platform,” although people sometimes refer to us like that, because people picture a database of lawyers with better typography, basically. We’re not putting lawyers on a website and calling it innovation, right? It’s a totally different thing.

Traditionally, a law firm bundled several things together. The talent, the brand, the clients, the technology, compliance, operations, and professional community. When a great lawyer became independent, that lawyer did not lose their expertise, but they often lost access to that entire institutional bundle.

What Ambar is doing is rebuilding those capabilities as a shared infrastructure without asking the lawyer or boutique law firm to give up their independence, their economics, brand, or professional identity.

For independent lawyers and specialist boutiques, which are the types of lawyers who are part of Ambar, we think about an operating system through four Bs.

One is belonging. You are an independent lawyer, but you’re also part of something bigger. Independence is professionally liberating when people leave certain institutions, but it also becomes lonely. We provide a curated professional community, practice groups, city chapters, trusted peers, knowledge-sharing spaces, and opportunities to collaborate with other exceptional lawyers. That’s the first one, belonging.

The second one is business. We provide access to sophisticated referrals among the different lawyers, cross-selling opportunities, team formation, and the infrastructure required to transform expertise into a sustainable legal business. Many outstanding lawyers do not want to spend half their lives becoming salespeople. That’s typically something lawyers don’t like to do, or chasing invoices, or learning digital marketing, or whatever. They want to practice law at the highest level. We help create those conditions around them to make that possible. So that’s the second B, business.

The third one is backbone. That’s the operational machinery around legal work. The intake, the proposals for the client, building the proposal with your scope, your fees, the contractual engagement letter, the specific conflict processes, KYC, anti-money laundering, insurance, invoicing, collections, all the red tape around sophisticated legal service delivery. We take care of that. The lawyer remains independent but no longer has to rebuild an entire institution alone with all the compliance and KYC around that. So that’s the third part, backbone.

The fourth one is the badge. That’s certification of your expertise, reputation, positioning, and institutional credibility, which is important in our sector and, obviously, in any professional service. We give independent experts and boutiques the ability to compete for sophisticated legal work that was historically reserved for larger organizations while preserving their own identity.

We are also building that with AI. AI is becoming more important in any profession, but obviously in legal. There are many players, frontier models building legal verticals, and AI legal-specific companies building legal tools. We have agreements with certain legal AI tools to deliver that legal AI expertise and those tools for our lawyers.

Greg Lambert (16:08)

Let me point at that last, almost like a fifth level here on the tech stack. How do you set up the attorneys on the technology stack so they have access to the tools, but then they also understand, say, how to use the tools, the interoperability between them, and all of that? How do you look at your tech stack?

Marlene Gebauer (16:28)

Responsibly.

Manuel Deó (16:34)

Right. I think we’re moving into a moment in which knowledge will be a commodity. That’s something hard for a lawyer because we’ve been, and I still consider myself a lawyer, tested for our knowledge of specific verticals, specific cases, specific contracts. But that’s a commodity.

I think the key here is that lawyers need to move from that sort of mantra where knowledge was key to a mantra where judgment will be the key driver of sophisticated legal services moving forward.

That’s our focus. Giving lawyers the ability to control, train, and use these tools, have full knowledge of the different AI tools available, and know that certain tools are available and helpful for specific needs while others are better for other needs. And second, providing judgment to that sort of legal service delivery.

The technology we’re building with other partners is allowing that judgment to develop memory, speed, and consistency, which are likely the three things clients are asking for.

Memory in terms of, “I want all my proprietary data, all my proprietary information to be stored. I want the AI to learn from my prior experience and from my knowledge.” Speed in terms of efficiency and time of delivery. And consistency. “I want to control that all my answers, all my legal advice, are consistent with my prior advice.”

That’s what we’re building. We’re building it with training, obviously, with different dedicated people in the different AI companies we’re partnering with. It’s not a one-shot process. It’s long.

We have more than 300 lawyers in our community currently from different backgrounds, different seniorities, and different geographies. I think sophisticated lawyers are typically tech savvy, but obviously not all of them have the same appetite to learn and the same ability to be aware of what AI is and what AI delivers to them.

Marlene Gebauer (18:59)

It’s interesting. You distinguish between knowledge as a commodity versus judgment, where I think a lot of firms would say their internal knowledge of things is kind of their IP. So how do you distinguish the two?

Manuel Deó (19:17)

There’s been a great debate around that topic lately in Big Law, basically because many firms have realized that if you hire an AI frontier model or AI legal solution, and you upload certain documentation to that AI tool, you might lose control over that information and not even know where that knowledge will end up.

So we’re building what we call the expert twins, which is a specific layer of information and documentation for each and every lawyer who is part of our community.

That’s basically a controlled, evolving system built so that the specific expert has approved knowledge. Knowledge that they approve, which is valid, which is dedicated. Picture a contract, picture a court case, something they’ve been validly involved in and they know that knowledge is valid. It’s been certified by them, with playbooks and experience specifically uploaded by that lawyer, and building a second layer of information validated by that lawyer.

That helps the lawyer trust the information being used by the AI tool.

Greg Lambert (20:46)

I want to switch over and look at Ambar from your clients’ perspective. I know a lot of them are looking at adding internal headcount or engaging with a traditional law firm for their legal needs. So how do you approach your clients and kind of sell to them what your platform at Ambar provides instead of these traditional methods?

Manuel Deó (21:15)

Sure. We use a simple rule. Fixed demand deserves fixed cost. Variable demand deserves variable capacity. Rare expertise deserves access and not ownership.

What I mean by that is that a company should hire internally, as an FTE, as a full-time employee, when the work is permanent, predictable, and central to its institutional knowledge, even typically sensitive core strategic information or knowledge in that organization.

But it should engage a traditional law firm when it needs institutional firepower, a highly leveraged team, court representation, a regulated local structure, or the resources required for a bet-the-company matter. M&A, capital markets, all those major, important transactions in the life of a company.

But Ambar sits between those two options when the need is important but variable, when they require senior expertise quickly, or when they need someone operating close to the business without making a permanent addition to the organization.

So you might have a fractional lawyer working 10, 20, 30 hours a week. It might be a leave of absence of one of your colleagues, post-acquisition integration, an internal expansion, a specialist regulatory problem, an investigation, litigation support for a specific period of time, or additional capacity during a peak of work.

In 2025, we handled approximately 300 projects, 293 projects, for 140 clients. More than 70% of that work was fractional counsel work, which is an external lawyer dedicated to your team as a natural extension of your in-house team.

This is important because fractional work is not simply a short secondment. The lawyer in a fractional project becomes part of the client’s operating rhythm, but the capacity remains adjustable. The client gains seniority, continuity, and business context without automatically adding permanent fixed cost.

Sometimes the problem is urgent, but that doesn’t mean the organization chart should become permanent. Clients do not always need another lawyer. They need the right expertise at the precise moment when that expertise becomes valuable.

More especially now, we’re in an uncertain moment where people don’t know, from a macroeconomic perspective, what’s going to happen. In many verticals and industries, there’s a lot of uncertainty. Of course, AI is bringing even more uncertainty in the legal sector.

Many clients, many general counsel, look at the fractional model as a quick, efficient, and flexible manner of accessing capacity without adding permanent headcount.

Marlene Gebauer (24:46)

So operating across jurisdictions with hundreds of independent lawyers, I picture, creates difficult questions around conflicts, privilege, professional ethics, data security, and AI use.

Greg Lambert (24:55)

A little bit.

Marlene Gebauer (24:59)

So how are all those responsibilities allocated and managed, and who’s ultimately accountable to the client?

Manuel Deó (25:11)

Sure. Independence must never mean ambiguity. In fact, a distributed model like ours only works when responsibility is more explicit, not less.

The allocation is clear. The lawyer remains professionally responsible for legal judgment, conflict checks, privilege, and all the ethical obligations applicable in the relevant jurisdiction in which they are barred or qualified.

Ambar is accountable for the operating environment we control. The certification of that lawyer, the selection, the contracting, the insurance we provide to our clients, the cybersecurity standards, governance, process, compliance, anti-money laundering, monitoring, and escalation.

In particular, one of the things you’ve mentioned is conflicts, conflict checks, which are a critical part of our business. Those are assessed in relation to the individual lawyer and the particular engagement.

We do not create one central environment in which independent lawyers access confidential information belonging to the clients of other lawyers in the community. We would never do that.

Each professional, each lawyer, retains responsibility for their own practice and professional obligations, supported by our engagement processes and infrastructure.

Privilege is addressed through the structure of the engagement and the rules of the relevant jurisdiction applicable to that engagement. It’s never something we simply assume because technology is involved.

AI use is also governed by clear principles of client requirements, approved tools, defined data boundaries, confidentiality, and human review. As you might expect, we have B2B enterprise clients who are sophisticated. They all have AI governance and data privacy policies which we need to comply with and be audited against.

Where local rules require work to be delivered through a regulated law firm or a particular professional structure, the engagement must reflect that.

So that’s what we do. We’re careful to do that and do not use the independence of those lawyers as a liability escape hatch.

We think distributed delivery should distribute capability, not responsibility. The client should never have to guess who owns the risk. We’re clear. The engagement letter covers that.

We have an engagement letter where the client, the lawyer, and Ambar are parties to the same relationship because we believe in transparency and each party knowing who owns what and who’s responsible for each part of the legal delivery.

Greg Lambert (28:21)

Well, I think this ties in nicely with the next question. I want to talk to you about Ambar’s community, its culture, and some of the interesting things I’ve seen.

As I was driving into work this morning, I was listening to something and someone mentioned culture at an organization. They defined it in a way that makes sense, but I hadn’t heard it quite this way before. Culture is a set of rules and guidelines on how a person should perform and act in the environment in order to succeed within this community.

When you have a distributed network of independent lawyers, let me ask you up front, how do you prevent this independent contractor from feeling like a transactional database of freelance attorneys? Let me start with that.

Manuel Deó (29:30)

Sure. It’s a long process. One is the right selection of the lawyers, so the vetting of that lawyer, being super demanding on the type of lawyer who is part of the community. We’re picky on that.

I hate to say that, but 95% of the lawyers who request being admitted to Ambar are not able to do it. So we have less than a 4% admission rate right now.

We require those lawyers to come from, or to have expertise in, a closed-ended list of law firms or in-house departments, which gives us certainty on the type of lawyer, the type of culture, and the work ethic around that lawyer.

We take that certification and admission process seriously. We’re part of it through the vetting of that lawyer with third-party references, but also with an in-person meeting with my co-founder, Rosa, who sits with each and every lawyer who’s part of Ambar and wants to be part of it. So we’re careful with that.

That’s one thing. Second, we have a clear code of practice. Obviously, when you’re talking about sophisticated lawyers, these lawyers come from specific backgrounds. They are subject to the ethical standards of their jurisdictions. This is a small market, and there’s tremendous ownership of each person’s reputation and brand.

We’ve never had any issue with that. We’re careful with it. We always have to be listening to our clients regarding the feedback we get on our lawyers in connection with our projects.

We’ve been disruptive there, too, because we’ve been proactively asking for feedback, which is something that’s not common in this sector, and getting the client to verbalize the feedback they have on their lawyers, put that on the record, and make that transparent to the lawyer.

So we’re careful about that. Obviously, it’s a distributed model. Ambar is accountable for the system and the lawyer is accountable for the legal judgment and the legal delivery. That’s clear. I think that’s something that’s also been clear for our clients.

Greg Lambert (32:24)

There’s one more thing you do in community that’s interesting to me, and that’s your communications with your clients.

You’ve put out a newsletter that I think you call Dr. No, and it’s read by thousands of general counsel. But instead of talking about case law updates and changes in regulation, you write about leadership, legal operations, and strategy.

How do your clients respond to a different approach like this?

Manuel Deó (33:10)

Sure. Dr. No, and by the way, the name comes from how certain in-house lawyers are described in their companies because they’re always saying no.

What we try to say there is that we need to put our clients at the center of the conversation, which is something that’s not common in the legal sector.

We’ve always been talking about the rainmakers in this law firm. We talked about Chambers before. Chambers has a lot of rankings, but they’ve always concentrated on law firms and not on in-house lawyers and general counsel.

What we’re trying to suggest here is that we should change the conversation, put the general counsel in the middle of the conversation, and offer different tools those lawyers in those companies need.

I’m not going to teach law to a general counsel or a senior lawyer in an important company. I would never intend to do that. But we talk about things they need to become more influential in their companies, to become more central.

Think about HR managers 20 years ago, who became influential people in their companies through many different changes. One of them is a change of name, which changed to Chief People Officer or Chief Talent Officer, and they became senior and influential people on the board.

Or CFOs, who went from being financial analysts or controllers to becoming influential people in their companies.

What we’re trying to provide are those tools for general counsel to become as influential as a Chief Legal Officer, someone who’s talking about metrics, return on investment, how much savings the department provides to the company, and treating a legal department as another separate business unit of a company.

Greg Lambert (35:25)

All right, well, we are at the end. We want to have you look back before we ask you our crystal ball question. So Manuel, what is something that’s different today, or wasn’t necessarily true a year ago?

Manuel Deó (35:48)

I don’t want to talk about AI because it would be too obvious. Obviously, every week we have a new development in AI.

Marlene Gebauer (35:59)

So boring, AI.

Manuel Deó (36:01)

Right. So it’s something I would avoid mentioning. It’s a difficult question, but I think we’ve run through a process where general counsel don’t want to be seen as a cost center in their companies and want to be seen as something different.

That’s changing the conversation, and it’s changing the type of solutions we as legal suppliers need to provide our clients.

Because it’s not a matter of an hourly rate or, “I’m the best expert in this specific industry.” It’s a matter of how much value did I provide to my client? Because I need to help that client explain to their CEO, their CFO, the value they’re bringing.

That’s something we’re starting to see now that we didn’t see a year, a year and a half ago. How important value contribution is, and being a true investment as a legal department in a company, is changing the mindset of many general counsel.

Marlene Gebauer (37:11)

Well, if that question was hard, this one might be a little harder.

Greg Lambert (37:14)

Yeah.

Marlene Gebauer (37:15)

This time we’re going to ask you to look into the future.

Manuel Deó (37:17)

Wow.

Marlene Gebauer (37:18)

Our crystal ball question is, what’s the single biggest change you see coming for how firms operate and interact with their clients?

Manuel Deó (37:29)

That’s also a hard one, yeah. I think the single biggest change will be that traditional firms will stop being the only meaningful unit of legal delivery.

Law firms will be important, but they will become more porous. The future legal team will combine small strategic in-house core teams. They will engage traditional firms when they need them, but they will also engage independent senior specialists, specialist boutiques, managed services, and AI agents to deliver legal solutions appropriate to the specifics of that legal need.

Those capabilities will be assembled by each legal department in a dynamic manner around each problem.

I think clients will move from asking, “Which firm should we hire?” to, “What is the best configuration of people, technology, knowledge, and risk for this particular matter?”

The traditional pyramid was designed for a world in which legal knowledge was scarce and coordination was expensive. That will be totally different.

AI makes information, as I said before, increasingly abundant and truly a commodity, in my opinion. Technology makes coordination much cheaper. I think that will change the geometry of the industry.

I think we’ll be seeing legal departments more as trusted orchestrators of capabilities. That’s how I see the future going forward. AI, boring matter, but I know it’s something we always...

Greg Lambert (39:26)

Well, I think you handled both of those questions well.

Marlene Gebauer (39:27)

Yeah, I think you did a great job.

Manuel Deó (39:28)

Yeah.

Greg Lambert (39:30)

Well, Manuel Deó, thank you so much for taking time out of your day to talk to us. This has been great.

Marlene Gebauer (39:37)

Thank you.

Manuel Deó (39:37)

Thank you. Thanks for having me.

Marlene Gebauer (39:40)

And thanks to all of you for listening. And don’t forget to like and subscribe on Substack, YouTube, and LinkedIn.

Greg Lambert (39:48)

And Manuel, where’s the best place for people to follow your content and to learn more about Ambar?

Manuel Deó (39:54)

Well, the best place to follow me is LinkedIn. I write regularly about the business of law, leadership, innovation, entrepreneurship, and AI.

I also publish a daily newsletter called Dr. No for legal leaders who want to be more innovative, about mental models, about cool stories. Less than a one-minute read each and every day.

Many clients tell me they resend it and forward it to their teams to send specific messages.

And that’s pretty much it. You can find Dr. No on our webpage, ambarpartners.com, under Dr. No.

Marlene Gebauer (40:38)

And as always, the music here is from Jerry David DeCicca. Thank you, Jerry, and bye, everybody.

]]> What happens when you separate elite legal talent from the traditional law firm structure? This week on The Geek in Review, we talk with Manuel Deó, co-founder and co-CEO of Ambar Partners, about a model designed around senior independent lawyers, flexible capacity, enterprise technology, and client choice. Deó explains why he and co-founder Rosa Espín did not set out to replace Big Law, but instead to address a gap between permanent in-house hiring and traditional outside counsel.

At the center of Ambar’s model is a simple idea: legal demand comes in different shapes, and the delivery model should match the problem. Deó describes how Ambar gives lawyers control over the clients, projects, fees, and schedules they take on, while giving clients greater visibility into cost and the individual lawyers doing the work. He also discusses Ambar’s recent Chambers recognition and argues that the term “alternative” is starting to lose some of its usefulness as clients grow more comfortable assembling legal services from a wider range of providers.

Deó walks through what Ambar calls its legal operating system, built around belonging, business, backbone, and badge. The model combines a professional community with business development, compliance, contracting, billing, technology, and institutional credibility for independent lawyers and specialist boutiques. Ambar’s public materials describe a shared technology environment that includes tools such as Harvey, Microsoft Copilot, Legora, and other legal technology products. The goal, according to Deó, is to give independent lawyers access to the infrastructure associated with a large firm without requiring them to give up professional independence.

The conversation also turns to AI, knowledge, and professional judgment. Deó argues that legal knowledge is becoming more abundant while judgment grows more valuable. He describes Ambar’s work on “expert twins,” where approved knowledge, prior work, playbooks, and experience associated with an individual lawyer form a trusted layer for AI-assisted work. That emphasis on institutional knowledge and permissions echoes a broader trend across legal AI, where vendors are increasingly focused on connecting AI systems to trusted internal work product and organizational context.

Finally, Deó offers a broader view of where legal delivery is heading. He sees legal departments assembling teams dynamically from in-house lawyers, traditional firms, independent specialists, boutiques, managed services, and AI agents based on the needs of a particular matter. Instead of asking which firm to hire, clients increasingly have reason to ask what combination of people, technology, expertise, and risk structure best fits the work. For Deó, the future belongs less to a single dominant delivery model and more to legal departments acting as orchestrators of capability.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

LINKS

Transcript

Greg Lambert (00:01)

Hey everyone, I’m Greg Lambert, and this week on The Geek in Review, we have with us Manuel Deó from Ambar Partners. But first, let’s get some insights from our friends at Legal Technology Hub.

Marlene Gebauer (00:14)

Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:23)

Thank you. Good to see you again. Well, in the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Now, most law firms and departments have at least an AI policy of some kind, and there are some broad standards which are starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and towards a more informative approach which defines different risk categories and makes distinctions between routine, low-risk use of AI and other more substantial use cases. I think that’s appropriate for where we are. It’s also what clients are coming to expect from their advisors. But one of the bigger challenges we’re seeing right now is, how do you express an AI use policy in such a way that any member of your legal team could explain it to a client? I think that’s still a big challenge for the industry.

Marlene Gebauer (00:41)

Okay.

Sam Moore (01:21)

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is using AI day-to-day. So they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not” and turn them into more easily readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit legaltechnologyhub.com.

Marlene Gebauer (02:05)

Thank you, Sam. I mean, it is important to have AI policy in plain language so people understand it.

Sam Moore (02:11)

Absolutely.

Marlene Gebauer (02:19)

Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:26)

And I’m Greg Lambert, and today we are examining the complete deconstruction of the traditional law firm. For decades, the legal industry was defined by centralization, kind of bringing the talent, the technology, the overhead, and the clients under one structured, highly leveraged roof and partnership pyramid. But a new model is proving you can unbundle law firms, keeping the elite talent and the enterprise technology while stripping away the rigid hierarchy.

Marlene Gebauer (02:56)

Our guest today is operating at the absolute forefront of this shift in the European and Latin American markets. Manuel Deó is the co-founder and co-CEO of Ambar Partners, which recently secured a Band One ranking in Chambers as a leading alternative legal service provider. Before launching Ambar, Manuel was the youngest Spanish lawyer to make partner at Latham & Watkins, eventually leading their corporate and M&A practice in Spain.

Greg Lambert (03:25)

And Manuel and his co-founder, Rosa Espín, have built what they describe as a legal operating system. It’s a digital platform called Ambar Haus that connects a highly vetted, curated community of over 400 senior independent lawyers with multinational enterprise clients. They are deploying enterprise tools like Harvey AI at scale, across borders, and completely rethinking how legal capacity is consumed and built.

Marlene Gebauer (03:55)

Manuel, welcome to The Geek in Review.

Manuel Deó (03:57)

Thank you, Marlene. Thank you, Greg, for having me. It’s a true pleasure. Thank you.

Marlene Gebauer (04:03)

So Manuel, you went from leading corporate and private equity at Latham & Watkins in Spain to co-founding Ambar. What did you see in the traditional legal market that convinced you to build a different kind of legal business?

Manuel Deó (04:18)

First, I should say that I loved Big Law, right? I always consider myself a son of the old model, and I’m a grateful son for many different reasons. Latham, in particular, where I was a partner, taught me what excellence, intensity, and global execution look like.

I did not build Ambar because I believed traditional law firms were broken. This is something people sometimes ask me. I built it with Rosa because no single institution is optimized for every type of legal need. We kept seeing two false choices when we were back at Big Law.

On one side, you had outstanding lawyers who had to choose between institutional prestige and professional autonomy. On the other side, you had clients who had to choose between adding permanent headcount to their teams or engaging a traditional law firm, usually through a narrow model, an hourly-rate basis.

But a significant part of corporate legal demand is neither permanent nor junior. It is senior, important, urgent, and variable. The market has never been short of talent. I think quite the opposite. What it’s been short of is an architecture capable of deploying that talent differently.

We wanted to separate expertise from real estate in specific offices on specific streets, seniority from hierarchy, and value from time. I think the traditional legal market had two gears. One is hire a lawyer, or hire a law firm. What we built is a gearbox. I think that’s the broader opportunity we’re building here. Not to replace law firms or internal legal departments, but to give clients a more intelligent, flexible way of building legal capacity around the problem they have.

So that’s what we’re doing and why we went from Big Law to building Ambar.

Greg Lambert (06:38)

As you were building this with Rosa, how were you thinking of structuring it in a way that was both enticing for the lawyers you wanted to recruit and also for the clients to understand what the business model was and what you were offering?

Manuel Deó (07:02)

Right. I think for lawyers the most important part of our model is freedom. We give lawyers freedom. Freedom to decide which projects they are engaged on, for which clients, for what fees, on what terms, and at what moment of the year they want to work. They have full control to decide which type of projects they are a part of. That’s the main value proposition for lawyers.

On the other side, for clients, it’s twofold. One is having full control. Full control over the cost, and also over the specific lawyer delivering the legal service. When you’re part of other, more traditional models, you hire a firm, but you don’t always have full control over who’s doing the job, right? Who’s doing the legal work? Because they have a number of people there doing the legal delivery.

So from one specific point, that’s full control over cost and delivery. And second, access to highly vetted lawyers where you have full control over the talent, expertise, and knowledge of the lawyer, who is highly vetted by third parties, not only by us, but by other clients, other colleagues, and, obviously, traditional firms.

Marlene Gebauer (08:17)

See, this is interesting. When we talk about value, we’re usually talking about value to the client, but I think you’re talking about it going both ways. What does the lawyer find valuable to work on, as well as delivering value to the client?

Manuel Deó (08:29)

Yeah, absolutely.

Greg Lambert (08:32)

Marlene mentioned this in the intro, but congratulations on Ambar’s recent Chambers recognition as Band One for ALSPs in Europe. Both you and Rosa were also named individually in Band One as well. The Chambers submissions, I know outside of legal no one knows what those are, but it’s a big deal inside legal. So what does that recognition tell you about the legal market’s acceptance of the type of model you’re providing?

Manuel Deó (09:07)

Right. The recognition is specifically for what they call NewLaw, or alternative legal service firms. What that tells me is that the word “alternative,” which is a buzzword used in the sector for many years, is approaching its expiration date.

We’re grateful for a prestigious institution like Chambers recognizing Ambar as Band One with other global companies, but clients validated the model before Chambers did. Chambers has made visible something that was already happening.

I think the true thing is that sophisticated clients are increasingly indifferent to labels and brands. They care about quality, speed, accountability, commercial understanding, and value. The general counsel, which is the name given to the most senior person in the legal department at big corporations and S&P 500 companies, are sophisticated people with a lot of pressure in their companies. They’re typically trusted advisors to the CEO and have a lot of pressure to deliver more value with less.

This recognition demonstrates that top-tier standards exist outside the traditional partnership pyramid. Independence, which is part of our value for lawyers because they are independent lawyers, does not have to mean fragmentation. Flexibility, which is something we provide to our clients, does not have to mean lower quality. Quite the opposite.

A lawyer does not have to sit inside a large institution for that lawyer’s expertise to be institutional-grade, relevant, and appealing to a big client. We are proud of that recognition. But the ranking is a mirror, it’s not a moat. The question is no longer whether clients will accept these models. I think they already do.

The hard question for me is who will scale the model without diluting trust, quality, or accountability.

Marlene Gebauer (11:33)

So I want to dig in a little bit at how the platform works.

Manuel Deó (11:38)

Sure.

Marlene Gebauer (11:39)

Ambar describes itself as a global platform of independent senior legal talent and as an operating system for flexible, borderless legal work. So walk us through what that operating system provides to lawyers and clients.

Manuel Deó (11:55)

Sure. I sometimes regret using the word “platform,” although people sometimes refer to us like that, because people picture a database of lawyers with better typography, basically. We’re not putting lawyers on a website and calling it innovation, right? It’s a totally different thing.

Traditionally, a law firm bundled several things together. The talent, the brand, the clients, the technology, compliance, operations, and professional community. When a great lawyer became independent, that lawyer did not lose their expertise, but they often lost access to that entire institutional bundle.

What Ambar is doing is rebuilding those capabilities as a shared infrastructure without asking the lawyer or boutique law firm to give up their independence, their economics, brand, or professional identity.

For independent lawyers and specialist boutiques, which are the types of lawyers who are part of Ambar, we think about an operating system through four Bs.

One is belonging. You are an independent lawyer, but you’re also part of something bigger. Independence is professionally liberating when people leave certain institutions, but it also becomes lonely. We provide a curated professional community, practice groups, city chapters, trusted peers, knowledge-sharing spaces, and opportunities to collaborate with other exceptional lawyers. That’s the first one, belonging.

The second one is business. We provide access to sophisticated referrals among the different lawyers, cross-selling opportunities, team formation, and the infrastructure required to transform expertise into a sustainable legal business. Many outstanding lawyers do not want to spend half their lives becoming salespeople. That’s typically something lawyers don’t like to do, or chasing invoices, or learning digital marketing, or whatever. They want to practice law at the highest level. We help create those conditions around them to make that possible. So that’s the second B, business.

The third one is backbone. That’s the operational machinery around legal work. The intake, the proposals for the client, building the proposal with your scope, your fees, the contractual engagement letter, the specific conflict processes, KYC, anti-money laundering, insurance, invoicing, collections, all the red tape around sophisticated legal service delivery. We take care of that. The lawyer remains independent but no longer has to rebuild an entire institution alone with all the compliance and KYC around that. So that’s the third part, backbone.

The fourth one is the badge. That’s certification of your expertise, reputation, positioning, and institutional credibility, which is important in our sector and, obviously, in any professional service. We give independent experts and boutiques the ability to compete for sophisticated legal work that was historically reserved for larger organizations while preserving their own identity.

We are also building that with AI. AI is becoming more important in any profession, but obviously in legal. There are many players, frontier models building legal verticals, and AI legal-specific companies building legal tools. We have agreements with certain legal AI tools to deliver that legal AI expertise and those tools for our lawyers.

Greg Lambert (16:08)

Let me point at that last, almost like a fifth level here on the tech stack. How do you set up the attorneys on the technology stack so they have access to the tools, but then they also understand, say, how to use the tools, the interoperability between them, and all of that? How do you look at your tech stack?

Marlene Gebauer (16:28)

Responsibly.

Manuel Deó (16:34)

Right. I think we’re moving into a moment in which knowledge will be a commodity. That’s something hard for a lawyer because we’ve been, and I still consider myself a lawyer, tested for our knowledge of specific verticals, specific cases, specific contracts. But that’s a commodity.

I think the key here is that lawyers need to move from that sort of mantra where knowledge was key to a mantra where judgment will be the key driver of sophisticated legal services moving forward.

That’s our focus. Giving lawyers the ability to control, train, and use these tools, have full knowledge of the different AI tools available, and know that certain tools are available and helpful for specific needs while others are better for other needs. And second, providing judgment to that sort of legal service delivery.

The technology we’re building with other partners is allowing that judgment to develop memory, speed, and consistency, which are likely the three things clients are asking for.

Memory in terms of, “I want all my proprietary data, all my proprietary information to be stored. I want the AI to learn from my prior experience and from my knowledge.” Speed in terms of efficiency and time of delivery. And consistency. “I want to control that all my answers, all my legal advice, are consistent with my prior advice.”

That’s what we’re building. We’re building it with training, obviously, with different dedicated people in the different AI companies we’re partnering with. It’s not a one-shot process. It’s long.

We have more than 300 lawyers in our community currently from different backgrounds, different seniorities, and different geographies. I think sophisticated lawyers are typically tech savvy, but obviously not all of them have the same appetite to learn and the same ability to be aware of what AI is and what AI delivers to them.

Marlene Gebauer (18:59)

It’s interesting. You distinguish between knowledge as a commodity versus judgment, where I think a lot of firms would say their internal knowledge of things is kind of their IP. So how do you distinguish the two?

Manuel Deó (19:17)

There’s been a great debate around that topic lately in Big Law, basically because many firms have realized that if you hire an AI frontier model or AI legal solution, and you upload certain documentation to that AI tool, you might lose control over that information and not even know where that knowledge will end up.

So we’re building what we call the expert twins, which is a specific layer of information and documentation for each and every lawyer who is part of our community.

That’s basically a controlled, evolving system built so that the specific expert has approved knowledge. Knowledge that they approve, which is valid, which is dedicated. Picture a contract, picture a court case, something they’ve been validly involved in and they know that knowledge is valid. It’s been certified by them, with playbooks and experience specifically uploaded by that lawyer, and building a second layer of information validated by that lawyer.

That helps the lawyer trust the information being used by the AI tool.

Greg Lambert (20:46)

I want to switch over and look at Ambar from your clients’ perspective. I know a lot of them are looking at adding internal headcount or engaging with a traditional law firm for their legal needs. So how do you approach your clients and kind of sell to them what your platform at Ambar provides instead of these traditional methods?

Manuel Deó (21:15)

Sure. We use a simple rule. Fixed demand deserves fixed cost. Variable demand deserves variable capacity. Rare expertise deserves access and not ownership.

What I mean by that is that a company should hire internally, as an FTE, as a full-time employee, when the work is permanent, predictable, and central to its institutional knowledge, even typically sensitive core strategic information or knowledge in that organization.

But it should engage a traditional law firm when it needs institutional firepower, a highly leveraged team, court representation, a regulated local structure, or the resources required for a bet-the-company matter. M&A, capital markets, all those major, important transactions in the life of a company.

But Ambar sits between those two options when the need is important but variable, when they require senior expertise quickly, or when they need someone operating close to the business without making a permanent addition to the organization.

So you might have a fractional lawyer working 10, 20, 30 hours a week. It might be a leave of absence of one of your colleagues, post-acquisition integration, an internal expansion, a specialist regulatory problem, an investigation, litigation support for a specific period of time, or additional capacity during a peak of work.

In 2025, we handled approximately 300 projects, 293 projects, for 140 clients. More than 70% of that work was fractional counsel work, which is an external lawyer dedicated to your team as a natural extension of your in-house team.

This is important because fractional work is not simply a short secondment. The lawyer in a fractional project becomes part of the client’s operating rhythm, but the capacity remains adjustable. The client gains seniority, continuity, and business context without automatically adding permanent fixed cost.

Sometimes the problem is urgent, but that doesn’t mean the organization chart should become permanent. Clients do not always need another lawyer. They need the right expertise at the precise moment when that expertise becomes valuable.

More especially now, we’re in an uncertain moment where people don’t know, from a macroeconomic perspective, what’s going to happen. In many verticals and industries, there’s a lot of uncertainty. Of course, AI is bringing even more uncertainty in the legal sector.

Many clients, many general counsel, look at the fractional model as a quick, efficient, and flexible manner of accessing capacity without adding permanent headcount.

Marlene Gebauer (24:46)

So operating across jurisdictions with hundreds of independent lawyers, I picture, creates difficult questions around conflicts, privilege, professional ethics, data security, and AI use.

Greg Lambert (24:55)

A little bit.

Marlene Gebauer (24:59)

So how are all those responsibilities allocated and managed, and who’s ultimately accountable to the client?

Manuel Deó (25:11)

Sure. Independence must never mean ambiguity. In fact, a distributed model like ours only works when responsibility is more explicit, not less.

The allocation is clear. The lawyer remains professionally responsible for legal judgment, conflict checks, privilege, and all the ethical obligations applicable in the relevant jurisdiction in which they are barred or qualified.

Ambar is accountable for the operating environment we control. The certification of that lawyer, the selection, the contracting, the insurance we provide to our clients, the cybersecurity standards, governance, process, compliance, anti-money laundering, monitoring, and escalation.

In particular, one of the things you’ve mentioned is conflicts, conflict checks, which are a critical part of our business. Those are assessed in relation to the individual lawyer and the particular engagement.

We do not create one central environment in which independent lawyers access confidential information belonging to the clients of other lawyers in the community. We would never do that.

Each professional, each lawyer, retains responsibility for their own practice and professional obligations, supported by our engagement processes and infrastructure.

Privilege is addressed through the structure of the engagement and the rules of the relevant jurisdiction applicable to that engagement. It’s never something we simply assume because technology is involved.

AI use is also governed by clear principles of client requirements, approved tools, defined data boundaries, confidentiality, and human review. As you might expect, we have B2B enterprise clients who are sophisticated. They all have AI governance and data privacy policies which we need to comply with and be audited against.

Where local rules require work to be delivered through a regulated law firm or a particular professional structure, the engagement must reflect that.

So that’s what we do. We’re careful to do that and do not use the independence of those lawyers as a liability escape hatch.

We think distributed delivery should distribute capability, not responsibility. The client should never have to guess who owns the risk. We’re clear. The engagement letter covers that.

We have an engagement letter where the client, the lawyer, and Ambar are parties to the same relationship because we believe in transparency and each party knowing who owns what and who’s responsible for each part of the legal delivery.

Greg Lambert (28:21)

Well, I think this ties in nicely with the next question. I want to talk to you about Ambar’s community, its culture, and some of the interesting things I’ve seen.

As I was driving into work this morning, I was listening to something and someone mentioned culture at an organization. They defined it in a way that makes sense, but I hadn’t heard it quite this way before. Culture is a set of rules and guidelines on how a person should perform and act in the environment in order to succeed within this community.

When you have a distributed network of independent lawyers, let me ask you up front, how do you prevent this independent contractor from feeling like a transactional database of freelance attorneys? Let me start with that.

Manuel Deó (29:30)

Sure. It’s a long process. One is the right selection of the lawyers, so the vetting of that lawyer, being super demanding on the type of lawyer who is part of the community. We’re picky on that.

I hate to say that, but 95% of the lawyers who request being admitted to Ambar are not able to do it. So we have less than a 4% admission rate right now.

We require those lawyers to come from, or to have expertise in, a closed-ended list of law firms or in-house departments, which gives us certainty on the type of lawyer, the type of culture, and the work ethic around that lawyer.

We take that certification and admission process seriously. We’re part of it through the vetting of that lawyer with third-party references, but also with an in-person meeting with my co-founder, Rosa, who sits with each and every lawyer who’s part of Ambar and wants to be part of it. So we’re careful with that.

That’s one thing. Second, we have a clear code of practice. Obviously, when you’re talking about sophisticated lawyers, these lawyers come from specific backgrounds. They are subject to the ethical standards of their jurisdictions. This is a small market, and there’s tremendous ownership of each person’s reputation and brand.

We’ve never had any issue with that. We’re careful with it. We always have to be listening to our clients regarding the feedback we get on our lawyers in connection with our projects.

We’ve been disruptive there, too, because we’ve been proactively asking for feedback, which is something that’s not common in this sector, and getting the client to verbalize the feedback they have on their lawyers, put that on the record, and make that transparent to the lawyer.

So we’re careful about that. Obviously, it’s a distributed model. Ambar is accountable for the system and the lawyer is accountable for the legal judgment and the legal delivery. That’s clear. I think that’s something that’s also been clear for our clients.

Greg Lambert (32:24)

There’s one more thing you do in community that’s interesting to me, and that’s your communications with your clients.

You’ve put out a newsletter that I think you call Dr. No, and it’s read by thousands of general counsel. But instead of talking about case law updates and changes in regulation, you write about leadership, legal operations, and strategy.

How do your clients respond to a different approach like this?

Manuel Deó (33:10)

Sure. Dr. No, and by the way, the name comes from how certain in-house lawyers are described in their companies because they’re always saying no.

What we try to say there is that we need to put our clients at the center of the conversation, which is something that’s not common in the legal sector.

We’ve always been talking about the rainmakers in this law firm. We talked about Chambers before. Chambers has a lot of rankings, but they’ve always concentrated on law firms and not on in-house lawyers and general counsel.

What we’re trying to suggest here is that we should change the conversation, put the general counsel in the middle of the conversation, and offer different tools those lawyers in those companies need.

I’m not going to teach law to a general counsel or a senior lawyer in an important company. I would never intend to do that. But we talk about things they need to become more influential in their companies, to become more central.

Think about HR managers 20 years ago, who became influential people in their companies through many different changes. One of them is a change of name, which changed to Chief People Officer or Chief Talent Officer, and they became senior and influential people on the board.

Or CFOs, who went from being financial analysts or controllers to becoming influential people in their companies.

What we’re trying to provide are those tools for general counsel to become as influential as a Chief Legal Officer, someone who’s talking about metrics, return on investment, how much savings the department provides to the company, and treating a legal department as another separate business unit of a company.

Greg Lambert (35:25)

All right, well, we are at the end. We want to have you look back before we ask you our crystal ball question. So Manuel, what is something that’s different today, or wasn’t necessarily true a year ago?

Manuel Deó (35:48)

I don’t want to talk about AI because it would be too obvious. Obviously, every week we have a new development in AI.

Marlene Gebauer (35:59)

So boring, AI.

Manuel Deó (36:01)

Right. So it’s something I would avoid mentioning. It’s a difficult question, but I think we’ve run through a process where general counsel don’t want to be seen as a cost center in their companies and want to be seen as something different.

That’s changing the conversation, and it’s changing the type of solutions we as legal suppliers need to provide our clients.

Because it’s not a matter of an hourly rate or, “I’m the best expert in this specific industry.” It’s a matter of how much value did I provide to my client? Because I need to help that client explain to their CEO, their CFO, the value they’re bringing.

That’s something we’re starting to see now that we didn’t see a year, a year and a half ago. How important value contribution is, and being a true investment as a legal department in a company, is changing the mindset of many general counsel.

Marlene Gebauer (37:11)

Well, if that question was hard, this one might be a little harder.

Greg Lambert (37:14)

Yeah.

Marlene Gebauer (37:15)

This time we’re going to ask you to look into the future.

Manuel Deó (37:17)

Wow.

Marlene Gebauer (37:18)

Our crystal ball question is, what’s the single biggest change you see coming for how firms operate and interact with their clients?

Manuel Deó (37:29)

That’s also a hard one, yeah. I think the single biggest change will be that traditional firms will stop being the only meaningful unit of legal delivery.

Law firms will be important, but they will become more porous. The future legal team will combine small strategic in-house core teams. They will engage traditional firms when they need them, but they will also engage independent senior specialists, specialist boutiques, managed services, and AI agents to deliver legal solutions appropriate to the specifics of that legal need.

Those capabilities will be assembled by each legal department in a dynamic manner around each problem.

I think clients will move from asking, “Which firm should we hire?” to, “What is the best configuration of people, technology, knowledge, and risk for this particular matter?”

The traditional pyramid was designed for a world in which legal knowledge was scarce and coordination was expensive. That will be totally different.

AI makes information, as I said before, increasingly abundant and truly a commodity, in my opinion. Technology makes coordination much cheaper. I think that will change the geometry of the industry.

I think we’ll be seeing legal departments more as trusted orchestrators of capabilities. That’s how I see the future going forward. AI, boring matter, but I know it’s something we always…

Greg Lambert (39:26)

Well, I think you handled both of those questions well.

Marlene Gebauer (39:27)

Yeah, I think you did a great job.

Manuel Deó (39:28)

Yeah.

Greg Lambert (39:30)

Well, Manuel Deó, thank you so much for taking time out of your day to talk to us. This has been great.

Marlene Gebauer (39:37)

Thank you.

Manuel Deó (39:37)

Thank you. Thanks for having me.

Marlene Gebauer (39:40)

And thanks to all of you for listening. And don’t forget to like and subscribe on Substack, YouTube, and LinkedIn.

Greg Lambert (39:48)

And Manuel, where’s the best place for people to follow your content and to learn more about Ambar?

Manuel Deó (39:54)

Well, the best place to follow me is LinkedIn. I write regularly about the business of law, leadership, innovation, entrepreneurship, and AI.

I also publish a daily newsletter called Dr. No for legal leaders who want to be more innovative, about mental models, about cool stories. Less than a one-minute read each and every day.

Many clients tell me they resend it and forward it to their teams to send specific messages.

And that’s pretty much it. You can find Dr. No on our webpage, ambarpartners.com, under Dr. No.

Marlene Gebauer (40:38)

And as always, the music here is from Jerry David DeCicca. Thank you, Jerry, and bye, everybody.

]]> xlambert@gmail.com (Greg Lambert) Judicaid: Bringing AI Mediation to Everyday Legal Problems https://www.geeklawblog.com/2026/08/judicaid-bringing-ai-mediation-to-everyday-legal-problems.html Mon, 31 Aug 2026 10:24:59 +0000 https://www.geeklawblog.com/?p=19537

For millions of people, an everyday legal dispute never justifies the cost of a lawyer or a private mediator, no matter how much the outcome matters to them. Judge Victoria Wood saw that problem over and over during her years on the Napa County Superior Court bench. This week, Wood joins Judicaid Chief Strategy Officer Valerie Clemen to explain how those years led to Judicaid, an AI-assisted mediation platform built to help people resolve everyday disputes before time, expense, and emotion push them deeper into litigation.

Wood traces Judicaid’s origins to two problems she kept running into as a judge and mediator. Traditional settlement conferences arrive late in a case, after the parties have spent real money and dug into their positions. Then there are the lower-value landlord-tenant, contractor, neighbor, probate, and small-claims disputes where professional mediation rarely pencils out at all. Wood puts a number on the access problem. The State Bar of California’s 2024 Justice Gap Study, released in 2025, found that Californians received no legal help, or inadequate help, for 85 percent of their civil legal problems. Judicaid aims at a slice of that gap by giving people an earlier and cheaper chance to communicate and negotiate.

Clemen walks Greg Lambert and Marlene Gebauer through Judicaid’s “shuttle-style” mediation process. Each participant talks privately with an AI mediator named Jude, so the two sides never have to speak to each other directly. Jude gathers each side’s account of the dispute, identifies priorities and possible settlement terms, and moves between the participants while filtering out insults, anger, and inflammatory language. If the parties find common ground, the platform prepares a proposed settlement for review and electronic signature. Wood is careful about one boundary throughout the conversation. Jude is a facilitative mediator, and it stays away from evaluating legal rights. It will not decide who is legally correct, predict who will win, or give legal advice.

The conversation then turns to Judicaid’s pilot with Napa County Superior Court, and the role courts could play in expanding AI-assisted dispute resolution. In the court model, a court subscribes to the service and hands litigants’ access through a QR code, with no integration into the court’s technology systems. The pilot has already surfaced a behavioral lesson. Offering mediation as an optional service does not mean parties will use it, and Wood and Clemen see more potential where courts actively encourage or require litigants to attempt dispute resolution before proceeding. Language is another piece of the story, since Judicaid lets participants who speak different languages work through the same mediation without arranging multiple interpreters.

Wood and Clemen also see mediation as just the starting point. Wood uses the phrase “intelligent dispute resolution” for a broader category of AI-assisted tools covering mediator proposals, parent coordination, and other structured approaches to conflict. The bigger ambition is a change in habits, where people reach for structured communication and settlement before a disagreement hardens into a lawsuit. Clemen boils that aspiration down to three words she hopes become part of the vocabulary of everyday disputes: “Just Judicate it.”

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

 

LINKS

Transcript

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review, and this week we’re talking with Judge Victoria Wood and Chief Strategy Officer Valerie Clemen from Judicaid. But first up, here’s a word from our great sponsor and partners at Legal Technology Hub.

Marlene Gebauer (00:13)
I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:19)
Thank you. Good to see you again. Well, in the advisory team at Legal Tech Hub, we’ve been having a lot of conversations with clients about AI governance. Now, most law firms and departments have at least an AI policy of some kind, and there are some broad standards starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and toward a more informative approach, which

Marlene Gebauer (00:37)
Okay.

Sam Moore (00:47)
defines different risk categories and

Marlene Gebauer (00:48)
You...

Sam Moore (00:49)
makes distinctions between routine, low-risk use of AI and other more substantial use cases. I think that’s appropriate for where we are, and it’s also what clients are coming to expect from their advisors. But one of the bigger challenges we’re seeing right now is how you express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

Marlene Gebauer (00:55)
So, okay.

Sam Moore (01:17)
And I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is probably using AI day-to-day. So they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not”

Marlene Gebauer (01:18)
So...

Sam Moore (01:43)
and turn them into more readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit https://legaltechnologyhub.com.

Marlene Gebauer (02:01)
Thank you, Sam. I mean, it is really important to have AI policy in plain language so people understand it.

Sam Moore (02:07)
Absolutely.

Marlene Gebauer (02:15)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:22)
And I’m Greg Lambert.

Marlene Gebauer (02:24)
Today we’re exploring whether artificial intelligence can help address one of the justice system’s most persistent problems, resolving everyday disputes without months of delay, escalating costs, or procedures that ordinary people struggle to navigate.

Greg Lambert (02:39)
Yeah, and to help us do that, our guests today are the Honorable Victoria Wood, a retired judge from Napa County Superior Court and founder and CEO of Judicaid. Joining Judge Wood is Valerie Clemen, Judicaid’s Chief Strategy Officer. Judicaid is an online, AI-assisted mediation platform designed to help people resolve disputes privately, affordably, and without first filing a case.

Marlene Gebauer (03:08)
Judge Wood and Valerie, welcome to The Geek in Review.

Valerie Clemen (03:12)
Thank you for having us.

Vickie Wood (03:12)
Thank you. Happy to be here.

Marlene Gebauer (03:16)
Judge Wood, Judicaid grew out of what you witnessed during your years on the bench in Napa County. What recurring problems convinced you that the traditional court and mediation systems were not adequately serving people with everyday disputes, and that technology could provide a better path?

Vickie Wood (03:34)
There were a few things. One of them, I’d say, is that I handled a lot of settlement conferences while I was sitting on the trial court bench, but usually those come very near trial, later in the case, when people are very entrenched.

Marlene Gebauer (03:50)
When the judge says, “You go in the other room and figure it out.”

Vickie Wood (03:54)
Yeah, yeah.

And they’ve already become very angered in their positions. They’ve already invested so much emotionally and financially, usually through attorneys’ fees, and it’s hard to get those settled. Sometimes that’s what it takes because people understand the dynamics and the finances, and they’re finally at their wits’ end and they’ll settle. But it comes at a cost. There may be families that could have had relationships saved if they could have talked earlier on and understood each other’s positions better.

So that’s one thing, the opportunity for earlier mediation or communication facilitation. For example, I handle a fair amount of probate disputes, and some people say probate disputes are essentially family law after someone dies. They’re highly emotional for lots of reasons that go back many years. But what very often happens is it seems like there’s a lot of money at stake, but by the time they go through the process and get to mediation, sometimes the entire amount is spent on fighting. And it’s really sad. People never thought that would be the case.

I often think to myself, boy, if I only could have been with these people earlier in the process and let them know what would likely happen if they continued down the litigation path. So that’s one of the things that inspired it. The other thing is that there is this broad swath of disputes that are lower value and really don’t justify the expense of resources for attorney representation or human mediators. But those disputes still mean a lot to people, even though they’re low-dollar-value. In fact, that’s the reason why I decided to go to law school, being in a very low-dollar-value dispute with a landlord who I felt had done us wrong, and I was gonna

Greg Lambert (05:56)
Ha ha.

Vickie Wood (05:57)
fight it. And we did sort of win, but boy, it took a lot of time.

But these cases mean a lot to people, and they don’t have the same access to alternative dispute resolution as higher-value cases. So that was the other problem I was looking to fix. In fact, there is a State Bar of California justice gap study that was recently released. It was a follow-up study from 2019, then redone in 2024, with those results coming out in 2025. They reflect, let me get this right, that Californians receive no legal help or inadequate legal help for 85% of their disputes.

And I thought that was astonishing.

Marlene Gebauer (06:59)
I wish I thought it was astonishing. It’s like, I...

Greg Lambert (06:59)
Yeah. Yeah. And...

Marlene Gebauer (07:03)
I think...

Greg Lambert (07:03)
I don’t think Californians are unique in this either.

Marlene Gebauer (07:06)
No, no.

Vickie Wood (07:07)
Yeah. So this was a California study, obviously, but that’s 85% of disputes where people don’t have help, don’t have adequate help, or they throw their arms up and say, “The justice system can’t help me.”

After the 2019 study, the California Judicial Council really made efforts to try to close this justice gap. The problem was that the results from 2024 showed things got worse, not better, despite their efforts. So that was striking to me as well.

When I left the bench and started my mediation practice and started studying and using AI, it came to me that there is probably a portion of these cases that could benefit from what AI and LLM technology can do. That’s how Judicaid was born. I came up with the idea and the name, and then I happened to have a family member who is brilliant in the tech world. I mentioned it to him and he said, “I can build that for you.” And he said, “That will be easy.”

So, more than a year later...

Greg Lambert (08:27)
Yeah, I imagine in California...

Marlene Gebauer (08:28)
Words you like...

Greg Lambert (08:29)
Yeah. Well, it’s nice...

Marlene Gebauer (08:29)
to hear when you’re building tech, “It will be easy.”

Vickie Wood (08:32)
Yeah, yeah. “That’d be easy.”

Greg Lambert (08:33)
to be in California where you can’t swing a cat without hitting an engineer.

Vickie Wood (08:39)
Yeah, needless to say, it was not easy. It proved to be much more challenging, but he and the other part of the tech team did it. We’re really proud of the tech product, the rest of our product, and the platform, and excited to be getting it out in the world. So thank you for the opportunity to be here today.

Greg Lambert (09:00)
So, Valerie, I want to jump into what Judicaid does and how it’s set up. But let’s start off with the user experience. Let’s take a typical user of the system, maybe two people having a landlord-tenant, contractor, or small-claims dispute.

What happens from the moment one person opens a matter on Judicaid until the parties either reach a settlement or determine that the mediation won’t resolve it? Can you walk us through the process of Judicaid?

Valerie Clemen (09:37)
Sure. The product just went live, I want to say, like six weeks ago. There are actually two aspects to it. There’s the consumer-facing version, where people can go on, pay $34.99, and start a mediation. But we also have the court-subscription version. Napa Superior Court currently has a pilot in its small-claims cases, so it’s a little different depending on which one we’re talking about.

Essentially, it’s a website, a standalone website. Parties can go there, create their own account, and put in some basic information about who the other side is and what the dispute is about. They never speak directly with the party they’re having a dispute with. An email goes to the other side and says, “We’re sorry you’re in a dispute. The other side would like to try to work this out with you. Please sign up as well.”

Hopefully they sign up if they’re willing to talk. Once they sign up, there’s literally an AI mediator that first goes to the initial party and goes back and forth asking questions like: When did this happen? What occurred? What is owed? Usually it’s over money or property. What attempts have there been to resolve it? What do you want today to resolve it? Once it has this information, it says, “Sit tight,” and then it goes to the other side.

When it says “sit tight,” people can be doing this on their phone, laptop, or tablet, and they don’t have to be online at the same time. It basically sends a message to each side when it’s their turn to speak to the AI mediator. It’s essentially the LLM, the AI engine, asking the questions and doing this.

So it goes to the other side and asks the same kinds of questions. What’s your version of the dispute? How do you feel about it? What’s your bottom line? What do you want? Once it has that, it starts going back and forth and tries to ask questions, get more information, and bring both sides to an agreement, hopefully in the end. It’ll keep going back and forth as long as it takes.

We’ve done a lot of pressure testing. Vickie and I are both attorneys, so we were like, what would a litigant say if you’re really upset? They might tell the AI mediator to F off and say, “You’re an idiot,” or whatever. So we did all of these things, and it’s great. It’ll hear these things and say, “I understand you’re upset, but what is your bottom line?” It’ll never go to the other side and say that this side insulted you. It’s very good at filtering

Marlene Gebauer (12:16)
Yeah.

Valerie Clemen (12:17)
and focusing on the problem and how to resolve it. Once it senses there’s an agreement, maybe there’s a payment plan worked out, it’ll prepare a draft agreement and send it to both sides and say, “Could you live with this? Is this what you want? Is this the agreement you want?” If both sides say yes, it creates an agreement, sends it to both sides through DocuSign, and you have a signed settlement agreement.

Greg Lambert (12:43)
Okay. Well, let me jump in on one question. What incentive does the counterparty have? I can see an incentive where I want to pay the $35 to get the subscription, but what’s the incentive on the other side to join this mediation?

Valerie Clemen (13:08)
Usually there’s a threat of a lawsuit. Like, “I’m gonna take you to small claims. I’m gonna call the Contractors State License Board.” If it’s a home-improvement project, there’s some threat because someone’s upset and wants something. So either you resolve it with that person or they’re gonna take the next step, which could have professional consequences. You might have to deal with litigation. You’re gonna be sued. In the world of Google, if you get sued by someone, somebody Googles your name and that shows up forever.

So the ability to resolve something before it turns into something worse would hopefully be motivation. But, you know, humanity...

Vickie Wood (13:46)
Yeah, but Greg, you ask a really good question because that is a sticking point. The platform generates an automatic email invite to the other side, and it says this person has either filed a lawsuit or is considering filing a lawsuit, but they would like to first try to resolve this peacefully.

You don’t lose any of your rights if you’re not able to resolve it. I personally would be very motivated to try that, knowing what it’s about, but what we’re finding is that a lot of people don’t even know exactly what mediation is, and they don’t know what they’re getting into. We try to highlight that this is completely confidential. The court’s never gonna know anything that is said. You preserve all your rights.

But that is a tricky part of this, and we’re hoping that if it gets out there and becomes more normalized, that’ll take away some of that reluctance. On the other side of the coin, I think people in general would really like to avoid going to court, seeing this person face to face, and talking to a judge. These are scary things. People are getting less used to talking to people face to face, so we’re hoping it’s going to play into the natural progression of how people would rather address conflict.

Marlene Gebauer (15:18)
So I’m curious about how the AI is trained. I’m assuming that it trains on contract law, landlord-tenant law, things that would come up as part of these disputes. I’m also curious, we talk about personas here sometimes in terms of different types of training and things like that. I’m wondering if judge personas were part of the training.

And then how do you keep the system from crossing the line into giving legal advice? I heard a little bit, if we sense there’s an agreement, we’ll start with the decision, but how do you avoid legal advice or saying, okay, right, wrong?

Vickie Wood (16:03)
Mm-hmm. Great questions. First off, no, it’s not trained in the law.

It doesn’t give any legal advice whatsoever. It is essentially a facilitator of the communications. As a mediator, most mediators know there’s a spectrum of how you approach mediation. You can be on one end, mostly facilitative, where you’re exploring people’s values and priorities in trying to get the case resolved, and you’re facilitating communication about how they feel and their thoughts on the case.

On the other end of the spectrum is a mediator who becomes quite evaluative and says to someone, “You’re gonna lose this case,” or, you know, “Here’s...”

Marlene Gebauer (16:48)
You know, “You might want to think about this.”

Greg Lambert (16:50)
Mm-hmm.

Vickie Wood (16:52)
“This is where I’m really concerned. These are your risks.” Generally, at least in how I mediate, and I think it’s one of the better ways to approach mediation, you start out facilitative and then, as the day goes on, you can get a little more evaluative and try to push things toward that end.

Judicaid stays facilitative. So there’s no legal advice. It’s not trying to tell someone, “You’re gonna lose this case,” or, “They’re right on this point and you’re wrong,” or vice versa. It will remind them of the risk and hassle, the time and expense of going to court, and the risk that they may not win their case, encouraging them to continue to think about compromise and keep engaging so they inch their way toward a palatable compromise where eventually they go, “Okay, I can say yes to this and be done and not have to deal with court.”

So that’s the idea, and that’s why we’re able to offer it nationally, because it doesn’t deal with laws that would be individualized to each state.

The training aspect is really, there are two main tech pieces. There’s the application that’s coded, which is our AI engine for the shuttle-style back and forth. That took a really long time to create. Then there is the mediator disposition architecture, and that’s where I had a lot of input as the subject-matter expert. It’s like a 38-page prompt that...

Marlene Gebauer (18:42)
Ha ha ha.

Vickie Wood (18:42)
That...

Greg Lambert (18:42)
How?

Vickie Wood (18:43)
guides Jude, our mediator’s name is Jude, by the way. It tells Jude...

Marlene Gebauer (18:48)
Do you say, “Hey Jude,” when you talk to it?

Vickie Wood (18:51)
Jude. We actually...

Marlene Gebauer (18:53)
I’ll leave now.

Vickie Wood (18:54)
never intended to have a name, and it just sort of came about. Since it’s Judicaid, and Jude’s sort of a gender-neutral name, we thought, why not? We’ll go for it.

But it’s the disposition, sort of prompting architecture, that tells it how to respond and how to continue to inch people toward a compromise. There are lots of guardrails so it’s never giving legal advice. It can suggest, if someone says, “Do you have any ideas on how we might be able to resolve this?” it can say, “Well, one thing you might consider is this,” or, “You could take payments over time,” or, “Maybe take a smaller payment up front and then the balance over time.” It can suggest things like that, but it’s very generalized in that way.

That’s a big part of why it was never intended to replace attorneys or human mediators. As a mediator, I know that most complex cases that go to human mediation are far too complex, both legally and emotionally, for an LLM to manage. So this is not trying to replace that. This is focused on filling the gap for those lower-value cases that are generally fairly simple.

There is emotion involved, and that’s something an LLM is very good at managing in communication. It’ll calm the person down, and they can get as upset as they want. It can be empathetic and say, “I hear you’re feeling really frustrated about that, and that’s understandable. But why don’t you let me go talk to the other person?” It’ll go to the other side, and it’s never gonna convey the anger or the bad words being said in the other room. It’s gonna say, “They’re feeling concerned about this.” So it paraphrases things in a way that makes them far more palatable to hear when you’re the adversary.

That’s a big part of what I do as a human mediator. When somebody is cussing and swearing and saying horrible things about the other side, I go in the other room and say, “Yeah, well, they’re starting to come around.” So...

Greg Lambert (21:17)
Yeah, I was gonna say, do you have it default back to those instructions so it doesn’t get caught up in what each side is feeding it, but rather goes back to its baseline to make sure it stays on the course it needs to stay on?

Vickie Wood (21:40)
Yeah. Like I said, the prompting architecture is vast, and it’s very carefully thought out in phases to guide things through. But it has priorities around guardrails, and if there’s any sort of safety issue, somebody says, “This is terrible. I’m gonna kill myself,” we make sure it’s gonna say, “If you’re struggling with that, then you need to consult resources in your locality. This may not be the place to do this.”

And there’s a button to say, “Stop mediating,” and terminate the mediation at any point in time. So, yeah, a lot of thought went into that...

Marlene Gebauer (22:25)
I have one. I have one.

Vickie Wood (22:27)
Aspect.

Marlene Gebauer (22:29)
Does it learn? I mean, from session to session or are those isolated?

Vickie Wood (22:34)
They’re isolated. We have made the decision at this point that we’re not training the AI model directly in the way that an open-source LLM would be constantly training itself.

Yeah, it’s truly intended to be confidential, just like mediation is confidential. You’re not supposed to go tell a court, “Well, in mediation she said this or that,” or you’re not allowed to, at least in California.

Greg Lambert (23:03)
And that’s when you hold up your hand saying, “I don’t want to hear that. I don’t...”

Vickie Wood (23:06)
Yeah. Yep. Exactly.

Marlene Gebauer (23:08)
No, no.

Vickie Wood (23:09)
Exactly. Privileged. So...

Greg Lambert (23:12)
Well, we talked about the user side, so let’s go over and talk about the court side. Courts appear to be a really important path for Judicaid going forward. Valerie, I’ll start with you, and Judge Wood, if you want to jump in, please do. What would a court-facing model look like in practice, and where do you see it fitting into the life cycle of these disputes?

Is it before the case is filed? Is it after, but before a hearing? Is there someplace else? Where do you see the path of leveraging either the threat of the court or the actions of the court to help Judicaid?

Valerie Clemen (23:55)
Yeah. Initially, it would be good to have it in self-help centers, because a lot of times, especially for small claims, landlord-tenant, or those types of claims, those parties are unrepresented. You just can’t afford to pay an attorney’s hourly rate for a case that’s honestly below $20,000. It doesn’t make any sense to pay an attorney, so they’re trying to figure out the system themselves.

Knowing they have this option to resolve their dispute without going through the paperwork, getting it served, and filing is helpful. So having that information in self-help centers and in the courts pre-filing is helpful. Once a case is filed, it would be helpful for courts to provide that option and, honestly, not make it voluntary. Make it mandatory to give it a try, to talk to the other side using some type of dispute resolution or mediation, but also provide Judicaid as a free resource for litigants to use and try to resolve the dispute.

The information would come at the time of filing. Like, here’s your complaint, you have to serve the other side, and also serve the other side with this dispute-resolution information to see if you can resolve the case before your hearing date comes up. In some courts, when your hearing date is set out six months or a year, being able to resolve it and put the thing behind you before that happens, or before you spend money having the sheriff personally serve it or hiring a process server, is helpful. There are so many costs and so much time associated with litigation.

The hope is that letting litigants know there’s this option would be helpful. Of course, a lot of unrepresented litigants are doing this for the first time. They’ve never filed a small-claims suit. They’ve never been in a legal dispute. A lot of them probably don’t even know what mediation is. I’d say at least 10% get the flyer and are like, “Meditation? I know I’m angry, but I don’t need that.”

Marlene Gebauer (25:48)
Yeah.

Valerie Clemen (25:49)
Like, just not having any idea.

Vickie Wood (25:51)
It should go hand in hand, though.

Greg Lambert (25:51)
I need it, but not in this case.

Valerie Clemen (25:54)
Yeah. Because honestly...

Greg Lambert (25:57)
So are you educating law librarians and self-help centers on this, so they have it as a tool they can point people to as an alternative, or...?

Vickie Wood (26:11)
We’re trying to. We’re trying to. We just...

Marlene Gebauer (26:15)
That’s why they’re on the podcast, Greg.

Vickie Wood (26:18)
But yeah, in Napa County, where we’re running the pilot, we have definitely put it out there to all of the people who might be interested. We’ve got flyers in the law library and in the regular county library. While it’s being offered for free, government agencies have a line where they don’t want to be advertising something that’s for profit.

But while they’re piloting it, or if they were, and actually, let me jump back to one of your questions, Greg, about what this would look like through the court. The platform itself will be identical. The courts would pay for a monthly subscription, and then they would be given a QR code that they could provide to litigants upon filing. They could even put it on their website, in the small-claims area, for people who are thinking of filing a claim to try it before they file.

Those people would enter the site through a landing page for that court with that QR code. There’s a little gateway where they have to say how they got there. If they didn’t get there through that court, we’re going to redirect them to the paid portion. But if they got there through the court through the QR code appropriately, then they bypass the payment piece and jump into the platform exactly the way everybody else does.

So, yeah, we’re trying to get this out there. We’re on social media. We just got back from a national court conference in Delaware. We were having conversations with courts across the U.S., getting them aware. I think the trick in trying to get courts on board with this is that most of the technology out there needs to be integrated into the courts’ networks and processes.

Their first thought, I think, is, “Here’s yet another AI-based app or process that we’re gonna have to implement and then be tied to this company for however long, and tied to their schedule of upgrades and new features and their pricing changes and all of that.” We’re completely different from that. The only thing the court has to do is give that QR code to its litigants or potential litigants, and any questions go through our support page. That’s it. That is it.

I think courts are having a hard time comprehending that it could be that easy. Hopefully we’ll get the message out. What was that, Marlene?

Marlene Gebauer (29:01)
This could be very helpful for them.

Greg Lambert (29:02)
Yeah.

Marlene Gebauer (29:05)
It could be very helpful for them in terms of the docket. So...

Vickie Wood (29:11)
Yeah, that’s the idea. And it’s so affordable that it’s sort of a win-win-win proposition, and it feels like a no-brainer to us. But still, it’s gonna take some time to get people to first be aware of it and then have a true understanding of how simple it is.

Valerie Clemen (29:29)
Yeah. I’ve had clients come to me, we’re in a small town, so I have clients who come to me with certain issues, and sometimes they come with things that are just so minor. They have a dispute with their neighbor and the money involved is so low, and I’m like, “Look, I can’t honestly have you pay me for this, but try this.” I’ve had a couple of clients go try Judicaid, and they were like, “It was easy to sign up and send the invite, but the other side didn’t respond.”

But it was nice. Sometimes clients come to you and they have things that are too minor, and you’re like, “Look, the budget for this doesn’t make sense.” Giving them something, some tool where they might resolve it on their own, was nice instead of saying, “I can’t take this. Good luck.”

Vickie Wood (30:12)
Yeah. So that is another sort of distribution channel, if you will. Exactly what Valerie just said, for attorneys to be able to give something of value to those clients they have to turn away. Say, “There’s another tool here.” We think it’s something attorneys would really like having in their back pocket for that situation. The problem is there is that immediate reaction of, “What is this taking? Is this going to take away our business?” Or, for...

Greg Lambert (30:45)
Yeah.

Vickie Wood (30:45)
mediators, “Is this going to take away our business?”

And I’m saying, no, it’s not that. LLMs are obviously changing the legal scene drastically, but at this level of sophistication, they can’t replace humans. You absolutely need human mediators. So hopefully, if there are attorneys out there listening, this is something for you to have to give to the people who you can’t otherwise help.

Valerie Clemen (31:16)
Yeah. It’s like...

Marlene Gebauer (31:16)
Yeah, I’m kind of looking at it like it’s an initial step because you’re absolutely right. I was very curious, a human mediator can sense when there’s unequal bargaining power or someone’s more legally sophisticated, and they can run their mediation with that in mind. But I’m curious how Judicaid does that as a technology tool.

Vickie Wood (31:43)
Well, in some ways it’s more neutral. Because it’s just going to be kind of...

Marlene Gebauer (31:47)
Yeah. It’s neutral. That’s it.

Vickie Wood (31:54)
Yeah, yeah. It’s not gonna be impressed by the person who is sort of a bully or sounds like they really know what they’re talking about. It’s not gonna be more persuaded by this side or that side. Again, it’s facilitating that conversation in a really neutral way.

And to your point about it being a starting point, yes, I do think cases will get resolved through this. But if they don’t, I also think there’s another value that goes beyond monetary. Let’s talk about this, it isn’t limited to small claims. It could be used for limited civil cases. It could be used for eviction cases. We’re gonna add a feature to try to get this into family law for parent-coordination kinds of things. I have unlimited features in my head, so it’s just a question of getting them to happen.

What I would see with small claims is, like I said in the beginning, people are passionate about these cases and they spend days’ worth of time preparing for them and putting everything into them. Then they get to court and there are 40 cases on the docket for trials. They’re gonna sit there for a long time, their case gets called, and they get maybe 10 minutes. And...

Greg Lambert (33:13)
Yeah.

Vickie Wood (33:14)
even if they win, but especially if they lose, they walk away not feeling good about the process and about the justice system, because many times this is these people’s only real interface with the courts. They walk away not feeling good about it, and that’s not good for any of us. That’s not good for society in general.

So if they start with a product like Judicaid, where they’re able to kind of be “heard,” and I’m putting that in air quotes for anyone who’s only on audio, to air their feelings about it and also hear the other side’s position and thoughts, and then have to think through the potential outcome that it may not go their way and decide they want to take that risk, well, now they’ve had much more participation in the ultimate outcome. I feel like that’s going to be an improvement for everybody across the board.

Greg Lambert (34:12)
Yeah. Well, I want to touch on your pilot with the Napa County Superior Court. Tell us about what you’re piloting. Beyond finding out whether the technology works, what are you hoping the courts will find out, and what are you hoping to find out from this pilot on your side?

Vickie Wood (34:35)
Yeah, so we’re new with the pilot. We’re kind of just starting to get through the period from where we started to where cases would be coming on to trial. We have had people engaging, but we’re not getting the mediations to happen yet.

So we’ve learned one really important thing, which is that people are gonna be reluctant to do this right now if it’s purely voluntary. That was a very valuable thing to learn. We knew there would be reluctance, but there’s significant reluctance.

One note, though, is that Napa County is a tiny county, so the number of filings is a very small sample to be working off of.

Greg Lambert (35:18)
I’m envisioning the mediation being over a glass of wine. That’s all I see.

Vickie Wood (35:22)
Of course, Greg, of course.

Marlene Gebauer (35:22)
Ha ha.

Vickie Wood (35:25)
If they’re not drinking wine, then the dispute is about wine somehow. But yeah...

Greg Lambert (35:30)
Yes.

Vickie Wood (35:32)
It’s a small county. But yeah, once we start getting engagement, and we’re connecting with some larger counties, one in particular is a large county that already mandates mediation for both small claims and unlawful detainers. They offer some human mediation options that are lower-cost than most human mediation, but still significantly more than Judicaid. So this would be another thing to offer people.

One of the greatest features about Judicaid is that it can be done in many different languages. You can have somebody who speaks only Spanish and someone who speaks only Russian mediating together. That would be very expensive to provide with a human mediator because you would probably have to have two interpreters for one mediator, and the cost skyrockets.

So, yeah, we’re hoping to get a bigger sample, especially one where it’s mandated, because we think if it’s mandated, a lot of people will opt for this because it’s so easy. You don’t have to schedule a set time. You can do it literally over your phone, dictating.

Greg Lambert (36:47)
And you mentioned that this isn’t just California, right? You’re looking at going...

Vickie Wood (36:51)
Correct.

Greg Lambert (36:54)
going into other states as well.

Vickie Wood (36:56)
Correct, correct. Yeah. And maybe, I mean, honestly, I don’t know why it couldn’t go outside the U.S. too, because again, it’s not tied to any particular laws. So I don’t know. I don’t want to think too big just yet, but...

Greg Lambert (37:09)
Well, then you have to deal with the EU and GDPR and all that, so, you know...

Valerie Clemen (37:14)
Yeah, yeah.

Vickie Wood (37:15)
Yeah. Well, I’ve been really bored lately, Greg. That would give me something to do with...

Greg Lambert (37:19)
Yeah.

Vickie Wood (37:19)
my time.

Marlene Gebauer (37:19)
Yeah.

So we kind of close the podcast by looking back and then looking forward. This is kind of our “what’s different now” question. If we had spoken with you a year ago, other than the fact that maybe Judicaid didn’t exist, what would you have told us is materially different today? What have users and court partners, or the experience of building the platform, taught you that changed your original assumptions?

Vickie Wood (37:56)
When I conceived of it, which was closer to two years ago, the way ChatGPT or any of these large language model platforms functioned was astonishing. I mean, it still is astonishing, but more people were seeing it for the first time or had not seen it yet. So the concept of what Judicaid does was almost like magic. Now people are aware that having a computer talk to you like it’s a human can be done.

What we have, that I don’t think is done anywhere else, is this shuttle-style approach where it’s truly exchanging the communications. But when my CTO told me, “This will be easy,” I thought we would be launching in like two months. Then all these months later, it’s not quite as novel as it was at the time.

At the same time, there’s also a fair amount of skepticism of AI and concern about AI taking jobs. There are people who might see that it’s an AI-facilitated platform and want nothing to do with it, without considering that this is something trying to provide a positive benefit and fill a gap. Those were unexpected things, but we’re rolling with it because I think it’s gonna require a little bit of awareness about what we’re doing.

Also, AI is becoming so pervasive that I think eventually people are...

Greg Lambert (39:41)
Yeah.

Vickie Wood (39:42)
just gonna get tired of being so against it, I guess. But yeah, I think the other thing is we focused on mediation because that’s the piece we wanted to nail down. Now that we have that core AI engine, we can branch off in so many different dispute-resolution ways, which gets me so excited.

We’re now calling it intelligent dispute resolution because it may not just be mediation. We might start having mediator’s proposals. We might have a parent coordinator to help with little custody disputes.

ODR, online dispute resolution, has been around for quite some time, but I think intelligent dispute resolution is new. So I’m gonna say you heard it here first.

Greg Lambert (40:29)
All right, all right. Well, that leads in perfectly to our crystal ball question. Valerie, I’ll let you start this off if you don’t mind. So...

Valerie Clemen (40:38)
Sure.

Greg Lambert (40:39)
Picking up on Judge Wood’s discussion there about intelligent dispute resolution, in three to five years, where do you see things when it comes to AI-assisted dispute resolution? What’s it gonna look like, and what all do you think it will cover?

Valerie Clemen (40:58)
I mean, our hope is that it would be offered in courts nationwide, because this underserved population doesn’t have access to dispute resolution or mediation, and this would provide that. So we would like to see it used in courts nationwide.

I would also, I mean, this is hopeful, but it would be nice if people just thought to “Judicate it”...

Vickie Wood (41:19)
Yeah.

Valerie Clemen (41:19)
when they have disputes, to go there first. You see people arguing on Nextdoor. Sometimes you think we should have guerrilla marketing and just go in there and say, “Just Judicate it.” Like, there’s so many...

Vickie Wood (41:32)
I don’t know if Nextdoor is national, but it’s this neighborhood platform. And yeah, so...

Greg Lambert (41:35)
It is. You know. No, we all...

Valerie Clemen (41:36)
It is.

Greg Lambert (41:38)
know. “Was that fireworks or gunshots?” is the most popular...

Valerie Clemen (41:41)
Yes, exactly. Exactly. “Does anyone...”

Vickie Wood (41:46)
Yeah.

Valerie Clemen (41:44)
“recognize this car?”

Greg Lambert (41:46)
Ha ha.

Valerie Clemen (41:47)
But people have disputes, and it’s really hard to get people talking to each other. Oftentimes, when people start talking to each other, they resolve things. It’s that initial step, because it’s uncomfortable, it’s adversarial. But once people do it, they can often resolve a lot of issues.

So the idea that people could use this as a tool to help get things started, it’s really hard to walk up to someone...

Marlene Gebauer (42:12)
Okay.

Valerie Clemen (42:13)
and confront them and talk about something, even if you have it simmering. I mean, people have a hard time with that with family members. You’ve had this thing simmering forever, but you can’t address it.

This is a tool that helps you be a little protected but still share your thoughts and feelings and resolve disputes. The idea that people could use it for all kinds of disputes, I mean, it is a very civil way to resolve disputes and quash things, and not have things linger for a long time.

But obviously, in the legal sense, it would be nice when people do have disputes to the level where they’re ready to file a lawsuit or do something more, that they have a way to either solve it beforehand or try to solve it once the lawsuit has started. They don’t have attorneys helping them in a lot of these cases. They’re probably using Claude or ChatGPT to help them with their case. So this is another resource to help them resolve it instead of keep fighting.

Vickie Wood (43:09)
Yeah, or have it escalate to something more than it ever needed to be. And just to tack on to that, I think that’s exactly right. A lot of times it’s barriers to communication, and that’s what Judicaid is trying to resolve. Whether it’s lack of skill with communication, a power dynamic, or a language barrier, that’s really where Judicaid provides this platform at a very base level.

And hopefully, this is my tagline that I have in our little video, my hope is someday that instead of saying, “Let’s take it to court,” we’ll say, “Let’s just Judicate it” instead. So that’s our hope.

Marlene Gebauer (43:49)
I like it. I like it.

Well, Judge Wood and Valerie Clemen, thank you for joining us and giving us a closer look at Judicaid, at both the opportunities and difficult questions that come with using AI and expanding access to dispute resolution. And thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. We’d also love to hear from you on LinkedIn and Substack.

Greg Lambert (44:17)
And Judge Wood and Valerie, where can listeners go and find out more about Judicaid?

Vickie Wood (44:26)
Judicaid.com. We have a lot of information on there, including an instructional video that’s on YouTube, but you can access it from the homepage, along with how-to instructions. We’re on Instagram, Facebook, X, and LinkedIn, but that’s probably the best place to start.

Greg Lambert (44:47)
Soon to be on Nextdoor.

Marlene Gebauer (44:48)
All the places.

Valerie Clemen (44:50)
Yeah.

Vickie Wood (44:51)
I actually am on Nextdoor. That’s true. I haven’t really spent a lot of time on there yet, but yeah...

Marlene Gebauer (44:55)
Ha.

Vickie Wood (44:56)
I’m going there, Greg.

Greg Lambert (44:59)
Awesome, awesome.

Vickie Wood (45:00)
Thank you so much.

Marlene Gebauer (45:02)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry. Goodbye, everybody.

]]>

For millions of people, an everyday legal dispute never justifies the cost of a lawyer or a private mediator, no matter how much the outcome matters to them. Judge Victoria Wood saw that problem over and over during her years on the Napa County Superior Court bench. This week, Wood joins Judicaid Chief Strategy Officer Valerie Clemen to explain how those years led to Judicaid, an AI-assisted mediation platform built to help people resolve everyday disputes before time, expense, and emotion push them deeper into litigation.

Wood traces Judicaid’s origins to two problems she kept running into as a judge and mediator. Traditional settlement conferences arrive late in a case, after the parties have spent real money and dug into their positions. Then there are the lower-value landlord-tenant, contractor, neighbor, probate, and small-claims disputes where professional mediation rarely pencils out at all. Wood puts a number on the access problem. The State Bar of California’s 2024 Justice Gap Study, released in 2025, found that Californians received no legal help, or inadequate help, for 85 percent of their civil legal problems. Judicaid aims at a slice of that gap by giving people an earlier and cheaper chance to communicate and negotiate.

Clemen walks Greg Lambert and Marlene Gebauer through Judicaid’s “shuttle-style” mediation process. Each participant talks privately with an AI mediator named Jude, so the two sides never have to speak to each other directly. Jude gathers each side’s account of the dispute, identifies priorities and possible settlement terms, and moves between the participants while filtering out insults, anger, and inflammatory language. If the parties find common ground, the platform prepares a proposed settlement for review and electronic signature. Wood is careful about one boundary throughout the conversation. Jude is a facilitative mediator, and it stays away from evaluating legal rights. It will not decide who is legally correct, predict who will win, or give legal advice.

The conversation then turns to Judicaid’s pilot with Napa County Superior Court, and the role courts could play in expanding AI-assisted dispute resolution. In the court model, a court subscribes to the service and hands litigants’ access through a QR code, with no integration into the court’s technology systems. The pilot has already surfaced a behavioral lesson. Offering mediation as an optional service does not mean parties will use it, and Wood and Clemen see more potential where courts actively encourage or require litigants to attempt dispute resolution before proceeding. Language is another piece of the story, since Judicaid lets participants who speak different languages work through the same mediation without arranging multiple interpreters.

Wood and Clemen also see mediation as just the starting point. Wood uses the phrase “intelligent dispute resolution” for a broader category of AI-assisted tools covering mediator proposals, parent coordination, and other structured approaches to conflict. The bigger ambition is a change in habits, where people reach for structured communication and settlement before a disagreement hardens into a lawsuit. Clemen boils that aspiration down to three words she hopes become part of the vocabulary of everyday disputes: “Just Judicate it.”

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

 

LINKS

Transcript

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review, and this week we’re talking with Judge Victoria Wood and Chief Strategy Officer Valerie Clemen from Judicaid. But first up, here’s a word from our great sponsor and partners at Legal Technology Hub.

Marlene Gebauer (00:13)
I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:19)
Thank you. Good to see you again. Well, in the advisory team at Legal Tech Hub, we’ve been having a lot of conversations with clients about AI governance. Now, most law firms and departments have at least an AI policy of some kind, and there are some broad standards starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and toward a more informative approach, which

Marlene Gebauer (00:37)
Okay.

Sam Moore (00:47)
defines different risk categories and

Marlene Gebauer (00:48)
You…

Sam Moore (00:49)
makes distinctions between routine, low-risk use of AI and other more substantial use cases. I think that’s appropriate for where we are, and it’s also what clients are coming to expect from their advisors. But one of the bigger challenges we’re seeing right now is how you express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

Marlene Gebauer (00:55)
So, okay.

Sam Moore (01:17)
And I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is probably using AI day-to-day. So they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not”

Marlene Gebauer (01:18)
So…

Sam Moore (01:43)
and turn them into more readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit https://legaltechnologyhub.com.

Marlene Gebauer (02:01)
Thank you, Sam. I mean, it is really important to have AI policy in plain language so people understand it.

Sam Moore (02:07)
Absolutely.

Marlene Gebauer (02:15)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:22)
And I’m Greg Lambert.

Marlene Gebauer (02:24)
Today we’re exploring whether artificial intelligence can help address one of the justice system’s most persistent problems, resolving everyday disputes without months of delay, escalating costs, or procedures that ordinary people struggle to navigate.

Greg Lambert (02:39)
Yeah, and to help us do that, our guests today are the Honorable Victoria Wood, a retired judge from Napa County Superior Court and founder and CEO of Judicaid. Joining Judge Wood is Valerie Clemen, Judicaid’s Chief Strategy Officer. Judicaid is an online, AI-assisted mediation platform designed to help people resolve disputes privately, affordably, and without first filing a case.

Marlene Gebauer (03:08)
Judge Wood and Valerie, welcome to The Geek in Review.

Valerie Clemen (03:12)
Thank you for having us.

Vickie Wood (03:12)
Thank you. Happy to be here.

Marlene Gebauer (03:16)
Judge Wood, Judicaid grew out of what you witnessed during your years on the bench in Napa County. What recurring problems convinced you that the traditional court and mediation systems were not adequately serving people with everyday disputes, and that technology could provide a better path?

Vickie Wood (03:34)
There were a few things. One of them, I’d say, is that I handled a lot of settlement conferences while I was sitting on the trial court bench, but usually those come very near trial, later in the case, when people are very entrenched.

Marlene Gebauer (03:50)
When the judge says, “You go in the other room and figure it out.”

Vickie Wood (03:54)
Yeah, yeah.

And they’ve already become very angered in their positions. They’ve already invested so much emotionally and financially, usually through attorneys’ fees, and it’s hard to get those settled. Sometimes that’s what it takes because people understand the dynamics and the finances, and they’re finally at their wits’ end and they’ll settle. But it comes at a cost. There may be families that could have had relationships saved if they could have talked earlier on and understood each other’s positions better.

So that’s one thing, the opportunity for earlier mediation or communication facilitation. For example, I handle a fair amount of probate disputes, and some people say probate disputes are essentially family law after someone dies. They’re highly emotional for lots of reasons that go back many years. But what very often happens is it seems like there’s a lot of money at stake, but by the time they go through the process and get to mediation, sometimes the entire amount is spent on fighting. And it’s really sad. People never thought that would be the case.

I often think to myself, boy, if I only could have been with these people earlier in the process and let them know what would likely happen if they continued down the litigation path. So that’s one of the things that inspired it. The other thing is that there is this broad swath of disputes that are lower value and really don’t justify the expense of resources for attorney representation or human mediators. But those disputes still mean a lot to people, even though they’re low-dollar-value. In fact, that’s the reason why I decided to go to law school, being in a very low-dollar-value dispute with a landlord who I felt had done us wrong, and I was gonna

Greg Lambert (05:56)
Ha ha.

Vickie Wood (05:57)
fight it. And we did sort of win, but boy, it took a lot of time.

But these cases mean a lot to people, and they don’t have the same access to alternative dispute resolution as higher-value cases. So that was the other problem I was looking to fix. In fact, there is a State Bar of California justice gap study that was recently released. It was a follow-up study from 2019, then redone in 2024, with those results coming out in 2025. They reflect, let me get this right, that Californians receive no legal help or inadequate legal help for 85% of their disputes.

And I thought that was astonishing.

Marlene Gebauer (06:59)
I wish I thought it was astonishing. It’s like, I…

Greg Lambert (06:59)
Yeah. Yeah. And…

Marlene Gebauer (07:03)
I think…

Greg Lambert (07:03)
I don’t think Californians are unique in this either.

Marlene Gebauer (07:06)
No, no.

Vickie Wood (07:07)
Yeah. So this was a California study, obviously, but that’s 85% of disputes where people don’t have help, don’t have adequate help, or they throw their arms up and say, “The justice system can’t help me.”

After the 2019 study, the California Judicial Council really made efforts to try to close this justice gap. The problem was that the results from 2024 showed things got worse, not better, despite their efforts. So that was striking to me as well.

When I left the bench and started my mediation practice and started studying and using AI, it came to me that there is probably a portion of these cases that could benefit from what AI and LLM technology can do. That’s how Judicaid was born. I came up with the idea and the name, and then I happened to have a family member who is brilliant in the tech world. I mentioned it to him and he said, “I can build that for you.” And he said, “That will be easy.”

So, more than a year later…

Greg Lambert (08:27)
Yeah, I imagine in California…

Marlene Gebauer (08:28)
Words you like…

Greg Lambert (08:29)
Yeah. Well, it’s nice…

Marlene Gebauer (08:29)
to hear when you’re building tech, “It will be easy.”

Vickie Wood (08:32)
Yeah, yeah. “That’d be easy.”

Greg Lambert (08:33)
to be in California where you can’t swing a cat without hitting an engineer.

Vickie Wood (08:39)
Yeah, needless to say, it was not easy. It proved to be much more challenging, but he and the other part of the tech team did it. We’re really proud of the tech product, the rest of our product, and the platform, and excited to be getting it out in the world. So thank you for the opportunity to be here today.

Greg Lambert (09:00)
So, Valerie, I want to jump into what Judicaid does and how it’s set up. But let’s start off with the user experience. Let’s take a typical user of the system, maybe two people having a landlord-tenant, contractor, or small-claims dispute.

What happens from the moment one person opens a matter on Judicaid until the parties either reach a settlement or determine that the mediation won’t resolve it? Can you walk us through the process of Judicaid?

Valerie Clemen (09:37)
Sure. The product just went live, I want to say, like six weeks ago. There are actually two aspects to it. There’s the consumer-facing version, where people can go on, pay $34.99, and start a mediation. But we also have the court-subscription version. Napa Superior Court currently has a pilot in its small-claims cases, so it’s a little different depending on which one we’re talking about.

Essentially, it’s a website, a standalone website. Parties can go there, create their own account, and put in some basic information about who the other side is and what the dispute is about. They never speak directly with the party they’re having a dispute with. An email goes to the other side and says, “We’re sorry you’re in a dispute. The other side would like to try to work this out with you. Please sign up as well.”

Hopefully they sign up if they’re willing to talk. Once they sign up, there’s literally an AI mediator that first goes to the initial party and goes back and forth asking questions like: When did this happen? What occurred? What is owed? Usually it’s over money or property. What attempts have there been to resolve it? What do you want today to resolve it? Once it has this information, it says, “Sit tight,” and then it goes to the other side.

When it says “sit tight,” people can be doing this on their phone, laptop, or tablet, and they don’t have to be online at the same time. It basically sends a message to each side when it’s their turn to speak to the AI mediator. It’s essentially the LLM, the AI engine, asking the questions and doing this.

So it goes to the other side and asks the same kinds of questions. What’s your version of the dispute? How do you feel about it? What’s your bottom line? What do you want? Once it has that, it starts going back and forth and tries to ask questions, get more information, and bring both sides to an agreement, hopefully in the end. It’ll keep going back and forth as long as it takes.

We’ve done a lot of pressure testing. Vickie and I are both attorneys, so we were like, what would a litigant say if you’re really upset? They might tell the AI mediator to F off and say, “You’re an idiot,” or whatever. So we did all of these things, and it’s great. It’ll hear these things and say, “I understand you’re upset, but what is your bottom line?” It’ll never go to the other side and say that this side insulted you. It’s very good at filtering

Marlene Gebauer (12:16)
Yeah.

Valerie Clemen (12:17)
and focusing on the problem and how to resolve it. Once it senses there’s an agreement, maybe there’s a payment plan worked out, it’ll prepare a draft agreement and send it to both sides and say, “Could you live with this? Is this what you want? Is this the agreement you want?” If both sides say yes, it creates an agreement, sends it to both sides through DocuSign, and you have a signed settlement agreement.

Greg Lambert (12:43)
Okay. Well, let me jump in on one question. What incentive does the counterparty have? I can see an incentive where I want to pay the $35 to get the subscription, but what’s the incentive on the other side to join this mediation?

Valerie Clemen (13:08)
Usually there’s a threat of a lawsuit. Like, “I’m gonna take you to small claims. I’m gonna call the Contractors State License Board.” If it’s a home-improvement project, there’s some threat because someone’s upset and wants something. So either you resolve it with that person or they’re gonna take the next step, which could have professional consequences. You might have to deal with litigation. You’re gonna be sued. In the world of Google, if you get sued by someone, somebody Googles your name and that shows up forever.

So the ability to resolve something before it turns into something worse would hopefully be motivation. But, you know, humanity…

Vickie Wood (13:46)
Yeah, but Greg, you ask a really good question because that is a sticking point. The platform generates an automatic email invite to the other side, and it says this person has either filed a lawsuit or is considering filing a lawsuit, but they would like to first try to resolve this peacefully.

You don’t lose any of your rights if you’re not able to resolve it. I personally would be very motivated to try that, knowing what it’s about, but what we’re finding is that a lot of people don’t even know exactly what mediation is, and they don’t know what they’re getting into. We try to highlight that this is completely confidential. The court’s never gonna know anything that is said. You preserve all your rights.

But that is a tricky part of this, and we’re hoping that if it gets out there and becomes more normalized, that’ll take away some of that reluctance. On the other side of the coin, I think people in general would really like to avoid going to court, seeing this person face to face, and talking to a judge. These are scary things. People are getting less used to talking to people face to face, so we’re hoping it’s going to play into the natural progression of how people would rather address conflict.

Marlene Gebauer (15:18)
So I’m curious about how the AI is trained. I’m assuming that it trains on contract law, landlord-tenant law, things that would come up as part of these disputes. I’m also curious, we talk about personas here sometimes in terms of different types of training and things like that. I’m wondering if judge personas were part of the training.

And then how do you keep the system from crossing the line into giving legal advice? I heard a little bit, if we sense there’s an agreement, we’ll start with the decision, but how do you avoid legal advice or saying, okay, right, wrong?

Vickie Wood (16:03)
Mm-hmm. Great questions. First off, no, it’s not trained in the law.

It doesn’t give any legal advice whatsoever. It is essentially a facilitator of the communications. As a mediator, most mediators know there’s a spectrum of how you approach mediation. You can be on one end, mostly facilitative, where you’re exploring people’s values and priorities in trying to get the case resolved, and you’re facilitating communication about how they feel and their thoughts on the case.

On the other end of the spectrum is a mediator who becomes quite evaluative and says to someone, “You’re gonna lose this case,” or, you know, “Here’s…”

Marlene Gebauer (16:48)
You know, “You might want to think about this.”

Greg Lambert (16:50)
Mm-hmm.

Vickie Wood (16:52)
“This is where I’m really concerned. These are your risks.” Generally, at least in how I mediate, and I think it’s one of the better ways to approach mediation, you start out facilitative and then, as the day goes on, you can get a little more evaluative and try to push things toward that end.

Judicaid stays facilitative. So there’s no legal advice. It’s not trying to tell someone, “You’re gonna lose this case,” or, “They’re right on this point and you’re wrong,” or vice versa. It will remind them of the risk and hassle, the time and expense of going to court, and the risk that they may not win their case, encouraging them to continue to think about compromise and keep engaging so they inch their way toward a palatable compromise where eventually they go, “Okay, I can say yes to this and be done and not have to deal with court.”

So that’s the idea, and that’s why we’re able to offer it nationally, because it doesn’t deal with laws that would be individualized to each state.

The training aspect is really, there are two main tech pieces. There’s the application that’s coded, which is our AI engine for the shuttle-style back and forth. That took a really long time to create. Then there is the mediator disposition architecture, and that’s where I had a lot of input as the subject-matter expert. It’s like a 38-page prompt that…

Marlene Gebauer (18:42)
Ha ha ha.

Vickie Wood (18:42)
That…

Greg Lambert (18:42)
How?

Vickie Wood (18:43)
guides Jude, our mediator’s name is Jude, by the way. It tells Jude…

Marlene Gebauer (18:48)
Do you say, “Hey Jude,” when you talk to it?

Vickie Wood (18:51)
Jude. We actually…

Marlene Gebauer (18:53)
I’ll leave now.

Vickie Wood (18:54)
never intended to have a name, and it just sort of came about. Since it’s Judicaid, and Jude’s sort of a gender-neutral name, we thought, why not? We’ll go for it.

But it’s the disposition, sort of prompting architecture, that tells it how to respond and how to continue to inch people toward a compromise. There are lots of guardrails so it’s never giving legal advice. It can suggest, if someone says, “Do you have any ideas on how we might be able to resolve this?” it can say, “Well, one thing you might consider is this,” or, “You could take payments over time,” or, “Maybe take a smaller payment up front and then the balance over time.” It can suggest things like that, but it’s very generalized in that way.

That’s a big part of why it was never intended to replace attorneys or human mediators. As a mediator, I know that most complex cases that go to human mediation are far too complex, both legally and emotionally, for an LLM to manage. So this is not trying to replace that. This is focused on filling the gap for those lower-value cases that are generally fairly simple.

There is emotion involved, and that’s something an LLM is very good at managing in communication. It’ll calm the person down, and they can get as upset as they want. It can be empathetic and say, “I hear you’re feeling really frustrated about that, and that’s understandable. But why don’t you let me go talk to the other person?” It’ll go to the other side, and it’s never gonna convey the anger or the bad words being said in the other room. It’s gonna say, “They’re feeling concerned about this.” So it paraphrases things in a way that makes them far more palatable to hear when you’re the adversary.

That’s a big part of what I do as a human mediator. When somebody is cussing and swearing and saying horrible things about the other side, I go in the other room and say, “Yeah, well, they’re starting to come around.” So…

Greg Lambert (21:17)
Yeah, I was gonna say, do you have it default back to those instructions so it doesn’t get caught up in what each side is feeding it, but rather goes back to its baseline to make sure it stays on the course it needs to stay on?

Vickie Wood (21:40)
Yeah. Like I said, the prompting architecture is vast, and it’s very carefully thought out in phases to guide things through. But it has priorities around guardrails, and if there’s any sort of safety issue, somebody says, “This is terrible. I’m gonna kill myself,” we make sure it’s gonna say, “If you’re struggling with that, then you need to consult resources in your locality. This may not be the place to do this.”

And there’s a button to say, “Stop mediating,” and terminate the mediation at any point in time. So, yeah, a lot of thought went into that…

Marlene Gebauer (22:25)
I have one. I have one.

Vickie Wood (22:27)
Aspect.

Marlene Gebauer (22:29)
Does it learn? I mean, from session to session or are those isolated?

Vickie Wood (22:34)
They’re isolated. We have made the decision at this point that we’re not training the AI model directly in the way that an open-source LLM would be constantly training itself.

Yeah, it’s truly intended to be confidential, just like mediation is confidential. You’re not supposed to go tell a court, “Well, in mediation she said this or that,” or you’re not allowed to, at least in California.

Greg Lambert (23:03)
And that’s when you hold up your hand saying, “I don’t want to hear that. I don’t…”

Vickie Wood (23:06)
Yeah. Yep. Exactly.

Marlene Gebauer (23:08)
No, no.

Vickie Wood (23:09)
Exactly. Privileged. So…

Greg Lambert (23:12)
Well, we talked about the user side, so let’s go over and talk about the court side. Courts appear to be a really important path for Judicaid going forward. Valerie, I’ll start with you, and Judge Wood, if you want to jump in, please do. What would a court-facing model look like in practice, and where do you see it fitting into the life cycle of these disputes?

Is it before the case is filed? Is it after, but before a hearing? Is there someplace else? Where do you see the path of leveraging either the threat of the court or the actions of the court to help Judicaid?

Valerie Clemen (23:55)
Yeah. Initially, it would be good to have it in self-help centers, because a lot of times, especially for small claims, landlord-tenant, or those types of claims, those parties are unrepresented. You just can’t afford to pay an attorney’s hourly rate for a case that’s honestly below $20,000. It doesn’t make any sense to pay an attorney, so they’re trying to figure out the system themselves.

Knowing they have this option to resolve their dispute without going through the paperwork, getting it served, and filing is helpful. So having that information in self-help centers and in the courts pre-filing is helpful. Once a case is filed, it would be helpful for courts to provide that option and, honestly, not make it voluntary. Make it mandatory to give it a try, to talk to the other side using some type of dispute resolution or mediation, but also provide Judicaid as a free resource for litigants to use and try to resolve the dispute.

The information would come at the time of filing. Like, here’s your complaint, you have to serve the other side, and also serve the other side with this dispute-resolution information to see if you can resolve the case before your hearing date comes up. In some courts, when your hearing date is set out six months or a year, being able to resolve it and put the thing behind you before that happens, or before you spend money having the sheriff personally serve it or hiring a process server, is helpful. There are so many costs and so much time associated with litigation.

The hope is that letting litigants know there’s this option would be helpful. Of course, a lot of unrepresented litigants are doing this for the first time. They’ve never filed a small-claims suit. They’ve never been in a legal dispute. A lot of them probably don’t even know what mediation is. I’d say at least 10% get the flyer and are like, “Meditation? I know I’m angry, but I don’t need that.”

Marlene Gebauer (25:48)
Yeah.

Valerie Clemen (25:49)
Like, just not having any idea.

Vickie Wood (25:51)
It should go hand in hand, though.

Greg Lambert (25:51)
I need it, but not in this case.

Valerie Clemen (25:54)
Yeah. Because honestly…

Greg Lambert (25:57)
So are you educating law librarians and self-help centers on this, so they have it as a tool they can point people to as an alternative, or…?

Vickie Wood (26:11)
We’re trying to. We’re trying to. We just…

Marlene Gebauer (26:15)
That’s why they’re on the podcast, Greg.

Vickie Wood (26:18)
But yeah, in Napa County, where we’re running the pilot, we have definitely put it out there to all of the people who might be interested. We’ve got flyers in the law library and in the regular county library. While it’s being offered for free, government agencies have a line where they don’t want to be advertising something that’s for profit.

But while they’re piloting it, or if they were, and actually, let me jump back to one of your questions, Greg, about what this would look like through the court. The platform itself will be identical. The courts would pay for a monthly subscription, and then they would be given a QR code that they could provide to litigants upon filing. They could even put it on their website, in the small-claims area, for people who are thinking of filing a claim to try it before they file.

Those people would enter the site through a landing page for that court with that QR code. There’s a little gateway where they have to say how they got there. If they didn’t get there through that court, we’re going to redirect them to the paid portion. But if they got there through the court through the QR code appropriately, then they bypass the payment piece and jump into the platform exactly the way everybody else does.

So, yeah, we’re trying to get this out there. We’re on social media. We just got back from a national court conference in Delaware. We were having conversations with courts across the U.S., getting them aware. I think the trick in trying to get courts on board with this is that most of the technology out there needs to be integrated into the courts’ networks and processes.

Their first thought, I think, is, “Here’s yet another AI-based app or process that we’re gonna have to implement and then be tied to this company for however long, and tied to their schedule of upgrades and new features and their pricing changes and all of that.” We’re completely different from that. The only thing the court has to do is give that QR code to its litigants or potential litigants, and any questions go through our support page. That’s it. That is it.

I think courts are having a hard time comprehending that it could be that easy. Hopefully we’ll get the message out. What was that, Marlene?

Marlene Gebauer (29:01)
This could be very helpful for them.

Greg Lambert (29:02)
Yeah.

Marlene Gebauer (29:05)
It could be very helpful for them in terms of the docket. So…

Vickie Wood (29:11)
Yeah, that’s the idea. And it’s so affordable that it’s sort of a win-win-win proposition, and it feels like a no-brainer to us. But still, it’s gonna take some time to get people to first be aware of it and then have a true understanding of how simple it is.

Valerie Clemen (29:29)
Yeah. I’ve had clients come to me, we’re in a small town, so I have clients who come to me with certain issues, and sometimes they come with things that are just so minor. They have a dispute with their neighbor and the money involved is so low, and I’m like, “Look, I can’t honestly have you pay me for this, but try this.” I’ve had a couple of clients go try Judicaid, and they were like, “It was easy to sign up and send the invite, but the other side didn’t respond.”

But it was nice. Sometimes clients come to you and they have things that are too minor, and you’re like, “Look, the budget for this doesn’t make sense.” Giving them something, some tool where they might resolve it on their own, was nice instead of saying, “I can’t take this. Good luck.”

Vickie Wood (30:12)
Yeah. So that is another sort of distribution channel, if you will. Exactly what Valerie just said, for attorneys to be able to give something of value to those clients they have to turn away. Say, “There’s another tool here.” We think it’s something attorneys would really like having in their back pocket for that situation. The problem is there is that immediate reaction of, “What is this taking? Is this going to take away our business?” Or, for…

Greg Lambert (30:45)
Yeah.

Vickie Wood (30:45)
mediators, “Is this going to take away our business?”

And I’m saying, no, it’s not that. LLMs are obviously changing the legal scene drastically, but at this level of sophistication, they can’t replace humans. You absolutely need human mediators. So hopefully, if there are attorneys out there listening, this is something for you to have to give to the people who you can’t otherwise help.

Valerie Clemen (31:16)
Yeah. It’s like…

Marlene Gebauer (31:16)
Yeah, I’m kind of looking at it like it’s an initial step because you’re absolutely right. I was very curious, a human mediator can sense when there’s unequal bargaining power or someone’s more legally sophisticated, and they can run their mediation with that in mind. But I’m curious how Judicaid does that as a technology tool.

Vickie Wood (31:43)
Well, in some ways it’s more neutral. Because it’s just going to be kind of…

Marlene Gebauer (31:47)
Yeah. It’s neutral. That’s it.

Vickie Wood (31:54)
Yeah, yeah. It’s not gonna be impressed by the person who is sort of a bully or sounds like they really know what they’re talking about. It’s not gonna be more persuaded by this side or that side. Again, it’s facilitating that conversation in a really neutral way.

And to your point about it being a starting point, yes, I do think cases will get resolved through this. But if they don’t, I also think there’s another value that goes beyond monetary. Let’s talk about this, it isn’t limited to small claims. It could be used for limited civil cases. It could be used for eviction cases. We’re gonna add a feature to try to get this into family law for parent-coordination kinds of things. I have unlimited features in my head, so it’s just a question of getting them to happen.

What I would see with small claims is, like I said in the beginning, people are passionate about these cases and they spend days’ worth of time preparing for them and putting everything into them. Then they get to court and there are 40 cases on the docket for trials. They’re gonna sit there for a long time, their case gets called, and they get maybe 10 minutes. And…

Greg Lambert (33:13)
Yeah.

Vickie Wood (33:14)
even if they win, but especially if they lose, they walk away not feeling good about the process and about the justice system, because many times this is these people’s only real interface with the courts. They walk away not feeling good about it, and that’s not good for any of us. That’s not good for society in general.

So if they start with a product like Judicaid, where they’re able to kind of be “heard,” and I’m putting that in air quotes for anyone who’s only on audio, to air their feelings about it and also hear the other side’s position and thoughts, and then have to think through the potential outcome that it may not go their way and decide they want to take that risk, well, now they’ve had much more participation in the ultimate outcome. I feel like that’s going to be an improvement for everybody across the board.

Greg Lambert (34:12)
Yeah. Well, I want to touch on your pilot with the Napa County Superior Court. Tell us about what you’re piloting. Beyond finding out whether the technology works, what are you hoping the courts will find out, and what are you hoping to find out from this pilot on your side?

Vickie Wood (34:35)
Yeah, so we’re new with the pilot. We’re kind of just starting to get through the period from where we started to where cases would be coming on to trial. We have had people engaging, but we’re not getting the mediations to happen yet.

So we’ve learned one really important thing, which is that people are gonna be reluctant to do this right now if it’s purely voluntary. That was a very valuable thing to learn. We knew there would be reluctance, but there’s significant reluctance.

One note, though, is that Napa County is a tiny county, so the number of filings is a very small sample to be working off of.

Greg Lambert (35:18)
I’m envisioning the mediation being over a glass of wine. That’s all I see.

Vickie Wood (35:22)
Of course, Greg, of course.

Marlene Gebauer (35:22)
Ha ha.

Vickie Wood (35:25)
If they’re not drinking wine, then the dispute is about wine somehow. But yeah…

Greg Lambert (35:30)
Yes.

Vickie Wood (35:32)
It’s a small county. But yeah, once we start getting engagement, and we’re connecting with some larger counties, one in particular is a large county that already mandates mediation for both small claims and unlawful detainers. They offer some human mediation options that are lower-cost than most human mediation, but still significantly more than Judicaid. So this would be another thing to offer people.

One of the greatest features about Judicaid is that it can be done in many different languages. You can have somebody who speaks only Spanish and someone who speaks only Russian mediating together. That would be very expensive to provide with a human mediator because you would probably have to have two interpreters for one mediator, and the cost skyrockets.

So, yeah, we’re hoping to get a bigger sample, especially one where it’s mandated, because we think if it’s mandated, a lot of people will opt for this because it’s so easy. You don’t have to schedule a set time. You can do it literally over your phone, dictating.

Greg Lambert (36:47)
And you mentioned that this isn’t just California, right? You’re looking at going…

Vickie Wood (36:51)
Correct.

Greg Lambert (36:54)
going into other states as well.

Vickie Wood (36:56)
Correct, correct. Yeah. And maybe, I mean, honestly, I don’t know why it couldn’t go outside the U.S. too, because again, it’s not tied to any particular laws. So I don’t know. I don’t want to think too big just yet, but…

Greg Lambert (37:09)
Well, then you have to deal with the EU and GDPR and all that, so, you know…

Valerie Clemen (37:14)
Yeah, yeah.

Vickie Wood (37:15)
Yeah. Well, I’ve been really bored lately, Greg. That would give me something to do with…

Greg Lambert (37:19)
Yeah.

Vickie Wood (37:19)
my time.

Marlene Gebauer (37:19)
Yeah.

So we kind of close the podcast by looking back and then looking forward. This is kind of our “what’s different now” question. If we had spoken with you a year ago, other than the fact that maybe Judicaid didn’t exist, what would you have told us is materially different today? What have users and court partners, or the experience of building the platform, taught you that changed your original assumptions?

Vickie Wood (37:56)
When I conceived of it, which was closer to two years ago, the way ChatGPT or any of these large language model platforms functioned was astonishing. I mean, it still is astonishing, but more people were seeing it for the first time or had not seen it yet. So the concept of what Judicaid does was almost like magic. Now people are aware that having a computer talk to you like it’s a human can be done.

What we have, that I don’t think is done anywhere else, is this shuttle-style approach where it’s truly exchanging the communications. But when my CTO told me, “This will be easy,” I thought we would be launching in like two months. Then all these months later, it’s not quite as novel as it was at the time.

At the same time, there’s also a fair amount of skepticism of AI and concern about AI taking jobs. There are people who might see that it’s an AI-facilitated platform and want nothing to do with it, without considering that this is something trying to provide a positive benefit and fill a gap. Those were unexpected things, but we’re rolling with it because I think it’s gonna require a little bit of awareness about what we’re doing.

Also, AI is becoming so pervasive that I think eventually people are…

Greg Lambert (39:41)
Yeah.

Vickie Wood (39:42)
just gonna get tired of being so against it, I guess. But yeah, I think the other thing is we focused on mediation because that’s the piece we wanted to nail down. Now that we have that core AI engine, we can branch off in so many different dispute-resolution ways, which gets me so excited.

We’re now calling it intelligent dispute resolution because it may not just be mediation. We might start having mediator’s proposals. We might have a parent coordinator to help with little custody disputes.

ODR, online dispute resolution, has been around for quite some time, but I think intelligent dispute resolution is new. So I’m gonna say you heard it here first.

Greg Lambert (40:29)
All right, all right. Well, that leads in perfectly to our crystal ball question. Valerie, I’ll let you start this off if you don’t mind. So…

Valerie Clemen (40:38)
Sure.

Greg Lambert (40:39)
Picking up on Judge Wood’s discussion there about intelligent dispute resolution, in three to five years, where do you see things when it comes to AI-assisted dispute resolution? What’s it gonna look like, and what all do you think it will cover?

Valerie Clemen (40:58)
I mean, our hope is that it would be offered in courts nationwide, because this underserved population doesn’t have access to dispute resolution or mediation, and this would provide that. So we would like to see it used in courts nationwide.

I would also, I mean, this is hopeful, but it would be nice if people just thought to “Judicate it”…

Vickie Wood (41:19)
Yeah.

Valerie Clemen (41:19)
when they have disputes, to go there first. You see people arguing on Nextdoor. Sometimes you think we should have guerrilla marketing and just go in there and say, “Just Judicate it.” Like, there’s so many…

Vickie Wood (41:32)
I don’t know if Nextdoor is national, but it’s this neighborhood platform. And yeah, so…

Greg Lambert (41:35)
It is. You know. No, we all…

Valerie Clemen (41:36)
It is.

Greg Lambert (41:38)
know. “Was that fireworks or gunshots?” is the most popular…

Valerie Clemen (41:41)
Yes, exactly. Exactly. “Does anyone…”

Vickie Wood (41:46)
Yeah.

Valerie Clemen (41:44)
“recognize this car?”

Greg Lambert (41:46)
Ha ha.

Valerie Clemen (41:47)
But people have disputes, and it’s really hard to get people talking to each other. Oftentimes, when people start talking to each other, they resolve things. It’s that initial step, because it’s uncomfortable, it’s adversarial. But once people do it, they can often resolve a lot of issues.

So the idea that people could use this as a tool to help get things started, it’s really hard to walk up to someone…

Marlene Gebauer (42:12)
Okay.

Valerie Clemen (42:13)
and confront them and talk about something, even if you have it simmering. I mean, people have a hard time with that with family members. You’ve had this thing simmering forever, but you can’t address it.

This is a tool that helps you be a little protected but still share your thoughts and feelings and resolve disputes. The idea that people could use it for all kinds of disputes, I mean, it is a very civil way to resolve disputes and quash things, and not have things linger for a long time.

But obviously, in the legal sense, it would be nice when people do have disputes to the level where they’re ready to file a lawsuit or do something more, that they have a way to either solve it beforehand or try to solve it once the lawsuit has started. They don’t have attorneys helping them in a lot of these cases. They’re probably using Claude or ChatGPT to help them with their case. So this is another resource to help them resolve it instead of keep fighting.

Vickie Wood (43:09)
Yeah, or have it escalate to something more than it ever needed to be. And just to tack on to that, I think that’s exactly right. A lot of times it’s barriers to communication, and that’s what Judicaid is trying to resolve. Whether it’s lack of skill with communication, a power dynamic, or a language barrier, that’s really where Judicaid provides this platform at a very base level.

And hopefully, this is my tagline that I have in our little video, my hope is someday that instead of saying, “Let’s take it to court,” we’ll say, “Let’s just Judicate it” instead. So that’s our hope.

Marlene Gebauer (43:49)
I like it. I like it.

Well, Judge Wood and Valerie Clemen, thank you for joining us and giving us a closer look at Judicaid, at both the opportunities and difficult questions that come with using AI and expanding access to dispute resolution. And thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. We’d also love to hear from you on LinkedIn and Substack.

Greg Lambert (44:17)
And Judge Wood and Valerie, where can listeners go and find out more about Judicaid?

Vickie Wood (44:26)
Judicaid.com. We have a lot of information on there, including an instructional video that’s on YouTube, but you can access it from the homepage, along with how-to instructions. We’re on Instagram, Facebook, X, and LinkedIn, but that’s probably the best place to start.

Greg Lambert (44:47)
Soon to be on Nextdoor.

Marlene Gebauer (44:48)
All the places.

Valerie Clemen (44:50)
Yeah.

Vickie Wood (44:51)
I actually am on Nextdoor. That’s true. I haven’t really spent a lot of time on there yet, but yeah…

Marlene Gebauer (44:55)
Ha.

Vickie Wood (44:56)
I’m going there, Greg.

Greg Lambert (44:59)
Awesome, awesome.

Vickie Wood (45:00)
Thank you so much.

Marlene Gebauer (45:02)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry. Goodbye, everybody.

]]> xlambert@gmail.com (Greg Lambert) From Search Rankings to Vibe Coding: How Best Lawyers Is Rebuilding for the AI Era https://www.geeklawblog.com/2026/08/from-search-rankings-to-vibe-coding-how-best-lawyers-is-rebuilding-for-the-ai-era.html Mon, 24 Aug 2026 11:06:37 +0000 https://www.geeklawblog.com/?p=19531

What happens when a 40-year-old legal data company decides its employees should start building their own software? This week on we talk with Best Lawyers CEO Phillip Greer and Senior Vice President of Research and Product Strategy Elizabeth Petit about an internal AI transformation that reaches far beyond adding ChatGPT to the corporate toolkit. Best Lawyers is experimenting with generative engine optimization, internal agentic systems, vibe coding, and an AI development environment where employees across research, finance, marketing, and other departments build applications around the company’s data.

Greer begins with a challenge facing every law firm marketing team: traditional search is changing. Google AI Overviews and answer engines such as ChatGPT, Claude, and Gemini increasingly give users answers without sending them to the familiar list of blue links. Greer argues that SEO still matters, but law firms now need to think about Generative Engine Optimization, or GEO, and the signals AI systems use when deciding which sources deserve trust. Structured data, schema markup, substantive content, and third-party validation all become part of the equation. For Best Lawyers, its long history of peer-reviewed rankings offers an interesting advantage. The company’s data serves as an independent signal that AI systems might weigh differently from content produced by a firm’s own marketing department.

Petit explains how Best Lawyers is applying the same thinking to legal marketing through Smithy AI, a system designed to help attorneys and law firm marketers develop profile content without endlessly copying the same biography across websites. Smithy draws from Best Lawyers’ structured information and existing lawyer content to produce a starting point that attorneys and marketers then edit. The larger goal is authenticity. As generative systems make producing generic legal content almost effortless, Greer argues that distinctive expertise, voice, and credible third-party signals become more valuable rather than less.

The conversation then moves inside Best Lawyers, where Greer has taken a far more unusual approach to AI adoption. After building a secure data layer connecting systems including SQL databases, HubSpot, Gong, Google Analytics, and accounting data, he created an internal Best Lawyers App Store where employees use natural language to build applications against company data. What began with roughly 30 percent of the workforce vibe coding has grown to around 40 percent, according to Greer. Petit describes building research and KPI dashboards despite coming from a research rather than software engineering background. Projects that once required Excel formulas, Power BI reports, development queues, and weeks of waiting now sometimes move from a question at 9:30 to a working internal application by 10:30.

That shift also changes the role of professional software engineers. Rather than spending their time building another reporting screen or internal form, Best Lawyers’ engineers increasingly concentrate on architecture, data infrastructure, performance, governance, and the guardrails surrounding employee-built applications. Greer describes moving parts of the company’s data architecture toward Elasticsearch and developing “Bestie,” an internal agentic AI team member. Yet speed introduces another problem. Petit and Greer describe an “AI vampire” effect, where instant feedback encourages people to keep working because the machine never gets tired, goes home, or stops responding. Human judgment includes knowing when the human needs to stop.

The discussion closes with what these changes mean for legal practice itself. Best Lawyers introduced Artificial Intelligence Law as a formal practice area in the 2026 edition of The Best Lawyers in America, reflecting how AI work has spread across technology, intellectual property, privacy, employment, compliance, litigation, and counseling. Looking further ahead, Greer and Petit do not expect AI to erase the billable hour overnight. They do expect clients to ask harder questions about what they are paying for, how legal work was produced, where AI contributed, and where the lawyer’s judgment created value. If AI makes routine production dramatically faster, the economic question for law firms becomes less about how many hours technology saves and more about how firms explain, price, and defend the value of human expertise.

LINKS

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Greg Lambert (00:01)
Hey everyone, I’m Greg Lambert from The Geek in Review, and this week we are talking with Phillip Greer and Elizabeth Petit from Best Lawyers. But first up, here’s a word from our great sponsor and partners at Legal Technology Hub.

Marlene Gebauer (00:15)
I have Sarah Glassmeyer here from Legal Technology Hub, and since it’s almost time for ILTACON, Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:30)
Yeah, so this is probably one of the more exciting times of the year for legal tech, kind of leading into ILTA. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legal Technology Hub can help you pregame and get ready to get the most out of it because, while you’re there, it does seem like you live now in Nashville and you’ve been there your entire life, but it goes fast. You only have two and a half, three days to talk to vendors.

So a couple of things I would suggest you do. One, look at the list of who is exhibiting at ILTACON. If you go to the ILTA website, they have a legal tech directory, but guess what? That is us.

We partner with ILTA. It is a mirror of our directory. You can see the little badges, and that will tell you who is exhibiting at ILTACON. Don’t wander in there. Think ahead and think, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list, see who’s going to be there, and start thinking ahead. Through the directory, you can see, these people don’t integrate with X tool, so they’re kind of off the list for us. You can do a little pre-filtering, pregaming, and figure out who you want to talk to, who is going to make the most of your time.

And then from there, if you are back on our platform in the Legal Technology Hub directory, if you’re logged in, you can make notes. So as you’re wandering through the exhibit hall visiting the people you want to visit, you can make notes saying, here’s this person’s email address that I spoke to and I want to follow up with them, or maybe show Bob this when I get back to my office, or these people are off the list, forget about them.

And so you keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legal Technology Hub directory, not a swag bag full of business cards and flyers. So that’s how we help you get the most out of what you’re doing.

But also, I do suggest wandering around the exhibit hall, because everyone who’s there does have a listing on Legal Technology Hub, so you can double-check. But I think it’s good to wander, see who catches your eye, have a spontaneous conversation, because you might find a vendor you never considered or something you weren’t thinking about ahead of time. So it is a fun time to get yourself absorbed in what’s happening new in legal tech.

Marlene Gebauer (02:46)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legal Technology Hub directory because it can be overwhelming, and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave.

Marlene Gebauer (03:14)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:21)
And I’m Greg Lambert, and today we are exploring how a legacy data institution is completely rearchitecting itself from the inside out for the generative AI era.

Marlene Gebauer (03:34)
We are thrilled to welcome Phillip Greer, CEO of Best Lawyers, and Elizabeth Petit, Senior Vice President of Research and Product Strategy. Phillip and Elizabeth have been leading a radical internal and external transformation at Best Lawyers, turning the organization into a pioneer for agentic workflows, vibe coding, and generative engine optimization. Elizabeth and Phillip, welcome to The Geek in Review.

Phillip Greer (03:58)
Thank you so much for having us. We’re quite excited to be here today.

Greg Lambert (04:03)
Right. Well, Phillip, I’m excited about this because I think a lot of us have been hearing about SEO for years, and now recently, with all the AI, we’re hearing more about generative engine optimization, or GEO.

Phillip Greer (04:18)
Mm-hmm.

Greg Lambert (04:20)
Last year you noticed that there was a massive drop in traditional web traffic due to all of these AI overviews that you see with Google and others, to where people are getting answers rather than getting links out to the websites and then going and finding the answer themselves.

So you talk about your pivot away, or, well, I’m sure you still use SEO, but you’re also having to implement this new generative engine optimization, and that differs a lot from the SEO that a lot of us here at law firms have grown up on and realized, in order for us to get high up on the Google rankings, we need to be good at that.

So how does GEO differ from traditional SEO? And why is it that these third-party reviews or peer reviews are becoming the primary, what you call a trust signal, that keeps firms from being compressed out of the AI search answers that we need to get involved in?

Phillip Greer (05:26)
Yeah, so I think it’s helpful to give a little bit more context. You started that process.

We’ve all been chasing SEO for over two decades, right? Google came out, and then, how do we get to the top of that Google ranking so that our customers and new clients can see the work that we’re doing? And we did that through so many things, backlinking efforts, keywords, thought leadership pieces, marketing associations, marketing partnerships with different types of organizations, et cetera, et cetera.

It’s been popular to say that SEO is dead. So first off, I don’t think SEO is dead. Traditional SEO is still important, and it’s the foundation of AIO.

So a few years ago, Google changed their search engine, and when you started searching, all of a sudden the middle of the funnel, or that blue-links world, started to go away, and you were using that Google Gemini. So you would say, “Hey, I need to find a lawyer for an issue I’m having,” and then it would give you a summary.

And then what was happening was so many law firms, and any type of business, were losing 15, 20, 30 percent of their web traffic because the blue links aren’t being clicked anymore. The users are going in, they’re getting that overview, they’re getting what they need, and they’re kind of moving on.

Well, those original AIO types of overviews were being fed from those SEO efforts.

Marlene Gebauer (06:51)
Yeah.

Phillip Greer (06:51)
The shift that we did over the last couple of years is, first, make sure that our content is structured in a way that, when these LLMs such as ChatGPT or Google Gemini or Claude are coming to our website and looking at our data in traditional SEO forms, we’re tagging it and presenting it in ways that are helpful.

So you think about SEO stuff you used to do. It was all about meta descriptions, keywords, page titles, and then, again, we talked about some of the backlinking techniques. Those things were structuring it so that, when the crawlers are finding your website, they know how to get that data.

Well, that’s kind of amped up with AIO and then eventually what we’ll talk about with GEO. You not only needed the meta descriptions, you needed to have good schema markup. You need to describe sections on the page, to be able to ask questions, answer questions, so that when the AI engines were gathering data, it wasn’t only finding it, they could start to interpret it, because that interpretation is what becomes a little more important when you think of things like ChatGPT or Google Gemini.

It has to take the structured data, and then it has to interpret it and create insights and say, okay, now you can start showing up in these overviews.

The latest shift, and I think it’s the biggest shift, is this GEO, and that was your opening question.

Greg Lambert (08:14)
Right.

Phillip Greer (08:14)
The generative engine optimization approach. And that is making these AI systems not only get your data, but understand that they can trust your data. And so, how do you trust data?

Data has to come from quality places, things that are not produced by a marketing staff. So we’ve all gamed the SEO world. Every year, Google changed the algorithm again. What do we do? How do we get to the top? We spend more dollars. We make sure that we have more content. We flood the internet with it. We do backlink strategies, right?

That would work for SEO, and then it would work until they changed the next algorithm again.

With GEO, you have these AI systems that have some insight and intellect in the sense that it knows whether or not the content is being produced by you, your law firm, putting more information out there, or are you reading a service like Best Lawyers that’s known for producing peer review surveys and, ultimately, lists of lawyers? It’s an annual process. It’s been around for 40 years. It’s got a strong standing, a big domain authority. It is a trusted source. It’s third party to the industry that is putting out their data, right?

So that type of association is important. That’s where GEO plays a role.

So we think about our customers and clients who are part of Best Lawyers, who make those rankings. They get a leg up immediately by making it onto our list because they’re getting a signal, an AI signal in the industry for these LLMs to say, okay, if they’re on Best Lawyers and they’re ranked in corporate law, then their value has a higher weight.

And there are other types of services that you should be paying more attention to, and you want to make sure they’re credible. And so that’s why, when you think about what is your plan going forward, I think for law firms you should continue to think SEO is important, but it’s not the only thing. And that’s the takeaway.

SEO is important. It’s not the only thing. Structuring our data is important, and let’s make sure that we’re also thinking deeper about the schema so that we can have questions asked and answered. And then how are we putting our data in? How are we associating? What are we saying? Go see us on, go connect with, what type of services, and what’s their credibility level?

That’s what is creating the big buzzword for the last year, the AI signals. And all those together create a strong AI signal for your law firm.

Greg Lambert (10:39)
Yeah.

Well, one of the things that I’ve been hearing from marketing folks is much more long-form articles, client alerts, more showing your expertise in an area. Are you also seeing the generative AI or the AIO, and it sounds like a song,

Phillip Greer (10:58)
Yeah.

Greg Lambert (11:00)
or the GEO relying upon, instead of straight-out structure and how things are presented, deeper content as well? Are you seeing that?

Marlene Gebauer (10:59)
Yeah.

Phillip Greer (11:13)
So first off, I refer to this as the E-I-E-I-O because it is 100 percent that effect. The Old MacDonald version.

Greg Lambert (11:18)
Yeah, there you go. The Old MacDonald version.

Phillip Greer (11:24)
For a new world. Yeah, so I think the goal is not only to have longer content, but it’s authentic content that speaks to your messaging.

Greg Lambert (11:31)
Okay.

Phillip Greer (11:31)
So you can create a lot of long-form content with AI, but the problem, if it’s not authentic to your brand and your expertise, you’re going to lose a lot of credibility.

So, thinking about what is it that makes your lawyer special? What makes them stand out? How do you speak on it? And how do you keep that authentic?

And sometimes it’s not directly in the world of law for the authenticity to stand out. So figuring out what your niche is there and then driving it home. That is what is going to create unique content that gives you that authority and will stand out among these GEO initiatives because they can say, this is not a whitewashed, white-labeled version of the same type of content that continues to be posted online. It’s something unique.

And if you have a series of this type of content, it can create that narrative of, okay, this is the type of lawyer, this is the type of firm, this is the type of credibility, this is the type of messaging that you can expect, and stand out among your peers. So yeah, I definitely agree with the longer-form opportunities there.

Marlene Gebauer (12:39)
So Elizabeth, for law firms trying to adapt to this new AI search environment, creating unique, machine-readable content is a difficult hurdle. Best Lawyers recently launched Smithy AI to help lawyers and marketers scale their profile content. How does this tool solve the duplicate-content penalty while keeping human editorial control firmly in the loop?

Elizabeth Petit (13:05)
Yeah. So first of all, we’ve always viewed ourselves as partners with lawyers and law firms. So the products that we build and deliver, we want to solve your problems. And one of the problems we know that you have is too many things to do and not enough time to do them.

So one of the things that Smithy AI was developed to address is getting lawyers to fill out their profiles.

Have either of you gotten lawyers to fill out their profiles? That’s a tall order.

Greg Lambert (13:35)
Yeah, all the time. All the time.

Phillip Greer (13:37)
Yeah.

Elizabeth Petit (13:41)
So the easiest thing, the old way to do this, the easiest thing to do is that most teams would copy and paste whatever you finally got written on your own website. You’re going to go copy and paste it everywhere else on the internet. And you got penalized for that.

So we wanted to, again, partner with law firms and develop an easier way to assist you with your workload.

So what Smithy AI does is it reads our structured data and the content you’ve already developed on your lawyer profiles on your website, and it makes a recommendation for you. Now, everybody knows that AI is not a replacement, it is an assistant, and you should still use your human judgment.

So it does say, recommended generative content. It pulls in information about the awards, things that it thinks are appropriate. You review it, and then you publish it with the click of a button.

The name for Smithy AI does come from one of our original co-founders, Gregory White Smith, who was a Pulitzer Prize-winning biographer.

And so we also wanted to name it in his honor for both founding Best Lawyers and as a writer who was a craftsman as well. So Smithy is developed to help you with your mountain of work, both with profiles, but also there are features now to help with press releases, email announcements, social assets. So we’re trying to be your extra hands when you’re looking at the clock, looking at your watch, looking at your calendar, and you don’t have enough hours in the day.

Greg Lambert (15:18)
I’m curious when it comes to that because I know, for example, one of the things that got pushed out on platforms like Substack is there’s kind of that AI content identifier.

Phillip Greer (15:34)
Mm-hmm.

Greg Lambert (15:35)
And so is that one of the things that you try to look for with using a tool like Smithy, giving them something to work with, but then, is it a full copy and paste, or is it something that they take and then they kind of do their own smithing on it as well? How do you encourage your clients to use the tool?

Elizabeth Petit (16:00)
Yep, so we envision it to get you like 90 percent of the way. So it’s not a copy and paste. It doesn’t pull exactly what you’ve already written. You can also hit that button a few times and say, slightly rewrite it, slightly rewrite it, polish it for me. So it can iterate a little bit for you.

But as with all wordsmithing through AI that we all do, we all know we do this, you should never take whatever it spits out for you and then publish that. This is where human judgment always needs to be that final set of eyes, that final check before you are comfortable publishing to your website, our website, or anywhere that you publish content.

Phillip Greer (16:44)
Yeah, and I would say also, going back to your point about the long-form content and where I was going with brand authenticity. So one of the great things about Smithy is, as Elizabeth is saying, we’re going to get the way you’re talking about yourself on your profiles, and we’re going to take the data we have on Best Lawyers and put it through our own system to say, how do we think it should build content?

So it’s going to have your unique voice with our data, but you still want to ask yourself, is this me?

If not, you do have the ability to be replacing it with another version of yourself. And I know with AI, it’s a whole different topic where there’s a lot of, what does it mean to clone a lawyer? Well, we don’t want you to clone your brand. We want you to play a role in helping that editing of the brand.

I think back to college, the hardest thing was always to start writing the paper. The first paragraph is always the hardest. The thing about Smithy is it’ll write the first four paragraphs for you, but you should come in with that closer. You should come in with that closer and do a little bit of editing on the first four that it produces for you.

Greg Lambert (17:52)
Phillip, I want to turn to something that you’ve done that I think we’ve seen a few CEOs do, and that was there at Best Lawyers, you kind of famously mandated that your entire team continue doing their job, but also look at becoming essentially software builders.

Phillip Greer (18:13)
Mm.

Greg Lambert (18:14)
Now you’ve got, and I think the last estimate was about 30 percent of the company, vibe coding their own apps. And Elizabeth, I think you’re a prime example of this. You’re a researcher, not an engineer, yet you were involved in building a real-time KPI dashboard there at Best Lawyers.

So it seems like people are taking you up on your challenge, Phillip. So I want you to walk us through something that you’ve built that takes the worst part of your job off the plate. And then, once the repetitive data wrangling is automated, how do managers shift their focus from doing that kind of grunt work to focusing on more of the higher creative work?

Because that’s something that we’re insisting our lawyers do. So I’m hoping you’ve got the secret sauce and can help us on that.

Phillip Greer (19:20)
Yeah, I’ll start here, and then I’ll want to pass it over to Elizabeth. But I’ll start.

So the end of last year, I’ve been playing with a lot of AI and product development for years now. My background is engineering. I used to build a lot of software. I built a lot of the core systems we still use here at Best Lawyers for producing our rankings and doing our survey processes.

And then I’ve been the CEO and running it for quite some time. And when AI came on the scene, I started experimenting. What can we do? What can we do?

And about December of last year, I had the breakthrough. Claude Opus came out, and its coding abilities were far superior to every other AI coding that had existed before.

So I had this wild thought where, what if I build an environment that was safe, that our team could fail fast in and not worry about hurting our data, and it wouldn’t go and be spread out to the outside world? It wouldn’t be trained on outside systems. We wouldn’t have to worry about them doing some shadow IT type environment where they’re taking sensitive data and going straight to ChatGPT and dumping it out there.

So I felt an obligation to create that environment. And it’s an exciting time because it felt possible. So I spent all of the holiday break in December and then all of January building out, first, an environment.

And what I mean by that is a place where all our data connects. So I needed to create this API layer so that all of our data, when I say our data, I mean our SQL data, our accounting data, our HubSpot, Gong, Google Analytics, any type of systems that we are accessing and using on a daily basis, I wanted it all to come to one place and then inform itself and know about its own data.

So I created a data lake, if you will, custom. And ultimately, I don’t know, for those who are a little more technical on these calls, it’s a deep coverage model where the data in Gong understands how it relates to our data inside of HubSpot, and that understands how it relates to our data inside of Google Analytics, and so on.

So when you’re asking questions, it references each of the data sets, and it knows what a firm is or a lawyer is inside of those areas.

That’s a complicated way of saying I made a place where all our data came together safely and made sure it knew how it was associated.

And then I needed to create that safe environment. So I built a system called the Best Lawyers App Store, where you could go in and start vibe coding through our Claude environment.

And when you vibe code, you’re safely in that App Store. It’s using only our data sets, and you can build, eventually, what we now are using as an operating layer for most of our business.

So I did this. I put all this together. I got it in a prototyped form, and I met with the team at the end of January and I said, “All right, all right, I got an idea. Everyone’s going to become a vibe coder. Every department’s going to be a builder. Elizabeth is going to be a builder. Nancy is going to be a builder.”

I went full Oprah Winfrey on, you’re going to, you’re going to, you’re going to.

Greg Lambert (22:25)
Yeah.

Phillip Greer (22:26)
And they were all on board. I was like, okay, no convincing needed, you’re totally in for this.

Elizabeth was one of the first people I sat down with. I think I spent all of 30 minutes with you, Elizabeth. It was a short session. I got you set up in the environment. I gave you the tools, told you how the data works and how to connect it, and I said, go and dream and build.

And Elizabeth, I’d love to hear your take on it. That’s my more technical explanation of what happened. I’d love to hear what your side was.

Elizabeth Petit (22:56)
Yeah.

Phillip Greer (22:56)
I’d love to hear what your side was.

Elizabeth Petit (22:58)
Yeah. So I knew Phil had been working on this passion project. Phil’s always got multiple passion projects going. So I knew he’d been working on this, and then he made a statement and he’s like, “I’m going to ask you guys to come join me in this.”

And then he did that ping and he’s like, “No, I mean it.”

And I’m like, “Yeah, I know. I’m here. I’m ready.”

So my personal mantra has always been, my team knows this, work smarter, not harder. And that doesn’t mean we don’t work hard. I love to work hard. But it means work more efficiently. If there’s a better way to solve your problem, do it the more productive way. Don’t do something the same way over and over again because you’ve always done it that way.

So what vibe coding unlocked for me was this new ability to work smarter and not harder, and take some of the repetitive, mundane parts of everybody’s job and make them more fun, make them more creative, automate them.

Whether it’s through KPI reporting that eats up time every day, or taking some of these important projects or problems we want to solve that sit on that shelf, that you know have value, but you never have the time to take off the shelf.

And say, okay, now that I have optimized some of this basic operational work, I’m not spending all day, two days of the week, pulling together my KPI reports, let’s take a bigger, meatier project off the shelf and let’s go start working on that.

Phillip Greer (24:38)
Yeah.

It’s been exciting watching how everybody came to the table to build.

Elizabeth built research dashboards to monitor the process for Best Lawyers as the nominations were coming in and voting was happening. Nancy, our head of content, developed expense-paid tracking systems for all of our budgets that were going out for paid media, as well as her marketing team developed this marketing dashboard that pulled in our Google Analytics and gave full-funnel conversion visibility.

I mean, we went from, like a lot of companies, working heavily in Excel and Power BI or Tableau. We’ve stopped using Power BI. We don’t use any Tableau. We do everything now inside of our dashboards that are developed independently by departments.

And the only time we’re in Excel these days is when we’re exporting data sets to give to our business partners or if we’ve got to send them out somewhere. But we work primarily now inside of our BL App Store environments.

When you’re asking for data and looking for new trends and analysis, it used to be, okay, we’ll export everything out to Excel. We’ll write a bunch of complicated formulas. I’m Googling how to write that complicated formula.

Greg Lambert (25:55)
Yeah.

Phillip Greer (25:55)
My God, why do we have to write these complicated formulas?

Greg Lambert (25:58)
Yeah. How do I do that VLOOKUP table again?

Phillip Greer (26:00)
Yeah, that VLOOKUP table.

Elizabeth Petit (26:01)
Yeah.

Phillip Greer (26:03)
My God. And then you always have the one person in the office who’s your Excel person, and that’s what their life is all about. “Ask me about my sheets,” you know?

But now everyone goes straight into their environment. The data’s all there in our data layer. It’s trained, it’s learned, it knows itself in the deep coverage model.

And you work through our App Store and ask it questions. And instead of developing an Excel sheet, you develop an entire new page that is the best visual representation of your data and answers a question. And you use that to start making decisions.

So our speed to decision is pretty great. A week-long, two-week-long Excel project is now done. We have an idea in a 9:30 meeting. By 10:30, we have a new page that’s been pumped out through our finance team that’s explaining the answer.

And then we need to make that next decision and, okay, how do we execute on this data?

Greg Lambert (27:02)
The follow-up I have on that, and this is something I’ve been writing on, especially since you have so much data that you’re working with, and it sounds like you’ve solved part of the problem through creating these data lakes or this ability to feed information into the right places so that you set up your AI to access clean information in a relatively simplistic way rather than having to go out to 15 different places, some external, some internal. You’ve got it all built.

So the topic that I’ve been writing on a lot, and I think this is going to be, I thought it was going to be 2027 fodder, but it might hit before then, is a lot of us are building these harnesses around both our data and our AI tools with the expectation of a lot of this information.

You don’t need to dump everything into the LLM and get an answer. You need it to be precise, and you need it to be consistently right so that the inputs and the outputs are accurate.

So I know, Phillip, you designed the initial, you vibe coded the system around it. Do you have a technical team now that keeps that up to date, updates or adds new things that come in, or are you still able to use the talent that you have on staff that they build on top of what’s already built?

How do you take that 9:30 question and turn it into a 10:30 report?

Phillip Greer (28:47)
Yeah, so I built all the environment for the initial App Store, and my intention was always to hand off the day-to-day maintenance because adding more data to our data lake or data API library is going to be a constant workflow.

And any company who’s working with any type of data environment, governance, or what have you, whether it’s SharePoint or an actual Azure data lake, et cetera, you’re going to have to have somebody managing that.

So once I got everything built, the initial systems, we found one person internally who had the most interest in the AI infrastructure and the future of AI and how it’s used in programming. He was one of our programmers, and his name’s Seth.

And so I trained him for a week on the system, and he took it over. And now he is running that API library.

And people, all the vibe coders, as they need more data or they need more feature abilities in the App Store, they go through Seth. And it’s been a good system.

Because my original vision was, I knew what Elizabeth and I knew what our research and our finance and our content team would need to get started, but I didn’t know what they were going to build, right? And that’s the wild thing, what they end up building and how they use the data.

I’m watching some of the requests come through and some of the systems they’re building, and I know because I designed the actual data that came together, and I would go, that’s not going to work. They don’t have that data. That’s not going to work. They don’t have that data.

So I had that moment where I was like, I need to hand this off to someone. I’m the CEO of this company. I can’t keep adding to that API layer.

Greg Lambert (30:28)
You don’t want to be the head vibe coder and CEO?

Phillip Greer (30:31)
Head vibe coder and CEO.

So I got Seth involved, and now whenever they start to build with more data, he continues to extend the actual API layer.

But what is great is the actual building is still happening from the non-builders. Well, they used to be non-builders. I’d call them all builders now.

That 30 percent number, as of, I onboarded a few more people this last week, we’re up to 40 percent of our company who are vibe coders now. It’s growing because the mentality is, if you want to do this, we want to help you do it. There’s no reason you have to sit on the sidelines.

So I handed that over, and then what I realized quickly by handing this over is there’s an opportunity to build more things.

So that’s been kind of exciting. And we can get into it. But I built out what we lovingly call here Bestie, which is our first type of AI support team member that uses our different models and data sets.

So yeah, you have to have someone who’s going to own it. You’ve got to have someone who’s going to make sure that they’re adding to it. But that’s the easy part.

The harder part is thinking about what you want to build. Because when you can build endlessly, narrowing that scope down to building things that are functionally helpful is that next muscle you have to start learning how to train.

Greg Lambert (31:55)
Yeah, it’s not asking what do I build, it’s what should I build, right?

Phillip Greer (32:01)
Yeah. One of the opening things you mentioned was about, how do you open up room and bandwidth for creativity?

Well, you have to allow yourself to be creative. And there’s a lot of people who work in an office environment that have not worked that muscle in a long time because you make that Excel report, you put it through Power BI, you drop it out as a PDF, and then you take it to the board. And you do it again, and you give the lawyer the thing.

The creativity is lacking.

And now all of a sudden, all of that administrative burden is done within 9:30, the idea, 10:30, the actual result. Now what’s the creative solution that you move forward and make something functional to execute on?

Marlene Gebauer (32:43)
So transforming a workforce as quickly as you have doesn’t happen without some friction. Phillip, you’ve spoken about the AI vampire effect causing extreme decision fatigue.

Phillip Greer (32:52)
Mm-hmm.

Marlene Gebauer (32:53)
And Elizabeth, you’ve had to manage an existential crisis among your software engineers. How did you help these technical engineers transition to feeling like they’re authoritative system architects rather than feeling displaced?

And how are you managing that cognitive burnout when AI offers answers in seconds instead of in weeks?

Elizabeth Petit (33:20)
Yeah. So first of all, the existential crisis is understandable.

These team members of ours, many of whom Phil and I have worked with for over 10 years, went to school to be experts in these fields. They are craftsmen. They are builders. This is their superpower.

And then seemingly overnight, IKEA came to town, and no longer is the craftsman superpower the same. And that’s hard. And I understand that, and we did understand that.

And it helped that Phil was a developer and came from that background, so he understood that as well.

And it was always our goal, continues to be our goal, to use AI to upskill our team members as an opportunity for professional development, whether you are an engineer or another member of the business team. It is not to replace anybody’s job.

And so while the business team is learning a superpower they never thought they would have, they never thought that they were going to be this type of craftsman, and they’re not going to be this technical craftsman, they do some things.

What it has done for our engineers is it has allowed them to also build faster, solve more complex problems. All the things that AI does. It allows for better debugging, it allows for better regression testing, it removes some of that mundane work that they were doing.

So we went into this as, we are all going to learn this together. This is new emerging technology. We don’t have all the answers, and we want to figure out how this makes the most sense for you and your roles, and us within our business.

Phillip Greer (35:16)
Yeah. At the end of January, at the same time I was meeting with the executive team, Elizabeth pulled all of our engineers together in an in-person meeting, and we had a Q1 huddle.

And I sat there and explained to them that I’ve built this App Store. I’ve got this huge data layer. All our data comes together nice and clean. It’s learning off of each other. And now everyone’s going to become builders.

And it was a lot of disbelief because, again, craftsmen, like, okay, I make quality code. There’s no way a machine’s going to do it.

And so there was a lot of disbelief.

After that, I met with Elizabeth and team and taught them how to vibe code, and they started building apps.

We brought everyone back together again and showed them what these non-builders were putting together and how it was pulling data from all of our systems.

And then even our engineers were in the mindset of, my God, this is a real thing. This is not some fancy fad happening, Phil says everyone’s going to be doing this. They’re building financial systems. They’re building research systems, marketing systems, sales systems.

And it was clear that they wanted to be part of it too.

And so Elizabeth put on, I thought it was an intelligent thing, as we had the adoption, every two weeks we’d have sessions where we’d encourage the engineers to show, in a safe space, the other engineers what they’d been vibe coding.

Because there’s also this stigma, like, I’m an engineer. I don’t vibe code. I know how to code.

But that’s not what vibe coding is. Vibe coding is not a replacement for, I don’t know, a good analogy. Here’s a good analogy.

I play guitar. But when someone says, “I play Guitar Hero,” and they’re pushing those buttons on the little plastic machines,

Marlene Gebauer (37:02)
Ha ha ha.

Phillip Greer (37:03)
I would go, “Ha, you don’t play guitar. I play.”

It’s not that. It’s a completely different thing. It’s not analogous to Guitar Hero versus playing guitar.

Both people are coding. Both people are building. It’s only one person’s doing it craftsman style, line by line. The other person is effectively doing it through a learned generative system that knows how to put all the pieces together, and you get to be that creative fountainhead.

So watching them come along the journey, it took a little while. We had to walk with them. We had to have these sessions where they would show what’s possible.

And the way it’s aha moments. After you start to realize, I made my day a little less boring, or I removed this ridiculous, redundant task that I have to do over and over, you start to go, now what do I do?

So that’s been a fun thing to watch. And I’m using the word fun because everything’s hindsight. In the moment, it was like, come on, guys, you don’t understand. I promise we’re not playing Guitar Hero. This is real. We’re producing real things.

Greg Lambert (38:06)
Yeah. I can’t imagine the eye strain from the eye rolling from your engineers when you first announced this.

Phillip Greer (38:16)
Yeah. Yeah, it was significant.

Marlene Gebauer (38:16)
Ha ha ha.

Greg Lambert (38:20)
But I’m wondering, Phillip, how do you leverage the intelligence and the experience of your engineers? Is there anything that they help people like Elizabeth better understand about structure and what the end results are and how you want to get there?

How do you leverage that expertise to help the non-engineers?

Phillip Greer (38:50)
So Elizabeth made the comment that our goal was not to reduce our engineering staff in this process. We wanted to support and invest more.

And they ultimately have, to the point you’re making, this understanding of our data.

No matter how much we bring it together and add to our API and have it learning on itself, they understand the repository, where it comes from, how it was put there, how it’s designed, the schema markup, how you scale it, and when you’re having more and more people access it, how much you pull on those data sets.

I’ll give you an example.

One person was writing an analytics system as a builder, and they were saying, give me all of our analytics in our BL App Store. We’re talking about hundreds of millions of data points, and they couldn’t get their app to do it. It was crashing constantly.

And so we had to have one of our architects come in and go, let’s walk through this.

And what they built, you’re using the word harness or harnesses earlier, or guardrails, they decided to change our API layer to understand that the user doesn’t understand the data when they say, give me all of our analytical data, give me all of our financial data.

So it’s my job to create ways that I feed it to them in an appropriate way.

Because when you have access to all of this and you don’t know how it’s formed, functioned, and put together, you create a lot of overly inefficient, heavy systems.

So what we’ve been doing is we’ve opened up the doorway and the communication to say, go talk initially to Seth. He’s in charge of the App Store. And then from there, you spend some time meeting with one of the engineers and asking questions about, how do I make my app do something a little faster? It’s getting stuck here.

And they walk you through their why and the how.

Because what they’re doing is they’re not only in charge of where the data is coming from, they understand it, and they’re building better ways to feed it.

So we had a big session with our engineers where we’re saying, how do we feed things even faster to our API layer?

So we had an architectural meeting where we’re trying to understand, if things are coming from SQL and those are our bottlenecks, it’s single-server environments, how do we extend that further? Let’s have a stopgap where it feeds into a system like Elasticsearch.

I’m so sorry for all the technicality here. Let’s have it feeding to a faster system.

Greg Lambert (41:11)
I think a lot of people that listen to this have heard Elasticsearch before.

Phillip Greer (41:16)
Yeah, yeah, yeah.

So we decided to move our API model from SQL to Elasticsearch, so the first read comes from a faster environment.

And these are the new types of things that our programmers get to think about. No longer are they creating forms on an intranet to feed data back to an end user in an Excel environment.

Now our engineers are freed up thinking, okay, if I restructure my data and take it from a single-server environment to a multi-server replica environment where I feed it faster, what do we accomplish?

How do I create a harness that feeds this data in a way that has nice guardrails, and they don’t have to think about ruining the data, over-encumbering the server? We’re not going to have server crashes.

And those are the projects.

Engineers always talk about, Elizabeth made the comment, taking that hard project off the shelf. That’s what the engineers want to do. They don’t want to have to write one more page on the intranet. They don’t want to write one more page that tells you what your voting reports are.

They will do it because that’s their job.

What they want to work on is making our server faster, building a better backend system, creating a new way to isolate our data and feed it in a multi-tenant scenario.

So that’s when their aha moments and their expertise start to be able to work on those types of systems.

For the first time, those are the types of things where we go, okay, you get the last two weeks of December. This is what you get. Build whatever you want in two weeks.

And now you have more opportunity to start thinking about that longer-term pipeline because each department, they’re servicing their own needs with the data.

Greg Lambert (42:59)
That sounds good, and I think it’s something that we all kind of face for how we transition into whatever the new version of, whether you’re a manager or an engineer, it seems like we’re all taking on more.

And I’ve noticed I’ve not seen people go home earlier on Fridays for some reason.

Phillip Greer (43:23)
Yeah.

Greg Lambert (43:24)
So I’m still waiting for my, it’s time for me to be rolling in income and sitting on the beach the whole time. So perhaps that’s a 2028 goal.

Elizabeth Petit (43:33)
Well,

Greg Lambert (43:34)
2028 goal.

Elizabeth Petit (43:35)
I think one of the reasons for that is because you move so much faster with AI, and specifically with vibe coding, it has unlocked all of this creativity and opportunity that, for those of you who’ve done it, is quite addictive.

Greg Lambert (43:55)
Yeah.

Elizabeth Petit (43:55)
Once you get in there and you start building, whatever you are, you’re prototyping something, you’re building something you’ve been wanting for years.

That used to be this cumbersome process of meetings, requirements, design, getting in the development queue. Three years later, you get something you asked for that you don’t need anymore.

You now all of a sudden get it quickly.

Okay, that’s great. But also, the feedback loop comes immediately. So instead of Friday morning you send off your information and think, I’ll get something on Monday to address, it comes right back to you.

Phillip Greer (44:32)
Mm.

Elizabeth Petit (44:33)
So now we have this cognitive fatigue concern because you are engaging so rapidly that the machine will never get tired. It will never go to dinner with its family. It will never log off for the weekend.

So at a certain point, you have to have the critical judgment to say, I am exhausted. You will never be exhausted, but I am.

And Phil shared this story that he found himself at one point being so in the go, and Phil and I are the same in that way. Once we get on that path, we are running.

And then he realized that he was not making the same little decisions that he would make. And not big, impactful things, but things that usually he would make a decision about.

He had that aha moment that he was having cognitive fatigue. And you don’t want AI to make those decisions for you.

So that’s when,

Phillip Greer (45:33)
Yeah.

Elizabeth Petit (45:34)
you have to remind yourself, AI will never say, go spend time with your kids, you know, go home early on a Friday.

Marlene Gebauer (45:41)
Not unless you

Phillip Greer (45:42)
Yeah.

Marlene Gebauer (45:42)
create an agent to remind you.

Greg Lambert (45:44)
Yeah, exactly.

Elizabeth Petit (45:44)
Yeah, exactly.

Phillip Greer (45:45)
Yeah.

Greg Lambert (45:45)
Exactly.

Phillip Greer (45:46)
And that runs into, you mentioned earlier, the AI vampire. That’s what it is. It’s feeding off of you, you’re feeding off of it, and you have to recognize, okay, I get to walk out in the sunlight. So I need to go do that some.

Marlene Gebauer (46:01)
Tear my eyes away from the screen, yes.

Greg Lambert (46:04)
Yeah, well, I always say when somebody asks me what’s the addictive game that I’m playing on my phone, I say it’s my $200-a-month Claude account. I’m on it all the time.

Phillip Greer (46:15)
Yeah, yeah.

Elizabeth Petit (46:16)
Yeah. Yeah.

Greg Lambert (46:18)
So yeah, you’re right.

Elizabeth, before we start wrapping up, I know that AI, of course, isn’t only affecting how we manage engineers, how we manage managers, and how we all become vibe coders. It’s becoming a part of Best Lawyers content as well.

And one of the things that you oversaw was the introduction of Artificial Intelligence Law as a formal recognition category in the 2026 edition.

Do you mind talking about what that encompasses, how you came up with it, and what you found?

Elizabeth Petit (47:00)
Yep. So, you know, it is always our goal to help connect clients in need of legal services, whether that’s professional or personal, with the right attorney for their case.

And so we always keep our eyes and ears open for developments in the legal industry to add new practice areas as they emerge over time.

So cannabis law, that was one that we added a few years ago because that was bubbling up.

And so about three years ago, we started to see that law firms were adding AI practice groups. And so we added this and started researching AI as its own practice area.

Now, when this first started, I envisioned this would be sort of a subset of emerging tech.

And we have technology law, and that this would kind of,

Greg Lambert (47:51)
This is what the blockchain lawyers morphed into, right?

Elizabeth Petit (47:54)
Yeah, exactly. This is sort of what I thought this would evolve into.

Phillip Greer (47:54)
Yeah. Yeah.

Marlene Gebauer (47:54)
Yeah.

Elizabeth Petit (47:57)
But as AI is so dynamic and evolving so quickly, the definition of an AI lawyer is quite nebulous.

Are you counseling AI corporations? Are you focused on data privacy? Are you looking at government issues? Even labor and employment. I mean, AI is truly touching everything.

And so because of that, it started off more as a subset of technology. But we will have to evolve with AI in our awards as this industry evolves so rapidly to make sure that it is reflective of how legal is addressing AI and the needs of clients.

Greg Lambert (48:49)
Are you thinking about

Phillip Greer (48:49)
We didn’t even get it.

Greg Lambert (48:52)
an AI-native law firm category? That seems to be the big buzz phrase these days.

Elizabeth Petit (49:01)
Yeah. We are getting ready to evaluate adding new practice areas for next year. So look for that webinar series coming up soon.

Phillip Greer (49:10)
Yeah, and whether or not we have AI law firms that we recognize, that’s currently not on the horizon.

But it is the question, when do I have my work done by AI and AI Inc.?

Greg Lambert (49:27)
Yes.

Marlene Gebauer (49:27)
Ha ha.

Greg Lambert (49:29)
All right. Well, before we get to our crystal ball question and we start looking at the future, I want to ask you guys to look back in the past. And I think I know where this is going based on our conversation.

Phillip and Elizabeth, what’s something that’s true today that wasn’t necessarily true for you and Best Lawyers a year ago? What’s changed in that year?

Phillip Greer (49:52)
Yeah.

Greg Lambert (49:52)
What’s changed in that year?

Phillip Greer (49:53)
I mean, the biggest thing, it’s everything we talked about, that we have non-builders, and they’re taking their data and they’re solving their own problems.

We have a decent engineering team as far as size because our company is research and tech. We use a lot of tech to build rankings and recognitions in over 76 countries.

And so we always built a lot of custom software, and we’ve always been able to do dynamic things for each department. But there was always a process. You got in line, got queued up, and then it handed off to the engineering team.

Greg Lambert (50:28)
Q3. Q3.

Phillip Greer (50:30)
And now that’s changed greatly because everybody is handling the majority of their own needs internally.

We’re still externally using our engineers. We’re not developing any software. All software that’s hitting our customers is still handled by our engineering team because that’s the right way to handle it.

But for all our internal needs, everyone’s a builder. So that was a big change I’ve seen. That is different now.

What about you, Elizabeth?

Elizabeth Petit (50:59)
I think the surprise that has come out of this is, I’ve been with Best Lawyers for over 15 years now, and I did not expect this career development opportunity.

Phillip Greer (51:14)
Mm.

Elizabeth Petit (51:15)
So I thought that I would continue to grow in leadership or other traditional career development paths, but all of a sudden to have an entire skill set that I did not have last year, that’s pretty amazing.

And I’m grateful to have been given that opportunity.

Marlene Gebauer (51:37)
All right, we’ve looked a little bit at the past, and now it’s time to look in the future.

If you would gaze into your crystal ball and look ahead a few years, as autonomous agents and vibe coding become standard inside both data organizations and law firms, what’s the single biggest shift you see coming for the legal profession’s economic model, specifically regarding the billable hour?

Phillip Greer (52:03)
Hmm. So I don’t think the billable hour is going to disappear overnight, right? But it will shift.

People are going to have more scrutiny of how you’re backing up the value of what’s coming from the work product.

And there’ll be more questions about how did you derive that actual result set.

It was already the question, you hire the partner and you ask yourself, am I getting the partner’s hours or am I getting the junior associate? That’s already been on the table for a long time. And some clients have been wanting to know that. And they want to know, I am paying for the partner’s time.

I think there’s going to be a lot of questions that more clients are going to be asking. I want you to break down the justification. Am I paying for the senior partner’s time, or how much am I paying for the AI’s time?

I don’t know what that’s going to look like as far as how it wants to be shown in transparency, but I think that’s going to be a big question.

Marlene Gebauer (53:01)
How about Elizabeth?

Elizabeth Petit (53:04)
I agree. I don’t think that this is, legal doesn’t revolutionize overnight. We’ve all been in this industry for a minute.

But a lawyer is not going to become twice as productive with AI. They’re not going to bill twice as many hours a day. That’s not possible.

But they’re expected to still bring in that same amount of revenue for the firm.

And so figuring out that balance between what is the appropriate use for AI, but still what requires that human judgment that AI will never replace.

And that’s ultimately what a client comes to a law firm for, not the administrative work that AI assists with. It is the human judgment.

That’s where I feel like, if firms tell their story to clients successfully, they sort of balance that out, get that efficiency that they need, but still have the confidence of their clients.

Greg Lambert (54:04)
Mm-hmm. All right. Well, I think that’s a good spot to end on.

So Phillip Greer and Elizabeth Petit, thank you so much for joining us and breaking down what it looks like behind the walls there at Best Lawyers. Appreciate it.

Marlene Gebauer (54:18)
Yeah, thank you.

Phillip Greer (54:18)
Yeah, thank you so much for having us.

Elizabeth Petit (54:19)
Thank you for having us.

Marlene Gebauer (54:21)
And thanks to all of you for listening to The Geek in Review. If you’ve enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn, YouTube, and Substack.

Greg Lambert (54:30)
And Phillip and Elizabeth, where’s the best place for listeners to learn more about you, more about Smithy AI, Best Lawyers, Bestie, all those things?

Phillip Greer (54:40)
So first off, for more information, go to bestlawyers.com. Also look us up on LinkedIn.

I’m posting a lot of videos about some of the AI work that I’m doing, some of the apps we’ve been building. And yeah, feel free to follow us there and check out what we’re doing.

Marlene Gebauer (54:56)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye, everybody.

]]> What happens when a 40-year-old legal data company decides its employees should start building their own software? This week on we talk with Best Lawyers CEO Phillip Greer and Senior Vice President of Research and Product Strategy Elizabeth Petit about an internal AI transformation that reaches far beyond adding ChatGPT to the corporate toolkit. Best Lawyers is experimenting with generative engine optimization, internal agentic systems, vibe coding, and an AI development environment where employees across research, finance, marketing, and other departments build applications around the company’s data.

Greer begins with a challenge facing every law firm marketing team: traditional search is changing. Google AI Overviews and answer engines such as ChatGPT, Claude, and Gemini increasingly give users answers without sending them to the familiar list of blue links. Greer argues that SEO still matters, but law firms now need to think about Generative Engine Optimization, or GEO, and the signals AI systems use when deciding which sources deserve trust. Structured data, schema markup, substantive content, and third-party validation all become part of the equation. For Best Lawyers, its long history of peer-reviewed rankings offers an interesting advantage. The company’s data serves as an independent signal that AI systems might weigh differently from content produced by a firm’s own marketing department.

Petit explains how Best Lawyers is applying the same thinking to legal marketing through Smithy AI, a system designed to help attorneys and law firm marketers develop profile content without endlessly copying the same biography across websites. Smithy draws from Best Lawyers’ structured information and existing lawyer content to produce a starting point that attorneys and marketers then edit. The larger goal is authenticity. As generative systems make producing generic legal content almost effortless, Greer argues that distinctive expertise, voice, and credible third-party signals become more valuable rather than less.

The conversation then moves inside Best Lawyers, where Greer has taken a far more unusual approach to AI adoption. After building a secure data layer connecting systems including SQL databases, HubSpot, Gong, Google Analytics, and accounting data, he created an internal Best Lawyers App Store where employees use natural language to build applications against company data. What began with roughly 30 percent of the workforce vibe coding has grown to around 40 percent, according to Greer. Petit describes building research and KPI dashboards despite coming from a research rather than software engineering background. Projects that once required Excel formulas, Power BI reports, development queues, and weeks of waiting now sometimes move from a question at 9:30 to a working internal application by 10:30.

That shift also changes the role of professional software engineers. Rather than spending their time building another reporting screen or internal form, Best Lawyers’ engineers increasingly concentrate on architecture, data infrastructure, performance, governance, and the guardrails surrounding employee-built applications. Greer describes moving parts of the company’s data architecture toward Elasticsearch and developing “Bestie,” an internal agentic AI team member. Yet speed introduces another problem. Petit and Greer describe an “AI vampire” effect, where instant feedback encourages people to keep working because the machine never gets tired, goes home, or stops responding. Human judgment includes knowing when the human needs to stop.

The discussion closes with what these changes mean for legal practice itself. Best Lawyers introduced Artificial Intelligence Law as a formal practice area in the 2026 edition of The Best Lawyers in America, reflecting how AI work has spread across technology, intellectual property, privacy, employment, compliance, litigation, and counseling. Looking further ahead, Greer and Petit do not expect AI to erase the billable hour overnight. They do expect clients to ask harder questions about what they are paying for, how legal work was produced, where AI contributed, and where the lawyer’s judgment created value. If AI makes routine production dramatically faster, the economic question for law firms becomes less about how many hours technology saves and more about how firms explain, price, and defend the value of human expertise.

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Greg Lambert (00:01)
Hey everyone, I’m Greg Lambert from The Geek in Review, and this week we are talking with Phillip Greer and Elizabeth Petit from Best Lawyers. But first up, here’s a word from our great sponsor and partners at Legal Technology Hub.

Marlene Gebauer (00:15)
I have Sarah Glassmeyer here from Legal Technology Hub, and since it’s almost time for ILTACON, Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:30)
Yeah, so this is probably one of the more exciting times of the year for legal tech, kind of leading into ILTA. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legal Technology Hub can help you pregame and get ready to get the most out of it because, while you’re there, it does seem like you live now in Nashville and you’ve been there your entire life, but it goes fast. You only have two and a half, three days to talk to vendors.

So a couple of things I would suggest you do. One, look at the list of who is exhibiting at ILTACON. If you go to the ILTA website, they have a legal tech directory, but guess what? That is us.

We partner with ILTA. It is a mirror of our directory. You can see the little badges, and that will tell you who is exhibiting at ILTACON. Don’t wander in there. Think ahead and think, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list, see who’s going to be there, and start thinking ahead. Through the directory, you can see, these people don’t integrate with X tool, so they’re kind of off the list for us. You can do a little pre-filtering, pregaming, and figure out who you want to talk to, who is going to make the most of your time.

And then from there, if you are back on our platform in the Legal Technology Hub directory, if you’re logged in, you can make notes. So as you’re wandering through the exhibit hall visiting the people you want to visit, you can make notes saying, here’s this person’s email address that I spoke to and I want to follow up with them, or maybe show Bob this when I get back to my office, or these people are off the list, forget about them.

And so you keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legal Technology Hub directory, not a swag bag full of business cards and flyers. So that’s how we help you get the most out of what you’re doing.

But also, I do suggest wandering around the exhibit hall, because everyone who’s there does have a listing on Legal Technology Hub, so you can double-check. But I think it’s good to wander, see who catches your eye, have a spontaneous conversation, because you might find a vendor you never considered or something you weren’t thinking about ahead of time. So it is a fun time to get yourself absorbed in what’s happening new in legal tech.

Marlene Gebauer (02:46)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legal Technology Hub directory because it can be overwhelming, and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave.

Marlene Gebauer (03:14)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:21)
And I’m Greg Lambert, and today we are exploring how a legacy data institution is completely rearchitecting itself from the inside out for the generative AI era.

Marlene Gebauer (03:34)
We are thrilled to welcome Phillip Greer, CEO of Best Lawyers, and Elizabeth Petit, Senior Vice President of Research and Product Strategy. Phillip and Elizabeth have been leading a radical internal and external transformation at Best Lawyers, turning the organization into a pioneer for agentic workflows, vibe coding, and generative engine optimization. Elizabeth and Phillip, welcome to The Geek in Review.

Phillip Greer (03:58)
Thank you so much for having us. We’re quite excited to be here today.

Greg Lambert (04:03)
Right. Well, Phillip, I’m excited about this because I think a lot of us have been hearing about SEO for years, and now recently, with all the AI, we’re hearing more about generative engine optimization, or GEO.

Phillip Greer (04:18)
Mm-hmm.

Greg Lambert (04:20)
Last year you noticed that there was a massive drop in traditional web traffic due to all of these AI overviews that you see with Google and others, to where people are getting answers rather than getting links out to the websites and then going and finding the answer themselves.

So you talk about your pivot away, or, well, I’m sure you still use SEO, but you’re also having to implement this new generative engine optimization, and that differs a lot from the SEO that a lot of us here at law firms have grown up on and realized, in order for us to get high up on the Google rankings, we need to be good at that.

So how does GEO differ from traditional SEO? And why is it that these third-party reviews or peer reviews are becoming the primary, what you call a trust signal, that keeps firms from being compressed out of the AI search answers that we need to get involved in?

Phillip Greer (05:26)
Yeah, so I think it’s helpful to give a little bit more context. You started that process.

We’ve all been chasing SEO for over two decades, right? Google came out, and then, how do we get to the top of that Google ranking so that our customers and new clients can see the work that we’re doing? And we did that through so many things, backlinking efforts, keywords, thought leadership pieces, marketing associations, marketing partnerships with different types of organizations, et cetera, et cetera.

It’s been popular to say that SEO is dead. So first off, I don’t think SEO is dead. Traditional SEO is still important, and it’s the foundation of AIO.

So a few years ago, Google changed their search engine, and when you started searching, all of a sudden the middle of the funnel, or that blue-links world, started to go away, and you were using that Google Gemini. So you would say, “Hey, I need to find a lawyer for an issue I’m having,” and then it would give you a summary.

And then what was happening was so many law firms, and any type of business, were losing 15, 20, 30 percent of their web traffic because the blue links aren’t being clicked anymore. The users are going in, they’re getting that overview, they’re getting what they need, and they’re kind of moving on.

Well, those original AIO types of overviews were being fed from those SEO efforts.

Marlene Gebauer (06:51)
Yeah.

Phillip Greer (06:51)
The shift that we did over the last couple of years is, first, make sure that our content is structured in a way that, when these LLMs such as ChatGPT or Google Gemini or Claude are coming to our website and looking at our data in traditional SEO forms, we’re tagging it and presenting it in ways that are helpful.

So you think about SEO stuff you used to do. It was all about meta descriptions, keywords, page titles, and then, again, we talked about some of the backlinking techniques. Those things were structuring it so that, when the crawlers are finding your website, they know how to get that data.

Well, that’s kind of amped up with AIO and then eventually what we’ll talk about with GEO. You not only needed the meta descriptions, you needed to have good schema markup. You need to describe sections on the page, to be able to ask questions, answer questions, so that when the AI engines were gathering data, it wasn’t only finding it, they could start to interpret it, because that interpretation is what becomes a little more important when you think of things like ChatGPT or Google Gemini.

It has to take the structured data, and then it has to interpret it and create insights and say, okay, now you can start showing up in these overviews.

The latest shift, and I think it’s the biggest shift, is this GEO, and that was your opening question.

Greg Lambert (08:14)
Right.

Phillip Greer (08:14)
The generative engine optimization approach. And that is making these AI systems not only get your data, but understand that they can trust your data. And so, how do you trust data?

Data has to come from quality places, things that are not produced by a marketing staff. So we’ve all gamed the SEO world. Every year, Google changed the algorithm again. What do we do? How do we get to the top? We spend more dollars. We make sure that we have more content. We flood the internet with it. We do backlink strategies, right?

That would work for SEO, and then it would work until they changed the next algorithm again.

With GEO, you have these AI systems that have some insight and intellect in the sense that it knows whether or not the content is being produced by you, your law firm, putting more information out there, or are you reading a service like Best Lawyers that’s known for producing peer review surveys and, ultimately, lists of lawyers? It’s an annual process. It’s been around for 40 years. It’s got a strong standing, a big domain authority. It is a trusted source. It’s third party to the industry that is putting out their data, right?

So that type of association is important. That’s where GEO plays a role.

So we think about our customers and clients who are part of Best Lawyers, who make those rankings. They get a leg up immediately by making it onto our list because they’re getting a signal, an AI signal in the industry for these LLMs to say, okay, if they’re on Best Lawyers and they’re ranked in corporate law, then their value has a higher weight.

And there are other types of services that you should be paying more attention to, and you want to make sure they’re credible. And so that’s why, when you think about what is your plan going forward, I think for law firms you should continue to think SEO is important, but it’s not the only thing. And that’s the takeaway.

SEO is important. It’s not the only thing. Structuring our data is important, and let’s make sure that we’re also thinking deeper about the schema so that we can have questions asked and answered. And then how are we putting our data in? How are we associating? What are we saying? Go see us on, go connect with, what type of services, and what’s their credibility level?

That’s what is creating the big buzzword for the last year, the AI signals. And all those together create a strong AI signal for your law firm.

Greg Lambert (10:39)
Yeah.

Well, one of the things that I’ve been hearing from marketing folks is much more long-form articles, client alerts, more showing your expertise in an area. Are you also seeing the generative AI or the AIO, and it sounds like a song,

Phillip Greer (10:58)
Yeah.

Greg Lambert (11:00)
or the GEO relying upon, instead of straight-out structure and how things are presented, deeper content as well? Are you seeing that?

Marlene Gebauer (10:59)
Yeah.

Phillip Greer (11:13)
So first off, I refer to this as the E-I-E-I-O because it is 100 percent that effect. The Old MacDonald version.

Greg Lambert (11:18)
Yeah, there you go. The Old MacDonald version.

Phillip Greer (11:24)
For a new world. Yeah, so I think the goal is not only to have longer content, but it’s authentic content that speaks to your messaging.

Greg Lambert (11:31)
Okay.

Phillip Greer (11:31)
So you can create a lot of long-form content with AI, but the problem, if it’s not authentic to your brand and your expertise, you’re going to lose a lot of credibility.

So, thinking about what is it that makes your lawyer special? What makes them stand out? How do you speak on it? And how do you keep that authentic?

And sometimes it’s not directly in the world of law for the authenticity to stand out. So figuring out what your niche is there and then driving it home. That is what is going to create unique content that gives you that authority and will stand out among these GEO initiatives because they can say, this is not a whitewashed, white-labeled version of the same type of content that continues to be posted online. It’s something unique.

And if you have a series of this type of content, it can create that narrative of, okay, this is the type of lawyer, this is the type of firm, this is the type of credibility, this is the type of messaging that you can expect, and stand out among your peers. So yeah, I definitely agree with the longer-form opportunities there.

Marlene Gebauer (12:39)
So Elizabeth, for law firms trying to adapt to this new AI search environment, creating unique, machine-readable content is a difficult hurdle. Best Lawyers recently launched Smithy AI to help lawyers and marketers scale their profile content. How does this tool solve the duplicate-content penalty while keeping human editorial control firmly in the loop?

Elizabeth Petit (13:05)
Yeah. So first of all, we’ve always viewed ourselves as partners with lawyers and law firms. So the products that we build and deliver, we want to solve your problems. And one of the problems we know that you have is too many things to do and not enough time to do them.

So one of the things that Smithy AI was developed to address is getting lawyers to fill out their profiles.

Have either of you gotten lawyers to fill out their profiles? That’s a tall order.

Greg Lambert (13:35)
Yeah, all the time. All the time.

Phillip Greer (13:37)
Yeah.

Elizabeth Petit (13:41)
So the easiest thing, the old way to do this, the easiest thing to do is that most teams would copy and paste whatever you finally got written on your own website. You’re going to go copy and paste it everywhere else on the internet. And you got penalized for that.

So we wanted to, again, partner with law firms and develop an easier way to assist you with your workload.

So what Smithy AI does is it reads our structured data and the content you’ve already developed on your lawyer profiles on your website, and it makes a recommendation for you. Now, everybody knows that AI is not a replacement, it is an assistant, and you should still use your human judgment.

So it does say, recommended generative content. It pulls in information about the awards, things that it thinks are appropriate. You review it, and then you publish it with the click of a button.

The name for Smithy AI does come from one of our original co-founders, Gregory White Smith, who was a Pulitzer Prize-winning biographer.

And so we also wanted to name it in his honor for both founding Best Lawyers and as a writer who was a craftsman as well. So Smithy is developed to help you with your mountain of work, both with profiles, but also there are features now to help with press releases, email announcements, social assets. So we’re trying to be your extra hands when you’re looking at the clock, looking at your watch, looking at your calendar, and you don’t have enough hours in the day.

Greg Lambert (15:18)
I’m curious when it comes to that because I know, for example, one of the things that got pushed out on platforms like Substack is there’s kind of that AI content identifier.

Phillip Greer (15:34)
Mm-hmm.

Greg Lambert (15:35)
And so is that one of the things that you try to look for with using a tool like Smithy, giving them something to work with, but then, is it a full copy and paste, or is it something that they take and then they kind of do their own smithing on it as well? How do you encourage your clients to use the tool?

Elizabeth Petit (16:00)
Yep, so we envision it to get you like 90 percent of the way. So it’s not a copy and paste. It doesn’t pull exactly what you’ve already written. You can also hit that button a few times and say, slightly rewrite it, slightly rewrite it, polish it for me. So it can iterate a little bit for you.

But as with all wordsmithing through AI that we all do, we all know we do this, you should never take whatever it spits out for you and then publish that. This is where human judgment always needs to be that final set of eyes, that final check before you are comfortable publishing to your website, our website, or anywhere that you publish content.

Phillip Greer (16:44)
Yeah, and I would say also, going back to your point about the long-form content and where I was going with brand authenticity. So one of the great things about Smithy is, as Elizabeth is saying, we’re going to get the way you’re talking about yourself on your profiles, and we’re going to take the data we have on Best Lawyers and put it through our own system to say, how do we think it should build content?

So it’s going to have your unique voice with our data, but you still want to ask yourself, is this me?

If not, you do have the ability to be replacing it with another version of yourself. And I know with AI, it’s a whole different topic where there’s a lot of, what does it mean to clone a lawyer? Well, we don’t want you to clone your brand. We want you to play a role in helping that editing of the brand.

I think back to college, the hardest thing was always to start writing the paper. The first paragraph is always the hardest. The thing about Smithy is it’ll write the first four paragraphs for you, but you should come in with that closer. You should come in with that closer and do a little bit of editing on the first four that it produces for you.

Greg Lambert (17:52)
Phillip, I want to turn to something that you’ve done that I think we’ve seen a few CEOs do, and that was there at Best Lawyers, you kind of famously mandated that your entire team continue doing their job, but also look at becoming essentially software builders.

Phillip Greer (18:13)
Mm.

Greg Lambert (18:14)
Now you’ve got, and I think the last estimate was about 30 percent of the company, vibe coding their own apps. And Elizabeth, I think you’re a prime example of this. You’re a researcher, not an engineer, yet you were involved in building a real-time KPI dashboard there at Best Lawyers.

So it seems like people are taking you up on your challenge, Phillip. So I want you to walk us through something that you’ve built that takes the worst part of your job off the plate. And then, once the repetitive data wrangling is automated, how do managers shift their focus from doing that kind of grunt work to focusing on more of the higher creative work?

Because that’s something that we’re insisting our lawyers do. So I’m hoping you’ve got the secret sauce and can help us on that.

Phillip Greer (19:20)
Yeah, I’ll start here, and then I’ll want to pass it over to Elizabeth. But I’ll start.

So the end of last year, I’ve been playing with a lot of AI and product development for years now. My background is engineering. I used to build a lot of software. I built a lot of the core systems we still use here at Best Lawyers for producing our rankings and doing our survey processes.

And then I’ve been the CEO and running it for quite some time. And when AI came on the scene, I started experimenting. What can we do? What can we do?

And about December of last year, I had the breakthrough. Claude Opus came out, and its coding abilities were far superior to every other AI coding that had existed before.

So I had this wild thought where, what if I build an environment that was safe, that our team could fail fast in and not worry about hurting our data, and it wouldn’t go and be spread out to the outside world? It wouldn’t be trained on outside systems. We wouldn’t have to worry about them doing some shadow IT type environment where they’re taking sensitive data and going straight to ChatGPT and dumping it out there.

So I felt an obligation to create that environment. And it’s an exciting time because it felt possible. So I spent all of the holiday break in December and then all of January building out, first, an environment.

And what I mean by that is a place where all our data connects. So I needed to create this API layer so that all of our data, when I say our data, I mean our SQL data, our accounting data, our HubSpot, Gong, Google Analytics, any type of systems that we are accessing and using on a daily basis, I wanted it all to come to one place and then inform itself and know about its own data.

So I created a data lake, if you will, custom. And ultimately, I don’t know, for those who are a little more technical on these calls, it’s a deep coverage model where the data in Gong understands how it relates to our data inside of HubSpot, and that understands how it relates to our data inside of Google Analytics, and so on.

So when you’re asking questions, it references each of the data sets, and it knows what a firm is or a lawyer is inside of those areas.

That’s a complicated way of saying I made a place where all our data came together safely and made sure it knew how it was associated.

And then I needed to create that safe environment. So I built a system called the Best Lawyers App Store, where you could go in and start vibe coding through our Claude environment.

And when you vibe code, you’re safely in that App Store. It’s using only our data sets, and you can build, eventually, what we now are using as an operating layer for most of our business.

So I did this. I put all this together. I got it in a prototyped form, and I met with the team at the end of January and I said, “All right, all right, I got an idea. Everyone’s going to become a vibe coder. Every department’s going to be a builder. Elizabeth is going to be a builder. Nancy is going to be a builder.”

I went full Oprah Winfrey on, you’re going to, you’re going to, you’re going to.

Greg Lambert (22:25)
Yeah.

Phillip Greer (22:26)
And they were all on board. I was like, okay, no convincing needed, you’re totally in for this.

Elizabeth was one of the first people I sat down with. I think I spent all of 30 minutes with you, Elizabeth. It was a short session. I got you set up in the environment. I gave you the tools, told you how the data works and how to connect it, and I said, go and dream and build.

And Elizabeth, I’d love to hear your take on it. That’s my more technical explanation of what happened. I’d love to hear what your side was.

Elizabeth Petit (22:56)
Yeah.

Phillip Greer (22:56)
I’d love to hear what your side was.

Elizabeth Petit (22:58)
Yeah. So I knew Phil had been working on this passion project. Phil’s always got multiple passion projects going. So I knew he’d been working on this, and then he made a statement and he’s like, “I’m going to ask you guys to come join me in this.”

And then he did that ping and he’s like, “No, I mean it.”

And I’m like, “Yeah, I know. I’m here. I’m ready.”

So my personal mantra has always been, my team knows this, work smarter, not harder. And that doesn’t mean we don’t work hard. I love to work hard. But it means work more efficiently. If there’s a better way to solve your problem, do it the more productive way. Don’t do something the same way over and over again because you’ve always done it that way.

So what vibe coding unlocked for me was this new ability to work smarter and not harder, and take some of the repetitive, mundane parts of everybody’s job and make them more fun, make them more creative, automate them.

Whether it’s through KPI reporting that eats up time every day, or taking some of these important projects or problems we want to solve that sit on that shelf, that you know have value, but you never have the time to take off the shelf.

And say, okay, now that I have optimized some of this basic operational work, I’m not spending all day, two days of the week, pulling together my KPI reports, let’s take a bigger, meatier project off the shelf and let’s go start working on that.

Phillip Greer (24:38)
Yeah.

It’s been exciting watching how everybody came to the table to build.

Elizabeth built research dashboards to monitor the process for Best Lawyers as the nominations were coming in and voting was happening. Nancy, our head of content, developed expense-paid tracking systems for all of our budgets that were going out for paid media, as well as her marketing team developed this marketing dashboard that pulled in our Google Analytics and gave full-funnel conversion visibility.

I mean, we went from, like a lot of companies, working heavily in Excel and Power BI or Tableau. We’ve stopped using Power BI. We don’t use any Tableau. We do everything now inside of our dashboards that are developed independently by departments.

And the only time we’re in Excel these days is when we’re exporting data sets to give to our business partners or if we’ve got to send them out somewhere. But we work primarily now inside of our BL App Store environments.

When you’re asking for data and looking for new trends and analysis, it used to be, okay, we’ll export everything out to Excel. We’ll write a bunch of complicated formulas. I’m Googling how to write that complicated formula.

Greg Lambert (25:55)
Yeah.

Phillip Greer (25:55)
My God, why do we have to write these complicated formulas?

Greg Lambert (25:58)
Yeah. How do I do that VLOOKUP table again?

Phillip Greer (26:00)
Yeah, that VLOOKUP table.

Elizabeth Petit (26:01)
Yeah.

Phillip Greer (26:03)
My God. And then you always have the one person in the office who’s your Excel person, and that’s what their life is all about. “Ask me about my sheets,” you know?

But now everyone goes straight into their environment. The data’s all there in our data layer. It’s trained, it’s learned, it knows itself in the deep coverage model.

And you work through our App Store and ask it questions. And instead of developing an Excel sheet, you develop an entire new page that is the best visual representation of your data and answers a question. And you use that to start making decisions.

So our speed to decision is pretty great. A week-long, two-week-long Excel project is now done. We have an idea in a 9:30 meeting. By 10:30, we have a new page that’s been pumped out through our finance team that’s explaining the answer.

And then we need to make that next decision and, okay, how do we execute on this data?

Greg Lambert (27:02)
The follow-up I have on that, and this is something I’ve been writing on, especially since you have so much data that you’re working with, and it sounds like you’ve solved part of the problem through creating these data lakes or this ability to feed information into the right places so that you set up your AI to access clean information in a relatively simplistic way rather than having to go out to 15 different places, some external, some internal. You’ve got it all built.

So the topic that I’ve been writing on a lot, and I think this is going to be, I thought it was going to be 2027 fodder, but it might hit before then, is a lot of us are building these harnesses around both our data and our AI tools with the expectation of a lot of this information.

You don’t need to dump everything into the LLM and get an answer. You need it to be precise, and you need it to be consistently right so that the inputs and the outputs are accurate.

So I know, Phillip, you designed the initial, you vibe coded the system around it. Do you have a technical team now that keeps that up to date, updates or adds new things that come in, or are you still able to use the talent that you have on staff that they build on top of what’s already built?

How do you take that 9:30 question and turn it into a 10:30 report?

Phillip Greer (28:47)
Yeah, so I built all the environment for the initial App Store, and my intention was always to hand off the day-to-day maintenance because adding more data to our data lake or data API library is going to be a constant workflow.

And any company who’s working with any type of data environment, governance, or what have you, whether it’s SharePoint or an actual Azure data lake, et cetera, you’re going to have to have somebody managing that.

So once I got everything built, the initial systems, we found one person internally who had the most interest in the AI infrastructure and the future of AI and how it’s used in programming. He was one of our programmers, and his name’s Seth.

And so I trained him for a week on the system, and he took it over. And now he is running that API library.

And people, all the vibe coders, as they need more data or they need more feature abilities in the App Store, they go through Seth. And it’s been a good system.

Because my original vision was, I knew what Elizabeth and I knew what our research and our finance and our content team would need to get started, but I didn’t know what they were going to build, right? And that’s the wild thing, what they end up building and how they use the data.

I’m watching some of the requests come through and some of the systems they’re building, and I know because I designed the actual data that came together, and I would go, that’s not going to work. They don’t have that data. That’s not going to work. They don’t have that data.

So I had that moment where I was like, I need to hand this off to someone. I’m the CEO of this company. I can’t keep adding to that API layer.

Greg Lambert (30:28)
You don’t want to be the head vibe coder and CEO?

Phillip Greer (30:31)
Head vibe coder and CEO.

So I got Seth involved, and now whenever they start to build with more data, he continues to extend the actual API layer.

But what is great is the actual building is still happening from the non-builders. Well, they used to be non-builders. I’d call them all builders now.

That 30 percent number, as of, I onboarded a few more people this last week, we’re up to 40 percent of our company who are vibe coders now. It’s growing because the mentality is, if you want to do this, we want to help you do it. There’s no reason you have to sit on the sidelines.

So I handed that over, and then what I realized quickly by handing this over is there’s an opportunity to build more things.

So that’s been kind of exciting. And we can get into it. But I built out what we lovingly call here Bestie, which is our first type of AI support team member that uses our different models and data sets.

So yeah, you have to have someone who’s going to own it. You’ve got to have someone who’s going to make sure that they’re adding to it. But that’s the easy part.

The harder part is thinking about what you want to build. Because when you can build endlessly, narrowing that scope down to building things that are functionally helpful is that next muscle you have to start learning how to train.

Greg Lambert (31:55)
Yeah, it’s not asking what do I build, it’s what should I build, right?

Phillip Greer (32:01)
Yeah. One of the opening things you mentioned was about, how do you open up room and bandwidth for creativity?

Well, you have to allow yourself to be creative. And there’s a lot of people who work in an office environment that have not worked that muscle in a long time because you make that Excel report, you put it through Power BI, you drop it out as a PDF, and then you take it to the board. And you do it again, and you give the lawyer the thing.

The creativity is lacking.

And now all of a sudden, all of that administrative burden is done within 9:30, the idea, 10:30, the actual result. Now what’s the creative solution that you move forward and make something functional to execute on?

Marlene Gebauer (32:43)
So transforming a workforce as quickly as you have doesn’t happen without some friction. Phillip, you’ve spoken about the AI vampire effect causing extreme decision fatigue.

Phillip Greer (32:52)
Mm-hmm.

Marlene Gebauer (32:53)
And Elizabeth, you’ve had to manage an existential crisis among your software engineers. How did you help these technical engineers transition to feeling like they’re authoritative system architects rather than feeling displaced?

And how are you managing that cognitive burnout when AI offers answers in seconds instead of in weeks?

Elizabeth Petit (33:20)
Yeah. So first of all, the existential crisis is understandable.

These team members of ours, many of whom Phil and I have worked with for over 10 years, went to school to be experts in these fields. They are craftsmen. They are builders. This is their superpower.

And then seemingly overnight, IKEA came to town, and no longer is the craftsman superpower the same. And that’s hard. And I understand that, and we did understand that.

And it helped that Phil was a developer and came from that background, so he understood that as well.

And it was always our goal, continues to be our goal, to use AI to upskill our team members as an opportunity for professional development, whether you are an engineer or another member of the business team. It is not to replace anybody’s job.

And so while the business team is learning a superpower they never thought they would have, they never thought that they were going to be this type of craftsman, and they’re not going to be this technical craftsman, they do some things.

What it has done for our engineers is it has allowed them to also build faster, solve more complex problems. All the things that AI does. It allows for better debugging, it allows for better regression testing, it removes some of that mundane work that they were doing.

So we went into this as, we are all going to learn this together. This is new emerging technology. We don’t have all the answers, and we want to figure out how this makes the most sense for you and your roles, and us within our business.

Phillip Greer (35:16)
Yeah. At the end of January, at the same time I was meeting with the executive team, Elizabeth pulled all of our engineers together in an in-person meeting, and we had a Q1 huddle.

And I sat there and explained to them that I’ve built this App Store. I’ve got this huge data layer. All our data comes together nice and clean. It’s learning off of each other. And now everyone’s going to become builders.

And it was a lot of disbelief because, again, craftsmen, like, okay, I make quality code. There’s no way a machine’s going to do it.

And so there was a lot of disbelief.

After that, I met with Elizabeth and team and taught them how to vibe code, and they started building apps.

We brought everyone back together again and showed them what these non-builders were putting together and how it was pulling data from all of our systems.

And then even our engineers were in the mindset of, my God, this is a real thing. This is not some fancy fad happening, Phil says everyone’s going to be doing this. They’re building financial systems. They’re building research systems, marketing systems, sales systems.

And it was clear that they wanted to be part of it too.

And so Elizabeth put on, I thought it was an intelligent thing, as we had the adoption, every two weeks we’d have sessions where we’d encourage the engineers to show, in a safe space, the other engineers what they’d been vibe coding.

Because there’s also this stigma, like, I’m an engineer. I don’t vibe code. I know how to code.

But that’s not what vibe coding is. Vibe coding is not a replacement for, I don’t know, a good analogy. Here’s a good analogy.

I play guitar. But when someone says, “I play Guitar Hero,” and they’re pushing those buttons on the little plastic machines,

Marlene Gebauer (37:02)
Ha ha ha.

Phillip Greer (37:03)
I would go, “Ha, you don’t play guitar. I play.”

It’s not that. It’s a completely different thing. It’s not analogous to Guitar Hero versus playing guitar.

Both people are coding. Both people are building. It’s only one person’s doing it craftsman style, line by line. The other person is effectively doing it through a learned generative system that knows how to put all the pieces together, and you get to be that creative fountainhead.

So watching them come along the journey, it took a little while. We had to walk with them. We had to have these sessions where they would show what’s possible.

And the way it’s aha moments. After you start to realize, I made my day a little less boring, or I removed this ridiculous, redundant task that I have to do over and over, you start to go, now what do I do?

So that’s been a fun thing to watch. And I’m using the word fun because everything’s hindsight. In the moment, it was like, come on, guys, you don’t understand. I promise we’re not playing Guitar Hero. This is real. We’re producing real things.

Greg Lambert (38:06)
Yeah. I can’t imagine the eye strain from the eye rolling from your engineers when you first announced this.

Phillip Greer (38:16)
Yeah. Yeah, it was significant.

Marlene Gebauer (38:16)
Ha ha ha.

Greg Lambert (38:20)
But I’m wondering, Phillip, how do you leverage the intelligence and the experience of your engineers? Is there anything that they help people like Elizabeth better understand about structure and what the end results are and how you want to get there?

How do you leverage that expertise to help the non-engineers?

Phillip Greer (38:50)
So Elizabeth made the comment that our goal was not to reduce our engineering staff in this process. We wanted to support and invest more.

And they ultimately have, to the point you’re making, this understanding of our data.

No matter how much we bring it together and add to our API and have it learning on itself, they understand the repository, where it comes from, how it was put there, how it’s designed, the schema markup, how you scale it, and when you’re having more and more people access it, how much you pull on those data sets.

I’ll give you an example.

One person was writing an analytics system as a builder, and they were saying, give me all of our analytics in our BL App Store. We’re talking about hundreds of millions of data points, and they couldn’t get their app to do it. It was crashing constantly.

And so we had to have one of our architects come in and go, let’s walk through this.

And what they built, you’re using the word harness or harnesses earlier, or guardrails, they decided to change our API layer to understand that the user doesn’t understand the data when they say, give me all of our analytical data, give me all of our financial data.

So it’s my job to create ways that I feed it to them in an appropriate way.

Because when you have access to all of this and you don’t know how it’s formed, functioned, and put together, you create a lot of overly inefficient, heavy systems.

So what we’ve been doing is we’ve opened up the doorway and the communication to say, go talk initially to Seth. He’s in charge of the App Store. And then from there, you spend some time meeting with one of the engineers and asking questions about, how do I make my app do something a little faster? It’s getting stuck here.

And they walk you through their why and the how.

Because what they’re doing is they’re not only in charge of where the data is coming from, they understand it, and they’re building better ways to feed it.

So we had a big session with our engineers where we’re saying, how do we feed things even faster to our API layer?

So we had an architectural meeting where we’re trying to understand, if things are coming from SQL and those are our bottlenecks, it’s single-server environments, how do we extend that further? Let’s have a stopgap where it feeds into a system like Elasticsearch.

I’m so sorry for all the technicality here. Let’s have it feeding to a faster system.

Greg Lambert (41:11)
I think a lot of people that listen to this have heard Elasticsearch before.

Phillip Greer (41:16)
Yeah, yeah, yeah.

So we decided to move our API model from SQL to Elasticsearch, so the first read comes from a faster environment.

And these are the new types of things that our programmers get to think about. No longer are they creating forms on an intranet to feed data back to an end user in an Excel environment.

Now our engineers are freed up thinking, okay, if I restructure my data and take it from a single-server environment to a multi-server replica environment where I feed it faster, what do we accomplish?

How do I create a harness that feeds this data in a way that has nice guardrails, and they don’t have to think about ruining the data, over-encumbering the server? We’re not going to have server crashes.

And those are the projects.

Engineers always talk about, Elizabeth made the comment, taking that hard project off the shelf. That’s what the engineers want to do. They don’t want to have to write one more page on the intranet. They don’t want to write one more page that tells you what your voting reports are.

They will do it because that’s their job.

What they want to work on is making our server faster, building a better backend system, creating a new way to isolate our data and feed it in a multi-tenant scenario.

So that’s when their aha moments and their expertise start to be able to work on those types of systems.

For the first time, those are the types of things where we go, okay, you get the last two weeks of December. This is what you get. Build whatever you want in two weeks.

And now you have more opportunity to start thinking about that longer-term pipeline because each department, they’re servicing their own needs with the data.

Greg Lambert (42:59)
That sounds good, and I think it’s something that we all kind of face for how we transition into whatever the new version of, whether you’re a manager or an engineer, it seems like we’re all taking on more.

And I’ve noticed I’ve not seen people go home earlier on Fridays for some reason.

Phillip Greer (43:23)
Yeah.

Greg Lambert (43:24)
So I’m still waiting for my, it’s time for me to be rolling in income and sitting on the beach the whole time. So perhaps that’s a 2028 goal.

Elizabeth Petit (43:33)
Well,

Greg Lambert (43:34)
2028 goal.

Elizabeth Petit (43:35)
I think one of the reasons for that is because you move so much faster with AI, and specifically with vibe coding, it has unlocked all of this creativity and opportunity that, for those of you who’ve done it, is quite addictive.

Greg Lambert (43:55)
Yeah.

Elizabeth Petit (43:55)
Once you get in there and you start building, whatever you are, you’re prototyping something, you’re building something you’ve been wanting for years.

That used to be this cumbersome process of meetings, requirements, design, getting in the development queue. Three years later, you get something you asked for that you don’t need anymore.

You now all of a sudden get it quickly.

Okay, that’s great. But also, the feedback loop comes immediately. So instead of Friday morning you send off your information and think, I’ll get something on Monday to address, it comes right back to you.

Phillip Greer (44:32)
Mm.

Elizabeth Petit (44:33)
So now we have this cognitive fatigue concern because you are engaging so rapidly that the machine will never get tired. It will never go to dinner with its family. It will never log off for the weekend.

So at a certain point, you have to have the critical judgment to say, I am exhausted. You will never be exhausted, but I am.

And Phil shared this story that he found himself at one point being so in the go, and Phil and I are the same in that way. Once we get on that path, we are running.

And then he realized that he was not making the same little decisions that he would make. And not big, impactful things, but things that usually he would make a decision about.

He had that aha moment that he was having cognitive fatigue. And you don’t want AI to make those decisions for you.

So that’s when,

Phillip Greer (45:33)
Yeah.

Elizabeth Petit (45:34)
you have to remind yourself, AI will never say, go spend time with your kids, you know, go home early on a Friday.

Marlene Gebauer (45:41)
Not unless you

Phillip Greer (45:42)
Yeah.

Marlene Gebauer (45:42)
create an agent to remind you.

Greg Lambert (45:44)
Yeah, exactly.

Elizabeth Petit (45:44)
Yeah, exactly.

Phillip Greer (45:45)
Yeah.

Greg Lambert (45:45)
Exactly.

Phillip Greer (45:46)
And that runs into, you mentioned earlier, the AI vampire. That’s what it is. It’s feeding off of you, you’re feeding off of it, and you have to recognize, okay, I get to walk out in the sunlight. So I need to go do that some.

Marlene Gebauer (46:01)
Tear my eyes away from the screen, yes.

Greg Lambert (46:04)
Yeah, well, I always say when somebody asks me what’s the addictive game that I’m playing on my phone, I say it’s my $200-a-month Claude account. I’m on it all the time.

Phillip Greer (46:15)
Yeah, yeah.

Elizabeth Petit (46:16)
Yeah. Yeah.

Greg Lambert (46:18)
So yeah, you’re right.

Elizabeth, before we start wrapping up, I know that AI, of course, isn’t only affecting how we manage engineers, how we manage managers, and how we all become vibe coders. It’s becoming a part of Best Lawyers content as well.

And one of the things that you oversaw was the introduction of Artificial Intelligence Law as a formal recognition category in the 2026 edition.

Do you mind talking about what that encompasses, how you came up with it, and what you found?

Elizabeth Petit (47:00)
Yep. So, you know, it is always our goal to help connect clients in need of legal services, whether that’s professional or personal, with the right attorney for their case.

And so we always keep our eyes and ears open for developments in the legal industry to add new practice areas as they emerge over time.

So cannabis law, that was one that we added a few years ago because that was bubbling up.

And so about three years ago, we started to see that law firms were adding AI practice groups. And so we added this and started researching AI as its own practice area.

Now, when this first started, I envisioned this would be sort of a subset of emerging tech.

And we have technology law, and that this would kind of,

Greg Lambert (47:51)
This is what the blockchain lawyers morphed into, right?

Elizabeth Petit (47:54)
Yeah, exactly. This is sort of what I thought this would evolve into.

Phillip Greer (47:54)
Yeah. Yeah.

Marlene Gebauer (47:54)
Yeah.

Elizabeth Petit (47:57)
But as AI is so dynamic and evolving so quickly, the definition of an AI lawyer is quite nebulous.

Are you counseling AI corporations? Are you focused on data privacy? Are you looking at government issues? Even labor and employment. I mean, AI is truly touching everything.

And so because of that, it started off more as a subset of technology. But we will have to evolve with AI in our awards as this industry evolves so rapidly to make sure that it is reflective of how legal is addressing AI and the needs of clients.

Greg Lambert (48:49)
Are you thinking about

Phillip Greer (48:49)
We didn’t even get it.

Greg Lambert (48:52)
an AI-native law firm category? That seems to be the big buzz phrase these days.

Elizabeth Petit (49:01)
Yeah. We are getting ready to evaluate adding new practice areas for next year. So look for that webinar series coming up soon.

Phillip Greer (49:10)
Yeah, and whether or not we have AI law firms that we recognize, that’s currently not on the horizon.

But it is the question, when do I have my work done by AI and AI Inc.?

Greg Lambert (49:27)
Yes.

Marlene Gebauer (49:27)
Ha ha.

Greg Lambert (49:29)
All right. Well, before we get to our crystal ball question and we start looking at the future, I want to ask you guys to look back in the past. And I think I know where this is going based on our conversation.

Phillip and Elizabeth, what’s something that’s true today that wasn’t necessarily true for you and Best Lawyers a year ago? What’s changed in that year?

Phillip Greer (49:52)
Yeah.

Greg Lambert (49:52)
What’s changed in that year?

Phillip Greer (49:53)
I mean, the biggest thing, it’s everything we talked about, that we have non-builders, and they’re taking their data and they’re solving their own problems.

We have a decent engineering team as far as size because our company is research and tech. We use a lot of tech to build rankings and recognitions in over 76 countries.

And so we always built a lot of custom software, and we’ve always been able to do dynamic things for each department. But there was always a process. You got in line, got queued up, and then it handed off to the engineering team.

Greg Lambert (50:28)
Q3. Q3.

Phillip Greer (50:30)
And now that’s changed greatly because everybody is handling the majority of their own needs internally.

We’re still externally using our engineers. We’re not developing any software. All software that’s hitting our customers is still handled by our engineering team because that’s the right way to handle it.

But for all our internal needs, everyone’s a builder. So that was a big change I’ve seen. That is different now.

What about you, Elizabeth?

Elizabeth Petit (50:59)
I think the surprise that has come out of this is, I’ve been with Best Lawyers for over 15 years now, and I did not expect this career development opportunity.

Phillip Greer (51:14)
Mm.

Elizabeth Petit (51:15)
So I thought that I would continue to grow in leadership or other traditional career development paths, but all of a sudden to have an entire skill set that I did not have last year, that’s pretty amazing.

And I’m grateful to have been given that opportunity.

Marlene Gebauer (51:37)
All right, we’ve looked a little bit at the past, and now it’s time to look in the future.

If you would gaze into your crystal ball and look ahead a few years, as autonomous agents and vibe coding become standard inside both data organizations and law firms, what’s the single biggest shift you see coming for the legal profession’s economic model, specifically regarding the billable hour?

Phillip Greer (52:03)
Hmm. So I don’t think the billable hour is going to disappear overnight, right? But it will shift.

People are going to have more scrutiny of how you’re backing up the value of what’s coming from the work product.

And there’ll be more questions about how did you derive that actual result set.

It was already the question, you hire the partner and you ask yourself, am I getting the partner’s hours or am I getting the junior associate? That’s already been on the table for a long time. And some clients have been wanting to know that. And they want to know, I am paying for the partner’s time.

I think there’s going to be a lot of questions that more clients are going to be asking. I want you to break down the justification. Am I paying for the senior partner’s time, or how much am I paying for the AI’s time?

I don’t know what that’s going to look like as far as how it wants to be shown in transparency, but I think that’s going to be a big question.

Marlene Gebauer (53:01)
How about Elizabeth?

Elizabeth Petit (53:04)
I agree. I don’t think that this is, legal doesn’t revolutionize overnight. We’ve all been in this industry for a minute.

But a lawyer is not going to become twice as productive with AI. They’re not going to bill twice as many hours a day. That’s not possible.

But they’re expected to still bring in that same amount of revenue for the firm.

And so figuring out that balance between what is the appropriate use for AI, but still what requires that human judgment that AI will never replace.

And that’s ultimately what a client comes to a law firm for, not the administrative work that AI assists with. It is the human judgment.

That’s where I feel like, if firms tell their story to clients successfully, they sort of balance that out, get that efficiency that they need, but still have the confidence of their clients.

Greg Lambert (54:04)
Mm-hmm. All right. Well, I think that’s a good spot to end on.

So Phillip Greer and Elizabeth Petit, thank you so much for joining us and breaking down what it looks like behind the walls there at Best Lawyers. Appreciate it.

Marlene Gebauer (54:18)
Yeah, thank you.

Phillip Greer (54:18)
Yeah, thank you so much for having us.

Elizabeth Petit (54:19)
Thank you for having us.

Marlene Gebauer (54:21)
And thanks to all of you for listening to The Geek in Review. If you’ve enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn, YouTube, and Substack.

Greg Lambert (54:30)
And Phillip and Elizabeth, where’s the best place for listeners to learn more about you, more about Smithy AI, Best Lawyers, Bestie, all those things?

Phillip Greer (54:40)
So first off, for more information, go to bestlawyers.com. Also look us up on LinkedIn.

I’m posting a lot of videos about some of the AI work that I’m doing, some of the apps we’ve been building. And yeah, feel free to follow us there and check out what we’re doing.

Marlene Gebauer (54:56)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye, everybody.

]]> xlambert@gmail.com (Greg Lambert) Patrick Forquer on Legora’s Agentic AI, Legal Engineering, and Consumption-Based Pricing https://www.geeklawblog.com/2026/08/patrick-forquer-on-legoras-agentic-ai-legal-engineering-and-consumption-based-pricing.html Mon, 17 Aug 2026 11:42:25 +0000 https://www.geeklawblog.com/?p=19524

In this episode of The Geek in Review, we talk with Patrick Forquer, Chief Revenue Officer at Legora, about legal AI’s move from experimentation into daily legal work. Forquer explains why Legora has invested heavily in legal engineers, lawyers with practice experience who work alongside clients on adoption, workflow design, prompt and context engineering, and change management. The conversation also explores an emerging career path for lawyers who pair substantive legal knowledge with AI fluency, especially as firms search for people able to translate practice needs into working systems.

Legora’s acquisition strategy provides another lens on the company’s ambitions. Forquer describes a strategy aimed at building breadth across legal work while adding depth in litigation, commercial real estate, regulatory monitoring, and legal research. Recent acquisitions such as Wexler, Cadastral, and Graceview bring specialized capabilities into a broader agentic platform. Legora’s own 13-day acquisition process also serves as an example of how M&A diligence, document review, drafting, and analysis are beginning to move through shared AI environments.

A major portion of the discussion focuses on the difference between traditional workflow automation and agentic AI for legal work. Forquer draws a line between prebuilt automation and agentic systems: workflows follow predetermined steps, while agents receive a goal, gather context, form a plan, call tools, and work across longer tasks with human review. Context engineering therefore becomes increasingly important. Matter data, firm knowledge, permissions, legal skills, and connections to systems through tools such as MCP all shape the quality of agentic work. M&A due diligence already represents one area where longer-horizon agentic processes are gaining traction. Legora describes the same architecture through its agentic operating system, or aOS.

The discussion then turns to economics, pricing, and proof of adoption. Greg points to Crowell & Moring’s reported 91 percent attorney adoption and nearly 70 percent weekly usage, while Forquer argues login counts and activated licenses tell only part of the story. Legora tracks daily activity and depth of feature use, including tools such as Tabular Review, skills, playbooks, and extraction templates. Agent Pro’s shift to consumption-based pricing introduces another measurement challenge, with credits tied to usage alongside dashboards, spending controls, and project-level attribution. For law firms, a broader question follows: when AI compresses hours while increasing speed, scope, and output quality, traditional measures of efficiency and value start pulling in different directions.

The episode closes with a look at what law firm innovation leaders should prepare for next. Forquer identifies the data layer as one of the central issues behind successful agentic AI. Secure access to documents, matter-level permissions, governance, firm knowledge, and well-structured context determines how far agents progress into complex legal work. Talent matters alongside infrastructure, which brings the conversation back to legal engineers and new hybrid roles spanning law, AI, knowledge management, and data governance. The episode leaves innovation and KM leaders with a practical agenda: improve data governance, build legal engineering skills, align stakeholders around risk and outcomes, and measure value through work product, adoption depth, and client impact.

LINKS

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sarah Glassmeyer here from Legaltech Hub. Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:12)
Yeah, so this is probably one of the more exciting times of the year for legal tech kind of leading into ILTACON. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legaltech Hub can really help you pre-game and get ready to get the most out of it because while you’re there, it does seem like you live in Nashville and you’ve been there your entire life, but it goes really fast. You really only have like two and a half, three days to talk to vendors. So a couple of things I would suggest you do.

One, look at the list of who is exhibiting at ILTACON. So if you go to the ILTA website, they have a legal tech directory but guess what? That is us. We partner with ILTA. It is a mirror of our directory.

You can see the little badges, and that will tell you who’s exhibiting at ILTACON. Don’t just wander in there. Think ahead and think like, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list to see who’s going to be there, and start thinking ahead. And through the directory, you can see, for example, that some companies don’t integrate with X tool, so they’re off the list for us. So you can kind of do a little pre-filtering, pre-gaming, and figure out who you want to talk to, who’s going to make the most of your time. And then from there, if you’re back on our platform in the Legaltech Hub directory and you’re logged in, you can make notes. So as you’re wandering through the exhibit hall, visiting the people you want to visit, you can kind of make notes saying, “Here’s this person’s email address that I spoke to and I want to follow up with them,” or, “Maybe show Bob this when I get back to my office,” or, “These people are off the list.

Forget about them.” And so you just kind of keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legaltech Hub directory, not just a swag bag full of business cards and flyers. So that’s how we can help you get the most out of what you’re doing. But also, I do suggest just wandering around the exhibit hall because everyone who’s there does have a listing on Legaltech Hub. So you can kind of double-check it. But I think it’s really good just to kind of wander, see who catches your eye, have a spontaneous conversation because you might find a vendor that you never considered or something you weren’t really thinking about ahead of time.

So it is kind of a fun time to get yourself absorbed in what’s happening in legal tech.

Marlene Gebauer (02:27)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legaltech Hub directory because it can be overwhelming and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave. Thank you.

Marlene Gebauer (02:54)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:00)
And I’m Greg Lambert And today we are going to be looking at the next phase of legal AI work where vendors are moving beyond, or perhaps more accurately, adding to assistants and isolated pilots, toward more agentic work, more firmwide deployment and new pricing models.

Marlene Gebauer (03:20)
And our guest sits at the center of those commercial and operational questions. Patrick Forquer is Chief Revenue Officer at Legora, where he leads the company’s global go-to-market strategy and works with law firms and legal teams moving AI from experimentation into daily legal work. Patrick, welcome to The Geek in Review.

Patrick Forquer (03:40)
Hey, Marlene. Hey, Greg., thanks so much for having me.

Marlene Gebauer (03:43)
So one thing caught my eye recently. Legora is hiring a significant number of attorneys, not just software engineers. I heard Max on a podcast saying that you guys may have more lawyers than engineers, and that’s kind of unique in the industry. So you’re hiring legal engineers, legal associates, and lawyers with practice experience. What do those lawyers actually do day-to-day?

What skills make someone successful in those roles? And what does that tell you about where the profession’s headed?

Patrick Forquer (04:15)
Yeah, it’s a great question. I get this question all the time. And I think legal engineering is like one of the most exciting roles in technology now, just generally, because they’re on the front lines of AI and the AI revolution, doing the hard work of change management and adoption in firms. And so I think if you wind all the way back, I mean Legora was founded by engineers, right? One of the founding principles of the company is that we want to build with lawyers, not simply for lawyers.

That means taking their feedback and understanding the deep levels of requirements and subject matter expertise needed to build a successful legal AI platform. For us, that’s always been a founding principle, having lawyers in the business helping guide the direction of the product. More importantly these days, there’s this idea of transformation. We’re fortunate that Legora is popular right now. We get to have lots of conversations with many great firms and great companies out there in the market.

But at the end of the day, we have to help lawyers adapt AI into their ways of working, into their workflows. And candidly, you can’t just do that with standard consulting practices. You have to have deep levels of subject matter expertise. So, for example, people from an M&A background understand how an M&A deal gets run and the due diligence process. The same applies to litigation and the different aspects and practice areas of legal work.

And so our lawyers predominantly work in our legal engineering function. They embed with our sales and post-sales teams. First, they have deep levels of practice-area expertise. So they bring credibility to our clients: we understand your business, and we understand the types of deals, cases, and matters you’re working on. And then they have high levels of technology and AI proficiency, meaning they understand how to do things like prompt and context engineering.

They understand how agentic workflows work and how to build them. And they’re highly curious and adaptive in a client-facing environment where you can go in and work on a live deal or a live matter and help build sort of long-horizon workflows within Legora to help our clients deliver faster and better work. It’s really exciting, and I love spending time with our legal engineers. They’re some of the smartest, best people I’ve ever worked with.

Marlene Gebauer (06:36)
That’s a great endorsement.

Greg Lambert (06:36)
See Marlene, this is why we’re having a hard time finding these people. They’re taking them from us.

Marlene Gebauer (06:39)
That’s what I was going to say. I’m seeing discussions about what’s going to happen to our junior people, and those are all legitimate questions. I’m glad we’re having these discussions about other job opportunities that may be out there for people.

Patrick Forquer (06:58)
Yeah, and we definitely think that, Marlene. We think legal engineering is a new career path. It has always existed in different forms at different companies, and Greg and I were talking about that earlier. But the current form of building these bespoke workflows, skills, and capabilities within tools like Legora is not something that was done as commonly as it is now. And they’re making a meaningful impact on the client work that’s getting delivered.

And it’s showing up for our clients in their work with their clients. And I think that’s the most exciting part. I definitely think it’s a new career path that’s opening up a ton of opportunity for the folks that work for us. And we’re really excited about that.

Greg Lambert (07:45)
Just curious, Patrick, about the types of talent that you’re looking for. Is there a certain practice range? Are they, say, seventh-year or tenth-year lawyers? What’s the sweet spot you tend to look for in the talent?

Patrick Forquer (08:02)
We actually hire across the entire spectrum. We have everything from folks who’ve done a couple of years as an associate through partners who work with us and they tend to focus on obviously solving different problems. Especially within BigLaw, there’s so many different types of transactions and deals and matters and cases that are happening on a day-to-day basis and there are different needs, and how you train and enable a partner is very different from how you train and enable a first-year. And we just try to map practice area and sort of seniority to help solve the different problems we’re looking at so we can add the most value to the firms that we work with.

Greg Lambert (08:44)
You’re the revenue guy, so let’s talk a little money now.

Patrick Forquer (08:49)
Okay.

Greg Lambert (08:50)
I know that you guys have raised significant capital. I think I was looking at Series D so far. And you’ve gotten busy on the acquisition side including a recent acquisition of Wexler, which I think is like the fifth

Patrick Forquer (09:03)
Yeah.

Greg Lambert (09:04)
or sixth acquisition. So, instead of asking you specifically about the individual acquisitions, what’s kind of the strategy that you have when you’re looking at acquiring and building through the acquisition process?

Patrick Forquer (09:21)
Yeah, for sure. It’s a couple of different things. First and foremost, we’re hiring amazing talent. And so the folks that are coming into the business through this acquisition are high-slope, highly intelligent, hardworking folks that we want to work with. And they’ve built platforms that are solving big problems that are relevant to our customers and the firms we work with.

In this world, as you know, there’s so much going on in legal tech. There’s so much noise and so much chatter. A lot of times when you’re comparing sort of legal AI platform A to legal platform B, you’re looking at it in terms of like apples and apples, right? It’s like you’ve got drafting, we’ve got drafting, how are we doing it? How are they doing it?

And so on and so forth. So part of the strategy also is we want to be a wide application layer for the firms we work with and a platform that any practice area could work within, but we also want to be really deep. The deeper the capabilities we add, for example the litigation capabilities Wexler provides, the more we can be both wide and deep and that sort of keeps the comparison from an apples-to-apples comparison. So Max likes to make the comparison of apples to fruit salad. And so we want to add more of these bespoke, deep levels of capabilities.

We bought Cadastral for real estate work. We’ve got Wexler for litigation work. We brought a company called Graceview that we integrated into the platform in under 60 days for regulatory horizon scanning as an example. So not only do we want to be able to work with every type of lawyer, we don’t want to just do surface-level work. We want to go deep within each practice area and within each area.

So it’s really important to us that we provide the highest level of value possible. And we’re able to do that through our M&A strategy. And so we’re trying to provide more value and more bang for your buck when you work with Legora.

Greg Lambert (11:12)
Yeah. And when you’re doing these acquisitions, you talked about making it both wide and deep. Are you really looking at kind of becoming almost like a matter management platform where the attorneys are constantly working within the Legora platform over a wide range of activities? It’s not just legal research or document drafting. It may be directly accessing your document management system and bringing things in and working with them there or maybe even the accounting system.

How wide and deep are you going to go?

Patrick Forquer (11:49)
Yeah. With any type of work that you do, no one likes bouncing around to different systems to do different things. And insofar as we can add capabilities to our platform that make sense for the type of work that we’re doing, we’re going to look at it. And certainly for us, we want to look at M&A as an accelerant to adding value and keeping folks spending more time in the platform. So yeah, we’d love for you to be able to do an end-to-end M&A due diligence process, for example, in Legora.

Many firms are doing that today. And actually, that was our fifth acquisition of the year. And we’ve run all those acquisitions on Legora. And we did one of those acquisitions in 13 days, using our product to run the M&A due diligence. Obviously, we work with some amazing law firms on that as well.

So it’s been really fun to see it in action. The more capabilities that we have, the longer folks are going to spend in Legora, and that’s good for them and good for us.

Marlene Gebauer (12:46)
So let’s keep talking a bit about the workflows. We’ve certainly moved from asking, “Can AI draft or summarize?” And now we’re really talking more about these agents that can execute multi-step automated legal work. We hear a lot about it, and I’m sure that there are pockets where it’s done. Which legal workflows are genuinely ready for that sort of next step today, and where do you think it’s still a little bit of theater?

Patrick Forquer (13:22)
Yeah. Well, it’s a great call out, Marlene. From my perspective, you hear a lot about agents out in the market. Everyone’s got an agent doing X, Y, or Z. But if you really look under the hood, a common pitch is, “We have this number of agents.” Anyone who tells you they have like hundreds of agents, I think is perhaps misdirecting a bit.

So, is it a repackaged workflow? Workflows have existed for a long time. n8n technology has existed for a long time. And then there’s lots of different workflow tools that you can buy that have if-then logic. Are you buying something that’s a repackaged workflow or are you buying something that’s truly agentic? And for us, the way to think about it would be

Marlene Gebauer (14:04)
And maybe explain what’s truly agentic versus not.

Patrick Forquer (14:08)
Yeah, so the agent in Legora, like the Agent OS that we launched earlier this year, it can click every button you can click in the platform. So it’s not just a set of rules that it’s following, like “if this, then do this; if that, then do that,” which you have to then go in and like edit those rules or that logic within a particular pre-baked package. With the agent, the big difference is you’re sort of moving from prompt engineering to context engineering, right? So with agentic work, you want to give it a definable scope. You also want to have the data and the sources made available within the project in Legora.

So everything that you’re going to be doing is grounded in source and truth. And then you also want to have a human in the loop at each step to customize and make sure everything is working like you want it to. So if you give the agent a goal in our system, and then you give it some context of that goal around like what it is you’re trying to do, sort of guardrails and things to watch for, then our agent is going to come back and it’s going to ask you some questions, some clarifying questions around the scope, around the goal. It’s going to look through the documents and make sure it has all the context it needs to achieve the goal. Then it’s going to present you with a bespoke plan for that particular goal that you’ve outlined and you can layer in skills, tools, and resources within Legora to make it even more bespoke and more granular to improve the work product.

The plan that it’s showing you is then the prompt. So in the old school world, the “Improve Your Prompt” button was a big feature that we still have that people like, but with an agent, you don’t really need the “Improve Your Prompt” button as long as you give it enough context with a very well-defined goal and all the resources that it needs. It’ll create the plan. And the plan is in many cases a long-horizon task that can run for 20, 30, 60, 120 minutes in one go to solve these increasingly complex problems. As long as as it has access to all the legal skills that we provide it in the tool, all the context you give from the firm or from your client documents, the agent can then go run these long-horizon tasks and solve increasingly complex problems instead of saying, “Go run this four-step workflow” that was pre-baked when I logged in.

And so it’s quite different and it’s extremely flexible and bespoke to the particular problem that you’re working on. That education, though, and that sort of change have been a journey. These are new capabilities that only really emerged as the new models came out around Christmas and the beginning of this year. And so we’ve had to educate our teams on what they can do, what they can’t do. M&A due diligence, Marlene, to answer your question specifically, is an area where we’re seeing an increasing amount of that work being able to get done with the agentic work.

And we’re hoping there are increased capabilities with Wexler as we sort of bake their capabilities in the platform over time that you’ll be able to do that in the long run. But yeah, we’re still working on this together and figuring it out with our customers.

Marlene Gebauer (17:22)
Thanks for thinking of the litigators.

Patrick Forquer (17:24)
Yes, of course. Always thinking of the litigators.

Greg Lambert (17:28)
You were speaking about things coming out over the Christmas break. It was kind of funny because I was talking with one of our AI-forward partners that had developed a process earlier in the year, and he was sending an update and then one of the other partners was like, “Do you realize it’s only been six months since you created this?” And we were all

Patrick Forquer (17:49)
Yeah.

Greg Lambert (17:49)
like, “Holy smokes, is that all it’s been? It feels like forever ago.” I’m wondering, sticking with the agents, and especially with all the transformation we’re seeing, I have a two-sided question. What are you finding that law firms may be a little slow on when it comes to putting an agent to work? And then for those that may be overachievers, what are some things they think it can do that it’s not ready for yet?

Patrick Forquer (18:21)
Yeah, that’s a great question, Greg. I think one thing is to be transparent. The rate of change in AI capabilities, in some cases, outstrips what the organizations we work with are able to absorb. The change is happening so fast. We all have to be transparent and honest about the fact that these things are changing.

What you could do six months ago or the thing you designed six months ago may not work the same way. There are all kinds of organizational questions around that, like approvals to use different capabilities and all the risk and the training and the change management. So I always tell folks, if you’re using AI today only to summarize documents and draft emails, you’re probably not doing enough, right? But in

Greg Lambert (19:09)
Yeah, yeah.

Patrick Forquer (19:10)
many ways, the question is: Does the agent have access to all the context and data and resources that it needs to complete the task? We’re seeing an increase in requests for things like MCP builds. So we have a team of folks, for example, that can build secure MCP access to different systems that’ll enable multi-system execution across a number of different areas, but you have to have the right security and guardrails around data governance in place before you can do that. I think a lot of folks think, “I can eventually just tell the agent, go do this thing, and it’ll figure it out.” And the agents, in theory, have the capability to do that. But do you have that sort of matter-level isolation?

Do you have the document-level permissioning structure down? Have you given it enough context and enabled it with your firm’s embedded skills and context so it knows, “This is how we do this particular piece of legal research,” or, “This is how we do this particular type of drafting,” so it can produce content that’s in line with your expectations? And that’s a journey. Again, going back to our legal engineers, that’s why we have so many legal engineers, so that we can work with our clients in a more embedded way and help them develop those more complex use cases. Does it have access to all the skills and content that it needs to produce the output at the quality that you want?

Marlene Gebauer (20:34)
We’ve heard in the past that a lot of AI companies are saying lawyers are saving time using the tools. But we’re also starting to hear, because of that, how are we sort of looking at the economics of legal services? Danielle Benecke, on the podcast Max was on, was saying that they do this by outcome. There are a lot of different ways to do this. Her team works directly with clients.

So, when are we going to start talking about how to measure the changing economics of legal services and how are we going to price things when using AI? I think people want to talk about it, but I don’t know that people are ready to do anything about it. So I’m curious what your thoughts are on that.

Patrick Forquer (21:27)
Yeah, well, look, we could spend the whole podcast talking about this. We could probably spend all dinner

Marlene Gebauer (21:31)
True.

Patrick Forquer (21:31)
and all night talking about all that stuff too. And certainly I’m not here to tell lawyers how to price and package their work. But I have some thoughts on what you were going through. First, I think it’s a misnomer just to think about AI in terms of efficiency, right? If you’re using AI only to save time on things, I think that’s mostly the way the first wave of AI worked.

People are doing that sort of look back, right? I used to do this thing this way and it took me 10 hours, and now I do it with AI and it took me 30 minutes. That type of stuff has existed for a while. And I don’t think that’s particularly exciting to the market anymore. What we try to look at in terms of a look forward is, okay, if you’re commoditizing certain types of work product that used to take you 10 hours and now it takes you 30 minutes in terms of time, then what does that enable in terms of quality of output?

What does that enable in terms of adding new features or new services that maybe you couldn’t have done previously? What can you package or repackage as part of new work product that wasn’t available? So there are all kinds of 50-state surveys and all these different examples of things that would have been too onerous in a world pre-AI that you can now deliver in a really effective way using platforms like Legora. And when we think about value, anytime you’re looking at an economic system more broadly, if certain pieces of the inputs into that system are getting commoditized in terms of time, then what you typically see in other industries is that the ecosystem and the economy itself gets bigger. So we think there’ll be more legal work, that there will be more legal services, that this is accretive to the overall market.

And it’s a really exciting time to be a lawyer and be working in legal services. And, as I’m sure you both know, like if you talk to any litigation partner, like they’re busier than ever, right? There are more cases coming in, there’s more work to do. People talk about time as it relates to you know the billable hour. But I was talking to a CLO of a big company a few weeks ago and he was mentioning, if we were working with an external counsel and they used to say, “Hey, if we can do this M&A deal in six months,” today they might say, “Maybe we can do it in three or four months.” Is it more or less valuable to do it in three months than six months?

Again, I’m not here to answer that question for folks or tell people how to price things, but I certainly think there’s an interesting conversation to be had around value. And if you’ve got an amazing attorney today who’s working just with Microsoft Word and Outlook, and you give them a platform like Legora, is that attorney more or less valuable to their clients? And what are the economics of that work? Certainly I have every confidence that legal research went from analog to online, data rooms went from banker boxes in a conference room to platforms like Intralinks and Datasite, and paper moved to email. There’s been all kinds of these examples over time and legal services has only grown and thrived.

We fully expect that to be the case with AI. How that looks and the shape of it between lawyers and their clients, things will change and evolve for sure, but we think it’ll be very positive and that there’ll be a lot of opportunity.

Greg Lambert (24:56)
Before I go further into the economics, there was something that you mentioned in your last answer where you talked about change management being one of the hardest parts of the deployment. I read something earlier where I think Crowell & Moring was talking about how they had a 91 percent attorney adoption rate. A year ago, we were fighting to tell people whether it was safe to use these tools or not, and now we’re

Patrick Forquer (25:23)
Yeah, yeah. Yeah.

Greg Lambert (25:25)
looking at 91 percent adoption. And I think they said they also had 70 percent-plus weekly usage within like six months of deployment. So I’m curious when you do these types of rollouts, what are the success metrics Legora has? I can look at metrics and I can see people have logged in or people have run prompts, but I don’t necessarily see

Marlene Gebauer (25:52)
Or the skills they’ve used. Yeah.

Greg Lambert (25:54)
Yeah, I don’t necessarily see some of the details. So what are you looking at on your side as far as success and how your users are using the product?

Patrick Forquer (26:05)
It’s a great question. And this is an area where we’ve evolved and grown so much over the past few years. And it’s a huge emphasis for me. It’s one of the top three things that I focus on every day when I go to work. How are we doing on the implementations that we have active?

And we actually have an entire team dedicated to this particular piece of our customer engagement. So this isn’t something that’s done by sales or our post-sales engagement team. We have a team that’s dedicated and focused on this. We’re continuing to evolve our strategies as we learn. We’ve done thousands of these now, so there are a couple of areas to think about.

For us, the speed and quality of implementation is the number one leading indicator of client satisfaction and overall renewal metrics. So we look at whether we got it done in the timeline we had agreed to with the client, right? So we look at speed. Did we get it done on time? And that time varies by client, by size, by the type of implementation.

We have different implementation options. We run through those and collaborate with our clients to create bespoke plans for all these big rollouts that we do. And then, of course, we look at a ton of data. And as you mentioned, Greg, for us, we don’t look much at activated licenses or number of logins. What we really look for is deeper than that.

Greg Lambert (27:32)
Those vanity metrics are really nice.

Marlene Gebauer (27:36)
Love it.

Patrick Forquer (27:36)
Of course, monthly active usage is nice. But we want daily active usage. And we don’t only want daily active users. Going back to my earlier sort of anecdote. If folks are coming in and only summarizing documents or saying, “Respond to this email for me,” that’s relatively shallow usage, and it’s easily commodified across other platforms.

We know that if folks are using more of our products and doing more complex tasks in Legora, and, as Marlene mentioned, are they using skills? Are they using playbooks? Are they using extraction templates? How many parts of the product are they using? What parts of the product are they using?

So we have a product called Tabular Review. Tabular Review gives us the ability to do bulk document review and extraction. When I first saw Tabular Review and met Max, I thought, “My gosh, this is amazing, and this is going to change the way work gets done. Period. The end.” If people use Tabular Review, they have their light bulb moment with AI.

It’s like, “My gosh, I can get all this data out of this big document set and then turn that into a RAG that I can draft, review, research, and so on, on top of with the agent.” And it unlocks an incredible amount of complexity in terms of the things that you can do. Those are the types of things we look at. Of course, we look at monthly and daily active usage, but we also look at quality of usage. So we have a number of different mechanisms and nomenclature we use internally. We might see that Marlene logged in a certain number of times this week and used a certain number of tools within the platform.

We can’t see the data or prompts, but we can see the tools she’s using. That’s the deeper level of analysis that we look at. And typically when we see certain feature usage, we know that we’re going to have a happy customer who’s solving big problems and getting great results from Legora.

Greg Lambert (29:31)
Now I’m going to hit you with the hard questions so...

Patrick Forquer (29:34)
Great.

Greg Lambert (29:35)
Yeah, butter you up.

Marlene Gebauer (29:35)
We save them for the end. We butter you up, and then we save them for the end.

Patrick Forquer (29:37)
[Laughs]

Greg Lambert (29:41)
and then hit you.

Marlene Gebauer (29:42)
Yeah.

Greg Lambert (29:43)
So a few weeks ago you announced that Agent Pro, which is one of the high-end tools in Legora, is moving to consumption-based pricing. The more tokens you use, the more it’s going to cost. And I think this was not a surprise to the industry that this was coming, but you were one of the first to move to this. So as a customer of Legora, what should people be thinking about as far as planning? It’s easy to plan per-seat pricing because you know what the cost is going to be.

When you start thinking about tokens, credits, or consumption-based pricing, how are you coaching your clients on how to approach that?

Patrick Forquer (30:27)
Yeah, it’s definitely a model from a technology provider that’s newer for legal tech. It’s used very heavily in other databases and technology systems, as you know. So for us, there are pros and cons to every pricing model. So on a per-seat model, the question we used to get was, “Okay, if you buy 1,000 seats from Legora, but what if these people don’t use it? What if Marlene goes on vacation and it’s sitting there?”

Greg Lambert (30:52)
Can I get a refund on that?

Patrick Forquer (30:54)
Yeah. “Do I get credit back if she doesn’t use it or something like that?” I can tell you that there’s no perfect pricing model. There are pros and cons to everything. The second thing is, look, this is new, this is a journey that we’re all on together. We try to lead with a bunch of empathy and understanding and try to be extremely collaborative and transparent with our clients as we work through this together.

I’ve worked at companies that sold consumption before. There are some key tenets I like to think about. First, we have to be able to predict consumption across various scenarios pretty well. The example I always use is: if we’re going to sell you 100 licenses, sorry, 100 credits of Legora. And to be clear, we’re not simply taking the tokens we use and just adding a margin on that and doing cost-plus pricing.

The credits are an amalgamation of different document API and token usage in the platform. So we don’t simply take the number of tokens that you used on a given day and add some money on top of that. But if I sell you 100 credits of Legora and then three months in you use 1,000 and I send you a surprise bill for 900 tokens, sorry, 900 credits, guess what? You’re going to get super pissed and you’ll probably churn, right? That’s an easy way to lose a customer.

That’s not a good scenario to be in. On the flip side of that, if I sell you 1,000 credits and you’re very happy, and then you use 100, and then you renew your contract at 100 credits. If we take a 900-credit churn, that’s bad for us. And then our investors are like, “What the heck? You just took a 90 percent churn on this very happy customer.

What’s going on?” So it aligns incentives in a really nice way. It aligns incentives around whether you’re using the product and whether we’re able to accurately predict that. Does the usage account for experimentation and different usage patterns? And do certain user types use the product differently than others? Are we able to report on that?

Are we providing visibility and governance? So, can we do user-level caps? In our case, you can put usage metrics against a particular project or matter in Legora. So you have governance and control you can put in place to make everyone comfortable and prevent certain situations from happening. But at the end of the day, it aligns incentives in a really nice way.

And if we’re able to predict the way we should, and communicate your consumption on a weekly, monthly, and real-time basis, so you know exactly how much you’re using and the patterns we’ve laid out hold, then you typically get to a good outcome. At the end of the day, I always ask our teams: for every dollar of Legora you buy, like what do our clients get? For every credit of Legora you use, what do you get in the outcome? Is it only efficiency, or do we deliver the other value-added benefits that we were talking about? And one of the more exciting things for the second half of this year is that we’ve hired a value engineering team who are working with our customers on what is the ROI of working with a platform like Legora.

And it can’t simply be, “It saved Greg some time on something.” It’s got to be more robust. We’ve done some work with Ari Kaplan on this and published some reports, but this is really the first inning on this stuff. But if we can help our customers tell a narrative, tell a story with data showing that for every dollar of Legora we put in, this is what we get out, and we can do that in a way where we’re communicating and executing on the consumption pricing as I described, then I think it’ll be fine. But it’s new, and we’re working through it with our clients in a really collaborative way. I think we’ll be in a good place.

And like you say, this is the direction of travel for the industry more broadly and I think we’ll be in a good space.

Greg Lambert (34:57)
I think one of the questions I hear the most as we start looking at this, because it’s not just going to be Legora, there are going to be other

Patrick Forquer (35:06)
Yeah.

Greg Lambert (35:06)
AI companies that we work with that aren’t necessarily going to use credits and consumption, but instead use a pure model where if you use a million output tokens, you’re going to get charged a million output tokens. And if you let an inefficient process go awry, that’s on you because they’re not upcharging you, they’re charging for what you use. Are you guys thinking about safeguards so that, if somebody sets up an inefficient agent that runs into a loop and can’t get out of it, it breaks, or are there some kind of safeguards that you set up?

Patrick Forquer (35:45)
Yeah, a hundred percent. We already have a lot of these in place and we’re continuing to invest in this area around model selection, model routing, right? You don’t need to use whatever the latest model is for every single task in Legora, right? So model selection, model routing, guardrails against everything from prompt injection, matter-level isolation, all these different things. We we have to have a lot of guardrails in place in the product which we already have, and then we’re continuing to build what we need to make our clients feel more comfortable using Agent Pro in particular.

Marlene Gebauer (36:22)
So Patrick, we have a new question. We sort of look backward to the present and then for our crystal ball, we look for the future. So this is sort of looking backward and looking to the present. What’s one thing that strikes you that’s different today than last year in our industry?

Patrick Forquer (36:43)
I’m glad you asked because I was actually looking back on Max’s interview with you guys last year, and he made the prediction about these long-horizon agents. He said, “They’re coming. It’s going to come faster than you think.” Before we had the agent, we had the assistant. And the assistant could do a couple of steps of a workflow. I think our workflow builder capped out at about five steps because the prior models would run out of gas.

They get what they call context anxiety, right? And wrap something up before getting to the end. The ability for the agents to do increasingly complex long-horizon tasks is here. And it’s something that we were working toward last year, but the reality was we were still in workflow mode. Last summer we were in workflow mode, whereas this summer we’re in Agent OS, with all the things we were just talking about.

So that’s been really exciting to see that come to life. More importantly, we’re seeing our clients use it. And I hear from clients every day about something cool they did with Agent that they never would have been able to do. I was with a client yesterday down in Atlanta. He was talking to me about some exciting work he was doing for a business development use case.

It generated a deck he was excited to use in a pitch meeting. I think the agentic revolution, so to speak, is here and it’s all new and we’re figuring out new applications, new use cases for it every day, even those of us who work at Legora. I was in a demo the other day and one of our legal engineers asked the agent to not only redline a document, but create an HTML page of the issues list based on the redline. That way, the lawyer could have a visual representation they could share with their client based on the redline it had generated for the customer. I honestly didn’t know Legora could generate HTML like that.

I thought, “That’s so cool. I didn’t know

Marlene Gebauer (38:41)
Mm-hmm.

Patrick Forquer (38:41)
that.” And so there are all these things that we’re uncovering and, as you can tell, I get excited about it. Our team does too.

Greg Lambert (38:49)
Yeah, I heard someone say HTML is now the new markdown for agentic AI tools especially.

Patrick Forquer (38:58)
Yeah, for sure.

Greg Lambert (39:00)
So let’s jump to the crystal ball question and as we told you in prepping for the interview, you can answer this however you want, but I’m going to phrase it a little bit differently this time because I think this is an interesting and a little bit selfish of me to do this. Let’s say you and I swapped roles. You came in as a chief innovation officer in an AmLaw firm. What’s something that you think someone like me should prepare for over the next few months or a few years?

Patrick Forquer (39:36)
Well, I mean, I think your jobs will be increasingly essential to the success of the firms that you work with. And we think it’s really exciting. Yes, more money. More money. Well,

Greg Lambert (39:46)
So you’re saying we’ll make more money, right?

Marlene Gebauer (39:48)
[Laughs]

Patrick Forquer (39:50)
Hey, man, I read LinkedIn too. There’s a lot of stuff going on in knowledge management these days.

Marlene Gebauer (39:53)
Send that out to the...

Greg Lambert (39:57)
Yeah, we will neither confirm nor deny that we’ve passed that around to...

Patrick Forquer (40:02)
Yeah. It’s a talent war out there right now. But we might have to talk afterwards, Greg. I think the first thing is, and we’ve encountered this because we use Legora internally as well, and we use a number of AI tools. But the layer that gets often overlooked is the data layer.

And with all the talk about agents and all these different things, as we were discussing earlier in response to Marlene’s question, where does the data sit that you need to go execute these long-horizon tasks? And how do we teach systems thinking and design thinking to our internal teams? And maybe that’s the lawyers, or maybe that’s part of the KM function around how we think in terms of these step-by-step workflows that need access to specific pieces of context. The more context you give agents, the better they do, right? But accessing that context within the firm, for many reasons, is such a challenge.

We talk to firms all the time about this. It’s about getting the data in a way that’s secure within the right governance frameworks, but also understanding what your risk tolerance is for the different use cases that you want to do with different agents. That data layer is more important than ever. And that’s an area where our legal engineers are constantly collaborating and working with our customers to understand how it exists today and what we can change to make the agent more powerful. So the data layer is definitely an area that I would look at.

And I do think, going back to the legal engineering sort of type of conversation, that there are all kinds of new roles and new capabilities within those roles that are going to be available. So making sure you have the right team in place probably means I would ask for a bunch more headcount, Greg and Marlene, as I’m

Greg Lambert (41:54)
Mm.

Patrick Forquer (41:55)
sure you’re always doing yourselves and all the knowledge management leaders we work with, doing the same. There are new skills and new capabilities around AI specifically, data governance specifically, that I would want to hire for and make sure that we had that in-house so we could move faster on the AI adoption curve. Without this sort of data layer and without the talent in place, you’re going to be limited in the complexity of the tasks that you can do. And again, we’re all on this journey together as we sort of try to adapt these increasingly advanced capabilities and technologies into our platforms. And I think from our side, one thing that Legora wants to do a much better job of is to leverage the collective knowledge from our peers.

And that’s why I love this podcast and what you guys are doing because there’s so many smart people out there trying to solve these problems. But it can’t be KM or innovation alone. You need partners, customers, KM, innovation, and you need everyone on the same page. Can we align on what the goals and intentions are of the firm as it relates to AI? What are your expectations from us?

We need to make that alignment clear. For the people I talk to who do what you guys do, getting stakeholder alignment is difficult. Everyone has a perspective. People come from different places and may have higher or lower risk tolerance. But at the end of the day, you’re held to a standard, right?

You’re held to a level of expectation. And sometimes you have a great opportunity to go hit that expectation, and sometimes you’re limited because of the constraints put on you by the business. But I’m not sure everyone always appreciates that. From Legora’s side, our job is to try to bring those folks together so people have a deeper understanding of the challenges so that we can unlock the true potential here. Those are some things I would think about.

And you’re probably thinking, “I’ve already thought of all those things. This guy.”

Greg Lambert (43:50)
All right. Nope, I’m writing them all down. That’ll go in my monthly report now.

Marlene Gebauer (43:53)
We’re taking notes, man.

Patrick Forquer (43:59)
I know I’m not telling you anything you don’t know, but just to reinforce, as I’m sure you guys have talked about many times.

Greg Lambert (44:06)
Well, I appreciate that. And Patrick Forquer, CRO at Legora, thank you very much for taking the time to talk with us today. We’ve covered a lot of ground here and it’s been a lot of fun. Thanks.

Marlene Gebauer (44:19)
Thank you, Patrick.

Patrick Forquer (44:19)
Thanks, Marlene. Thanks, Greg.

Marlene Gebauer (44:21)
And thanks to all our listeners for listening to The Geek in Review. If you enjoyed the show, share it with a friend, share it with a colleague, and we’d love to hear from you on LinkedIn and Substack.

Greg Lambert (44:32)
And Patrick, where’s the best place for listeners to reach out and learn more about you and Legora?

Patrick Forquer (44:38)
So you can send me an email anytime, patrick@legora.com. We’ve got an amazing website, https://legora.com, and our LinkedIn page has all the latest announcements from the company. So give us a follow. Send me a LinkedIn connection or an email. We’d love to chat with your listeners, and I appreciate everyone listening today.

Marlene Gebauer (44:55)
And as always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry, and goodbye, everybody.

Patrick Forquer (45:02)
See you.

]]> In this episode of The Geek in Review, we talk with Patrick Forquer, Chief Revenue Officer at Legora, about legal AI’s move from experimentation into daily legal work. Forquer explains why Legora has invested heavily in legal engineers, lawyers with practice experience who work alongside clients on adoption, workflow design, prompt and context engineering, and change management. The conversation also explores an emerging career path for lawyers who pair substantive legal knowledge with AI fluency, especially as firms search for people able to translate practice needs into working systems.

Legora’s acquisition strategy provides another lens on the company’s ambitions. Forquer describes a strategy aimed at building breadth across legal work while adding depth in litigation, commercial real estate, regulatory monitoring, and legal research. Recent acquisitions such as Wexler, Cadastral, and Graceview bring specialized capabilities into a broader agentic platform. Legora’s own 13-day acquisition process also serves as an example of how M&A diligence, document review, drafting, and analysis are beginning to move through shared AI environments.

A major portion of the discussion focuses on the difference between traditional workflow automation and agentic AI for legal work. Forquer draws a line between prebuilt automation and agentic systems: workflows follow predetermined steps, while agents receive a goal, gather context, form a plan, call tools, and work across longer tasks with human review. Context engineering therefore becomes increasingly important. Matter data, firm knowledge, permissions, legal skills, and connections to systems through tools such as MCP all shape the quality of agentic work. M&A due diligence already represents one area where longer-horizon agentic processes are gaining traction. Legora describes the same architecture through its agentic operating system, or aOS.

The discussion then turns to economics, pricing, and proof of adoption. Greg points to Crowell & Moring’s reported 91 percent attorney adoption and nearly 70 percent weekly usage, while Forquer argues login counts and activated licenses tell only part of the story. Legora tracks daily activity and depth of feature use, including tools such as Tabular Review, skills, playbooks, and extraction templates. Agent Pro’s shift to consumption-based pricing introduces another measurement challenge, with credits tied to usage alongside dashboards, spending controls, and project-level attribution. For law firms, a broader question follows: when AI compresses hours while increasing speed, scope, and output quality, traditional measures of efficiency and value start pulling in different directions.

The episode closes with a look at what law firm innovation leaders should prepare for next. Forquer identifies the data layer as one of the central issues behind successful agentic AI. Secure access to documents, matter-level permissions, governance, firm knowledge, and well-structured context determines how far agents progress into complex legal work. Talent matters alongside infrastructure, which brings the conversation back to legal engineers and new hybrid roles spanning law, AI, knowledge management, and data governance. The episode leaves innovation and KM leaders with a practical agenda: improve data governance, build legal engineering skills, align stakeholders around risk and outcomes, and measure value through work product, adoption depth, and client impact.

LINKS

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sarah Glassmeyer here from Legaltech Hub. Sarah’s going to share with us how you can use the Legal Technology Directory to prep for ILTACON.

Sarah Glassmeyer (00:12)
Yeah, so this is probably one of the more exciting times of the year for legal tech kind of leading into ILTACON. Everyone’s excited to see all the new changes, see people we haven’t seen for a year. So Legaltech Hub can really help you pre-game and get ready to get the most out of it because while you’re there, it does seem like you live in Nashville and you’ve been there your entire life, but it goes really fast. You really only have like two and a half, three days to talk to vendors. So a couple of things I would suggest you do.

One, look at the list of who is exhibiting at ILTACON. So if you go to the ILTA website, they have a legal tech directory but guess what? That is us. We partner with ILTA. It is a mirror of our directory.

You can see the little badges, and that will tell you who’s exhibiting at ILTACON. Don’t just wander in there. Think ahead and think like, what am I looking for this year? Am I looking for a new kind of document automation tool? Am I looking for a new AI legal assistant?

So you do some filtering, make a list to see who’s going to be there, and start thinking ahead. And through the directory, you can see, for example, that some companies don’t integrate with X tool, so they’re off the list for us. So you can kind of do a little pre-filtering, pre-gaming, and figure out who you want to talk to, who’s going to make the most of your time. And then from there, if you’re back on our platform in the Legaltech Hub directory and you’re logged in, you can make notes. So as you’re wandering through the exhibit hall, visiting the people you want to visit, you can kind of make notes saying, “Here’s this person’s email address that I spoke to and I want to follow up with them,” or, “Maybe show Bob this when I get back to my office,” or, “These people are off the list.

Forget about them.” And so you just kind of keep track of who you’ve talked to, what you’re thinking about things live, and it’s all in one place on the Legaltech Hub directory, not just a swag bag full of business cards and flyers. So that’s how we can help you get the most out of what you’re doing. But also, I do suggest just wandering around the exhibit hall because everyone who’s there does have a listing on Legaltech Hub. So you can kind of double-check it. But I think it’s really good just to kind of wander, see who catches your eye, have a spontaneous conversation because you might find a vendor that you never considered or something you weren’t really thinking about ahead of time.

So it is kind of a fun time to get yourself absorbed in what’s happening in legal tech.

Marlene Gebauer (02:27)
Yeah, serendipity is definitely part of the ILTACON experience, but this is great advice in terms of using the Legaltech Hub directory because it can be overwhelming and this will allow you to focus on the vendors and people that you definitely need to talk to before you leave. Thank you.

Marlene Gebauer (02:54)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (03:00)
And I’m Greg Lambert And today we are going to be looking at the next phase of legal AI work where vendors are moving beyond, or perhaps more accurately, adding to assistants and isolated pilots, toward more agentic work, more firmwide deployment and new pricing models.

Marlene Gebauer (03:20)
And our guest sits at the center of those commercial and operational questions. Patrick Forquer is Chief Revenue Officer at Legora, where he leads the company’s global go-to-market strategy and works with law firms and legal teams moving AI from experimentation into daily legal work. Patrick, welcome to The Geek in Review.

Patrick Forquer (03:40)
Hey, Marlene. Hey, Greg., thanks so much for having me.

Marlene Gebauer (03:43)
So one thing caught my eye recently. Legora is hiring a significant number of attorneys, not just software engineers. I heard Max on a podcast saying that you guys may have more lawyers than engineers, and that’s kind of unique in the industry. So you’re hiring legal engineers, legal associates, and lawyers with practice experience. What do those lawyers actually do day-to-day?

What skills make someone successful in those roles? And what does that tell you about where the profession’s headed?

Patrick Forquer (04:15)
Yeah, it’s a great question. I get this question all the time. And I think legal engineering is like one of the most exciting roles in technology now, just generally, because they’re on the front lines of AI and the AI revolution, doing the hard work of change management and adoption in firms. And so I think if you wind all the way back, I mean Legora was founded by engineers, right? One of the founding principles of the company is that we want to build with lawyers, not simply for lawyers.

That means taking their feedback and understanding the deep levels of requirements and subject matter expertise needed to build a successful legal AI platform. For us, that’s always been a founding principle, having lawyers in the business helping guide the direction of the product. More importantly these days, there’s this idea of transformation. We’re fortunate that Legora is popular right now. We get to have lots of conversations with many great firms and great companies out there in the market.

But at the end of the day, we have to help lawyers adapt AI into their ways of working, into their workflows. And candidly, you can’t just do that with standard consulting practices. You have to have deep levels of subject matter expertise. So, for example, people from an M&A background understand how an M&A deal gets run and the due diligence process. The same applies to litigation and the different aspects and practice areas of legal work.

And so our lawyers predominantly work in our legal engineering function. They embed with our sales and post-sales teams. First, they have deep levels of practice-area expertise. So they bring credibility to our clients: we understand your business, and we understand the types of deals, cases, and matters you’re working on. And then they have high levels of technology and AI proficiency, meaning they understand how to do things like prompt and context engineering.

They understand how agentic workflows work and how to build them. And they’re highly curious and adaptive in a client-facing environment where you can go in and work on a live deal or a live matter and help build sort of long-horizon workflows within Legora to help our clients deliver faster and better work. It’s really exciting, and I love spending time with our legal engineers. They’re some of the smartest, best people I’ve ever worked with.

Marlene Gebauer (06:36)
That’s a great endorsement.

Greg Lambert (06:36)
See Marlene, this is why we’re having a hard time finding these people. They’re taking them from us.

Marlene Gebauer (06:39)
That’s what I was going to say. I’m seeing discussions about what’s going to happen to our junior people, and those are all legitimate questions. I’m glad we’re having these discussions about other job opportunities that may be out there for people.

Patrick Forquer (06:58)
Yeah, and we definitely think that, Marlene. We think legal engineering is a new career path. It has always existed in different forms at different companies, and Greg and I were talking about that earlier. But the current form of building these bespoke workflows, skills, and capabilities within tools like Legora is not something that was done as commonly as it is now. And they’re making a meaningful impact on the client work that’s getting delivered.

And it’s showing up for our clients in their work with their clients. And I think that’s the most exciting part. I definitely think it’s a new career path that’s opening up a ton of opportunity for the folks that work for us. And we’re really excited about that.

Greg Lambert (07:45)
Just curious, Patrick, about the types of talent that you’re looking for. Is there a certain practice range? Are they, say, seventh-year or tenth-year lawyers? What’s the sweet spot you tend to look for in the talent?

Patrick Forquer (08:02)
We actually hire across the entire spectrum. We have everything from folks who’ve done a couple of years as an associate through partners who work with us and they tend to focus on obviously solving different problems. Especially within BigLaw, there’s so many different types of transactions and deals and matters and cases that are happening on a day-to-day basis and there are different needs, and how you train and enable a partner is very different from how you train and enable a first-year. And we just try to map practice area and sort of seniority to help solve the different problems we’re looking at so we can add the most value to the firms that we work with.

Greg Lambert (08:44)
You’re the revenue guy, so let’s talk a little money now.

Patrick Forquer (08:49)
Okay.

Greg Lambert (08:50)
I know that you guys have raised significant capital. I think I was looking at Series D so far. And you’ve gotten busy on the acquisition side including a recent acquisition of Wexler, which I think is like the fifth

Patrick Forquer (09:03)
Yeah.

Greg Lambert (09:04)
or sixth acquisition. So, instead of asking you specifically about the individual acquisitions, what’s kind of the strategy that you have when you’re looking at acquiring and building through the acquisition process?

Patrick Forquer (09:21)
Yeah, for sure. It’s a couple of different things. First and foremost, we’re hiring amazing talent. And so the folks that are coming into the business through this acquisition are high-slope, highly intelligent, hardworking folks that we want to work with. And they’ve built platforms that are solving big problems that are relevant to our customers and the firms we work with.

In this world, as you know, there’s so much going on in legal tech. There’s so much noise and so much chatter. A lot of times when you’re comparing sort of legal AI platform A to legal platform B, you’re looking at it in terms of like apples and apples, right? It’s like you’ve got drafting, we’ve got drafting, how are we doing it? How are they doing it?

And so on and so forth. So part of the strategy also is we want to be a wide application layer for the firms we work with and a platform that any practice area could work within, but we also want to be really deep. The deeper the capabilities we add, for example the litigation capabilities Wexler provides, the more we can be both wide and deep and that sort of keeps the comparison from an apples-to-apples comparison. So Max likes to make the comparison of apples to fruit salad. And so we want to add more of these bespoke, deep levels of capabilities.

We bought Cadastral for real estate work. We’ve got Wexler for litigation work. We brought a company called Graceview that we integrated into the platform in under 60 days for regulatory horizon scanning as an example. So not only do we want to be able to work with every type of lawyer, we don’t want to just do surface-level work. We want to go deep within each practice area and within each area.

So it’s really important to us that we provide the highest level of value possible. And we’re able to do that through our M&A strategy. And so we’re trying to provide more value and more bang for your buck when you work with Legora.

Greg Lambert (11:12)
Yeah. And when you’re doing these acquisitions, you talked about making it both wide and deep. Are you really looking at kind of becoming almost like a matter management platform where the attorneys are constantly working within the Legora platform over a wide range of activities? It’s not just legal research or document drafting. It may be directly accessing your document management system and bringing things in and working with them there or maybe even the accounting system.

How wide and deep are you going to go?

Patrick Forquer (11:49)
Yeah. With any type of work that you do, no one likes bouncing around to different systems to do different things. And insofar as we can add capabilities to our platform that make sense for the type of work that we’re doing, we’re going to look at it. And certainly for us, we want to look at M&A as an accelerant to adding value and keeping folks spending more time in the platform. So yeah, we’d love for you to be able to do an end-to-end M&A due diligence process, for example, in Legora.

Many firms are doing that today. And actually, that was our fifth acquisition of the year. And we’ve run all those acquisitions on Legora. And we did one of those acquisitions in 13 days, using our product to run the M&A due diligence. Obviously, we work with some amazing law firms on that as well.

So it’s been really fun to see it in action. The more capabilities that we have, the longer folks are going to spend in Legora, and that’s good for them and good for us.

Marlene Gebauer (12:46)
So let’s keep talking a bit about the workflows. We’ve certainly moved from asking, “Can AI draft or summarize?” And now we’re really talking more about these agents that can execute multi-step automated legal work. We hear a lot about it, and I’m sure that there are pockets where it’s done. Which legal workflows are genuinely ready for that sort of next step today, and where do you think it’s still a little bit of theater?

Patrick Forquer (13:22)
Yeah. Well, it’s a great call out, Marlene. From my perspective, you hear a lot about agents out in the market. Everyone’s got an agent doing X, Y, or Z. But if you really look under the hood, a common pitch is, “We have this number of agents.” Anyone who tells you they have like hundreds of agents, I think is perhaps misdirecting a bit.

So, is it a repackaged workflow? Workflows have existed for a long time. n8n technology has existed for a long time. And then there’s lots of different workflow tools that you can buy that have if-then logic. Are you buying something that’s a repackaged workflow or are you buying something that’s truly agentic? And for us, the way to think about it would be

Marlene Gebauer (14:04)
And maybe explain what’s truly agentic versus not.

Patrick Forquer (14:08)
Yeah, so the agent in Legora, like the Agent OS that we launched earlier this year, it can click every button you can click in the platform. So it’s not just a set of rules that it’s following, like “if this, then do this; if that, then do that,” which you have to then go in and like edit those rules or that logic within a particular pre-baked package. With the agent, the big difference is you’re sort of moving from prompt engineering to context engineering, right? So with agentic work, you want to give it a definable scope. You also want to have the data and the sources made available within the project in Legora.

So everything that you’re going to be doing is grounded in source and truth. And then you also want to have a human in the loop at each step to customize and make sure everything is working like you want it to. So if you give the agent a goal in our system, and then you give it some context of that goal around like what it is you’re trying to do, sort of guardrails and things to watch for, then our agent is going to come back and it’s going to ask you some questions, some clarifying questions around the scope, around the goal. It’s going to look through the documents and make sure it has all the context it needs to achieve the goal. Then it’s going to present you with a bespoke plan for that particular goal that you’ve outlined and you can layer in skills, tools, and resources within Legora to make it even more bespoke and more granular to improve the work product.

The plan that it’s showing you is then the prompt. So in the old school world, the “Improve Your Prompt” button was a big feature that we still have that people like, but with an agent, you don’t really need the “Improve Your Prompt” button as long as you give it enough context with a very well-defined goal and all the resources that it needs. It’ll create the plan. And the plan is in many cases a long-horizon task that can run for 20, 30, 60, 120 minutes in one go to solve these increasingly complex problems. As long as as it has access to all the legal skills that we provide it in the tool, all the context you give from the firm or from your client documents, the agent can then go run these long-horizon tasks and solve increasingly complex problems instead of saying, “Go run this four-step workflow” that was pre-baked when I logged in.

And so it’s quite different and it’s extremely flexible and bespoke to the particular problem that you’re working on. That education, though, and that sort of change have been a journey. These are new capabilities that only really emerged as the new models came out around Christmas and the beginning of this year. And so we’ve had to educate our teams on what they can do, what they can’t do. M&A due diligence, Marlene, to answer your question specifically, is an area where we’re seeing an increasing amount of that work being able to get done with the agentic work.

And we’re hoping there are increased capabilities with Wexler as we sort of bake their capabilities in the platform over time that you’ll be able to do that in the long run. But yeah, we’re still working on this together and figuring it out with our customers.

Marlene Gebauer (17:22)
Thanks for thinking of the litigators.

Patrick Forquer (17:24)
Yes, of course. Always thinking of the litigators.

Greg Lambert (17:28)
You were speaking about things coming out over the Christmas break. It was kind of funny because I was talking with one of our AI-forward partners that had developed a process earlier in the year, and he was sending an update and then one of the other partners was like, “Do you realize it’s only been six months since you created this?” And we were all

Patrick Forquer (17:49)
Yeah.

Greg Lambert (17:49)
like, “Holy smokes, is that all it’s been? It feels like forever ago.” I’m wondering, sticking with the agents, and especially with all the transformation we’re seeing, I have a two-sided question. What are you finding that law firms may be a little slow on when it comes to putting an agent to work? And then for those that may be overachievers, what are some things they think it can do that it’s not ready for yet?

Patrick Forquer (18:21)
Yeah, that’s a great question, Greg. I think one thing is to be transparent. The rate of change in AI capabilities, in some cases, outstrips what the organizations we work with are able to absorb. The change is happening so fast. We all have to be transparent and honest about the fact that these things are changing.

What you could do six months ago or the thing you designed six months ago may not work the same way. There are all kinds of organizational questions around that, like approvals to use different capabilities and all the risk and the training and the change management. So I always tell folks, if you’re using AI today only to summarize documents and draft emails, you’re probably not doing enough, right? But in

Greg Lambert (19:09)
Yeah, yeah.

Patrick Forquer (19:10)
many ways, the question is: Does the agent have access to all the context and data and resources that it needs to complete the task? We’re seeing an increase in requests for things like MCP builds. So we have a team of folks, for example, that can build secure MCP access to different systems that’ll enable multi-system execution across a number of different areas, but you have to have the right security and guardrails around data governance in place before you can do that. I think a lot of folks think, “I can eventually just tell the agent, go do this thing, and it’ll figure it out.” And the agents, in theory, have the capability to do that. But do you have that sort of matter-level isolation?

Do you have the document-level permissioning structure down? Have you given it enough context and enabled it with your firm’s embedded skills and context so it knows, “This is how we do this particular piece of legal research,” or, “This is how we do this particular type of drafting,” so it can produce content that’s in line with your expectations? And that’s a journey. Again, going back to our legal engineers, that’s why we have so many legal engineers, so that we can work with our clients in a more embedded way and help them develop those more complex use cases. Does it have access to all the skills and content that it needs to produce the output at the quality that you want?

Marlene Gebauer (20:34)
We’ve heard in the past that a lot of AI companies are saying lawyers are saving time using the tools. But we’re also starting to hear, because of that, how are we sort of looking at the economics of legal services? Danielle Benecke, on the podcast Max was on, was saying that they do this by outcome. There are a lot of different ways to do this. Her team works directly with clients.

So, when are we going to start talking about how to measure the changing economics of legal services and how are we going to price things when using AI? I think people want to talk about it, but I don’t know that people are ready to do anything about it. So I’m curious what your thoughts are on that.

Patrick Forquer (21:27)
Yeah, well, look, we could spend the whole podcast talking about this. We could probably spend all dinner

Marlene Gebauer (21:31)
True.

Patrick Forquer (21:31)
and all night talking about all that stuff too. And certainly I’m not here to tell lawyers how to price and package their work. But I have some thoughts on what you were going through. First, I think it’s a misnomer just to think about AI in terms of efficiency, right? If you’re using AI only to save time on things, I think that’s mostly the way the first wave of AI worked.

People are doing that sort of look back, right? I used to do this thing this way and it took me 10 hours, and now I do it with AI and it took me 30 minutes. That type of stuff has existed for a while. And I don’t think that’s particularly exciting to the market anymore. What we try to look at in terms of a look forward is, okay, if you’re commoditizing certain types of work product that used to take you 10 hours and now it takes you 30 minutes in terms of time, then what does that enable in terms of quality of output?

What does that enable in terms of adding new features or new services that maybe you couldn’t have done previously? What can you package or repackage as part of new work product that wasn’t available? So there are all kinds of 50-state surveys and all these different examples of things that would have been too onerous in a world pre-AI that you can now deliver in a really effective way using platforms like Legora. And when we think about value, anytime you’re looking at an economic system more broadly, if certain pieces of the inputs into that system are getting commoditized in terms of time, then what you typically see in other industries is that the ecosystem and the economy itself gets bigger. So we think there’ll be more legal work, that there will be more legal services, that this is accretive to the overall market.

And it’s a really exciting time to be a lawyer and be working in legal services. And, as I’m sure you both know, like if you talk to any litigation partner, like they’re busier than ever, right? There are more cases coming in, there’s more work to do. People talk about time as it relates to you know the billable hour. But I was talking to a CLO of a big company a few weeks ago and he was mentioning, if we were working with an external counsel and they used to say, “Hey, if we can do this M&A deal in six months,” today they might say, “Maybe we can do it in three or four months.” Is it more or less valuable to do it in three months than six months?

Again, I’m not here to answer that question for folks or tell people how to price things, but I certainly think there’s an interesting conversation to be had around value. And if you’ve got an amazing attorney today who’s working just with Microsoft Word and Outlook, and you give them a platform like Legora, is that attorney more or less valuable to their clients? And what are the economics of that work? Certainly I have every confidence that legal research went from analog to online, data rooms went from banker boxes in a conference room to platforms like Intralinks and Datasite, and paper moved to email. There’s been all kinds of these examples over time and legal services has only grown and thrived.

We fully expect that to be the case with AI. How that looks and the shape of it between lawyers and their clients, things will change and evolve for sure, but we think it’ll be very positive and that there’ll be a lot of opportunity.

Greg Lambert (24:56)
Before I go further into the economics, there was something that you mentioned in your last answer where you talked about change management being one of the hardest parts of the deployment. I read something earlier where I think Crowell & Moring was talking about how they had a 91 percent attorney adoption rate. A year ago, we were fighting to tell people whether it was safe to use these tools or not, and now we’re

Patrick Forquer (25:23)
Yeah, yeah. Yeah.

Greg Lambert (25:25)
looking at 91 percent adoption. And I think they said they also had 70 percent-plus weekly usage within like six months of deployment. So I’m curious when you do these types of rollouts, what are the success metrics Legora has? I can look at metrics and I can see people have logged in or people have run prompts, but I don’t necessarily see

Marlene Gebauer (25:52)
Or the skills they’ve used. Yeah.

Greg Lambert (25:54)
Yeah, I don’t necessarily see some of the details. So what are you looking at on your side as far as success and how your users are using the product?

Patrick Forquer (26:05)
It’s a great question. And this is an area where we’ve evolved and grown so much over the past few years. And it’s a huge emphasis for me. It’s one of the top three things that I focus on every day when I go to work. How are we doing on the implementations that we have active?

And we actually have an entire team dedicated to this particular piece of our customer engagement. So this isn’t something that’s done by sales or our post-sales engagement team. We have a team that’s dedicated and focused on this. We’re continuing to evolve our strategies as we learn. We’ve done thousands of these now, so there are a couple of areas to think about.

For us, the speed and quality of implementation is the number one leading indicator of client satisfaction and overall renewal metrics. So we look at whether we got it done in the timeline we had agreed to with the client, right? So we look at speed. Did we get it done on time? And that time varies by client, by size, by the type of implementation.

We have different implementation options. We run through those and collaborate with our clients to create bespoke plans for all these big rollouts that we do. And then, of course, we look at a ton of data. And as you mentioned, Greg, for us, we don’t look much at activated licenses or number of logins. What we really look for is deeper than that.

Greg Lambert (27:32)
Those vanity metrics are really nice.

Marlene Gebauer (27:36)
Love it.

Patrick Forquer (27:36)
Of course, monthly active usage is nice. But we want daily active usage. And we don’t only want daily active users. Going back to my earlier sort of anecdote. If folks are coming in and only summarizing documents or saying, “Respond to this email for me,” that’s relatively shallow usage, and it’s easily commodified across other platforms.

We know that if folks are using more of our products and doing more complex tasks in Legora, and, as Marlene mentioned, are they using skills? Are they using playbooks? Are they using extraction templates? How many parts of the product are they using? What parts of the product are they using?

So we have a product called Tabular Review. Tabular Review gives us the ability to do bulk document review and extraction. When I first saw Tabular Review and met Max, I thought, “My gosh, this is amazing, and this is going to change the way work gets done. Period. The end.” If people use Tabular Review, they have their light bulb moment with AI.

It’s like, “My gosh, I can get all this data out of this big document set and then turn that into a RAG that I can draft, review, research, and so on, on top of with the agent.” And it unlocks an incredible amount of complexity in terms of the things that you can do. Those are the types of things we look at. Of course, we look at monthly and daily active usage, but we also look at quality of usage. So we have a number of different mechanisms and nomenclature we use internally. We might see that Marlene logged in a certain number of times this week and used a certain number of tools within the platform.

We can’t see the data or prompts, but we can see the tools she’s using. That’s the deeper level of analysis that we look at. And typically when we see certain feature usage, we know that we’re going to have a happy customer who’s solving big problems and getting great results from Legora.

Greg Lambert (29:31)
Now I’m going to hit you with the hard questions so…

Patrick Forquer (29:34)
Great.

Greg Lambert (29:35)
Yeah, butter you up.

Marlene Gebauer (29:35)
We save them for the end. We butter you up, and then we save them for the end.

Patrick Forquer (29:37)
[Laughs]

Greg Lambert (29:41)
and then hit you.

Marlene Gebauer (29:42)
Yeah.

Greg Lambert (29:43)
So a few weeks ago you announced that Agent Pro, which is one of the high-end tools in Legora, is moving to consumption-based pricing. The more tokens you use, the more it’s going to cost. And I think this was not a surprise to the industry that this was coming, but you were one of the first to move to this. So as a customer of Legora, what should people be thinking about as far as planning? It’s easy to plan per-seat pricing because you know what the cost is going to be.

When you start thinking about tokens, credits, or consumption-based pricing, how are you coaching your clients on how to approach that?

Patrick Forquer (30:27)
Yeah, it’s definitely a model from a technology provider that’s newer for legal tech. It’s used very heavily in other databases and technology systems, as you know. So for us, there are pros and cons to every pricing model. So on a per-seat model, the question we used to get was, “Okay, if you buy 1,000 seats from Legora, but what if these people don’t use it? What if Marlene goes on vacation and it’s sitting there?”

Greg Lambert (30:52)
Can I get a refund on that?

Patrick Forquer (30:54)
Yeah. “Do I get credit back if she doesn’t use it or something like that?” I can tell you that there’s no perfect pricing model. There are pros and cons to everything. The second thing is, look, this is new, this is a journey that we’re all on together. We try to lead with a bunch of empathy and understanding and try to be extremely collaborative and transparent with our clients as we work through this together.

I’ve worked at companies that sold consumption before. There are some key tenets I like to think about. First, we have to be able to predict consumption across various scenarios pretty well. The example I always use is: if we’re going to sell you 100 licenses, sorry, 100 credits of Legora. And to be clear, we’re not simply taking the tokens we use and just adding a margin on that and doing cost-plus pricing.

The credits are an amalgamation of different document API and token usage in the platform. So we don’t simply take the number of tokens that you used on a given day and add some money on top of that. But if I sell you 100 credits of Legora and then three months in you use 1,000 and I send you a surprise bill for 900 tokens, sorry, 900 credits, guess what? You’re going to get super pissed and you’ll probably churn, right? That’s an easy way to lose a customer.

That’s not a good scenario to be in. On the flip side of that, if I sell you 1,000 credits and you’re very happy, and then you use 100, and then you renew your contract at 100 credits. If we take a 900-credit churn, that’s bad for us. And then our investors are like, “What the heck? You just took a 90 percent churn on this very happy customer.

What’s going on?” So it aligns incentives in a really nice way. It aligns incentives around whether you’re using the product and whether we’re able to accurately predict that. Does the usage account for experimentation and different usage patterns? And do certain user types use the product differently than others? Are we able to report on that?

Are we providing visibility and governance? So, can we do user-level caps? In our case, you can put usage metrics against a particular project or matter in Legora. So you have governance and control you can put in place to make everyone comfortable and prevent certain situations from happening. But at the end of the day, it aligns incentives in a really nice way.

And if we’re able to predict the way we should, and communicate your consumption on a weekly, monthly, and real-time basis, so you know exactly how much you’re using and the patterns we’ve laid out hold, then you typically get to a good outcome. At the end of the day, I always ask our teams: for every dollar of Legora you buy, like what do our clients get? For every credit of Legora you use, what do you get in the outcome? Is it only efficiency, or do we deliver the other value-added benefits that we were talking about? And one of the more exciting things for the second half of this year is that we’ve hired a value engineering team who are working with our customers on what is the ROI of working with a platform like Legora.

And it can’t simply be, “It saved Greg some time on something.” It’s got to be more robust. We’ve done some work with Ari Kaplan on this and published some reports, but this is really the first inning on this stuff. But if we can help our customers tell a narrative, tell a story with data showing that for every dollar of Legora we put in, this is what we get out, and we can do that in a way where we’re communicating and executing on the consumption pricing as I described, then I think it’ll be fine. But it’s new, and we’re working through it with our clients in a really collaborative way. I think we’ll be in a good place.

And like you say, this is the direction of travel for the industry more broadly and I think we’ll be in a good space.

Greg Lambert (34:57)
I think one of the questions I hear the most as we start looking at this, because it’s not just going to be Legora, there are going to be other

Patrick Forquer (35:06)
Yeah.

Greg Lambert (35:06)
AI companies that we work with that aren’t necessarily going to use credits and consumption, but instead use a pure model where if you use a million output tokens, you’re going to get charged a million output tokens. And if you let an inefficient process go awry, that’s on you because they’re not upcharging you, they’re charging for what you use. Are you guys thinking about safeguards so that, if somebody sets up an inefficient agent that runs into a loop and can’t get out of it, it breaks, or are there some kind of safeguards that you set up?

Patrick Forquer (35:45)
Yeah, a hundred percent. We already have a lot of these in place and we’re continuing to invest in this area around model selection, model routing, right? You don’t need to use whatever the latest model is for every single task in Legora, right? So model selection, model routing, guardrails against everything from prompt injection, matter-level isolation, all these different things. We we have to have a lot of guardrails in place in the product which we already have, and then we’re continuing to build what we need to make our clients feel more comfortable using Agent Pro in particular.

Marlene Gebauer (36:22)
So Patrick, we have a new question. We sort of look backward to the present and then for our crystal ball, we look for the future. So this is sort of looking backward and looking to the present. What’s one thing that strikes you that’s different today than last year in our industry?

Patrick Forquer (36:43)
I’m glad you asked because I was actually looking back on Max’s interview with you guys last year, and he made the prediction about these long-horizon agents. He said, “They’re coming. It’s going to come faster than you think.” Before we had the agent, we had the assistant. And the assistant could do a couple of steps of a workflow. I think our workflow builder capped out at about five steps because the prior models would run out of gas.

They get what they call context anxiety, right? And wrap something up before getting to the end. The ability for the agents to do increasingly complex long-horizon tasks is here. And it’s something that we were working toward last year, but the reality was we were still in workflow mode. Last summer we were in workflow mode, whereas this summer we’re in Agent OS, with all the things we were just talking about.

So that’s been really exciting to see that come to life. More importantly, we’re seeing our clients use it. And I hear from clients every day about something cool they did with Agent that they never would have been able to do. I was with a client yesterday down in Atlanta. He was talking to me about some exciting work he was doing for a business development use case.

It generated a deck he was excited to use in a pitch meeting. I think the agentic revolution, so to speak, is here and it’s all new and we’re figuring out new applications, new use cases for it every day, even those of us who work at Legora. I was in a demo the other day and one of our legal engineers asked the agent to not only redline a document, but create an HTML page of the issues list based on the redline. That way, the lawyer could have a visual representation they could share with their client based on the redline it had generated for the customer. I honestly didn’t know Legora could generate HTML like that.

I thought, “That’s so cool. I didn’t know

Marlene Gebauer (38:41)
Mm-hmm.

Patrick Forquer (38:41)
that.” And so there are all these things that we’re uncovering and, as you can tell, I get excited about it. Our team does too.

Greg Lambert (38:49)
Yeah, I heard someone say HTML is now the new markdown for agentic AI tools especially.

Patrick Forquer (38:58)
Yeah, for sure.

Greg Lambert (39:00)
So let’s jump to the crystal ball question and as we told you in prepping for the interview, you can answer this however you want, but I’m going to phrase it a little bit differently this time because I think this is an interesting and a little bit selfish of me to do this. Let’s say you and I swapped roles. You came in as a chief innovation officer in an AmLaw firm. What’s something that you think someone like me should prepare for over the next few months or a few years?

Patrick Forquer (39:36)
Well, I mean, I think your jobs will be increasingly essential to the success of the firms that you work with. And we think it’s really exciting. Yes, more money. More money. Well,

Greg Lambert (39:46)
So you’re saying we’ll make more money, right?

Marlene Gebauer (39:48)
[Laughs]

Patrick Forquer (39:50)
Hey, man, I read LinkedIn too. There’s a lot of stuff going on in knowledge management these days.

Marlene Gebauer (39:53)
Send that out to the…

Greg Lambert (39:57)
Yeah, we will neither confirm nor deny that we’ve passed that around to…

Patrick Forquer (40:02)
Yeah. It’s a talent war out there right now. But we might have to talk afterwards, Greg. I think the first thing is, and we’ve encountered this because we use Legora internally as well, and we use a number of AI tools. But the layer that gets often overlooked is the data layer.

And with all the talk about agents and all these different things, as we were discussing earlier in response to Marlene’s question, where does the data sit that you need to go execute these long-horizon tasks? And how do we teach systems thinking and design thinking to our internal teams? And maybe that’s the lawyers, or maybe that’s part of the KM function around how we think in terms of these step-by-step workflows that need access to specific pieces of context. The more context you give agents, the better they do, right? But accessing that context within the firm, for many reasons, is such a challenge.

We talk to firms all the time about this. It’s about getting the data in a way that’s secure within the right governance frameworks, but also understanding what your risk tolerance is for the different use cases that you want to do with different agents. That data layer is more important than ever. And that’s an area where our legal engineers are constantly collaborating and working with our customers to understand how it exists today and what we can change to make the agent more powerful. So the data layer is definitely an area that I would look at.

And I do think, going back to the legal engineering sort of type of conversation, that there are all kinds of new roles and new capabilities within those roles that are going to be available. So making sure you have the right team in place probably means I would ask for a bunch more headcount, Greg and Marlene, as I’m

Greg Lambert (41:54)
Mm.

Patrick Forquer (41:55)
sure you’re always doing yourselves and all the knowledge management leaders we work with, doing the same. There are new skills and new capabilities around AI specifically, data governance specifically, that I would want to hire for and make sure that we had that in-house so we could move faster on the AI adoption curve. Without this sort of data layer and without the talent in place, you’re going to be limited in the complexity of the tasks that you can do. And again, we’re all on this journey together as we sort of try to adapt these increasingly advanced capabilities and technologies into our platforms. And I think from our side, one thing that Legora wants to do a much better job of is to leverage the collective knowledge from our peers.

And that’s why I love this podcast and what you guys are doing because there’s so many smart people out there trying to solve these problems. But it can’t be KM or innovation alone. You need partners, customers, KM, innovation, and you need everyone on the same page. Can we align on what the goals and intentions are of the firm as it relates to AI? What are your expectations from us?

We need to make that alignment clear. For the people I talk to who do what you guys do, getting stakeholder alignment is difficult. Everyone has a perspective. People come from different places and may have higher or lower risk tolerance. But at the end of the day, you’re held to a standard, right?

You’re held to a level of expectation. And sometimes you have a great opportunity to go hit that expectation, and sometimes you’re limited because of the constraints put on you by the business. But I’m not sure everyone always appreciates that. From Legora’s side, our job is to try to bring those folks together so people have a deeper understanding of the challenges so that we can unlock the true potential here. Those are some things I would think about.

And you’re probably thinking, “I’ve already thought of all those things. This guy.”

Greg Lambert (43:50)
All right. Nope, I’m writing them all down. That’ll go in my monthly report now.

Marlene Gebauer (43:53)
We’re taking notes, man.

Patrick Forquer (43:59)
I know I’m not telling you anything you don’t know, but just to reinforce, as I’m sure you guys have talked about many times.

Greg Lambert (44:06)
Well, I appreciate that. And Patrick Forquer, CRO at Legora, thank you very much for taking the time to talk with us today. We’ve covered a lot of ground here and it’s been a lot of fun. Thanks.

Marlene Gebauer (44:19)
Thank you, Patrick.

Patrick Forquer (44:19)
Thanks, Marlene. Thanks, Greg.

Marlene Gebauer (44:21)
And thanks to all our listeners for listening to The Geek in Review. If you enjoyed the show, share it with a friend, share it with a colleague, and we’d love to hear from you on LinkedIn and Substack.

Greg Lambert (44:32)
And Patrick, where’s the best place for listeners to reach out and learn more about you and Legora?

Patrick Forquer (44:38)
So you can send me an email anytime, patrick@legora.com. We’ve got an amazing website, https://legora.com, and our LinkedIn page has all the latest announcements from the company. So give us a follow. Send me a LinkedIn connection or an email. We’d love to chat with your listeners, and I appreciate everyone listening today.

Marlene Gebauer (44:55)
And as always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry, and goodbye, everybody.

Patrick Forquer (45:02)
See you.

]]> xlambert@gmail.com (Greg Lambert) Who Governs Big Tech? Hannah Bloch-Wehba on AI Regulation, Police Surveillance, and Public Accountability https://www.geeklawblog.com/2026/08/who-governs-big-tech-hannah-bloch-wehba-on-ai-regulation-police-surveillance-and-public-accountability.html Mon, 10 Aug 2026 10:28:26 +0000 https://www.geeklawblog.com/?p=19518

It turns out that tech companies don’t sit outside of government as just ordinary vendors. This week on The Geek in Review podcast, we welcome back Texas A&M University School of Law professor Hannah Bloch-Wehba to talk about accountability of Big Tech, AI regulations, government surveillance, and the intertwining of public authority and private tech infrastructure. Bloch-Wehba traced the dependency between the two powers all the way back to the 1930s in her article “How Tech Took Over,” in how the tech sector became a foundation for national security and economic growth.

Today’s hybrid form of governance, where Bloch-Wehba explains how a handful of private companies supply data and cloud systems, along with decision-making infrastructures across multiple governmental agencies. It is a struggle for traditional constitutional doctrines to adjust to the modern technology and the operations provided by contractors that are providing their core foundational operations.

The issues also enter into the criminal law enforcement areas and Bloch-Wehba’s “Rights, Knowledge, and Capture in the Datafied State,” discusses how trade-secret claims are throwing a barrier between proprietary data systems and criminal defendant’s ability to examine the systems that are being used to convict them in the courts. There is a strangeness in the judicial systems where corporate choices are shaping the legal process being followed, rather than corporate governance following established legal norms.

Bloch-Wehba’s “Information Law Pluralism” covers how privacy rules, audits, impact assessments, disclosure duties, researcher access, and independent review as parts of a broader system governing exactly how knowledge is shared, validated, and even produced. There seems to be no single device that transparently provides accountability. In addition, she lists how a political campaign program against states attempting to regulate AI companies and products is weakening state transparency even further.

Finally, we cover Bloch-Wehba’s “Rethinking Federal Support for Journalism” where she argues that platform payments give rise to the risk of replacing a governmental dependency gets switched for journalist and new organizations being financially tied to companies they must scrutinize. Ideas floated like an AI tax provide some alternative funding possibilities for supporting local and public-interest journalists.

LINKS

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Marlene Gebauer (00:01)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:10)
Thank you. Good to see you again. In the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Most law firms and law departments have at least an AI policy of some kind, and some broad standards are starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and toward a more informative approach that defines different risk categories and makes distinctions between routine, low-risk uses of AI and other, more substantial use cases. I think that’s appropriate for where we are, and it’s also what clients are coming to expect from their advisors. One of the bigger challenges we’re seeing right now is how to express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is using AI day to day, so they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment, helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not” and turn them into more readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit legaltechnologyhub.com.

Marlene Gebauer (01:50)
Thank you, Sam. It is important for AI policy to be in plain language so people understand it.

Sam Moore (01:58)
Absolutely.

Marlene Gebauer (02:07)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:14)
And I’m Greg Lambert. Today we welcome back Hannah Bloch-Wehba, the law professor at Texas A&M and a leading scholar on law and technology. Hannah writes about how tech can be used to conceal power and evade accountability. Hannah, welcome back to The Geek in Review. We were just looking at the date. It’s only been seven years and two children.

Marlene Gebauer (02:34)
Ha ha.

Hannah Bloch-Wehba (02:37)
Thank you so much for having me back after seven years and two children.

Greg Lambert (02:42)
Ha ha ha.

Marlene Gebauer (02:44)
We are absolutely happy to have you, and you have been busy in your role at A&M. So we’re going to dive in.

Greg Lambert (02:51)
Yeah, well A&M’s been busy.

Congrats. What are you guys, like 22nd now in U.S. News & World Report and all the good stuff?

Hannah Bloch-Wehba (03:00)
Something like that. We have a new building, a new campus coming up. It’s a pretty exciting time at A&M.

Marlene Gebauer (03:07)
Terrific. Well, I want to dig into some of the things you’ve been writing about. In “How Tech Took Over,” you argue that the government didn’t merely fail to regulate Silicon Valley; it affirmatively helped make technology companies essential to public governance. What legal and policy choices were most responsible for creating that dependence?

Hannah Bloch-Wehba (03:30)
So in law and tech circles, we often talk about deregulation and hands-off methods of regulation that started when the internet was commercialized in the mid-’90s. But in “How Tech Took Over,” I go way back to the 1930s and ’40s, and I look at choices that were made in science policy and innovation policy then. Essentially, the government knew that technology and innovation were going to be critical both to economic progress and to safeguarding national security. So they built institutions that promoted both scientific research and technological innovation. Once that ball started rolling, the government became a primary consumer of those innovations. From almost a century back at this point, the government has been making legal and policy choices around science policy and tech policy, like the National Science Foundation Act and military funding of basic research, which were intended to cultivate this industry for public benefit. I think that set the stage for tech’s expansion, but not in a predictable or linear way. So over time those policy choices have changed and we’ve had these different political moments where, for example, the balance between economic progress and national security needs has shifted somewhat. But overall, I think this is a really important sort of reservoir of federal support for the tech industry that tech law scholarship hasn’t really explored. And so that’s why I was interested in digging into this history and seeing why this happened and how.

Marlene Gebauer (05:25)
What was the response to the article?

Greg Lambert (05:28)
Everyone came out and said It’s all lies. It’s all lies, Hannah.

Hannah Bloch-Wehba (05:33)
You know, I think people have been interested in it in this moment when it’s clear that the relationship between the federal government and tech companies is not an arm’s-length regulatory relationship. We see CEOs at the inauguration lining up to support the president. We have press conferences about the urgent need for federal funding to help build data centers. The U.S. government is intervening in a permitting lawsuit in Mississippi against xAI. We are seeing a lot of weird dynamics in the last couple of years that we haven’t seen before. The conventional narrative that the federal government has deregulated doesn’t capture what’s going on. So I think people are aware and hungry for a better explanation of this relationship. The response has been interested and supportive. I will say, people are like, “This isn’t an article, this is a book,” which is true. It will eventually be a book, but right now it’s one article at a time.

Marlene Gebauer (06:44)
And we’ll have you back for the book.

Greg Lambert (06:45)
Yeah, definitely. It’s

Hannah Bloch-Wehba (06:47)
Well, that’ll help me finish it.

Greg Lambert (06:50)
Well,

Marlene Gebauer (06:50)
Ha ha ha.

Greg Lambert (06:51)
it’s definitely interesting times. You started talking about this almost commingling of tech and government. I think we’ve always assumed that the traditional assumptions of constitutional accountability attach to the government.

But we’re almost seeing this blending of authority between the private sector in Silicon Valley and the government. So how are you viewing that in your now article, but soon-to-be book, and explaining this blending between private and public accountability?

Hannah Bloch-Wehba (07:34)
So I think this, what I call the emergence of a hybrid form of governance, that intentionally takes advantage of relationships between private actors and public entities, is complicated for conventional approaches to constitutional law. We’ve always had this problem to some degree.

There have always been efforts to extend constitutional accountability to contractors, for example. We have a complicated body of doctrine about how and when that is and isn’t appropriate. We have state action doctrine that limits the application of the Constitution in general. Then we have little exceptions around the edges of the state action doctrine. The problem here, I think, is that the kinds of functions that many tech companies are performing don’t look that familiar. So one of the arguments I make in the paper is that tech companies are really enmeshed in providing the informational infrastructure for government decision making. So take an example like cloud infrastructure for federal government agencies.

A huge amount of that infrastructure is provided by a handful of tech companies. Is that a government function? It’s hard to analogize that kind of function to something that would have happened even 20 or 30 years ago. So we don’t have a lot of good comparisons to say, okay, well, this looks like this other form of public-private cooperation, so here’s how we should think about it.

So I think we’re feeling around in the dark for the kinds of principles that we ought to apply. It’s much easier to apply the traditional state action doctrine when you have a traditional government contractor relationship than when the contractors are providing the thing that makes the government run, and that is, to a significant extent, what’s happening right now. So I think it’s getting much harder to pull them apart. At the same time, a lot of those infrastructural activities are hard for normal people to see and understand. An ordinary person doesn’t care whether the informational infrastructure for a federal agency is provided by Google or Oracle or the government itself. We don’t have a lot of insight into what’s going on. So it’s something that I think is important and yet overlooked in constitutional doctrine and in scholarship, frankly.

Greg Lambert (10:30)
One of the things this brings to mind as you’re talking is, typically, we have a transfer of power, different administrations, different Congresses, but the underlying structure is consistent. Do you see this as causing an inconsistency as we transfer power from one administration to another, or when a new Congress comes in, that we’re kind of the...

Marlene Gebauer (10:58)
We’re looking short-term. We’re looking in eight-year segments as opposed to long-term.

Hannah Bloch-Wehba (11:04)
I guess I see it as related, but a little bit different. It’s never been the case that when a president enters into a long-term contract with an outside party, you’re going to redo that contract every Congress, right? Or with the changeover of every presidential administration.

So the system of contracting has always been, in some way, vulnerable to the accusation that it’s not sufficiently politically accountable because the people who made the agreement long predated the current administration. I think this introduces an interesting wrinkle, right? This is not a Trump administration problem. This is a problem that has been occurring and building, as I argue, over decades, right? But if you enter into a long-duration contract with a tech company to provide something like cloud infrastructure for the Department of the Interior, I’m making this example up, but you enter into that contract with Google, you are not going to then cancel it and enter into a different contract with a different vendor for the next administration because everybody knows it’s too much trouble and cost to switch from one vendor to the other. It’s enough trouble to switch from Gmail to Outlook. Nobody does that in their personal lives. Now imagine the entire informational infrastructure of a federal government agency is locked into one provider. It’s hard to destabilize that relationship. Regardless of what elected officials might want to do, there are serious switching costs and serious anti-competitive effects.

Marlene Gebauer (13:08)
I’m curious if there’s another model out there that perhaps clarifies the relationship that we could consider.

Hannah Bloch-Wehba (13:18)
I’m curious if you have one in mind.

Marlene Gebauer (13:21)
I don’t have one in mind. I just...

Hannah Bloch-Wehba (13:23)
I can’t.

Marlene Gebauer (13:23)
I’m just, I mean, I realize this is all...

Greg Lambert (13:27)
Are you talking about another government, another country or state?

Marlene Gebauer (13:30)
Perhaps in another country or another government, or something that someone has surfaced as a potential model, if there’s any work being done in that space.

Hannah Bloch-Wehba (13:43)
So I’m not a comparativist, but I do think when I’ve talked about this issue in front of European audiences, they’ve been baffled. They’re like, “We don’t have this problem.” There are a couple of reasons why they don’t have that problem. One is that I think they do a lot more public provision than the United States tends to do. So one model is for the government to build it themselves or to recognize that government investments give rise to a certain degree of state control. Historically, the United States has not taken that approach, but I could see a parallel universe where we might take that approach of public provision. I also think that the U.S. constitutional doctrine of state action is not one that I’ve seen in robust form in most European constitutional democracies. So there’s no reason why you have to have that doctrine as the delineator of what’s subject to constitutional constraint and what’s not. You could wish away some of these features of our order, but in the real world where we live and work, these are the constraints we’re operating within.

Greg Lambert (15:17)
Yeah. Well, it’s kind of like Sean West, someone who’s been on the show before, said, Europe’s superpower is its regulation. I think part of the traditional American superpower has been its somewhat hands-off approach to regulation. I can see how everything we do baffles the Europeans, so that doesn’t surprise me.

Hannah Bloch-Wehba (15:41)
Yeah, but I think they also, the last time I gave this kind of paper, I gave an early version of this paper in Berlin in 2022. I think it was before the Europeans had turned away from American tech platforms as reliable partners. So I think now that the Ukraine war has been dragging on for so long, and we see that the Europeans are trying to make this pivot away from partnering too closely with U.S.-based tech companies, they’re more attuned to these kinds of problems of capture and enmeshment and the risks they might cause for Europe.

Right. It’s one set of risks when it’s U.S. companies within the U.S. government. It’s a totally different situation when we’re talking about U.S. companies providing the infrastructure for a foreign government, right? I think that illustrates what the structural problems might be, even though the risks themselves look different.

Marlene Gebauer (16:56)
All right, I’m going to pivot a little bit in terms of our questions. I mean...

Greg Lambert (17:01)
We could stay on this topic all hour.

Marlene Gebauer (17:03)
We really could. I am fascinated.

Hannah Bloch-Wehba (17:05)
Yeah.

Marlene Gebauer (17:08)
But when the book comes out, Hannah Bloch-Wehba (17:11)
No.

Marlene Gebauer (17:11)
we’re going to talk all about it. So in “Rights, Knowledge, and Capture in the Datafied State,” you examine government use of privately developed data and algorithmic tools, particularly in criminal law enforcement. What happens to due process when someone can’t examine or challenge a system because a vendor claims trade secret protection?

Hannah Bloch-Wehba (17:33)
Yeah, I mean, this is an emerging problem for criminal law enforcement, and it’s been emerging for several years now. I’m definitely not the first person to write about it, but it’s not going away. The problem is that when a vendor claims trade secrecy to conceal information about how a product works, a product that was used to produce evidence used to arrest or convict somebody, then the defense has a right to challenge, to confront the evidence against them. They can’t do that because the trade secrecy claim stands in their way. So I think it undermines people’s ability to access sufficient information to exercise their rights.

It undermines their ability to challenge the evidence against them, but it also creates a bigger, more systemic problem, which is that a lot of these tools aren’t tested or validated in a meaningful way before they get used. The process of confrontation between the defense and the prosecution is the way the public gets information about whether this tool works or not.

If you can’t confront it, then you can’t produce that information. Then we don’t know whether it works or not. That seems to me a problem that has social effects beyond the individual defendant in an individual case. We’re using technologies that haven’t been tested or validated, and then, in the course of these individual confrontations, undermining the ability to produce evidence about their validity.

Marlene Gebauer (19:25)
Your Tech Policy Press article shows Google effectively determining whether and how police could conduct geofence investigations. What does that example reveal about capture when a company controls the relevant data, the investigative possibilities available to police, and the information available to the courts?

Hannah Bloch-Wehba (19:49)
Yeah, so this is an example of the broader dynamic, which is that these companies wield control over the government’s ability to engage in certain courses of action, and that control isn’t always visible or checkable. Google is a great example. For many years, they kept individuals’ location information by default in something they called the Sensorvault, which is kind of creepy and not what

Marlene Gebauer (20:25)
Yikes.

Greg Lambert (20:26)
Yeah.

Hannah Bloch-Wehba (20:29)
I would have called it if I had known that name would have gotten into the public domain.

Marlene Gebauer (20:31)
They needed a PR person on that one. They did not have one.

Hannah Bloch-Wehba (20:35)
They didn’t anticipate that becoming public, but they kept everybody’s location information by default in the Sensorvault. That made it possible for law enforcement to go to Google and say, “Hey, we want to know everybody who was in this location at this particular time.” Only because Google had made that choice was it possible for government to do. So, starting several years ago, once law enforcement figured out they could make this kind of request, they started doing it again and again. This is a geofence warrant, and it quickly became the most frequent form of judicial warrant Google was receiving. It became an appealing tool for law enforcement. Strikingly, Google was the only company that would comply with these warrants.

So it’s not that other companies didn’t have the data, but Google was the one that would give the data to law enforcement. To their credit, they said, “Well, we’re not going to give it to you willy-nilly. Our corporate policy is that you need to get a warrant rather than a subpoena or a court order. If you get a warrant based on probable cause, we will work with you.” Then they created this elaborate process by which they worked with law enforcement to deliver this information. It’s a striking example of the law of Google. This is not the law that was developed by

Greg Lambert (22:04)
Yeah.

Hannah Bloch-Wehba (22:05)
a legislature or a court. This is not Google trying to apply a ruling that was already made. It’s Google saying, “This is what we want you to do.” Law enforcement is so eager to get this data that they do what Google wants them to do.

And I think Google was right that a warrant is required, but that’s not the point. The point is that it’s not their job to decide. Right.

Marlene Gebauer (22:32)
They’re making the call.

Greg Lambert (22:34)
Mm-hmm.

Hannah Bloch-Wehba (22:35)
So I think this is a clear way in which it’s corporate policy that law enforcement has to get a warrant. It’s also the data infrastructure or the data governance choices Google is making internally, the collection by default, the indefinite storage, right? Those are the things that make it possible for law enforcement to get this information. So I think, in a clear way, it was Google that shaped the geofence warrant.

Then, in 2023, Google decided, “You know what? We aren’t going to do this anymore. It’s incredibly burdensome for us.” They turned off default storage of location information in the Sensorvault. That’s another example of Google determining whether this is okay or not.

Marlene Gebauer (23:32)
Their choice, yeah.

Hannah Bloch-Wehba (23:33)
Like, okay, after several years of complying, now we’re going to change our data governance practices, and this isn’t going to be possible anymore. Again, I think that was a win for privacy. I don’t think it’s a win for democratic control to have individual tech companies determining, based on what’s convenient for them, whether they’re going to comply with a warrant, what legal standards should apply, and whether the government should be able to get this data. That makes me uncomfortable.

Greg Lambert (24:12)
Well, we talked recently about the OpenAI and Hugging Face situation, and one of the comments we made was, if this were an individual, this would be a crime.

Hannah Bloch-Wehba (24:24)
Absolutely.

Greg Lambert (24:25)
So this situation where Google decided, “Okay, I’m going to turn this off.” There was a situation, and it’s a little apples and oranges here, but where someone, I think, was coming back into the country and tricked the government official into wiping the data from his phone.

Hannah Bloch-Wehba (24:43)
Yeah.

Greg Lambert (24:44)
Now he’s charged with a felony for doing this. So, individual crime. If the company did that, it would be a policy change, a change in internal policy.

Hannah Bloch-Wehba (24:57)
Right.

Greg Lambert (24:58)
So, yeah, it seems like we’re almost a little upside down.

Hannah Bloch-Wehba (25:04)
Yeah, I mean, to be clear, I don’t think that should be a crime. I don’t think it should be a crime to decide you’re going to delete your information. But I definitely don’t think we should have one set of rules that apply to big tech companies and a different set of rules entirely that apply to individuals. That seems bad to me, and I don’t think we’ve appreciated the extent to which that’s the case.

But it is true that these companies are making decisions about, you know, how to test their products that may end up violating the law and they’re not being held to account the way that an individual might be. And I think we should conceive of that as a potential problem, right? That the law might not be applied evenly.

Marlene Gebauer (25:56)
Then it’s about the data they collect and how they choose to use it, and there’s no oversight on that.

Hannah Bloch-Wehba (26:03)
Well, I think that, to the extent oversight exists, for a long time we’ve assumed that we’ve opted to comply with the terms of service as they’re delivered to us, right? So if the

Marlene Gebauer (26:16)
Yes, our click agreements. Yeah, because everybody reads those.

Hannah Bloch-Wehba (26:22)
thing that makes the difference is whether I’m an individual or a company that produced 40-page terms of service. That seems obviously laughable, but I think that is part of what’s going on.

Greg Lambert (26:37)
Well, again, another one we could have a complete show on. As if you haven’t done enough writing, one of the other papers you wrote was...

Marlene Gebauer (26:46)
There’s more. There’s a couple more.

Greg Lambert (26:50)
information. Okay. Yeah.

Hannah Bloch-Wehba (26:50)
To be fair, it’s been seven years since you had me on the podcast.

Marlene Gebauer (26:54)
Yeah.

Greg Lambert (26:55)
So one of the other papers you wrote was “Information Law Pluralism.” First, do you mind defining what you mean by information law here?

Hannah Bloch-Wehba (27:06)
Yeah, so by information law, I mean the body of legal structures that governs how knowledge is produced, validated, and disseminated. A lot of the time when we talk about information law, people mean one part of it, like privacy law or public disclosure law. I’m trying to bring that all together and say these are all ways in which we govern flows of knowledge.

Greg Lambert (27:38)
Well, in that, you talk about emerging tools, audits, impact assessments, document requirements, and independent assessment. Which of these has the greatest potential for producing meaningful accountability at the end of the day?

Hannah Bloch-Wehba (28:02)
Yeah, so part of the impetus for writing this paper was that I took a step back. I decided to do a project where I looked at every piece of proposed and enacted state legislation about AI. This was a stupid project. It was a couple of years ago.

Greg Lambert (28:18)
So small, just a little weekend project.

Marlene Gebauer (28:21)
Well, a fun little thing to do, yeah.

Hannah Bloch-Wehba (28:24)
It was a couple of years ago. It wasn’t quite as crazy as it would be to try that project now. But I thought, what are they trying to do? My sense was that there were some broad-stroke similarities and some important differences, and I wanted to capture those. As I looked at these laws, I realized they’re all trying to do the same thing, which is, in different ways and for different audiences, force the AI providers, right? Whether that’s a company, an individual, or a system is often unclear in the legislation, but force these providers to generate and share certain kinds of information about their products. It might be shared with individuals who are affected by those products, like when you apply for a job and have to use an AI interviewing system.

It might be shared with the public, lawmakers, or regulators. We have all these different audiences for all these different kinds of information. So what are they trying to do? That’s the thing I think lawmakers were genuinely uncertain about. I wrote the paper to explain that these different ways of producing information serve different purposes and should be used in complementary ways. So I’m going to avoid your question entirely and say, I don’t think there

Greg Lambert (29:49)
Well done.

Hannah Bloch-Wehba (29:50)
is one tool that will be the silver bullet for accountability. Luckily, I didn’t think until pretty recently that we had to choose one tool because I thought it would be up to the states to regulate AI as they saw fit, so we were going to have federalist experimentation. Little did I know.

Greg Lambert (30:13)
I think I found the flaw in your logic.

Marlene Gebauer (30:15)
Yeah.

Hannah Bloch-Wehba (30:19)
I might still be right about that. I think the claim to state capacity to regulate AI is strong, and that, but for threats to preempt that experimentation through litigation and threats of cutting off different kinds of aid to the states, we would be seeing a lot of that experimentation in action. The last couple of years have tried to deter it from happening. I don’t think you have to choose one of these tools. They all need to work together. I think the public needs a certain degree of information about how these systems operate in practice.

We need expert insight into how they’re working. That’s researcher access to the under-the-hood data a lot of the time. I think it’s important for these companies to produce information directly to the regulators charged with overseeing their operations. Unfortunately, I also believe there’s a successful political campaign against these laws, and that’s going to stymie the development of legislation in this area. So this is a paper that got...

Greg Lambert (31:44)
Is that argument about national security? Because we’re hearing, well, if we regulate our AI companies, China and Russia are not going to regulate, and we’re going to lose the advantage. Is that the sole argument, or is there something more nuanced in that?

Hannah Bloch-Wehba (32:04)
I think there are two related arguments, and they’re familiar instances of this debate. One, AI is essential to national security. We shouldn’t do anything that’s bad for national security. Therefore, we shouldn’t do anything that might be bad for AI development because we need to win the arms race.

Greg Lambert (32:26)
Seems super logical.

Hannah Bloch-Wehba (32:27)
It’s super logical at a high level. The problem is that, first, China does regulate AI, so the whole idea that they have hands-off development and we need it too doesn’t work. Second, it’s this assumption that you need a totally deregulatory environment to safeguard national security, but we would never have said that about nuclear weapons, for example. So it seems like a strange leap. I’m also not sure you can win an arms race, but that’s neither here nor there. The other part is about the federal role. Having failed to pass AI legislation in the U.S. Congress that would preempt state legislation, we’ve instead had a series of threats by the president to convene a litigation task force to sue states that regulate AI in an overly burdensome way. We haven’t seen that happen yet, but many states recognize that the threat is real. We also have a litigation campaign dedicated to undermining these laws. So xAI has been suing in California and Colorado to undermine AI transparency rules, and they’ve been pretty successful. Colorado revisited and substantially weakened its laws partly in response to these threats. I think they all hang together as one ball of wax. Whether you call it national security or something else, it’s a successful campaign to get states to change their positions.

Marlene Gebauer (34:16)
Okay. One last pivot.

Greg Lambert (34:19)
Yeah, and you said it’s been seven years, but I looked at the dates. All of these are 2026 articles, so...

Marlene Gebauer (34:25)
Well, we’re trying...

Hannah Bloch-Wehba (34:25)
I had a good year.

Marlene Gebauer (34:27)
We’re trying to keep it fresh. You have definitely been busy. So in “Rethinking Federal Support for Journalism,” you argue that requiring platforms to compensate news organizations might deepen journalism’s dependence on tech companies. And you propose a national journalism foundation. How would you design something like that to support knowledge production while protecting editorial independence, resisting political interference, and ensuring that funding reaches public-interest and local journalism rather than primarily benefiting established media owners? This is an important question in my mind. I Hannah Bloch-Wehba (35:09)
Yeah.

Marlene Gebauer (35:10)
think our whole information and news dissemination platforms are struggling right now.

Hannah Bloch-Wehba (35:18)
Yes, and I think they’re going to struggle even more in the year to come. I came up as a press freedom lawyer. I spent four years litigating on behalf of journalists and media organizations, so this is an issue that’s important to me. I think a lot of news organizations are intuitively worried about taking money from the federal government, understandably, because they feel like it might compromise their independence. These proposals are floating around to enable news organizations to bargain with platforms for a share of the money they’ve been losing based on platform competition with their products.

I don’t think that solves the independence problem. I think you’re no more independent

Marlene Gebauer (36:14)
Just trading.

Hannah Bloch-Wehba (36:15)
if you take your money from Google than if you take your money from the government. Google is an extremely influential company, and we should want independent news organizations to report on influential actors, whether they are private or public. Right? So I think this is a bad structure, and I think it’s better to have a structure that’s public but independent in a meaningful way. What I’m thinking about is modeled on the National Science Foundation, which tried to thread some of these conflicts when it began funding university research and basic science.

Similarly, researchers were worried that if they took federal money, they would be captured by a federal policy agenda. The National Science Foundation dealt with that through institutions of independent peer review, essentially. Now, all of that is contested. I don’t mean to say it’s easy, make a National Science Foundation and call it the National Journalism Foundation.

But I think it speaks to the fact that these are issues we’ve dealt with before, conflicts between independence and federal support that we’ve been dealing with since 1952, when the NSF was first stood up. We’re revisiting them today with contemporary debates about science policy and research funding. I don’t think it’s impossible to manage. I think it’s important to direct federal funding and support to the kinds of news organizations producing news of genuine civic value, particularly in underserved areas. We have a lot of news deserts in this country. It’s hard for many Americans to access high-quality news. My first order of business would be geographically underserved communities.

Greg Lambert (38:29)
Yeah, that got me thinking, and this might be a little far-fetched, but you have the National Science Foundation that you talked about. Then there are other, not agencies, but other ways of distributing licensing, like ASCAP for the music industry.

Hannah Bloch-Wehba (38:49)
Yeah.

Greg Lambert (38:50)
Do you see there being some type of underlying control over how information gets distributed and then reimbursed?

Hannah Bloch-Wehba (39:04)
Yeah, I mean, I haven’t thought about that end of it. ASCAP is an interesting corollary that I hadn’t considered. The two poles I have thought about are the NSF on one side and the Corporation for Public Broadcasting, which no longer exists, on the other.

Greg Lambert (39:23)
Yeah.

Hannah Bloch-Wehba (39:25)
One of the things they have in common is that both have turned into significant political footballs because their budgets are appropriated by Congress. At the beginning of debates about the Corporation for Public Broadcasting, they considered an alternative, which was to fund CPB through an excise tax on television sets instead of through the appropriations process.

And that would

Greg Lambert (39:57)
Mm.

Hannah Bloch-Wehba (39:57)
have been the funding. I think about this because there are so many proposals to tax AI or take a golden share of AI, and then what do you do with that money? The conversation about supporting an ecosystem that includes journalism should be part of the question of how you tax AI and what you do with that fund. I could see a revenue source that comes from some kind of AI tax and removes at least some of the levers political actors have to bully these agencies or force them to adopt a political agenda that would compromise the integrity and independence of journalistic institutions.

So I think that’s one big question I have. Where do you get the money to support such an institution, and how do you make sure it’s not captured by political interests trying to strong-arm news coverage? I think that’s an important question.

Greg Lambert (41:12)
Whew. Well, on that happy note, let’s wind this down. We’ve started asking a new question before we jump into the crystal ball question. Hannah, you get to be one of the first people we ask this. What’s something that’s true for you today that wasn’t true one year ago?

Hannah Bloch-Wehba (41:39)
Well, I was going to say that a year ago we weren’t at war with Iran, and that’s true, but I don’t think it’s that interesting. I do think it’s important for the overall

Greg Lambert (41:50)
Right.

Hannah Bloch-Wehba (41:51)
question of how the relationship between tech companies and national security is changing based on the nation’s foreign policy. The thing that I think is interesting and emerging quite quickly is that there is a popular movement mobilizing around two things. One is data centers. Opposition to data centers is becoming a high-profile grassroots issue that I didn’t expect or see a year ago today, although I think the ball was rolling. The other thing is Flock surveillance cameras, which

Greg Lambert (42:33)
yeah.

Hannah Bloch-Wehba (42:35)
are everywhere and have been for several years, and are also galvanizing a wave of popular opposition. So the thing I would say, which is a positive spin on it, is that issues related to tech accountability and tech’s enmeshment in the state are much more visible to people than they were a year ago. That means the political conversation about these things is changing quite quickly, especially because it’s an election year.

Greg Lambert (43:08)
Yeah. I saw a story this morning where this guy sat underneath one of the Flock cameras and had his computer set up. The police kept coming by and checking on him because they thought he was trying to hack into the Flock camera. He’s like, “So I’m the problem, right? Not this?”

Marlene Gebauer (43:26)
Yeah.

Hannah Bloch-Wehba (43:28)
Yeah. I mean, I do think Greg Lambert (43:30)
Yeah.

Hannah Bloch-Wehba (43:31)
it can be a huge diversion of resources. Do you want the police going by to check on that guy again and again? Don’t they have something better they could be doing? That’s the question I ask about Flock.

Greg Lambert (43:43)
Apparently not.

Hannah Bloch-Wehba (43:45)
Yeah.

Marlene Gebauer (43:48)
Well, Hannah, it’s time for the crystal ball question. I like this new combo because Greg’s question is looking back to the present, and now we’re looking forward. What is the single biggest change you see coming that you think we need to prepare for in the next few years?

Hannah Bloch-Wehba (44:07)
Ugh, I am so bad at predicting the future, you guys.

Greg Lambert (44:11)
Everybody is.

Marlene Gebauer (44:12)
Everybody is.

Hannah Bloch-Wehba (44:15)
I hate being on record.

Marlene Gebauer (44:18)
The good thing is, if we wait another seven years, we don’t have to hold you accountable for it. But hopefully

Greg Lambert (44:24)
Ha.

Marlene Gebauer (44:25)
it won’t be another

Hannah Bloch-Wehba (44:24)
True.

Marlene Gebauer (44:25)
seven years.

Hannah Bloch-Wehba (44:26)
I mean, I think that from where I sit in higher ed, there are significant changes coming to higher education. I don’t know what they are. If you talk to my husband, he thinks our kids aren’t going to go to college because higher ed will have changed that dramatically in the next 20 years. I’m not sure whether he’s right or wrong because I hate to predict the future. I do think we have certain core assumptions about the kinds of things higher ed does that are going to be different. That’s partly because of technological change in AI. I think there are going to be questions about the role of higher education as the workforce changes, which I think is going to happen fast. I also think a lot of higher education institutions are changing from within. I do wonder what universities and research are going to look like in the next few years. For people who have kids in college, going to college in the future, or going to graduate school or law school, these are going to be different places than they are right now.

Marlene Gebauer (45:53)
Yeah, I agree with that. I saw something not too long ago. My alma mater dropped a number of degree programs and is narrowing its focus to certain ones. That could be a problem. We’re training people for their professions as opposed to broadening their education.

Hannah Bloch-Wehba (46:20)
Right. We’re doing that at the same time as, as you said, journalism is starting to collapse. Book publishing is collapsing. Search engines don’t serve the functions they used to serve. So the whole information ecosystem, how people learn, is changing quickly.

Greg Lambert (46:40)
Yeah. Well, again, on that happy note,

Marlene Gebauer (46:45)
Yeah.

Greg Lambert (46:47)
Hannah, I want to thank you for coming in and talking with us. This has been fascinating. If I had predicted how well Texas A&M Law School would do in its first 15 years, it’s skyrocketed, and I’m sure you have contributed to that as well. So thank you for coming in.

Marlene Gebauer (47:09)
Definitely.

Hannah Bloch-Wehba (47:10)
Thank you so much for having me. It’s been a pleasure.

Marlene Gebauer (47:14)
Thanks to all of you for listening, and don’t forget to like and subscribe.

Greg Lambert (47:18)
Yeah. Hannah, what’s the best place for people to find more of your content and learn more about your work?

Hannah Bloch-Wehba (47:30)
You can find my content on my website, hannahbw.com, or the Texas A&M website, and on SSRN, the Social Science Research Network.

Marlene Gebauer (47:43)
As always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry, and bye, everybody.

]]> It turns out that tech companies don’t sit outside of government as just ordinary vendors. This week on The Geek in Review podcast, we welcome back Texas A&M University School of Law professor Hannah Bloch-Wehba to talk about accountability of Big Tech, AI regulations, government surveillance, and the intertwining of public authority and private tech infrastructure. Bloch-Wehba traced the dependency between the two powers all the way back to the 1930s in her article “How Tech Took Over,” in how the tech sector became a foundation for national security and economic growth.

Today’s hybrid form of governance, where Bloch-Wehba explains how a handful of private companies supply data and cloud systems, along with decision-making infrastructures across multiple governmental agencies. It is a struggle for traditional constitutional doctrines to adjust to the modern technology and the operations provided by contractors that are providing their core foundational operations.

The issues also enter into the criminal law enforcement areas and Bloch-Wehba’s “Rights, Knowledge, and Capture in the Datafied State,” discusses how trade-secret claims are throwing a barrier between proprietary data systems and criminal defendant’s ability to examine the systems that are being used to convict them in the courts. There is a strangeness in the judicial systems where corporate choices are shaping the legal process being followed, rather than corporate governance following established legal norms.

Bloch-Wehba’s “Information Law Pluralism” covers how privacy rules, audits, impact assessments, disclosure duties, researcher access, and independent review as parts of a broader system governing exactly how knowledge is shared, validated, and even produced. There seems to be no single device that transparently provides accountability. In addition, she lists how a political campaign program against states attempting to regulate AI companies and products is weakening state transparency even further.

Finally, we cover Bloch-Wehba’s “Rethinking Federal Support for Journalism” where she argues that platform payments give rise to the risk of replacing a governmental dependency gets switched for journalist and new organizations being financially tied to companies they must scrutinize. Ideas floated like an AI tax provide some alternative funding possibilities for supporting local and public-interest journalists.

LINKS

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[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: Jerry David DeCicca

Transcript

Marlene Gebauer (00:01)
Hi, I’m Marlene Gebauer from The Geek in Review. I have Sam Moore here from Legal Technology Hub. He’s going to tell us about AI governance from the advisory side.

Sam Moore (00:10)
Thank you. Good to see you again. In the advisory team at Legal Technology Hub, we’ve been having a lot of conversations with clients about AI governance. Most law firms and law departments have at least an AI policy of some kind, and some broad standards are starting to emerge. A trend we’re seeing at the moment is a shift away from policies written in 2023 and 2024, which largely stated what you couldn’t do, and toward a more informative approach that defines different risk categories and makes distinctions between routine, low-risk uses of AI and other, more substantial use cases. I think that’s appropriate for where we are, and it’s also what clients are coming to expect from their advisors. One of the bigger challenges we’re seeing right now is how to express an AI use policy in such a way that any member of your legal team could explain it to a client. I think that’s still a big challenge for the industry.

I don’t think clients are all that happy if their attorney says, “I’ll have to get back to you about that,” because the attorney is using AI day to day, so they should be able to explain the policy themselves. How else do you know they’re complying with it? We’re doing some interesting work at the moment, helping law firms and law departments take those first-version AI use policies that are very much “thou shalt not” and turn them into more readable, appropriate governance positions that inform the conversation with clients. If anyone wants to find out more about our advisory services, they can find me or Cheryl Wilson Griffin on LinkedIn, or they can visit legaltechnologyhub.com.

Marlene Gebauer (01:50)
Thank you, Sam. It is important for AI policy to be in plain language so people understand it.

Sam Moore (01:58)
Absolutely.

Marlene Gebauer (02:07)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:14)
And I’m Greg Lambert. Today we welcome back Hannah Bloch-Wehba, the law professor at Texas A&M and a leading scholar on law and technology. Hannah writes about how tech can be used to conceal power and evade accountability. Hannah, welcome back to The Geek in Review. We were just looking at the date. It’s only been seven years and two children.

Marlene Gebauer (02:34)
Ha ha.

Hannah Bloch-Wehba (02:37)
Thank you so much for having me back after seven years and two children.

Greg Lambert (02:42)
Ha ha ha.

Marlene Gebauer (02:44)
We are absolutely happy to have you, and you have been busy in your role at A&M. So we’re going to dive in.

Greg Lambert (02:51)
Yeah, well A&M’s been busy.

Congrats. What are you guys, like 22nd now in U.S. News & World Report and all the good stuff?

Hannah Bloch-Wehba (03:00)
Something like that. We have a new building, a new campus coming up. It’s a pretty exciting time at A&M.

Marlene Gebauer (03:07)
Terrific. Well, I want to dig into some of the things you’ve been writing about. In “How Tech Took Over,” you argue that the government didn’t merely fail to regulate Silicon Valley; it affirmatively helped make technology companies essential to public governance. What legal and policy choices were most responsible for creating that dependence?

Hannah Bloch-Wehba (03:30)
So in law and tech circles, we often talk about deregulation and hands-off methods of regulation that started when the internet was commercialized in the mid-’90s. But in “How Tech Took Over,” I go way back to the 1930s and ’40s, and I look at choices that were made in science policy and innovation policy then. Essentially, the government knew that technology and innovation were going to be critical both to economic progress and to safeguarding national security. So they built institutions that promoted both scientific research and technological innovation. Once that ball started rolling, the government became a primary consumer of those innovations. From almost a century back at this point, the government has been making legal and policy choices around science policy and tech policy, like the National Science Foundation Act and military funding of basic research, which were intended to cultivate this industry for public benefit. I think that set the stage for tech’s expansion, but not in a predictable or linear way. So over time those policy choices have changed and we’ve had these different political moments where, for example, the balance between economic progress and national security needs has shifted somewhat. But overall, I think this is a really important sort of reservoir of federal support for the tech industry that tech law scholarship hasn’t really explored. And so that’s why I was interested in digging into this history and seeing why this happened and how.

Marlene Gebauer (05:25)
What was the response to the article?

Greg Lambert (05:28)
Everyone came out and said It’s all lies. It’s all lies, Hannah.

Hannah Bloch-Wehba (05:33)
You know, I think people have been interested in it in this moment when it’s clear that the relationship between the federal government and tech companies is not an arm’s-length regulatory relationship. We see CEOs at the inauguration lining up to support the president. We have press conferences about the urgent need for federal funding to help build data centers. The U.S. government is intervening in a permitting lawsuit in Mississippi against xAI. We are seeing a lot of weird dynamics in the last couple of years that we haven’t seen before. The conventional narrative that the federal government has deregulated doesn’t capture what’s going on. So I think people are aware and hungry for a better explanation of this relationship. The response has been interested and supportive. I will say, people are like, “This isn’t an article, this is a book,” which is true. It will eventually be a book, but right now it’s one article at a time.

Marlene Gebauer (06:44)
And we’ll have you back for the book.

Greg Lambert (06:45)
Yeah, definitely. It’s

Hannah Bloch-Wehba (06:47)
Well, that’ll help me finish it.

Greg Lambert (06:50)
Well,

Marlene Gebauer (06:50)
Ha ha ha.

Greg Lambert (06:51)
it’s definitely interesting times. You started talking about this almost commingling of tech and government. I think we’ve always assumed that the traditional assumptions of constitutional accountability attach to the government.

But we’re almost seeing this blending of authority between the private sector in Silicon Valley and the government. So how are you viewing that in your now article, but soon-to-be book, and explaining this blending between private and public accountability?

Hannah Bloch-Wehba (07:34)
So I think this, what I call the emergence of a hybrid form of governance, that intentionally takes advantage of relationships between private actors and public entities, is complicated for conventional approaches to constitutional law. We’ve always had this problem to some degree.

There have always been efforts to extend constitutional accountability to contractors, for example. We have a complicated body of doctrine about how and when that is and isn’t appropriate. We have state action doctrine that limits the application of the Constitution in general. Then we have little exceptions around the edges of the state action doctrine. The problem here, I think, is that the kinds of functions that many tech companies are performing don’t look that familiar. So one of the arguments I make in the paper is that tech companies are really enmeshed in providing the informational infrastructure for government decision making. So take an example like cloud infrastructure for federal government agencies.

A huge amount of that infrastructure is provided by a handful of tech companies. Is that a government function? It’s hard to analogize that kind of function to something that would have happened even 20 or 30 years ago. So we don’t have a lot of good comparisons to say, okay, well, this looks like this other form of public-private cooperation, so here’s how we should think about it.

So I think we’re feeling around in the dark for the kinds of principles that we ought to apply. It’s much easier to apply the traditional state action doctrine when you have a traditional government contractor relationship than when the contractors are providing the thing that makes the government run, and that is, to a significant extent, what’s happening right now. So I think it’s getting much harder to pull them apart. At the same time, a lot of those infrastructural activities are hard for normal people to see and understand. An ordinary person doesn’t care whether the informational infrastructure for a federal agency is provided by Google or Oracle or the government itself. We don’t have a lot of insight into what’s going on. So it’s something that I think is important and yet overlooked in constitutional doctrine and in scholarship, frankly.

Greg Lambert (10:30)
One of the things this brings to mind as you’re talking is, typically, we have a transfer of power, different administrations, different Congresses, but the underlying structure is consistent. Do you see this as causing an inconsistency as we transfer power from one administration to another, or when a new Congress comes in, that we’re kind of the…

Marlene Gebauer (10:58)
We’re looking short-term. We’re looking in eight-year segments as opposed to long-term.

Hannah Bloch-Wehba (11:04)
I guess I see it as related, but a little bit different. It’s never been the case that when a president enters into a long-term contract with an outside party, you’re going to redo that contract every Congress, right? Or with the changeover of every presidential administration.

So the system of contracting has always been, in some way, vulnerable to the accusation that it’s not sufficiently politically accountable because the people who made the agreement long predated the current administration. I think this introduces an interesting wrinkle, right? This is not a Trump administration problem. This is a problem that has been occurring and building, as I argue, over decades, right? But if you enter into a long-duration contract with a tech company to provide something like cloud infrastructure for the Department of the Interior, I’m making this example up, but you enter into that contract with Google, you are not going to then cancel it and enter into a different contract with a different vendor for the next administration because everybody knows it’s too much trouble and cost to switch from one vendor to the other. It’s enough trouble to switch from Gmail to Outlook. Nobody does that in their personal lives. Now imagine the entire informational infrastructure of a federal government agency is locked into one provider. It’s hard to destabilize that relationship. Regardless of what elected officials might want to do, there are serious switching costs and serious anti-competitive effects.

Marlene Gebauer (13:08)
I’m curious if there’s another model out there that perhaps clarifies the relationship that we could consider.

Hannah Bloch-Wehba (13:18)
I’m curious if you have one in mind.

Marlene Gebauer (13:21)
I don’t have one in mind. I just…

Hannah Bloch-Wehba (13:23)
I can’t.

Marlene Gebauer (13:23)
I’m just, I mean, I realize this is all…

Greg Lambert (13:27)
Are you talking about another government, another country or state?

Marlene Gebauer (13:30)
Perhaps in another country or another government, or something that someone has surfaced as a potential model, if there’s any work being done in that space.

Hannah Bloch-Wehba (13:43)
So I’m not a comparativist, but I do think when I’ve talked about this issue in front of European audiences, they’ve been baffled. They’re like, “We don’t have this problem.” There are a couple of reasons why they don’t have that problem. One is that I think they do a lot more public provision than the United States tends to do. So one model is for the government to build it themselves or to recognize that government investments give rise to a certain degree of state control. Historically, the United States has not taken that approach, but I could see a parallel universe where we might take that approach of public provision. I also think that the U.S. constitutional doctrine of state action is not one that I’ve seen in robust form in most European constitutional democracies. So there’s no reason why you have to have that doctrine as the delineator of what’s subject to constitutional constraint and what’s not. You could wish away some of these features of our order, but in the real world where we live and work, these are the constraints we’re operating within.

Greg Lambert (15:17)
Yeah. Well, it’s kind of like Sean West, someone who’s been on the show before, said, Europe’s superpower is its regulation. I think part of the traditional American superpower has been its somewhat hands-off approach to regulation. I can see how everything we do baffles the Europeans, so that doesn’t surprise me.

Hannah Bloch-Wehba (15:41)
Yeah, but I think they also, the last time I gave this kind of paper, I gave an early version of this paper in Berlin in 2022. I think it was before the Europeans had turned away from American tech platforms as reliable partners. So I think now that the Ukraine war has been dragging on for so long, and we see that the Europeans are trying to make this pivot away from partnering too closely with U.S.-based tech companies, they’re more attuned to these kinds of problems of capture and enmeshment and the risks they might cause for Europe.

Right. It’s one set of risks when it’s U.S. companies within the U.S. government. It’s a totally different situation when we’re talking about U.S. companies providing the infrastructure for a foreign government, right? I think that illustrates what the structural problems might be, even though the risks themselves look different.

Marlene Gebauer (16:56)
All right, I’m going to pivot a little bit in terms of our questions. I mean…

Greg Lambert (17:01)
We could stay on this topic all hour.

Marlene Gebauer (17:03)
We really could. I am fascinated.

Hannah Bloch-Wehba (17:05)
Yeah.

Marlene Gebauer (17:08)
But when the book comes out, Hannah Bloch-Wehba (17:11)
No.

Marlene Gebauer (17:11)
we’re going to talk all about it. So in “Rights, Knowledge, and Capture in the Datafied State,” you examine government use of privately developed data and algorithmic tools, particularly in criminal law enforcement. What happens to due process when someone can’t examine or challenge a system because a vendor claims trade secret protection?

Hannah Bloch-Wehba (17:33)
Yeah, I mean, this is an emerging problem for criminal law enforcement, and it’s been emerging for several years now. I’m definitely not the first person to write about it, but it’s not going away. The problem is that when a vendor claims trade secrecy to conceal information about how a product works, a product that was used to produce evidence used to arrest or convict somebody, then the defense has a right to challenge, to confront the evidence against them. They can’t do that because the trade secrecy claim stands in their way. So I think it undermines people’s ability to access sufficient information to exercise their rights.

It undermines their ability to challenge the evidence against them, but it also creates a bigger, more systemic problem, which is that a lot of these tools aren’t tested or validated in a meaningful way before they get used. The process of confrontation between the defense and the prosecution is the way the public gets information about whether this tool works or not.

If you can’t confront it, then you can’t produce that information. Then we don’t know whether it works or not. That seems to me a problem that has social effects beyond the individual defendant in an individual case. We’re using technologies that haven’t been tested or validated, and then, in the course of these individual confrontations, undermining the ability to produce evidence about their validity.

Marlene Gebauer (19:25)
Your Tech Policy Press article shows Google effectively determining whether and how police could conduct geofence investigations. What does that example reveal about capture when a company controls the relevant data, the investigative possibilities available to police, and the information available to the courts?

Hannah Bloch-Wehba (19:49)
Yeah, so this is an example of the broader dynamic, which is that these companies wield control over the government’s ability to engage in certain courses of action, and that control isn’t always visible or checkable. Google is a great example. For many years, they kept individuals’ location information by default in something they called the Sensorvault, which is kind of creepy and not what

Marlene Gebauer (20:25)
Yikes.

Greg Lambert (20:26)
Yeah.

Hannah Bloch-Wehba (20:29)
I would have called it if I had known that name would have gotten into the public domain.

Marlene Gebauer (20:31)
They needed a PR person on that one. They did not have one.

Hannah Bloch-Wehba (20:35)
They didn’t anticipate that becoming public, but they kept everybody’s location information by default in the Sensorvault. That made it possible for law enforcement to go to Google and say, “Hey, we want to know everybody who was in this location at this particular time.” Only because Google had made that choice was it possible for government to do. So, starting several years ago, once law enforcement figured out they could make this kind of request, they started doing it again and again. This is a geofence warrant, and it quickly became the most frequent form of judicial warrant Google was receiving. It became an appealing tool for law enforcement. Strikingly, Google was the only company that would comply with these warrants.

So it’s not that other companies didn’t have the data, but Google was the one that would give the data to law enforcement. To their credit, they said, “Well, we’re not going to give it to you willy-nilly. Our corporate policy is that you need to get a warrant rather than a subpoena or a court order. If you get a warrant based on probable cause, we will work with you.” Then they created this elaborate process by which they worked with law enforcement to deliver this information. It’s a striking example of the law of Google. This is not the law that was developed by

Greg Lambert (22:04)
Yeah.

Hannah Bloch-Wehba (22:05)
a legislature or a court. This is not Google trying to apply a ruling that was already made. It’s Google saying, “This is what we want you to do.” Law enforcement is so eager to get this data that they do what Google wants them to do.

And I think Google was right that a warrant is required, but that’s not the point. The point is that it’s not their job to decide. Right.

Marlene Gebauer (22:32)
They’re making the call.

Greg Lambert (22:34)
Mm-hmm.

Hannah Bloch-Wehba (22:35)
So I think this is a clear way in which it’s corporate policy that law enforcement has to get a warrant. It’s also the data infrastructure or the data governance choices Google is making internally, the collection by default, the indefinite storage, right? Those are the things that make it possible for law enforcement to get this information. So I think, in a clear way, it was Google that shaped the geofence warrant.

Then, in 2023, Google decided, “You know what? We aren’t going to do this anymore. It’s incredibly burdensome for us.” They turned off default storage of location information in the Sensorvault. That’s another example of Google determining whether this is okay or not.

Marlene Gebauer (23:32)
Their choice, yeah.

Hannah Bloch-Wehba (23:33)
Like, okay, after several years of complying, now we’re going to change our data governance practices, and this isn’t going to be possible anymore. Again, I think that was a win for privacy. I don’t think it’s a win for democratic control to have individual tech companies determining, based on what’s convenient for them, whether they’re going to comply with a warrant, what legal standards should apply, and whether the government should be able to get this data. That makes me uncomfortable.

Greg Lambert (24:12)
Well, we talked recently about the OpenAI and Hugging Face situation, and one of the comments we made was, if this were an individual, this would be a crime.

Hannah Bloch-Wehba (24:24)
Absolutely.

Greg Lambert (24:25)
So this situation where Google decided, “Okay, I’m going to turn this off.” There was a situation, and it’s a little apples and oranges here, but where someone, I think, was coming back into the country and tricked the government official into wiping the data from his phone.

Hannah Bloch-Wehba (24:43)
Yeah.

Greg Lambert (24:44)
Now he’s charged with a felony for doing this. So, individual crime. If the company did that, it would be a policy change, a change in internal policy.

Hannah Bloch-Wehba (24:57)
Right.

Greg Lambert (24:58)
So, yeah, it seems like we’re almost a little upside down.

Hannah Bloch-Wehba (25:04)
Yeah, I mean, to be clear, I don’t think that should be a crime. I don’t think it should be a crime to decide you’re going to delete your information. But I definitely don’t think we should have one set of rules that apply to big tech companies and a different set of rules entirely that apply to individuals. That seems bad to me, and I don’t think we’ve appreciated the extent to which that’s the case.

But it is true that these companies are making decisions about, you know, how to test their products that may end up violating the law and they’re not being held to account the way that an individual might be. And I think we should conceive of that as a potential problem, right? That the law might not be applied evenly.

Marlene Gebauer (25:56)
Then it’s about the data they collect and how they choose to use it, and there’s no oversight on that.

Hannah Bloch-Wehba (26:03)
Well, I think that, to the extent oversight exists, for a long time we’ve assumed that we’ve opted to comply with the terms of service as they’re delivered to us, right? So if the

Marlene Gebauer (26:16)
Yes, our click agreements. Yeah, because everybody reads those.

Hannah Bloch-Wehba (26:22)
thing that makes the difference is whether I’m an individual or a company that produced 40-page terms of service. That seems obviously laughable, but I think that is part of what’s going on.

Greg Lambert (26:37)
Well, again, another one we could have a complete show on. As if you haven’t done enough writing, one of the other papers you wrote was…

Marlene Gebauer (26:46)
There’s more. There’s a couple more.

Greg Lambert (26:50)
information. Okay. Yeah.

Hannah Bloch-Wehba (26:50)
To be fair, it’s been seven years since you had me on the podcast.

Marlene Gebauer (26:54)
Yeah.

Greg Lambert (26:55)
So one of the other papers you wrote was “Information Law Pluralism.” First, do you mind defining what you mean by information law here?

Hannah Bloch-Wehba (27:06)
Yeah, so by information law, I mean the body of legal structures that governs how knowledge is produced, validated, and disseminated. A lot of the time when we talk about information law, people mean one part of it, like privacy law or public disclosure law. I’m trying to bring that all together and say these are all ways in which we govern flows of knowledge.

Greg Lambert (27:38)
Well, in that, you talk about emerging tools, audits, impact assessments, document requirements, and independent assessment. Which of these has the greatest potential for producing meaningful accountability at the end of the day?

Hannah Bloch-Wehba (28:02)
Yeah, so part of the impetus for writing this paper was that I took a step back. I decided to do a project where I looked at every piece of proposed and enacted state legislation about AI. This was a stupid project. It was a couple of years ago.

Greg Lambert (28:18)
So small, just a little weekend project.

Marlene Gebauer (28:21)
Well, a fun little thing to do, yeah.

Hannah Bloch-Wehba (28:24)
It was a couple of years ago. It wasn’t quite as crazy as it would be to try that project now. But I thought, what are they trying to do? My sense was that there were some broad-stroke similarities and some important differences, and I wanted to capture those. As I looked at these laws, I realized they’re all trying to do the same thing, which is, in different ways and for different audiences, force the AI providers, right? Whether that’s a company, an individual, or a system is often unclear in the legislation, but force these providers to generate and share certain kinds of information about their products. It might be shared with individuals who are affected by those products, like when you apply for a job and have to use an AI interviewing system.

It might be shared with the public, lawmakers, or regulators. We have all these different audiences for all these different kinds of information. So what are they trying to do? That’s the thing I think lawmakers were genuinely uncertain about. I wrote the paper to explain that these different ways of producing information serve different purposes and should be used in complementary ways. So I’m going to avoid your question entirely and say, I don’t think there

Greg Lambert (29:49)
Well done.

Hannah Bloch-Wehba (29:50)
is one tool that will be the silver bullet for accountability. Luckily, I didn’t think until pretty recently that we had to choose one tool because I thought it would be up to the states to regulate AI as they saw fit, so we were going to have federalist experimentation. Little did I know.

Greg Lambert (30:13)
I think I found the flaw in your logic.

Marlene Gebauer (30:15)
Yeah.

Hannah Bloch-Wehba (30:19)
I might still be right about that. I think the claim to state capacity to regulate AI is strong, and that, but for threats to preempt that experimentation through litigation and threats of cutting off different kinds of aid to the states, we would be seeing a lot of that experimentation in action. The last couple of years have tried to deter it from happening. I don’t think you have to choose one of these tools. They all need to work together. I think the public needs a certain degree of information about how these systems operate in practice.

We need expert insight into how they’re working. That’s researcher access to the under-the-hood data a lot of the time. I think it’s important for these companies to produce information directly to the regulators charged with overseeing their operations. Unfortunately, I also believe there’s a successful political campaign against these laws, and that’s going to stymie the development of legislation in this area. So this is a paper that got…

Greg Lambert (31:44)
Is that argument about national security? Because we’re hearing, well, if we regulate our AI companies, China and Russia are not going to regulate, and we’re going to lose the advantage. Is that the sole argument, or is there something more nuanced in that?

Hannah Bloch-Wehba (32:04)
I think there are two related arguments, and they’re familiar instances of this debate. One, AI is essential to national security. We shouldn’t do anything that’s bad for national security. Therefore, we shouldn’t do anything that might be bad for AI development because we need to win the arms race.

Greg Lambert (32:26)
Seems super logical.

Hannah Bloch-Wehba (32:27)
It’s super logical at a high level. The problem is that, first, China does regulate AI, so the whole idea that they have hands-off development and we need it too doesn’t work. Second, it’s this assumption that you need a totally deregulatory environment to safeguard national security, but we would never have said that about nuclear weapons, for example. So it seems like a strange leap. I’m also not sure you can win an arms race, but that’s neither here nor there. The other part is about the federal role. Having failed to pass AI legislation in the U.S. Congress that would preempt state legislation, we’ve instead had a series of threats by the president to convene a litigation task force to sue states that regulate AI in an overly burdensome way. We haven’t seen that happen yet, but many states recognize that the threat is real. We also have a litigation campaign dedicated to undermining these laws. So xAI has been suing in California and Colorado to undermine AI transparency rules, and they’ve been pretty successful. Colorado revisited and substantially weakened its laws partly in response to these threats. I think they all hang together as one ball of wax. Whether you call it national security or something else, it’s a successful campaign to get states to change their positions.

Marlene Gebauer (34:16)
Okay. One last pivot.

Greg Lambert (34:19)
Yeah, and you said it’s been seven years, but I looked at the dates. All of these are 2026 articles, so…

Marlene Gebauer (34:25)
Well, we’re trying…

Hannah Bloch-Wehba (34:25)
I had a good year.

Marlene Gebauer (34:27)
We’re trying to keep it fresh. You have definitely been busy. So in “Rethinking Federal Support for Journalism,” you argue that requiring platforms to compensate news organizations might deepen journalism’s dependence on tech companies. And you propose a national journalism foundation. How would you design something like that to support knowledge production while protecting editorial independence, resisting political interference, and ensuring that funding reaches public-interest and local journalism rather than primarily benefiting established media owners? This is an important question in my mind. I Hannah Bloch-Wehba (35:09)
Yeah.

Marlene Gebauer (35:10)
think our whole information and news dissemination platforms are struggling right now.

Hannah Bloch-Wehba (35:18)
Yes, and I think they’re going to struggle even more in the year to come. I came up as a press freedom lawyer. I spent four years litigating on behalf of journalists and media organizations, so this is an issue that’s important to me. I think a lot of news organizations are intuitively worried about taking money from the federal government, understandably, because they feel like it might compromise their independence. These proposals are floating around to enable news organizations to bargain with platforms for a share of the money they’ve been losing based on platform competition with their products.

I don’t think that solves the independence problem. I think you’re no more independent

Marlene Gebauer (36:14)
Just trading.

Hannah Bloch-Wehba (36:15)
if you take your money from Google than if you take your money from the government. Google is an extremely influential company, and we should want independent news organizations to report on influential actors, whether they are private or public. Right? So I think this is a bad structure, and I think it’s better to have a structure that’s public but independent in a meaningful way. What I’m thinking about is modeled on the National Science Foundation, which tried to thread some of these conflicts when it began funding university research and basic science.

Similarly, researchers were worried that if they took federal money, they would be captured by a federal policy agenda. The National Science Foundation dealt with that through institutions of independent peer review, essentially. Now, all of that is contested. I don’t mean to say it’s easy, make a National Science Foundation and call it the National Journalism Foundation.

But I think it speaks to the fact that these are issues we’ve dealt with before, conflicts between independence and federal support that we’ve been dealing with since 1952, when the NSF was first stood up. We’re revisiting them today with contemporary debates about science policy and research funding. I don’t think it’s impossible to manage. I think it’s important to direct federal funding and support to the kinds of news organizations producing news of genuine civic value, particularly in underserved areas. We have a lot of news deserts in this country. It’s hard for many Americans to access high-quality news. My first order of business would be geographically underserved communities.

Greg Lambert (38:29)
Yeah, that got me thinking, and this might be a little far-fetched, but you have the National Science Foundation that you talked about. Then there are other, not agencies, but other ways of distributing licensing, like ASCAP for the music industry.

Hannah Bloch-Wehba (38:49)
Yeah.

Greg Lambert (38:50)
Do you see there being some type of underlying control over how information gets distributed and then reimbursed?

Hannah Bloch-Wehba (39:04)
Yeah, I mean, I haven’t thought about that end of it. ASCAP is an interesting corollary that I hadn’t considered. The two poles I have thought about are the NSF on one side and the Corporation for Public Broadcasting, which no longer exists, on the other.

Greg Lambert (39:23)
Yeah.

Hannah Bloch-Wehba (39:25)
One of the things they have in common is that both have turned into significant political footballs because their budgets are appropriated by Congress. At the beginning of debates about the Corporation for Public Broadcasting, they considered an alternative, which was to fund CPB through an excise tax on television sets instead of through the appropriations process.

And that would

Greg Lambert (39:57)
Mm.

Hannah Bloch-Wehba (39:57)
have been the funding. I think about this because there are so many proposals to tax AI or take a golden share of AI, and then what do you do with that money? The conversation about supporting an ecosystem that includes journalism should be part of the question of how you tax AI and what you do with that fund. I could see a revenue source that comes from some kind of AI tax and removes at least some of the levers political actors have to bully these agencies or force them to adopt a political agenda that would compromise the integrity and independence of journalistic institutions.

So I think that’s one big question I have. Where do you get the money to support such an institution, and how do you make sure it’s not captured by political interests trying to strong-arm news coverage? I think that’s an important question.

Greg Lambert (41:12)
Whew. Well, on that happy note, let’s wind this down. We’ve started asking a new question before we jump into the crystal ball question. Hannah, you get to be one of the first people we ask this. What’s something that’s true for you today that wasn’t true one year ago?

Hannah Bloch-Wehba (41:39)
Well, I was going to say that a year ago we weren’t at war with Iran, and that’s true, but I don’t think it’s that interesting. I do think it’s important for the overall

Greg Lambert (41:50)
Right.

Hannah Bloch-Wehba (41:51)
question of how the relationship between tech companies and national security is changing based on the nation’s foreign policy. The thing that I think is interesting and emerging quite quickly is that there is a popular movement mobilizing around two things. One is data centers. Opposition to data centers is becoming a high-profile grassroots issue that I didn’t expect or see a year ago today, although I think the ball was rolling. The other thing is Flock surveillance cameras, which

Greg Lambert (42:33)
yeah.

Hannah Bloch-Wehba (42:35)
are everywhere and have been for several years, and are also galvanizing a wave of popular opposition. So the thing I would say, which is a positive spin on it, is that issues related to tech accountability and tech’s enmeshment in the state are much more visible to people than they were a year ago. That means the political conversation about these things is changing quite quickly, especially because it’s an election year.

Greg Lambert (43:08)
Yeah. I saw a story this morning where this guy sat underneath one of the Flock cameras and had his computer set up. The police kept coming by and checking on him because they thought he was trying to hack into the Flock camera. He’s like, “So I’m the problem, right? Not this?”

Marlene Gebauer (43:26)
Yeah.

Hannah Bloch-Wehba (43:28)
Yeah. I mean, I do think Greg Lambert (43:30)
Yeah.

Hannah Bloch-Wehba (43:31)
it can be a huge diversion of resources. Do you want the police going by to check on that guy again and again? Don’t they have something better they could be doing? That’s the question I ask about Flock.

Greg Lambert (43:43)
Apparently not.

Hannah Bloch-Wehba (43:45)
Yeah.

Marlene Gebauer (43:48)
Well, Hannah, it’s time for the crystal ball question. I like this new combo because Greg’s question is looking back to the present, and now we’re looking forward. What is the single biggest change you see coming that you think we need to prepare for in the next few years?

Hannah Bloch-Wehba (44:07)
Ugh, I am so bad at predicting the future, you guys.

Greg Lambert (44:11)
Everybody is.

Marlene Gebauer (44:12)
Everybody is.

Hannah Bloch-Wehba (44:15)
I hate being on record.

Marlene Gebauer (44:18)
The good thing is, if we wait another seven years, we don’t have to hold you accountable for it. But hopefully

Greg Lambert (44:24)
Ha.

Marlene Gebauer (44:25)
it won’t be another

Hannah Bloch-Wehba (44:24)
True.

Marlene Gebauer (44:25)
seven years.

Hannah Bloch-Wehba (44:26)
I mean, I think that from where I sit in higher ed, there are significant changes coming to higher education. I don’t know what they are. If you talk to my husband, he thinks our kids aren’t going to go to college because higher ed will have changed that dramatically in the next 20 years. I’m not sure whether he’s right or wrong because I hate to predict the future. I do think we have certain core assumptions about the kinds of things higher ed does that are going to be different. That’s partly because of technological change in AI. I think there are going to be questions about the role of higher education as the workforce changes, which I think is going to happen fast. I also think a lot of higher education institutions are changing from within. I do wonder what universities and research are going to look like in the next few years. For people who have kids in college, going to college in the future, or going to graduate school or law school, these are going to be different places than they are right now.

Marlene Gebauer (45:53)
Yeah, I agree with that. I saw something not too long ago. My alma mater dropped a number of degree programs and is narrowing its focus to certain ones. That could be a problem. We’re training people for their professions as opposed to broadening their education.

Hannah Bloch-Wehba (46:20)
Right. We’re doing that at the same time as, as you said, journalism is starting to collapse. Book publishing is collapsing. Search engines don’t serve the functions they used to serve. So the whole information ecosystem, how people learn, is changing quickly.

Greg Lambert (46:40)
Yeah. Well, again, on that happy note,

Marlene Gebauer (46:45)
Yeah.

Greg Lambert (46:47)
Hannah, I want to thank you for coming in and talking with us. This has been fascinating. If I had predicted how well Texas A&M Law School would do in its first 15 years, it’s skyrocketed, and I’m sure you have contributed to that as well. So thank you for coming in.

Marlene Gebauer (47:09)
Definitely.

Hannah Bloch-Wehba (47:10)
Thank you so much for having me. It’s been a pleasure.

Marlene Gebauer (47:14)
Thanks to all of you for listening, and don’t forget to like and subscribe.

Greg Lambert (47:18)
Yeah. Hannah, what’s the best place for people to find more of your content and learn more about your work?

Hannah Bloch-Wehba (47:30)
You can find my content on my website, hannahbw.com, or the Texas A&M website, and on SSRN, the Social Science Research Network.

Marlene Gebauer (47:43)
As always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry, and bye, everybody.

]]> xlambert@gmail.com (Greg Lambert) Brad Blickstein on Private Equity Thinking, AI Pricing, and the Law Firm Business Model https://www.geeklawblog.com/2026/08/brad-blickstein-on-private-equity-thinking-ai-pricing-and-the-law-firm-business-model.html Mon, 03 Aug 2026 10:41:21 +0000 https://www.geeklawblog.com/?p=19512

We welcome back Brad Blickstein, CEO at Blickstein Group, to discuss how private equity principles may provide law firms with an alternative approach to profitability, governance, and even long-term growth. Blickstein’s new book, WWPED: What Would Private Equity Do? was written to walk firms through how treating topics like pricing, technology, talent, and client relationships as part of the enterprise value instead of overhead expenses after year-end partnership distributions.

Pulling from Jae Um’s topics of Cream, Core, and Commodity framework, Blickstein talks about the legal work as the primary competitive battleground. Much like businesses that provide baked goods, firms have to separate the customized legal judgment from the repeatable legal processes, technology, and what alternative legal services providers offer. Law firm leaders should understand what scalable work is, begin building consistent systems to deliver that work, and truly professionalize pricing over relying upon what a partner’s gut tells them.

We also cover the Blickstein Group’s 2026 Law Firm COO Survey where technology adoption and investment ranks as the leading strategic initiative with 38.1% identified practice silos as the largest structural issue and 27% of COOs listed lack of operational authority as another prime issue. COOs are struggling with being tasked with modernizing law firms, but not given the authority to actually overcome the base issues of decentralized partnerships, competing incentives, and overall firm political structures.

Add AI into the mix, and the pricing question becomes even more important. Some two-thirds of the COOs surveyed confessed that they were not formally measuring any return on investment (ROI) in which they could later measure any law productivity or direct revenue increases. Blickstein points out that faster work in a billable hour model is not the type of math that law firms want to calculate, and that firms have to address this directly and redesign their overall pricing model on value received by the client, not hours worked by the lawyers. We all discuss the issues of alternative fee arrangements (AFAs) have face in the more than 30 years since Blickstein originally published an article titled “Alternative Billing Making a Comeback.” AFAs bring with it issues of shadow billing, client trust factors, and the need to express value not tied to the amount to time spent on the work.

We also break down the corporate buyer side and address the Blickstein Group’s 18th Annual Law Department Operations Survey which identifies AI pilot projects in corporate legal departments, but very few operational deployments. These may be tied to the long running issue of poor data hygiene along with business objectives that are not clearly tied to overall corporate strategy.

Brad gets to be one of the first to answer our new question of “what’s true today that wasn’t true a year ago?” A nice lead in to our Crystal Ball question. We cover AI token pricing and having to compete with the new “AI native firms” that are spinning up from former BigLaw partners.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

LINKS

Transcript:

Stephanie Wilkins (00:01)
The GenAI conversation has been advancing faster than a lot of people can keep up with lately, but some new developments have brought older concepts like prompting back into the spotlight, thanks to a trending new topic: token cost. Token cost moved into the spotlight recently as tools like Claude gained traction in legal because most plans come with token limits, as well as the ability to request limit increases, which has resulted in tales of astronomical bills for some users. Recently, Legora also announced that it’s moving its Agent Pro offering to consumption-based pricing, which means it’s billing by what the agent does rather than by a flat-seat license. And what agents do is consume tokens. Eventually, other providers are sure to follow suit. What many don’t fully understand is just how quickly token usage can add up. A few extra follow-up questions, a document pasted in twice, a chat continuing long after it should have been reset. If that sounds familiar, token consumption compounds faster than you might expect, and you might be looking at higher token usage than you think.

And you might not even realize it until you’ve hit your usage limit or, worse, seen the bill. This is a blind spot we’ve been unpacking in one of our latest article series on Legaltech Hub: how to get more out of tools like Claude without burning time, decreasing accuracy, or racking up unnecessary bills. We’ve covered topics like what tokens are and why they function as a hidden meter running behind every chat, when to reset a conversation versus continue in the same chat, and what everyday prompting habits, from repasting whole documents to burying five questions in one prompt, might be driving up both cost and inaccuracy without you knowing it. Head over to legaltechnologyhub.com to read the full series and learn more about how to get the most out of your token limits and your use of tools like Claude.

Marlene Gebauer (01:51)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (01:57)
And I’m Greg Lambert. Today, we’re exploring the modern legal services market through a value-focused operational lens. We’re looking at what happens when you apply the strategic rigor of private equity to law firms, and how that intersects with the ground truth of legal operations on both the law firm and client sides.

Marlene Gebauer (02:24)
Yeah, we are absolutely thrilled to welcome back a frequent and favorite guest of the show, Brad Blickstein. Brad is the chief executive officer of Blickstein Group. He’s widely recognized in the legal industry as a futurist who’s spent nearly three decades analyzing how legal services are purchased and delivered.

Greg Lambert (02:42)
And, like us, he started when he was 12.

Marlene Gebauer (02:45)
That’s right.

Greg Lambert (02:46)
Brad, we’re going to talk about this, but you’ve been pretty busy lately. This past April, you published your new book, WWPED, which is What Would Private Equity Do?: Unlocking Value in the Law Firm You Already Own. Then, as if that wasn’t enough, you also released two massive benchmark studies: the 2026 Law Firm COO Survey Report in July and the 18th Annual Law Department Operations Survey. So you’ve been busy.

Marlene Gebauer (03:15)
Yeah, Brad the underachiever. Welcome back to The Geek in Review.

Greg Lambert (03:18)
[Laughs.]

Brad Blickstein (03:20)
I have been busy, and I do like to try new things, but I have to push back a little bit at the use of the second-person singular there. I have a great team at Blickstein Group, and they did most of the work that I get the credit for. So I’ve been busy, but they’ve been busy too, and it makes a big difference.

Greg Lambert (03:41)
That’s a good leader that gives credit where credit’s due.

Marlene Gebauer (03:44)
Absolutely.

Brad Blickstein (03:45)
Well, yeah. I mean, it is my name on the door, so...

Marlene Gebauer (03:48)
[Laughs.]

Brad Blickstein (03:48)
But we do what we can.

Marlene Gebauer (03:51)
So, Brad, in your new book, WWPED, you challenge law firm leaders to stop treating their firms purely as cash-flow vehicles for year-end partner distributions and start focusing on enterprise value. You use a great bakery analogy to explain Jae Um’s cream, core, and commodity market segmentation. Can you walk us through the difference between an artisanal legal bakery and a Crumbl Cookies-style system, and why the core segment, representing 70% of the market, is the ultimate battleground for law firms today?

Brad Blickstein (04:27)
Sure. Let’s start with cream, core, and commodity. The idea is that it’s a pyramid. The cream work is at the top. That’s what you think it is: the work where you need a seriously good lawyer to give you seriously good advice or provide seriously good work product. This is where we think about things artisanally. That work needs to be done artisanally. Then, at the bottom of the pyramid, skipping ahead a little bit, we have the commodity work.

Much of that is already not being done by law firms, or is in their captive ALSPs. That work has largely become systematized or scaled. But there’s tons of work in the middle. Jae calls it core work. Sometimes I prefer “run-the-company work.” The work, as you point out, Marlene, that we say is 70% of the market. To be honest with you, that’s a Fermi number, or really just a guess.

But it’s something like that. It’s a big chunk of the market. This is what law departments spend most of their money on and, frankly, where law firms earn most of their profits. This is where the leverage model plays in for law firms. Much of that work shouldn’t be done artisanally. It can be done differently than it is today, and it can be scaled. That’s where the bakery analysis comes in.

If you’re going to the bakery and it’s your 25th wedding anniversary, you’re throwing a big party, all your friends are going to be there, and you want the best cake you can find. You want something special for your spouse. You want to make it a big deal. You want to go to an artisanal bakery where an actual baker with skills is going to bake your cake because that’s appropriate for that situation.

On the other hand, your kid got a couple of B-pluses in school and you want to bring home some cookies to celebrate. That’s nice too, right? But you don’t need to go to that length for that. You can go to a more scaled bakery, something like Crumbl. And what’s the other one? The overnight cookie, the sleepy-time cookie. There’s something else like that. Insomnia Cookies, sorry. The opposite of sleepy time. You can go there and pick up a bundle. Think about how those different types of entities work.

At Crumbl Cookies, there’s likely not even a baker within 100 miles of the store. There’s a formula, a recipe, and technology that makes sure it goes right. They put out a fine product that works for most instances where you want cookies or a cake. I don’t think we always do a terribly good job in legal of differentiating between the two. There’s a lot of work that can be done with technology and different resources in a scaled way.

That work can be more profitable for firms if it’s done right, and it needs to be differentiated from the artisanal cream work, which shouldn’t be scaled. What we’re arguing for in the book, more than almost anything, is that you should figure out what that work is and scale appropriately for your firm.

Greg Lambert (07:26)
Yeah. I was afraid, Brad, that instead of saying the kid got a couple of B’s on the report card, you were going to say, “On your 24th wedding anniversary, you don’t go get a fancy cake,” and I was going to have to make sure

Marlene Gebauer (07:37)
[Laughs.]

Brad Blickstein (07:37)
Well

Greg Lambert (07:38)
my wife didn’t listen to this episode.

Brad Blickstein (07:40)
Well, I’ve been married more than 24 years, and I’ve learned a few things along the way, so you were at no risk of me stepping in that little pile.

Greg Lambert (07:50)
Well, I think that’s a pretty good argument. Part of the issue is firms trying to determine what falls into those categories. But one of the other issues we run into a lot, and we’re seeing it now around AI infrastructure, is the fact that, as partnerships, these firms are designed to distribute their profits at the end of the year and not have anything roll over. So when you see things like Kirkland announcing that they’re going to put $500 million into AI infrastructure over the next few years, one, not every firm can do that. And two, I laugh because I remember talking with a partner who said his wife remembers the one month he didn’t take a draw because they were putting some funds into the future. He said that was 10 years ago, and she still remembers it. It’s outside the norm. How do you convince firms that aren’t used to doing this to figure out a way to build for the future?

Brad Blickstein (09:08)
Yeah, that’s a great question. Not everything is for everyone, right? Not every strategy you might deploy here is for every firm. But it is one of the advantages of taking outside investment. That’s where you can smooth that over. You can use PE funds, or funds from whomever, for some original payout to your partners, to smooth that over time, or to make the investment. That’s one way to do it.

The other way, and I just alluded to it, is to pick your spots. Half a billion dollars is a lot of money for everybody. But over three years, which I believe is the period, it’s not that much money. I can’t believe I used those two things in the same sentence, but it’s not that much money for Kirkland partners, because they have

Marlene Gebauer (09:55)
[Laughs.]

Brad Blickstein (09:56)
enough partners to spread that out nicely, right? One thing I wonder about the partner you mentioned: did he tell his wife three years in advance that one month’s draw might not be coming? There are expectations that need to be set.

Greg Lambert (10:10)
No, I guarantee you he told her the month it happened.

Brad Blickstein (10:14)
Right. So there’s some of that too. But one of the book’s big arguments is that you don’t need to take outside investment to do some of the things a PE fund would say a firm should be doing. I list about 10 different things, but you don’t have to do them all at once. One thing I believe in is that law firms should professionalize pricing.

If I were a PE fund investing in law firms, one of the first things I would suggest is bringing on a serious, professional pricing operation to fix that. I’m not even talking about alternative fee arrangements or flat fees. That’s exactly what I mean by professionalizing.

Marlene Gebauer (10:50)
What does professionalizing pricing mean? Firms have pricing groups, so what are you saying that’s different?

Brad Blickstein (10:58)
Well, some firms have pricing groups. Some large firms have pricing groups. Most firms do not, and most midsized firms do not. In a lot of cases, those pricing groups have been neutered or aren’t listened to. At most firms, it’s still often a partner’s gut feeling about whether they have to discount or whether they can pass through a price increase. In many cases, the engagement partner feels they can’t get that across. They can’t sell that to clients right now without the right data to back it up.

Again, not everything is for every firm. But for many firms, it’s easy to see a couple, three, or five points across the board if you bring in the right type of pricing function. That goes right to the bottom line. Pick your spots. Don’t do everything we’re suggesting here at once. Where can some of this PE thinking truly affect value? Start there, with a bite your partners can digest.

Marlene Gebauer (12:03)
Let’s look at the findings from the July 2026 Law Firm COO Survey. A historic shift occurred this year. For the first time, technology investment and adoption surpassed talent acquisition as the top strategic initiative for law firms. Yet 38.1% of the COOs identified practice silos as the number-one structural issue they would fix, and 27% cited a lack of operational authority. Why is there such a persistent authority gap for professional managers tasked with executing these high-stakes technology strategies?

Brad Blickstein (12:39)
Yeah, I think there are two answers. One is the relative power of the people expected to do that work. At a midsized firm, the COO might have had a title like administrator a couple of years ago, perhaps even the same person. To what extent do the partners empower that person to run a firm the way a serious businessperson would be allowed to?

You hear a lot of this from COO-type folks: “This is what I’d like to do, but the partners won’t let me.” So there’s that. I guess there are three issues. There’s also a big delta between their opinion of leadership and their opinion of partners. Leadership is often more supportive of COOs’ business initiatives than the partners are. There’s that disconnect: “The managing partner is totally behind me, but then when I try to roll it out through the partnership...” You guys live and breathe this every day, right?

Greg Lambert (13:41)
No, I’ve never seen that. Never.

Brad Blickstein (13:44)
I do think, and this is evolved thinking for me, that practice silos are an underrated problem, especially at big firms. Different partners think differently, and different practice areas are built differently and designed to do different things. We understand why they’re put together in a firm, but in many ways they’re not alike enough for some of these systemic changes to be achieved entirely firmwide. The IP practice and the M&A practice, for example, are run and built quite differently.

Greg Lambert (14:23)
When Marlene was giving you that question, I was thinking that one of the worst things you can have in a position of authority is all the responsibility and none of the authority to do what you’re assigned to do. One thing you pointed out in the survey was a rise in COOs coming from outside legal to take over those roles, which makes sense. A lot of us want to run things more like a business, so we bring in businesspeople. But then we throw them into legacy law firm environments. How do you avoid bringing someone in and giving them all the responsibility and none of the authority?

Brad Blickstein (15:16)
That’s such a... Yeah, maybe.

Greg Lambert (15:18)
That’s another book, huh?

Marlene Gebauer (15:19)
Yeah.

Brad Blickstein (15:21)
That might be an encyclopedia. I think a lot of it comes down to where power is consolidated within the firm. If you’re talking about a small or midsized firm where leadership has consolidated a lot of the power, it’s easier. When the job becomes a consensus-building job, it’s difficult. Frankly, if you came from outside legal, that might be an impediment. If you come from corporate America, where it’s hierarchical, what the boss says goes, and people generally stay in line, working at a law firm where that’s inherently not the case is problematic.

One instructive example, and I’m oversimplifying, is that plaintiffs’ firms don’t have this problem as often. There’s usually one or two people’s names on the door. There’s a big boss who is the big boss of the firm, whether their name is on the door or it’s “Tiger Law” or something like that. They tend to row in the same direction, with fewer practice areas. They’ve been able to bring in a lot of talent and innovative business strategies that people on the corporate and defense side struggle to bring in. So it can be done. It takes a consolidation of power, or you need to be a world-class consensus builder, or there has to be some consolidation of power above you within the firm.

Greg Lambert (16:51)
Yeah.

I was going to ask: what advice would you give a COO coming in from outside legal?

Brad Blickstein (16:57)
A little patience, a little Zen. Make sure you re-up your blood pressure medication before you start. It takes the right kind of person.

Greg Lambert (17:09)
Enroll in some yoga and meditation.

Marlene Gebauer (17:11)
Breathing exercises.

Brad Blickstein (17:13)
And fight the battles you can win.

Greg Lambert (17:16)
Yeah.

Brad Blickstein (17:17)
But if you pick the battle, you have to win it. You can’t lose the ones you’ve decided to fight. That’s a good lesson for COOs too. Think carefully about how badly you need something to happen and how important it is. If you’re going to use a lot of political capital and possibly burn your reputation when you lose that battle, think about how badly you need to fight it.

Marlene Gebauer (17:44)
Makes sense.

One of the most striking statistics in the COO Survey is that 66% of COOs report their firms do not formally measure or document AI-related efficiency gains. And 39% of those same COOs expect AI to increase revenue through greater lawyer productivity. If firms are saving time but aren’t measuring it, are they actively devaluing their services? How can a firm defend pricing under a billable-hour model when AI is compressing those timelines and the firm has no baseline data for pricing alternative fee arrangements?

Brad Blickstein (18:33)
It’s a conundrum. It’s a problem. First, I think the number of people who are formally measuring or documenting is going to go up, right? There’s no way we’re going to continue

Marlene Gebauer (18:45)
Has to.

Brad Blickstein (18:46)
not documenting anything. And let’s be honest, while it can be hard to analyze and use, the data is there. These are people who measure everything they do in six-minute increments, right? We can talk about this later, but it’s going to be a much bigger problem in-house, where they don’t track what people do daily. At law firms, they do, so that number is going to change.

I’m fascinated by this AFA question because I’ve been tracking it for so long. You guys know I was one of the founders of Corporate Legal Times magazine, and in the fourth issue we published, in 1992, we ran an article with the headline, “Alternative Billing Making a Comeback.” So 30 years ago, Jesus, 35 years ago now, it was already making a comeback. This idea that...

Greg Lambert (19:36)
Any day now.

Brad Blickstein (19:37)
And we’re almost...

Marlene Gebauer (19:38)
Mm-hmm.

Brad Blickstein (19:38)
here. I think the idea that clients are going to make firms do this has largely been disproven. It’s true around the edges, and there’s 15% or whatever that do it, but the hypothesis that firms will adopt AFAs because clients will make them has been disproven over the last 30-plus years.

Now we’re getting to a point where it might be the firms that need to do this. It won’t be only for client satisfaction, because they think clients want it. It might become the only way to make money. So I’m not as concerned about the 66% that, in my mind, aren’t yet tracking. I’m much more concerned about the 39% who say they’re going to increase revenue through greater lawyer productivity. How? Under what model does that happen? Better productivity should decrease revenue. Perhaps it lets you bring on more clients and make enough of it back that way, but that’s a nuanced issue. I’m not sure the market sees it yet. People are starting to get their arms around it, but I don’t think anyone has solved it.

Firms are going to have to measure productivity better and understand it better. They’ll have to go to clients and say, “Look, these projects that used to take 10 hours, we can’t bill you for one hour. I guess we could bill you $7,000 for that hour, but that doesn’t work. How can we bill you in a way that gives you value and allows us to capture some of the efficiency gains?” To me, that’s

Greg Lambert (21:19)
Right.

Brad Blickstein (21:19)
the battleground. Like that’s what we’re looking at.

Greg Lambert (21:22)
Right. I’m going to switch up my question a little after hearing your answer. I heard this at a conference earlier this year: why would clients want AFAs now, when they think it takes only one hour instead of 10? Where’s the motivation? But my question is more about how we get past the trust factor and move to something beneficial for both sides. We tried this in 2008 and 2012. It was like, “Hey, we’re going to do alternative fees,” and clients responded, “Yeah, I don’t trust you. I want the alternative fee, but I also want you to tell me how many hours it took so you’re not getting one over on me,” to put it politely. How do we get past that?

Brad Blickstein (22:19)
That’s a great question, and it’s an underrated issue. People love to blame firms for the fact that AFAs largely haven’t been implemented, but clients don’t trust their own firms. That’s a big issue. The shadow-billing issue you mentioned is huge. If you’re a firm, you’re effectively putting a cap on the amount you can bill without putting a floor on the amount you could bill. It doesn’t work for firms. I think we’re approaching a crossroads.

The first thing to remember is that clients like sending work to their law firms. They like working with their firms. Despite all the talk about bringing work in-house, and I know that’s a giant trend, all things being equal, most general counsel would prefer to keep sending work to their firms. Now, all things aren’t equal, and they’re less equal than they used to be.

First, firms will have to be more transparent without going all the way to a specific number of hours. Firms have to do a better job, and this is hard, of articulating the value of their work in some way other than the number of hours it takes.

Frankly, I think firms need to stand up a little more. If you’re a firm and a client asks for shadow billing, the proper answer is no. “We won’t share the number of hours this work takes. Either you feel you’re getting a fair amount of value for the work we’re doing or you don’t. And if you don’t, we have a problem, and we’ll talk about that. But the hours we put in aren’t your business.” You have to say it much more nicely than I just did.

I don’t think firms push back enough when clients ask that. The whole point of an AFA is valuing the work rather than the time. And, by the way, the amount of time it takes to do the work is not the same as the value of the work. We have to figure out a way to work together to provide good work for good value, rather than go to work for a certain amount of time. I’m preaching at this point, though. I don’t know how to do that.

Marlene Gebauer (24:37)
It’s a good point, but it’s interesting because you’re getting that level of detail in pitches and RFPs. Clients want to know exactly how much time things take and how much time you’re saving. It’s difficult to respond the way you’re suggesting when they’re specifically asking for this information.

Brad Blickstein (25:08)
It’s hard to pivot that conversation. When they ask that question, the way to pivot is to say, “Let’s not talk about how much time we’re saving. Let’s talk about how much money we’re saving. Here’s what we used to bill you for this type of work, and here’s what we’ll bill you now.” You can ask how long it’s going to take, but what does it matter?

It’s easy for me to sit here on your podcast and say, “Go tell your clients we’re not going to answer your questions.” It’s hard to do, and it takes fortitude. But if you establish the value and build trust with clients over time, I think it works.

The other thing you might do, especially when talking about AI, is think about the other value propositions. Can you get the work done faster? I don’t mean in less time. I mean finished sooner. Can the deal close faster? Can the litigation be settled sooner? In due diligence, can you review more material than before? Classic due diligence used to mean, “Send us the contracts with your 25 biggest clients,” because that was all the due diligence team had time to review. Now you can use AI to look at all the contracts. What’s the value proposition around using AI that isn’t tied solely to efficiency?

Tie that to cost savings rather than time savings or hours saved. Stand up and say, “Look, we respect you, but we’ve thought a lot about this. We’ve spent a lot of energy on it, and we think you’re asking the wrong question. Let us answer the right one.” Some clients will say, “Well, they didn’t fill out our RFP properly.” Too bad. But I think many will respect that and want to work with a thoughtful firm.

Marlene Gebauer (26:54)
I’m going to shift to the buyer side. The 18th Annual Law Department Operations Survey, the LDO Survey, shows a corporate legal buyer that is stabilizing structurally while absorbing broader business mandates, including CFOs stepping directly into contracting. Meanwhile, corporate GenAI adoption is defined by piloting, at 52%, rather than fully operationalized systems, at 23%. Why are corporate legal departments stuck in pilot purgatory, and how does the middle-mile data hygiene problem play into this?

Brad Blickstein (27:34)
Yeah, that’s part of it. First, considering we’re only two or three years into the AI era, depending on how you count, I’m not sure it’s purgatory. In many ways, we’re working through this, and I expect that number to fall substantially over time.

Part of it is caused by all the noise. How do you determine whether a tool is worthwhile these days without giving it a shot? It’s hard. I think we’ll get there, but the data hygiene problem will grow. Whatever the old saying is, garbage in, garbage out. I think we’ll solve the data protection problem. People understand, and are starting to become more comfortable with, how their data is used.

The idea that we want our law firms to provide every possible ounce of efficiency through AI tools, but we won’t allow them to use our data as training data to help them do that, isn’t fair. I think we’ll get past many of those issues over time. But data in corporations is a mess. To get the full benefit from these tools, you’ll have to solve that in many cases. I wonder how many pilots will fail because the technology, adoption, and training are good, but the data isn’t there.

Marlene Gebauer (29:13)
It’s a big issue in terms

Brad Blickstein (29:16)
It’s a big issue.

Marlene Gebauer (29:16)
of getting this data usable, getting it where you need it to go, and making your technology useful. I mean, it’s

Brad Blickstein (29:23)
Yeah, my hope is...

Marlene Gebauer (29:26)
It’s not a new problem, but, yeah.

Greg Lambert (29:27)
Yeah, I was going to say this is a 25-year-old overnight problem.

Marlene Gebauer (29:30)
It’s not a new problem, but I don’t know whether it’s easier or harder. The technology is so much better, but the security issues are more difficult. I’m not sure.

Brad Blickstein (29:44)
What I’m hoping, I guess, is that if you look at e-discovery over the years, technology created the e-discovery problem, right? The ability to disperse data quickly, email everyone, and copy a million people created a data problem. Technology then largely solved it with tools such as auto review and platforms. I’m hoping AI will help solve the data hygiene problem itself, enabling AI to work better on the legal problems the data supports.

Greg Lambert (30:25)
I know this was the first time in the survey, and of course, AI has only been around for about three years, that usability received a higher score and beat out security as the top AI priority for legal operations teams.

I think this opens the door to unique opportunities for law firms to offer services to their clients. Are you seeing clients expect something they haven’t been offered before? Are they receptive to those services?

Brad Blickstein (31:10)
I think they’re absolutely receptive. If I ran a law firm, I’d try to get ahead of all this. I’d build things, find new use cases, and bring solutions to clients before someone else does or before the client tries to build something themselves.

For any work you’re concerned the client might bring in-house, think about what that work is and build a case for why they don’t need to bring it in-house. Explain why the firm can do it more efficiently, better, and at a better price than the law department going through the trouble of bringing it inside. I don’t think we see firms bringing that case to clients enough. When firms do, clients are extremely receptive.

ALSPs are starting to get involved too. If I were a firm or an ALSP, I would build offerings and bring them to the right clients before the client decides to find a solution.

Greg Lambert (32:11)
Yeah. Speaking of ALSPs, why haven’t they wiped the floor with BigLaw? Everyone seemed to expect that, because they have these repeatable processes. Why aren’t they kicking ass?

Brad Blickstein (32:34)
Well, first of all, I think some of them are kicking ass, right? When you look at them as a percentage of the legal market’s overall share, it seems somewhat stagnant. But many of these businesses are on big growth curves within the ALSP segment. There are many reasons, but the big one is that clients like working with law firms, and that’s their

Greg Lambert (32:59)
It’s what they know.

Brad Blickstein (33:00)
preference. If it isn’t working, or if it’s the kind of process or project that makes sense to take away from a firm, document review is a great example. Law firms largely proved they shouldn’t be in that business by throwing a million associates and no technology at document review 20 years ago. Legal service providers and e-discovery providers have thrived there.

For work that is clearly process-oriented, I think ALSPs are doing quite well. But much of what they have to do comes back to the artisanal argument we discussed. They have to convince clients that a large amount of legal work isn’t artisanal, that it benefits from process and technology, and that ALSPs are the people to deliver it.

Marlene Gebauer (33:48)
Okay, Brad, what’s true today that wasn’t true a year ago?

Brad Blickstein (33:55)
I think legal organizations, both in-house and outside, are starting to care about what AI costs. Legora, I believe, talked about moving to a consumption model. That’s a big issue. People are moving from, “Let’s get everyone to adopt whatever they can and use AI wherever they can,” to, “Hold on. What’s it going to cost when we start paying by the token? Does it make sense?”

The underlying assumption is that using AI to do this work is less expensive than using lawyers. At some point, if AI gets expensive enough, that might no longer be true. That wasn’t a concern a year ago. The gold rush was on, and now people are starting to wonder what stream they should mine. I think I mixed the metaphor. Where should we drill, look, or pan? Where should we pan for gold? That’s what I’m asking.

Will these price increases stick? Will open models become more valuable and viable? Those are good questions. I believe people are concerned about this today in a way they weren’t a year ago. I’m not sure how long the concern will last.

Greg Lambert (35:23)
Yeah, it’s going to be interesting to see, because six months ago it was, “Burn every token you can get your hands on,” and now it’s, “Whoa, wait a second.”

Marlene Gebauer (35:33)
Well, wait a minute. Hold on there.

Brad Blickstein (35:36)
I’ll also say, and it’s related, that we’re starting to see people distinguish between adoption and fluency. It felt like the standard was activation, and then adoption: “98% of our people have used the tool in the past month.” That’s a nice statistic, but it doesn’t say whether people have become AI-proficient or AI-fluent, whether they’re using it for the right things, or whether they’re using it enough.

Marlene Gebauer (36:01)
Is it useful for what they’re doing?

Brad Blickstein (36:04)
Yeah. As we start asking those questions, it’s going to affect where the value lies and what these tools are worth.

Greg Lambert (36:14)
Well, Brad, before we started recording, we looked back at your last prediction on the crystal ball question, and I think you did pretty well.

Marlene Gebauer (36:23)
Yeah, he did.

Greg Lambert (36:24)
Your prediction at the end of last year was that generative AI and LLMs would fundamentally accelerate how software and tools are built in the legal tech space, and that you thought there would be about an 80% improvement in turnaround speed from idea to product.

Brad Blickstein (36:47)
That does seem to be happening. Especially if you count the things being built within firms and law departments through vibe coding, Claude Code, or all those things, I think we can check that off as one I got right.

Greg Lambert (37:04)
All right. Now it’s time to ask you again. What kind of change or challenge do you see that law firms and law departments need to prepare for over the next few years?

Brad Blickstein (37:19)
I’ll answer two ways to double my shot at getting something right. Generally speaking, the issue of the business and billing model will have to come to a head at some point. Perhaps less so within the next two years, but eventually many law departments will put pressure on firms to reduce the number of hours they bill. I have some thoughts about how they’ll do that, but I don’t know.

If you’re a firm, that doesn’t work under the leverage model. At some point, many firms will have to come to terms with the issue. I also think about all these, and I’m not a big fan of the “AI-native” term, but these AI-native-type spin-offs from firms. Can partners leave a BigLaw firm, start a firm with no associates and use AI instead, then go to the same clients who trust them with that new model? External forces like that might push firms to make some of these changes more than their clients do.

Marlene Gebauer (38:25)
Well, that is food for thought. Brad Blickstein, thank you so much for joining us again and helping us cut through the noise and think like value-focused operators. And thanks to all of...

Greg Lambert (38:35)
Yeah. Thanks, Brad.

Marlene Gebauer (38:36)
you. Sorry, go ahead.

Greg Lambert (38:37)
Thanks, Brad.

Brad Blickstein (38:39)
Thanks, guys. I appreciate this. It’s always fun and thought-provoking talking to you. You ask questions that make me think hard about what the responses should be, which I don’t appreciate, but I do like talking to you guys.

Marlene Gebauer (38:50)
We appreciate it.

Greg Lambert (38:54)
Yep, he had a full set of hair before we started this.

Brad Blickstein (38:56)
It always reminds me of that scene in the movie Clueless where they think she has a concussion and say, “Ask her some questions.” They ask, “What’s seven times seven?” and someone says, “Stuff she knows.”

Greg Lambert (39:05)
[Laughs.]

Brad Blickstein (39:06)
I would prefer if you asked me stuff I know.

Marlene Gebauer (39:11)
And thanks to all of you, our listeners, for taking the time to listen to The Geek in Review. If you enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Greg Lambert (39:21)
And Brad, what’s the best place for listeners to find and buy What Would Private Equity Do? and learn more about

Brad Blickstein (39:29)
Easy.

Greg Lambert (39:30)
Blickstein Group?

Brad Blickstein (39:31)
Yeah, you can go to our website, BlicksteinGroup.com, or for the book itself, BlicksteinGroup.com/WWPED. And I’ve always wanted to say this: it’s available wherever fine books are sold online. You can get it from Amazon or BN.com. It’s not hard to find, and we appreciate the support.

Marlene Gebauer (39:52)
Absolutely. And, as always, the music you hear is from Jerry David DeCicca. Thank you very much, Jerry, and goodbye, everybody.

]]> We welcome back Brad Blickstein, CEO at Blickstein Group, to discuss how private equity principles may provide law firms with an alternative approach to profitability, governance, and even long-term growth. Blickstein’s new book, WWPED: What Would Private Equity Do? was written to walk firms through how treating topics like pricing, technology, talent, and client relationships as part of the enterprise value instead of overhead expenses after year-end partnership distributions.

Pulling from Jae Um’s topics of Cream, Core, and Commodity framework, Blickstein talks about the legal work as the primary competitive battleground. Much like businesses that provide baked goods, firms have to separate the customized legal judgment from the repeatable legal processes, technology, and what alternative legal services providers offer. Law firm leaders should understand what scalable work is, begin building consistent systems to deliver that work, and truly professionalize pricing over relying upon what a partner’s gut tells them.

We also cover the Blickstein Group’s 2026 Law Firm COO Survey where technology adoption and investment ranks as the leading strategic initiative with 38.1% identified practice silos as the largest structural issue and 27% of COOs listed lack of operational authority as another prime issue. COOs are struggling with being tasked with modernizing law firms, but not given the authority to actually overcome the base issues of decentralized partnerships, competing incentives, and overall firm political structures.

Add AI into the mix, and the pricing question becomes even more important. Some two-thirds of the COOs surveyed confessed that they were not formally measuring any return on investment (ROI) in which they could later measure any law productivity or direct revenue increases. Blickstein points out that faster work in a billable hour model is not the type of math that law firms want to calculate, and that firms have to address this directly and redesign their overall pricing model on value received by the client, not hours worked by the lawyers. We all discuss the issues of alternative fee arrangements (AFAs) have face in the more than 30 years since Blickstein originally published an article titled “Alternative Billing Making a Comeback.” AFAs bring with it issues of shadow billing, client trust factors, and the need to express value not tied to the amount to time spent on the work.

We also break down the corporate buyer side and address the Blickstein Group’s 18th Annual Law Department Operations Survey which identifies AI pilot projects in corporate legal departments, but very few operational deployments. These may be tied to the long running issue of poor data hygiene along with business objectives that are not clearly tied to overall corporate strategy.

Brad gets to be one of the first to answer our new question of “what’s true today that wasn’t true a year ago?” A nice lead in to our Crystal Ball question. We cover AI token pricing and having to compete with the new “AI native firms” that are spinning up from former BigLaw partners.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

LINKS

Transcript:

Stephanie Wilkins (00:01)
The GenAI conversation has been advancing faster than a lot of people can keep up with lately, but some new developments have brought older concepts like prompting back into the spotlight, thanks to a trending new topic: token cost. Token cost moved into the spotlight recently as tools like Claude gained traction in legal because most plans come with token limits, as well as the ability to request limit increases, which has resulted in tales of astronomical bills for some users. Recently, Legora also announced that it’s moving its Agent Pro offering to consumption-based pricing, which means it’s billing by what the agent does rather than by a flat-seat license. And what agents do is consume tokens. Eventually, other providers are sure to follow suit. What many don’t fully understand is just how quickly token usage can add up. A few extra follow-up questions, a document pasted in twice, a chat continuing long after it should have been reset. If that sounds familiar, token consumption compounds faster than you might expect, and you might be looking at higher token usage than you think.

And you might not even realize it until you’ve hit your usage limit or, worse, seen the bill. This is a blind spot we’ve been unpacking in one of our latest article series on Legaltech Hub: how to get more out of tools like Claude without burning time, decreasing accuracy, or racking up unnecessary bills. We’ve covered topics like what tokens are and why they function as a hidden meter running behind every chat, when to reset a conversation versus continue in the same chat, and what everyday prompting habits, from repasting whole documents to burying five questions in one prompt, might be driving up both cost and inaccuracy without you knowing it. Head over to legaltechnologyhub.com to read the full series and learn more about how to get the most out of your token limits and your use of tools like Claude.

Marlene Gebauer (01:51)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (01:57)
And I’m Greg Lambert. Today, we’re exploring the modern legal services market through a value-focused operational lens. We’re looking at what happens when you apply the strategic rigor of private equity to law firms, and how that intersects with the ground truth of legal operations on both the law firm and client sides.

Marlene Gebauer (02:24)
Yeah, we are absolutely thrilled to welcome back a frequent and favorite guest of the show, Brad Blickstein. Brad is the chief executive officer of Blickstein Group. He’s widely recognized in the legal industry as a futurist who’s spent nearly three decades analyzing how legal services are purchased and delivered.

Greg Lambert (02:42)
And, like us, he started when he was 12.

Marlene Gebauer (02:45)
That’s right.

Greg Lambert (02:46)
Brad, we’re going to talk about this, but you’ve been pretty busy lately. This past April, you published your new book, WWPED, which is What Would Private Equity Do?: Unlocking Value in the Law Firm You Already Own. Then, as if that wasn’t enough, you also released two massive benchmark studies: the 2026 Law Firm COO Survey Report in July and the 18th Annual Law Department Operations Survey. So you’ve been busy.

Marlene Gebauer (03:15)
Yeah, Brad the underachiever. Welcome back to The Geek in Review.

Greg Lambert (03:18)
[Laughs.]

Brad Blickstein (03:20)
I have been busy, and I do like to try new things, but I have to push back a little bit at the use of the second-person singular there. I have a great team at Blickstein Group, and they did most of the work that I get the credit for. So I’ve been busy, but they’ve been busy too, and it makes a big difference.

Greg Lambert (03:41)
That’s a good leader that gives credit where credit’s due.

Marlene Gebauer (03:44)
Absolutely.

Brad Blickstein (03:45)
Well, yeah. I mean, it is my name on the door, so…

Marlene Gebauer (03:48)
[Laughs.]

Brad Blickstein (03:48)
But we do what we can.

Marlene Gebauer (03:51)
So, Brad, in your new book, WWPED, you challenge law firm leaders to stop treating their firms purely as cash-flow vehicles for year-end partner distributions and start focusing on enterprise value. You use a great bakery analogy to explain Jae Um’s cream, core, and commodity market segmentation. Can you walk us through the difference between an artisanal legal bakery and a Crumbl Cookies-style system, and why the core segment, representing 70% of the market, is the ultimate battleground for law firms today?

Brad Blickstein (04:27)
Sure. Let’s start with cream, core, and commodity. The idea is that it’s a pyramid. The cream work is at the top. That’s what you think it is: the work where you need a seriously good lawyer to give you seriously good advice or provide seriously good work product. This is where we think about things artisanally. That work needs to be done artisanally. Then, at the bottom of the pyramid, skipping ahead a little bit, we have the commodity work.

Much of that is already not being done by law firms, or is in their captive ALSPs. That work has largely become systematized or scaled. But there’s tons of work in the middle. Jae calls it core work. Sometimes I prefer “run-the-company work.” The work, as you point out, Marlene, that we say is 70% of the market. To be honest with you, that’s a Fermi number, or really just a guess.

But it’s something like that. It’s a big chunk of the market. This is what law departments spend most of their money on and, frankly, where law firms earn most of their profits. This is where the leverage model plays in for law firms. Much of that work shouldn’t be done artisanally. It can be done differently than it is today, and it can be scaled. That’s where the bakery analysis comes in.

If you’re going to the bakery and it’s your 25th wedding anniversary, you’re throwing a big party, all your friends are going to be there, and you want the best cake you can find. You want something special for your spouse. You want to make it a big deal. You want to go to an artisanal bakery where an actual baker with skills is going to bake your cake because that’s appropriate for that situation.

On the other hand, your kid got a couple of B-pluses in school and you want to bring home some cookies to celebrate. That’s nice too, right? But you don’t need to go to that length for that. You can go to a more scaled bakery, something like Crumbl. And what’s the other one? The overnight cookie, the sleepy-time cookie. There’s something else like that. Insomnia Cookies, sorry. The opposite of sleepy time. You can go there and pick up a bundle. Think about how those different types of entities work.

At Crumbl Cookies, there’s likely not even a baker within 100 miles of the store. There’s a formula, a recipe, and technology that makes sure it goes right. They put out a fine product that works for most instances where you want cookies or a cake. I don’t think we always do a terribly good job in legal of differentiating between the two. There’s a lot of work that can be done with technology and different resources in a scaled way.

That work can be more profitable for firms if it’s done right, and it needs to be differentiated from the artisanal cream work, which shouldn’t be scaled. What we’re arguing for in the book, more than almost anything, is that you should figure out what that work is and scale appropriately for your firm.

Greg Lambert (07:26)
Yeah. I was afraid, Brad, that instead of saying the kid got a couple of B’s on the report card, you were going to say, “On your 24th wedding anniversary, you don’t go get a fancy cake,” and I was going to have to make sure

Marlene Gebauer (07:37)
[Laughs.]

Brad Blickstein (07:37)
Well

Greg Lambert (07:38)
my wife didn’t listen to this episode.

Brad Blickstein (07:40)
Well, I’ve been married more than 24 years, and I’ve learned a few things along the way, so you were at no risk of me stepping in that little pile.

Greg Lambert (07:50)
Well, I think that’s a pretty good argument. Part of the issue is firms trying to determine what falls into those categories. But one of the other issues we run into a lot, and we’re seeing it now around AI infrastructure, is the fact that, as partnerships, these firms are designed to distribute their profits at the end of the year and not have anything roll over. So when you see things like Kirkland announcing that they’re going to put $500 million into AI infrastructure over the next few years, one, not every firm can do that. And two, I laugh because I remember talking with a partner who said his wife remembers the one month he didn’t take a draw because they were putting some funds into the future. He said that was 10 years ago, and she still remembers it. It’s outside the norm. How do you convince firms that aren’t used to doing this to figure out a way to build for the future?

Brad Blickstein (09:08)
Yeah, that’s a great question. Not everything is for everyone, right? Not every strategy you might deploy here is for every firm. But it is one of the advantages of taking outside investment. That’s where you can smooth that over. You can use PE funds, or funds from whomever, for some original payout to your partners, to smooth that over time, or to make the investment. That’s one way to do it.

The other way, and I just alluded to it, is to pick your spots. Half a billion dollars is a lot of money for everybody. But over three years, which I believe is the period, it’s not that much money. I can’t believe I used those two things in the same sentence, but it’s not that much money for Kirkland partners, because they have

Marlene Gebauer (09:55)
[Laughs.]

Brad Blickstein (09:56)
enough partners to spread that out nicely, right? One thing I wonder about the partner you mentioned: did he tell his wife three years in advance that one month’s draw might not be coming? There are expectations that need to be set.

Greg Lambert (10:10)
No, I guarantee you he told her the month it happened.

Brad Blickstein (10:14)
Right. So there’s some of that too. But one of the book’s big arguments is that you don’t need to take outside investment to do some of the things a PE fund would say a firm should be doing. I list about 10 different things, but you don’t have to do them all at once. One thing I believe in is that law firms should professionalize pricing.

If I were a PE fund investing in law firms, one of the first things I would suggest is bringing on a serious, professional pricing operation to fix that. I’m not even talking about alternative fee arrangements or flat fees. That’s exactly what I mean by professionalizing.

Marlene Gebauer (10:50)
What does professionalizing pricing mean? Firms have pricing groups, so what are you saying that’s different?

Brad Blickstein (10:58)
Well, some firms have pricing groups. Some large firms have pricing groups. Most firms do not, and most midsized firms do not. In a lot of cases, those pricing groups have been neutered or aren’t listened to. At most firms, it’s still often a partner’s gut feeling about whether they have to discount or whether they can pass through a price increase. In many cases, the engagement partner feels they can’t get that across. They can’t sell that to clients right now without the right data to back it up.

Again, not everything is for every firm. But for many firms, it’s easy to see a couple, three, or five points across the board if you bring in the right type of pricing function. That goes right to the bottom line. Pick your spots. Don’t do everything we’re suggesting here at once. Where can some of this PE thinking truly affect value? Start there, with a bite your partners can digest.

Marlene Gebauer (12:03)
Let’s look at the findings from the July 2026 Law Firm COO Survey. A historic shift occurred this year. For the first time, technology investment and adoption surpassed talent acquisition as the top strategic initiative for law firms. Yet 38.1% of the COOs identified practice silos as the number-one structural issue they would fix, and 27% cited a lack of operational authority. Why is there such a persistent authority gap for professional managers tasked with executing these high-stakes technology strategies?

Brad Blickstein (12:39)
Yeah, I think there are two answers. One is the relative power of the people expected to do that work. At a midsized firm, the COO might have had a title like administrator a couple of years ago, perhaps even the same person. To what extent do the partners empower that person to run a firm the way a serious businessperson would be allowed to?

You hear a lot of this from COO-type folks: “This is what I’d like to do, but the partners won’t let me.” So there’s that. I guess there are three issues. There’s also a big delta between their opinion of leadership and their opinion of partners. Leadership is often more supportive of COOs’ business initiatives than the partners are. There’s that disconnect: “The managing partner is totally behind me, but then when I try to roll it out through the partnership…” You guys live and breathe this every day, right?

Greg Lambert (13:41)
No, I’ve never seen that. Never.

Brad Blickstein (13:44)
I do think, and this is evolved thinking for me, that practice silos are an underrated problem, especially at big firms. Different partners think differently, and different practice areas are built differently and designed to do different things. We understand why they’re put together in a firm, but in many ways they’re not alike enough for some of these systemic changes to be achieved entirely firmwide. The IP practice and the M&A practice, for example, are run and built quite differently.

Greg Lambert (14:23)
When Marlene was giving you that question, I was thinking that one of the worst things you can have in a position of authority is all the responsibility and none of the authority to do what you’re assigned to do. One thing you pointed out in the survey was a rise in COOs coming from outside legal to take over those roles, which makes sense. A lot of us want to run things more like a business, so we bring in businesspeople. But then we throw them into legacy law firm environments. How do you avoid bringing someone in and giving them all the responsibility and none of the authority?

Brad Blickstein (15:16)
That’s such a… Yeah, maybe.

Greg Lambert (15:18)
That’s another book, huh?

Marlene Gebauer (15:19)
Yeah.

Brad Blickstein (15:21)
That might be an encyclopedia. I think a lot of it comes down to where power is consolidated within the firm. If you’re talking about a small or midsized firm where leadership has consolidated a lot of the power, it’s easier. When the job becomes a consensus-building job, it’s difficult. Frankly, if you came from outside legal, that might be an impediment. If you come from corporate America, where it’s hierarchical, what the boss says goes, and people generally stay in line, working at a law firm where that’s inherently not the case is problematic.

One instructive example, and I’m oversimplifying, is that plaintiffs’ firms don’t have this problem as often. There’s usually one or two people’s names on the door. There’s a big boss who is the big boss of the firm, whether their name is on the door or it’s “Tiger Law” or something like that. They tend to row in the same direction, with fewer practice areas. They’ve been able to bring in a lot of talent and innovative business strategies that people on the corporate and defense side struggle to bring in. So it can be done. It takes a consolidation of power, or you need to be a world-class consensus builder, or there has to be some consolidation of power above you within the firm.

Greg Lambert (16:51)
Yeah.

I was going to ask: what advice would you give a COO coming in from outside legal?

Brad Blickstein (16:57)
A little patience, a little Zen. Make sure you re-up your blood pressure medication before you start. It takes the right kind of person.

Greg Lambert (17:09)
Enroll in some yoga and meditation.

Marlene Gebauer (17:11)
Breathing exercises.

Brad Blickstein (17:13)
And fight the battles you can win.

Greg Lambert (17:16)
Yeah.

Brad Blickstein (17:17)
But if you pick the battle, you have to win it. You can’t lose the ones you’ve decided to fight. That’s a good lesson for COOs too. Think carefully about how badly you need something to happen and how important it is. If you’re going to use a lot of political capital and possibly burn your reputation when you lose that battle, think about how badly you need to fight it.

Marlene Gebauer (17:44)
Makes sense.

One of the most striking statistics in the COO Survey is that 66% of COOs report their firms do not formally measure or document AI-related efficiency gains. And 39% of those same COOs expect AI to increase revenue through greater lawyer productivity. If firms are saving time but aren’t measuring it, are they actively devaluing their services? How can a firm defend pricing under a billable-hour model when AI is compressing those timelines and the firm has no baseline data for pricing alternative fee arrangements?

Brad Blickstein (18:33)
It’s a conundrum. It’s a problem. First, I think the number of people who are formally measuring or documenting is going to go up, right? There’s no way we’re going to continue

Marlene Gebauer (18:45)
Has to.

Brad Blickstein (18:46)
not documenting anything. And let’s be honest, while it can be hard to analyze and use, the data is there. These are people who measure everything they do in six-minute increments, right? We can talk about this later, but it’s going to be a much bigger problem in-house, where they don’t track what people do daily. At law firms, they do, so that number is going to change.

I’m fascinated by this AFA question because I’ve been tracking it for so long. You guys know I was one of the founders of Corporate Legal Times magazine, and in the fourth issue we published, in 1992, we ran an article with the headline, “Alternative Billing Making a Comeback.” So 30 years ago, Jesus, 35 years ago now, it was already making a comeback. This idea that…

Greg Lambert (19:36)
Any day now.

Brad Blickstein (19:37)
And we’re almost…

Marlene Gebauer (19:38)
Mm-hmm.

Brad Blickstein (19:38)
here. I think the idea that clients are going to make firms do this has largely been disproven. It’s true around the edges, and there’s 15% or whatever that do it, but the hypothesis that firms will adopt AFAs because clients will make them has been disproven over the last 30-plus years.

Now we’re getting to a point where it might be the firms that need to do this. It won’t be only for client satisfaction, because they think clients want it. It might become the only way to make money. So I’m not as concerned about the 66% that, in my mind, aren’t yet tracking. I’m much more concerned about the 39% who say they’re going to increase revenue through greater lawyer productivity. How? Under what model does that happen? Better productivity should decrease revenue. Perhaps it lets you bring on more clients and make enough of it back that way, but that’s a nuanced issue. I’m not sure the market sees it yet. People are starting to get their arms around it, but I don’t think anyone has solved it.

Firms are going to have to measure productivity better and understand it better. They’ll have to go to clients and say, “Look, these projects that used to take 10 hours, we can’t bill you for one hour. I guess we could bill you $7,000 for that hour, but that doesn’t work. How can we bill you in a way that gives you value and allows us to capture some of the efficiency gains?” To me, that’s

Greg Lambert (21:19)
Right.

Brad Blickstein (21:19)
the battleground. Like that’s what we’re looking at.

Greg Lambert (21:22)
Right. I’m going to switch up my question a little after hearing your answer. I heard this at a conference earlier this year: why would clients want AFAs now, when they think it takes only one hour instead of 10? Where’s the motivation? But my question is more about how we get past the trust factor and move to something beneficial for both sides. We tried this in 2008 and 2012. It was like, “Hey, we’re going to do alternative fees,” and clients responded, “Yeah, I don’t trust you. I want the alternative fee, but I also want you to tell me how many hours it took so you’re not getting one over on me,” to put it politely. How do we get past that?

Brad Blickstein (22:19)
That’s a great question, and it’s an underrated issue. People love to blame firms for the fact that AFAs largely haven’t been implemented, but clients don’t trust their own firms. That’s a big issue. The shadow-billing issue you mentioned is huge. If you’re a firm, you’re effectively putting a cap on the amount you can bill without putting a floor on the amount you could bill. It doesn’t work for firms. I think we’re approaching a crossroads.

The first thing to remember is that clients like sending work to their law firms. They like working with their firms. Despite all the talk about bringing work in-house, and I know that’s a giant trend, all things being equal, most general counsel would prefer to keep sending work to their firms. Now, all things aren’t equal, and they’re less equal than they used to be.

First, firms will have to be more transparent without going all the way to a specific number of hours. Firms have to do a better job, and this is hard, of articulating the value of their work in some way other than the number of hours it takes.

Frankly, I think firms need to stand up a little more. If you’re a firm and a client asks for shadow billing, the proper answer is no. “We won’t share the number of hours this work takes. Either you feel you’re getting a fair amount of value for the work we’re doing or you don’t. And if you don’t, we have a problem, and we’ll talk about that. But the hours we put in aren’t your business.” You have to say it much more nicely than I just did.

I don’t think firms push back enough when clients ask that. The whole point of an AFA is valuing the work rather than the time. And, by the way, the amount of time it takes to do the work is not the same as the value of the work. We have to figure out a way to work together to provide good work for good value, rather than go to work for a certain amount of time. I’m preaching at this point, though. I don’t know how to do that.

Marlene Gebauer (24:37)
It’s a good point, but it’s interesting because you’re getting that level of detail in pitches and RFPs. Clients want to know exactly how much time things take and how much time you’re saving. It’s difficult to respond the way you’re suggesting when they’re specifically asking for this information.

Brad Blickstein (25:08)
It’s hard to pivot that conversation. When they ask that question, the way to pivot is to say, “Let’s not talk about how much time we’re saving. Let’s talk about how much money we’re saving. Here’s what we used to bill you for this type of work, and here’s what we’ll bill you now.” You can ask how long it’s going to take, but what does it matter?

It’s easy for me to sit here on your podcast and say, “Go tell your clients we’re not going to answer your questions.” It’s hard to do, and it takes fortitude. But if you establish the value and build trust with clients over time, I think it works.

The other thing you might do, especially when talking about AI, is think about the other value propositions. Can you get the work done faster? I don’t mean in less time. I mean finished sooner. Can the deal close faster? Can the litigation be settled sooner? In due diligence, can you review more material than before? Classic due diligence used to mean, “Send us the contracts with your 25 biggest clients,” because that was all the due diligence team had time to review. Now you can use AI to look at all the contracts. What’s the value proposition around using AI that isn’t tied solely to efficiency?

Tie that to cost savings rather than time savings or hours saved. Stand up and say, “Look, we respect you, but we’ve thought a lot about this. We’ve spent a lot of energy on it, and we think you’re asking the wrong question. Let us answer the right one.” Some clients will say, “Well, they didn’t fill out our RFP properly.” Too bad. But I think many will respect that and want to work with a thoughtful firm.

Marlene Gebauer (26:54)
I’m going to shift to the buyer side. The 18th Annual Law Department Operations Survey, the LDO Survey, shows a corporate legal buyer that is stabilizing structurally while absorbing broader business mandates, including CFOs stepping directly into contracting. Meanwhile, corporate GenAI adoption is defined by piloting, at 52%, rather than fully operationalized systems, at 23%. Why are corporate legal departments stuck in pilot purgatory, and how does the middle-mile data hygiene problem play into this?

Brad Blickstein (27:34)
Yeah, that’s part of it. First, considering we’re only two or three years into the AI era, depending on how you count, I’m not sure it’s purgatory. In many ways, we’re working through this, and I expect that number to fall substantially over time.

Part of it is caused by all the noise. How do you determine whether a tool is worthwhile these days without giving it a shot? It’s hard. I think we’ll get there, but the data hygiene problem will grow. Whatever the old saying is, garbage in, garbage out. I think we’ll solve the data protection problem. People understand, and are starting to become more comfortable with, how their data is used.

The idea that we want our law firms to provide every possible ounce of efficiency through AI tools, but we won’t allow them to use our data as training data to help them do that, isn’t fair. I think we’ll get past many of those issues over time. But data in corporations is a mess. To get the full benefit from these tools, you’ll have to solve that in many cases. I wonder how many pilots will fail because the technology, adoption, and training are good, but the data isn’t there.

Marlene Gebauer (29:13)
It’s a big issue in terms

Brad Blickstein (29:16)
It’s a big issue.

Marlene Gebauer (29:16)
of getting this data usable, getting it where you need it to go, and making your technology useful. I mean, it’s

Brad Blickstein (29:23)
Yeah, my hope is…

Marlene Gebauer (29:26)
It’s not a new problem, but, yeah.

Greg Lambert (29:27)
Yeah, I was going to say this is a 25-year-old overnight problem.

Marlene Gebauer (29:30)
It’s not a new problem, but I don’t know whether it’s easier or harder. The technology is so much better, but the security issues are more difficult. I’m not sure.

Brad Blickstein (29:44)
What I’m hoping, I guess, is that if you look at e-discovery over the years, technology created the e-discovery problem, right? The ability to disperse data quickly, email everyone, and copy a million people created a data problem. Technology then largely solved it with tools such as auto review and platforms. I’m hoping AI will help solve the data hygiene problem itself, enabling AI to work better on the legal problems the data supports.

Greg Lambert (30:25)
I know this was the first time in the survey, and of course, AI has only been around for about three years, that usability received a higher score and beat out security as the top AI priority for legal operations teams.

I think this opens the door to unique opportunities for law firms to offer services to their clients. Are you seeing clients expect something they haven’t been offered before? Are they receptive to those services?

Brad Blickstein (31:10)
I think they’re absolutely receptive. If I ran a law firm, I’d try to get ahead of all this. I’d build things, find new use cases, and bring solutions to clients before someone else does or before the client tries to build something themselves.

For any work you’re concerned the client might bring in-house, think about what that work is and build a case for why they don’t need to bring it in-house. Explain why the firm can do it more efficiently, better, and at a better price than the law department going through the trouble of bringing it inside. I don’t think we see firms bringing that case to clients enough. When firms do, clients are extremely receptive.

ALSPs are starting to get involved too. If I were a firm or an ALSP, I would build offerings and bring them to the right clients before the client decides to find a solution.

Greg Lambert (32:11)
Yeah. Speaking of ALSPs, why haven’t they wiped the floor with BigLaw? Everyone seemed to expect that, because they have these repeatable processes. Why aren’t they kicking ass?

Brad Blickstein (32:34)
Well, first of all, I think some of them are kicking ass, right? When you look at them as a percentage of the legal market’s overall share, it seems somewhat stagnant. But many of these businesses are on big growth curves within the ALSP segment. There are many reasons, but the big one is that clients like working with law firms, and that’s their

Greg Lambert (32:59)
It’s what they know.

Brad Blickstein (33:00)
preference. If it isn’t working, or if it’s the kind of process or project that makes sense to take away from a firm, document review is a great example. Law firms largely proved they shouldn’t be in that business by throwing a million associates and no technology at document review 20 years ago. Legal service providers and e-discovery providers have thrived there.

For work that is clearly process-oriented, I think ALSPs are doing quite well. But much of what they have to do comes back to the artisanal argument we discussed. They have to convince clients that a large amount of legal work isn’t artisanal, that it benefits from process and technology, and that ALSPs are the people to deliver it.

Marlene Gebauer (33:48)
Okay, Brad, what’s true today that wasn’t true a year ago?

Brad Blickstein (33:55)
I think legal organizations, both in-house and outside, are starting to care about what AI costs. Legora, I believe, talked about moving to a consumption model. That’s a big issue. People are moving from, “Let’s get everyone to adopt whatever they can and use AI wherever they can,” to, “Hold on. What’s it going to cost when we start paying by the token? Does it make sense?”

The underlying assumption is that using AI to do this work is less expensive than using lawyers. At some point, if AI gets expensive enough, that might no longer be true. That wasn’t a concern a year ago. The gold rush was on, and now people are starting to wonder what stream they should mine. I think I mixed the metaphor. Where should we drill, look, or pan? Where should we pan for gold? That’s what I’m asking.

Will these price increases stick? Will open models become more valuable and viable? Those are good questions. I believe people are concerned about this today in a way they weren’t a year ago. I’m not sure how long the concern will last.

Greg Lambert (35:23)
Yeah, it’s going to be interesting to see, because six months ago it was, “Burn every token you can get your hands on,” and now it’s, “Whoa, wait a second.”

Marlene Gebauer (35:33)
Well, wait a minute. Hold on there.

Brad Blickstein (35:36)
I’ll also say, and it’s related, that we’re starting to see people distinguish between adoption and fluency. It felt like the standard was activation, and then adoption: “98% of our people have used the tool in the past month.” That’s a nice statistic, but it doesn’t say whether people have become AI-proficient or AI-fluent, whether they’re using it for the right things, or whether they’re using it enough.

Marlene Gebauer (36:01)
Is it useful for what they’re doing?

Brad Blickstein (36:04)
Yeah. As we start asking those questions, it’s going to affect where the value lies and what these tools are worth.

Greg Lambert (36:14)
Well, Brad, before we started recording, we looked back at your last prediction on the crystal ball question, and I think you did pretty well.

Marlene Gebauer (36:23)
Yeah, he did.

Greg Lambert (36:24)
Your prediction at the end of last year was that generative AI and LLMs would fundamentally accelerate how software and tools are built in the legal tech space, and that you thought there would be about an 80% improvement in turnaround speed from idea to product.

Brad Blickstein (36:47)
That does seem to be happening. Especially if you count the things being built within firms and law departments through vibe coding, Claude Code, or all those things, I think we can check that off as one I got right.

Greg Lambert (37:04)
All right. Now it’s time to ask you again. What kind of change or challenge do you see that law firms and law departments need to prepare for over the next few years?

Brad Blickstein (37:19)
I’ll answer two ways to double my shot at getting something right. Generally speaking, the issue of the business and billing model will have to come to a head at some point. Perhaps less so within the next two years, but eventually many law departments will put pressure on firms to reduce the number of hours they bill. I have some thoughts about how they’ll do that, but I don’t know.

If you’re a firm, that doesn’t work under the leverage model. At some point, many firms will have to come to terms with the issue. I also think about all these, and I’m not a big fan of the “AI-native” term, but these AI-native-type spin-offs from firms. Can partners leave a BigLaw firm, start a firm with no associates and use AI instead, then go to the same clients who trust them with that new model? External forces like that might push firms to make some of these changes more than their clients do.

Marlene Gebauer (38:25)
Well, that is food for thought. Brad Blickstein, thank you so much for joining us again and helping us cut through the noise and think like value-focused operators. And thanks to all of…

Greg Lambert (38:35)
Yeah. Thanks, Brad.

Marlene Gebauer (38:36)
you. Sorry, go ahead.

Greg Lambert (38:37)
Thanks, Brad.

Brad Blickstein (38:39)
Thanks, guys. I appreciate this. It’s always fun and thought-provoking talking to you. You ask questions that make me think hard about what the responses should be, which I don’t appreciate, but I do like talking to you guys.

Marlene Gebauer (38:50)
We appreciate it.

Greg Lambert (38:54)
Yep, he had a full set of hair before we started this.

Brad Blickstein (38:56)
It always reminds me of that scene in the movie Clueless where they think she has a concussion and say, “Ask her some questions.” They ask, “What’s seven times seven?” and someone says, “Stuff she knows.”

Greg Lambert (39:05)
[Laughs.]

Brad Blickstein (39:06)
I would prefer if you asked me stuff I know.

Marlene Gebauer (39:11)
And thanks to all of you, our listeners, for taking the time to listen to The Geek in Review. If you enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Greg Lambert (39:21)
And Brad, what’s the best place for listeners to find and buy What Would Private Equity Do? and learn more about

Brad Blickstein (39:29)
Easy.

Greg Lambert (39:30)
Blickstein Group?

Brad Blickstein (39:31)
Yeah, you can go to our website, BlicksteinGroup.com, or for the book itself, BlicksteinGroup.com/WWPED. And I’ve always wanted to say this: it’s available wherever fine books are sold online. You can get it from Amazon or BN.com. It’s not hard to find, and we appreciate the support.

Marlene Gebauer (39:52)
Absolutely. And, as always, the music you hear is from Jerry David DeCicca. Thank you very much, Jerry, and goodbye, everybody.

]]> xlambert@gmail.com (Greg Lambert) From AI Personas to Rogue Agents: Rethinking Legal Training, Security, and Value https://www.geeklawblog.com/2026/07/from-ai-personas-to-rogue-agents-rethinking-legal-training-security-and-value.html Mon, 27 Jul 2026 10:54:18 +0000 https://www.geeklawblog.com/?p=19505

Fresh from AALL in Cleveland, Greg reflects on a conference filled with legal information professionals who understand how technology performs under real working conditions. These librarians purchase products, train users, support law schools and courts, and often serve as internal advocates for legal technology. Their expertise makes vendor engagement especially valuable, yet major product announcements were scarce. Marlene balances Greg’s conference report with stories from her hiking trip through Zion and Bryce Canyon, plus a brief comparison of Ohio and Utah karaoke culture.

The conversation turns to the rapid growth of innovation attorney positions across law firms and legal organizations. Greg and Marlene describe these professionals as translators who connect legal practice, technology, workflow design, and organizational change. Firms are searching beyond traditional legal career paths for people who combine technical fluency with strong interpersonal skills. For law students and junior lawyers facing uncertainty around AI, these emerging roles offer broader career options beyond the familiar associate track.

Marlene explores the growing use of AI personas and simulations for professional development. Deposition witnesses, opposing counsel, negotiation partners, and drafting reviewers now appear as interactive characters with distinct goals and behaviors. Lawyers receive a place to practice, make decisions, and receive feedback before working with clients or appearing in court. Greg connects simulation-based learning with legal fiction, including his Beyond the Model series, which uses a fictional law firm to explain AI systems, business pressures, and changes in legal work.

The discussion takes a serious turn with a reported AI benchmarking incident involving an agentic model, a breached sandbox, and unauthorized access to Hugging Face resources in search of an answer key. Greg and Marlene examine the episode as a warning about containment, accountability, and excessive faith in technical guardrails. From there, they consider the renewed importance of knowledge management and security as AI systems gain access to documents, financial information, client data, and institutional expertise. Greg predicts growing attention around AI harnesses, structured software layers designed to guide model behavior and produce predictable outputs.

Marlene closes with examples of AI moving into client intake, business qualification, and workflow decisions, including an AI legal receptionist designed for smaller firms. The larger shift involves moving beyond simple tool adoption toward redesigned workflows, staffing models, pricing structures, and client service. Token costs are creating immediate budget pressure, while clients are questioning which AI expenses belong on their bills. Greg and Marlene argue firms must connect AI spending with legal judgment, measurable value, and responsible delivery, rather than treating consumption as a proxy for progress.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

LINKS:

Here is a chronological list of the substantive topics and sources referenced in the episode:

Transcript:

Marlene Gebauer (00:00)
Hey, this is Marlene Gebauer and Greg Lambert of The Geek in Review

Greg Lambert (00:04)
And this week we thought we would just have a little chat with each other, catch up on the latest in legal technology, AI, and talk about what could possibly go wrong if an AI tool breaks out of containment and hacks into another company’s database. I’ve never seen that movie.

Marlene Gebauer (00:20)
But first up, let’s get a bit of wisdom from our sponsor, Legal Technology Hub.

Stephanie Wilkins (00:25)
The Gen AI conversation has been advancing faster than a lot of people can keep up lately, but some new developments have brought older concepts like prompting back into the spotlight thanks to a trending new topic, token cost. Token cost moved into the spotlight recently as tools like Claude gained traction in legal, because most plans come with token limits, as well as the ability to request limit increases, which has resulted in tales of astronomical bills for some users. Recently, Legora also announced that it’s moving its Agent Pro offering to consumption-based pricing, which means it’s billing by what the agent does rather than by a flat seat license. And what agents do is consume tokens. Eventually, other providers are sure to follow suit. What many don’t fully understand is just how quickly token usage can add up. A few extra follow-up questions, a document pasted in twice, a chat continuing long after it should have been reset. If that sounds familiar, token consumption compounds faster than you might expect, and you may be looking at a higher token usage than you think. And you might not even realize it until you’ve hit your usage limit or worse, seen the bill. This is a blind spot we’ve been unpacking in one of our latest article series on Legal Tech Hub: how to get more out of tools like Claude without burning time, decreasing accuracy, or racking up unnecessary bills. We’ve covered topics like what tokens actually are and why they function as a hidden meter running behind every chat. When to reset a conversation versus continue in the same chat. And what everyday prompting habits, from repasting whole documents to burying five questions in one prompt, might be driving up both cost and inaccuracy without you knowing it. Head over to legaltechnologyhub.com to read the full series and learn more about how to get the most out of your token limits and your usage of tools like Claude.

Marlene Gebauer (02:04)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer

Greg Lambert (02:11)
And I’m Greg Lambert and we thought this week we would catch up. I’m just getting back from beautiful Cleveland, Ohio, Marlene’s favorite town in all of America.

Marlene Gebauer (02:21)
Ha ha

Greg Lambert (02:22)
And I was there for AALL. Our friend Jenny Foster got the conference kicked off and completed without a hitch. Congratulations. And now that she’s rotated off and Jessica Whytock is president, Jenny is a nobody anymore. So welcome to the club, Jenny.

Marlene Gebauer (02:39)
Yeah, not true, not true. She’s going to be working behind the scenes. I seem to recall how that goes.

Greg Lambert (02:45)
Yeah.

Marlene Gebauer (02:46)
But yes, I was sorry to miss AALL, but I was actually I wasn’t sorry to miss Cleveland.

Greg Lambert (02:52)
No, you weren’t. You weren’t sorry to miss Cleveland, I can tell you that.

Marlene Gebauer (02:57)
But I was doing some great stuff. I went on a trip to some of our beautiful national parks. I went to Zion and went to Bryce Canyon and did quite a bit of hiking for a week and I went with a very good friend of mine and it was amazing. It is breathtaking.

Greg Lambert (03:19)
Was it breathtaking from the view or from the hikes?

Marlene Gebauer (03:22)
It was from both. We were doing about ten miles a day and as you can imagine a lot of it was up. So we saw a lot of hoodoos at Bryce Canyon and saw great vistas at Zion. There was one hike that’s called the Narrows and you walk through a river pretty much the whole time in waterproof boots. So, pretty cool.

Greg Lambert (03:44)
Sounds perfectly safe. Glad you made it back, Marlene. So we’re filming in the morning so I’ve got my cup of coffee. I pulled out my Wolverine mug. So for those that are watching the video. Yeah? No, don’t worry about it. Only one of us

Marlene Gebauer (03:53)
He’s showing off because I have a Starbucks and I said, I got to go get a cool mug. And he’s like, No, don’t worry about it. So I have my boring Starbucks cup.

Greg Lambert (04:02)
Only one of us can bring a cool coffee cup at a time. Well, let me jump in and talk about what my experience was at AALL. If you’ve never been

Marlene Gebauer (04:12)
Yes, please. Besides karaoke.

Greg Lambert (04:15)
Besides karaoke. Well, man. All right. Well, let me talk about karaoke first. So we actually

Marlene Gebauer (04:18)
I just posted it on LinkedIn, so I figure it’s fair game. Get it over with, yes.

Greg Lambert (04:23)
had two karaoke events the same night. They kind of overlapped a little but you could go to both. Lexis did one and BankruptcyData along with our friend Andre Davison from Harris County Law Library here in beautiful Houston did a great job. I can tell you this, the DJ was not expecting a bunch of librarians on a Monday night to bring it and the librarians brought it. So I

Marlene Gebauer (04:49)
As usual.

Greg Lambert (04:50)
I think we saw a range of songs. My favorite was one of the librarians who got up who was still in his shirt and tie from the day and got up and sang Ice Ice Baby from Vanilla Ice and brought the house down. So good stuff. Thanks to Andre Davison for sponsoring that. And I even sang the Bowling for Soup song “Ohio”, a song about Texas and

Marlene Gebauer (05:15)
Mm-hmm.

Greg Lambert (05:17)
posted it on LinkedIn. So if

Marlene Gebauer (05:19)
So check that out, folks.

Greg Lambert (05:21)
If you’re curious to hear my singing voice, luckily the sound isn’t very good, so I’ll just blame it on that.

Marlene Gebauer (05:28)
That’s it.

Greg Lambert (05:29)
As far as the conference itself, I think again, second year in a row and I’m gonna call out the vendors, especially some of the big ones. These librarians are the people who buy your product, who use your product, who have to sell your product inside the law firms, who teach your products to law students, who provide them to the government, and absolutely no announcements out of AALL again this year. Harvey was a bronze sponsor this year. So thank you, Harvey, for doing that, but they didn’t send anybody. And I’m telling you, they would have been the belle of the ball because everyone wanted to talk Harvey and Legora while we were there. But one of the things that you have to know about the librarians and the library conference is again, these are not the people who install and support your products on the network. These are people that use it. So they were telling us about what’s working, what’s not working, what’s on the horizon, what students expect, what lawyers expect, what judges expect. I talked with Steve Embry while he was there. Steve was there, and Bob was not. Probably Bob’s got a little Cleveland bias too, maybe. I don’t know.

Marlene Gebauer (06:48)
See? Do we need to talk?

Greg Lambert (06:50)
But yeah. Yeah, you two could be good friends.

Marlene Gebauer (06:54)
Okay.

Greg Lambert (06:55)
But Steve, he and I were talking and He was like, this is just an amazing conference of people who understand how the technology works, how the products work. So kudos to Jenny for pulling it off. Great conference in Cleveland, Ohio. Hey, Cleveland rocks. I don’t

Marlene Gebauer (07:15)
Rocks.

Greg Lambert (07:15)
care what you say, Marlene.

Marlene Gebauer (07:16)
Cleveland Rocks. Cleveland Rocks. I told you where to go in the Rock and Roll Hall of Fame. So I have many good memories of my time in Cleveland.

Greg Lambert (07:21)
Man. Yeah. And I told you yesterday when we were talking, they had a women in rock exhibit in the 1990s and early 2000s.

Marlene Gebauer (07:31)
Mm-hmm. Yeah.

Greg Lambert (07:32)
It was fantastic. It was amazing. Yeah. So, all right, Marlene, what do

Marlene Gebauer (07:34)
Yeah, that’s cool. Well, I

Greg Lambert (07:38)
do you have up next?

Marlene Gebauer (07:39)
Well, I’ll quickly share that I also had a karaoke experience while I was away in Utah. Karaoke in Utah is very different than karaoke everywhere else. Yes.

Greg Lambert (07:49)
Karaoke in Ohio

Marlene Gebauer (07:51)
let’s just say, you know, you have to order food if you’re doing karaoke in Utah. Unfortunately we got there very late so we were not able to get a song in. However,

Greg Lambert (08:03)
Okay. Wait, wait. Tell people what very late is in Utah.

Marlene Gebauer (08:08)
Yes. Nine o’clock is very late. It’s very late. So they were playing “Goodnight, Sweetheart”. And so I jumped up and I finished it in an empty hall. Yeah, right. And there are no posts.

Greg Lambert (08:21)
It’s Utah’s version of closing time. So closing time’s a little too spicy.

Marlene Gebauer (08:29)
There are no posted videos, nor will there be. So sorry about that, everybody.

Greg Lambert (08:32)
That’s too bad. That’s too bad. Yeah.

Marlene Gebauer (08:35)
So, what was I going to start with? I’m noticing, and I’ve been talking to other people about this too, lots of innovation attorney jobs everywhere. It just seems that they’re in lots of different places. Yes.

Greg Lambert (08:49)
Yeah. I’m hiring. Are you hiring?

Marlene Gebauer (08:53)
We’re both hiring, in addition to a bunch of other firms and a bunch of other organizations. So I think it’s great. I mean, yes, I think there is going to be a lot of competition to get people, but that’s not a bad thing. I am elated that the industry is coming to see that these jobs are important, that people who have a deep knowledge of their practice but also understand technology, understand the workflows, understand how to translate between highly technical folks and the attorneys. This is kind of the glue that makes it happen. And so I think it’s great.

Greg Lambert (09:36)
Yeah. I know there was a story run a couple of weeks ago in Bloomberg, especially about the director and up positions, but I think even like entry-level, midlevel, and upper-level roles, this is a new set of skills and we’re looking for new talent. So I know I’m seeing people from outside of legal proper and outside of BigLaw that are applying. So it’s kind of interesting to see where we’re going and again, you know, we’ve made fun of law firms and the legal industry for, well, as long as I’ve been around about being slow

Marlene Gebauer (10:16)
Long time.

Greg Lambert (10:17)
to adopt technology. But I think we’re ahead of our peer industries when it comes to looking at a lot of the technology and beefing up internally. Even if you’re not spending half a billion dollars on infrastructure to help your firm out, you’re still spending a lot of money percentage-wise. I’d say it’s really good.

Marlene Gebauer (10:34)
Yeah. I mean, I do think this is the beginning. You’re starting to really see the beginning of kind of a change in staffing and therefore kind of a change in how the workflow operates because of the impact of Gen AI. So I think

Greg Lambert (10:55)
Yeah. Yeah.

Marlene Gebauer (10:56)
You’re going to see lots more of these kind of interesting new jobs. And I look forward to that.

Greg Lambert (11:02)
Yeah. I think it’s similar to the run that we saw in the early 2000s when Arthur Andersen folded and all of their knowledge management people came into the legal industry and kind of revamped how we looked at our data.

Marlene Gebauer (11:14)
Everything. Mm-hmm.

Greg Lambert (11:17)
I think we’re in that same kind of period of transition. So

Marlene Gebauer (11:22)
Yeah.

Greg Lambert (11:23)
Beef up your resumes, everybody. Send it to me and Marlene if you want.

Marlene Gebauer (11:25)
That’s right. Get tech skills, people skills.

Greg Lambert (11:29)
Yeah. All right, I’ve got one more conference that I’m going to that’s not ILTA. I actually got invited to speak at a conference that I’d never heard of before. Turns out there’s only like 11,000 people that go to this conference, and it’s called

Marlene Gebauer (11:43)
well.

Greg Lambert (11:43)
Ai4, the AI and the number four conference.

Marlene Gebauer (11:45)
In Vegas, baby.

Greg Lambert (11:47)
It’s in Vegas. It’s at the Venetian in Vegas in the best time of the year, early August. So I’m

Marlene Gebauer (11:54)
Only second to July when you fly in.

Greg Lambert (11:57)
Yeah. And again, it’s not a legal conference but AI focused. So it’s going to be interesting. I’m going to be speaking on the legal track. I’ve seen a few other law firm folks that are going to be there as well, hoping to learn a lot from outside the industry as well as what’s going on with some of our peers in legal.

Marlene Gebauer (12:21)
Yeah, I think our industry conferences are fantastic. I would never suggest anything otherwise. They really are second to none. But I think it’s good to go to conferences periodically that are outside of what you normally do. And I love the fact that they’re recognizing that they should bring in some people to hear from them about what they do because this is not something that’s normal for us. I think oftentimes that is the best way to learn about new things and get new ideas and see what’s being applied in other areas and whether we can apply it in our industry. So one of the things I’ve been really interested in is like personas, and you’ve probably seen in the news that AltaClaro now has DepoSim and you know, if you haven’t seen that, that’s an amazing tool. And essentially what it does is train people how to take depositions.

Greg Lambert (13:18)
Didn’t we interview the people on that?

Marlene Gebauer (13:21)
We have. Abdi was on, yes, for sure. And so what this does, in case you missed that one, is that it helps train you to take depositions or helps you improve your ability to take depositions. There are a number of personas, both for the deponent as well as opposing counsel. There are different fact patterns. And you can kind of go through the exercises and then you get a grade at the end. That’s a rather simplistic explanation, but you get the idea. But there are a lot of other things coming out. I mean, I attend the CodeX calls, the Stanford CodeX calls, and now I’m seeing a lot of students who are putting these things together. I am aware of former partners in BigLaw that are putting these things together for depositions and hearings. I was on a webinar that Opus 2 sponsored. I attended a couple of days ago and the chief innovation officer at Vorys was on and mentioned as part of that webinar that they have developed personas that they’re using to help people train for drafting, which is amazing. I’m a big fan of this. I think this is going to be a big deal in terms of training and getting people to learn. I’m waiting to see where this goes.

Greg Lambert (14:51)
Yeah. For someone who doesn’t understand when you say personas, do you have an example of what they should expect with a persona?

Marlene Gebauer (15:00)
Yeah, so a persona is basically a character. I mean, it has different, you know, qualities of a person. For example, in a deposition situation, it could be someone who’s very reticent to speak or someone who’s very outspoken and you would have different types of tasks with either one of these people. And that’s a simple example. You can get very sophisticated with some of the personas in terms of someone being a CEO at a small startup and these are their interests. You know, they like to go hiking, they like to ride bicycles, and personal time is important to them. They are concerned about protecting their IP. You can basically build these things to model, you know, whatever it is that you want to test against. Then you would interact with that persona, that model, and they would respond in a way that you would expect. And then you would kind of test yourself accordingly against that. So if you were drafting, then you would, you know, wait to see what their feedback was. You might ask questions. If you were doing a deposition, obviously you’d be asking questions of that persona and sort of dealing with both personas.

Greg Lambert (16:19)
And based on the persona, that’s the difference in the reaction you’re getting is that an outgoing person

Marlene Gebauer (16:24)
Yeah. And I

Greg Lambert (16:26)
may respond differently than an introverted person kind of deal, or

Marlene Gebauer (16:30)
Correct.

Greg Lambert (16:31)
a partner may respond differently than opposing counsel. So yeah, I can see that.

Marlene Gebauer (16:33)
Correct. And I should mention, Flatiron also has worked on this with Stanford and Megan Ma in terms of a negotiator simulator. I mean, very early on.

Greg Lambert (16:45)
Yeah.

Marlene Gebauer (16:46)
We had them on very early on for that.

Greg Lambert (16:48)
I was going to say if you want to know what’s going to happen in two years, talk to Megan Ma now. Well, you pointed one of these to me, but I’ve been seeing more now, you know, I’ve been writing a thing called Beyond the Model on our Substack since January.

Marlene Gebauer (17:05)
I do.

Greg Lambert (17:06)
I’ve been actually writing them since last year, where I’ve taken a fictional law firm and I apply technology to it.

Marlene Gebauer (17:12)
Fame comes slowly sometimes, you know? You just have to be patient.

Greg Lambert (17:16)
I’m the Megan Ma of legal fiction writing. How’s that?

Marlene Gebauer (17:20)
That’s it. That’s it. No.

Greg Lambert (17:24)
Of course, I always claim that I stole it from Anusia Gillespie after our interview with her. But I’ve noticed our friend Richard Tromans has the Innovators fictional story that he’s doing. Jennifer Leonard has a new book published by PLI that uses fictional characters to explain the story. It’s interesting. I think it’s a great way of taking facts or projections of how the technology can work in an actual law firm and applying it in a way that makes sense. In the latest story that I’ve done on our Substack page, I took a nonlegal-industry survey that was given to over 5,000 technology workers on how AI is affecting their job and their view of their importance within their job and applied it to a law firm where we gave our fictional law firm a survey to see how associates, partners and business professionals would take that survey and react to that survey. Again, fictional, but I feel pretty confident in where we were. The interesting thing that was in that survey was whether or not you would recommend your profession to someone who’s starting. In the tech survey, and I think this would apply in legal as well, the further up the chain you were, the more likely you were to suggest that somebody get into the industry to do what you do. So it’s

Marlene Gebauer (19:01)
Makes sense.

Greg Lambert (19:02)
And I think equity partners probably still see a lot of benefits. But I think associates right now, and even though I didn’t talk about law students, I’ve talked to a bunch of folks at law schools and they say the first-years, second-years, and third-years are all freaking out, mostly because of the early job offers that they’re getting, including summer associate gigs before they’ve even taken their first-semester tests and received their grades back. In fact, I heard at AALL that at one of the schools out on the East Coast, a T14 school, they saw earlier this year that an incoming 1L had already accepted a job before they’d even gone to their first class. I see this face. You’re not shocked at all. So it’s insane. The recruiters know it’s insane. The students know it’s insane. But other firms keep doing it.

Marlene Gebauer (20:05)
And yet we just keep doing it.

Greg Lambert (20:07)
Yeah. So I imagine that you know, if you score a 178 on your LSAT, you’ll probably get a job offer before you even apply to school. That’s going to be next. I don’t know. All right, high schoolers, we’re coming for you.

Marlene Gebauer (20:18)
Amazing. No pressure or anything. It’s like that, yeah, I know. It’s pressure enough. It’s like once you’re used to law school and how it’s done and then people are just sort of going in and taking jobs, having no idea. And they have to be

Greg Lambert (20:36)
Yeah. Well

Marlene Gebauer (20:38)
ready that soon without any type of legal prep.

Greg Lambert (20:40)
The other thing that I don’t think a lot of us are thinking about is that the students that don’t get these early offers are feeling like, Well, should I even be here? Is there a chance for me to get into a law firm now?

Marlene Gebauer (20:58)
I think sort of the key here is like you need to kind of have a flexible attitude toward this. I’m sure people went in thinking, okay, this is what it was going to be, but literally in like the last couple of years, the industry has changed remarkably and will continue to do that. And we were just talking about. Look at all these jobs that we’re hunting for. So, okay, it might or might not be an associate job at a law firm, but we’re hiring other types of roles where you can still use your legal smarts as well as any technical smarts you have. I know the big Gen AI companies, you know, are hiring people like this to be researchers, to be people that work on workflows. So I mean, there’s opportunity out there. You just kind of have to open your mind a little bit and the main thing is like, you know, get in there and get that experience. Now, in terms of your storytelling, I wanted to add that it totally makes sense because, look, there’s an entertainment value to fiction that, you know, isn’t present in these guidebooks, which are helpful and good, but you know, people respond to that. I think if you can combine something entertaining with a lesson or with knowledge, I mean that always goes a long way. And I think about how this is writing, but you know, we’ve had the oral tradition for thousands of years to pass on information. I don’t know about you, but you know, fairy tales and parables and Greek mythology, those were things I read when I was younger and I still remember. And I think this is just kind of a continuation of that.

Greg Lambert (22:55)
Yeah, and we’ll talk about this in a few minutes, but I was talking with one of the partners at my firm. There are really kind of two ways I write the story. One is teaching a technology and the reasoning behind it. It started off with why the legal research products suddenly got better. We talked about RAG technology and how the information is indexed, searched, and retrieved. He found that part very interesting. Then there’s a second style of story where I’m applying theory and business models to a law firm, and he’s like, yeah, I don’t like that part. Everyone has their own taste.

Marlene Gebauer (23:39)
Not as entertaining.

Greg Lambert (23:40)
So I don’t want that. I want to learn something. I already know how I’m running the business. I don’t need to know that

Marlene Gebauer (23:46)
Sure, but yeah, I mean, there are going to be other people for whom that’s their learning moment. So that’s, you know, you catch everybody. Okay. All right. So this one I thought was funny and it was kind of cute. So I wanted to bring it up that I was reading that Microsoft’s corporate external legal affairs group selected Harvey as their tool of choice. And I mean, yeah, it does make sense.

Greg Lambert (24:09)
Makes sense to me.

Marlene Gebauer (24:12)
It kind of makes sense and I’m thinking about again, I was reading that there’s sort of a deeper relationship now between Harvey and Microsoft and so it’s like, okay, well that totally makes sense, even though they have Copilot, but why not?

Greg Lambert (24:27)
I don’t know if I wanna say anything. Maybe they used Copilot and found out just how well it works.

Marlene Gebauer (24:35)
Well, I mean, you know, I think if you’re using it for legal, but I mean using it for other stuff is fine.

Greg Lambert (24:40)
Yeah. Copilot’s great because it connects to the M365 network and platform. So, yeah.

Marlene Gebauer (24:47)
Yeah, it connects to the stuff that you have and that is helpful. Very helpful.

Greg Lambert (24:52)
No, I use it every day, especially when

Marlene Gebauer (24:54)
Me too.

Greg Lambert (24:55)
I can’t find that stupid email I sent three weeks ago and I need to get it.

Marlene Gebauer (24:58)
How many would you say you use a day? Like different ones?

Greg Lambert (25:01)
Different ones?

Marlene Gebauer (25:02)
And for different things, I guess.

Greg Lambert (25:03)
Let’s see. I definitely use Harvey. Definitely use Claude every day. Okay, well let me tell you what I subscribe to.

Marlene Gebauer (25:13)
I use a lot.

Greg Lambert (25:15)
So I’ve got a $200 subscription to Claude,

Marlene Gebauer (25:18)
Crazy.

Greg Lambert (25:19)
and I’ve got a $20 subscription to Gemini. I’ve got a $20 subscription to OpenAI’s

Marlene Gebauer (25:26)
You’re an addict.

Greg Lambert (25:26)
ChatGPT. We have Harvey,

Marlene Gebauer (25:29)
You’re an LLM addict.

Greg Lambert (25:31)
we have Microsoft M365, so I have Copilot. So I use them all.

Marlene Gebauer (25:36)
Yeah, I mean I have OpenAI’s ChatGPT, I have the $20 plan. I have Claude, I have Copilot, I have Legora. There’s something else I have. What do I have? I feel like I’m missing one. I can’t remember right now, but not quite as many as you. Mm-mm, mm-mm. I refuse. I refuse.

Greg Lambert (25:54)
Just give your bank statement to Claude and it will tell you how many you have. Yeah. I actually had someone say on a LinkedIn post and I thought he was joking. He was like, “If you give Claude all of your data on your attorneys, their billing, their time entry, their forms, paperwork and emails that it does great for planning out these seven-figure deals.” And I know the person and I was going to do like a laughing emoji back to them and then I thought, nope, they’re actually being very serious about this.

Marlene Gebauer (26:28)
They’re serious.

Greg Lambert (26:30)
I’m like, if you want to watch my security ops team break into my office and tackle me away from my keyboard, that’s what I will start doing. Yeah.

Marlene Gebauer (26:37)
Mm. Wait, we’ll get the camera set up. Yep.

Greg Lambert (26:42)
Well, speaking of OpenAI, I don’t know if you saw the story since you were traveling, but apparently Hugging Face, the open-source platform, found that they had been hacked and they knew that whatever was hacking them must have been an agentic AI system because of the speed of what it was doing. A person couldn’t be doing this, so it had to be an automated attack. It turned out it was OpenAI doing a test on a benchmarking test that used a model and it was a combination of their new GPT-5.6 Sol and an unreleased model where it was given a test, put in a sandbox, and given limited access, supposedly. And like a resourceful college student, it found that rather than doing the work and doing the test as assigned, it was actually easier to go and break into the professor’s office and steal the key to the test and use it that way. So,

Marlene Gebauer (27:51)
Path of least resistance.

Greg Lambert (27:55)
It found a way through a multilevel hack to get internet access, went to Hugging Face, broke into Hugging Face using some stolen passcodes that it found, then found the answer key.

Marlene Gebauer (28:08)
Hugging Face had no idea what was going on.

Greg Lambert (28:10)
Found the answer key. So, I mean, it was interesting that it broke in and had access to all this, but was still on the mission of finishing the quest. It found the answer key, came back and answered the benchmarking test and apparently scored very high. But I will say that, you know, we’re joking around on this, but this is, I would say, this is a turning point in AI right now because the U.S. government as it’s set up today is very hands-off. There’s very little regulation. The federal government is actually stepping in and trying to keep states from regulating it. Now we’ve got this, you know, yeah, it’s kind of funny. But it’s also not funny. It’s not funny because

Marlene Gebauer (28:58)
This time it’s funny and

Greg Lambert (29:01)
Well, most of the podcast and writings I’ve seen on this said that if a human did this, it would be a crime. The FBI would be coming in and arresting people and instead you see Hugging Face’s and OpenAI’s leadership laughing it off and acting like this was a great thing that happened and if you’ve ever watched The Terminator, this is how it begins, people. This is how it begins.

Marlene Gebauer (29:25)
You know, I mean I know this isn’t human, but when you look at the whole picture, I mean it’s a very human experience. I mean, this is exactly what someone could do. A gray-hat or black-hat type of actor who says, okay, let’s see if I can do this and then does it. And what’s interesting here is that it basically assembled a bunch of actions that weren’t part of that environment and went with it and was very successful. Hugging Face was like, Okay, something’s happening but we don’t know which model is doing it. Then the model actually reveals later.

Greg Lambert (30:08)
what was even

Marlene Gebauer (30:09)
it’s like, it was me.

Greg Lambert (30:10)
What was even funnier was they tried to use both OpenAI’s foundational models and Claude’s foundational models to help fight off the

Marlene Gebauer (30:22)
Ha ha

Greg Lambert (30:22)
attack. But it wouldn’t let them because they had guardrails that wouldn’t allow them to use

Marlene Gebauer (30:27)
It’s like, sorry, we can’t do that.

Greg Lambert (30:29)
their tool to help because they could be going the other way. So they had to use the Chinese models. They had to use the Chinese models to fight it.

Marlene Gebauer (30:30)
So they were the white hats. It’s like we follow the rules. Yes, yes, I read that. It’s like, hey, can we borrow your models? Crazy. Mm-hmm. Mm-hmm.

Greg Lambert (30:42)
Yeah. It’s a great time to be alive. I’m going to end with that.

Marlene Gebauer (30:48)
Yeah. You know, I think KM is having a moment again. I’m very happy to see that. And I think you’re starting to see how, we had looked at, you know, Gen AI in terms of like efficiencies and doing things faster and better. And now I think the industry is turning towards how we can use AI to kind of harness knowledge that we have and surface knowledge that we have and combine knowledge that might be out there. So your traditional KM, you know, document-based, it’s like, okay, well, we’ll have things like that. But, you know, we may also have personas. We may also have financial information. We may also have information from the outside, you know, public information. Being able to synthesize all of this and surface all of it for different types of work is becoming important. So I know NetDocuments is rolling out some new AI-enabled capabilities. I know iManage is as well. I’m wondering if everybody was saving their announcements for ILTA. So we might hear something

Greg Lambert (31:59)
They are. They are.

Marlene Gebauer (32:01)
Something there. We’ve talked to you know a number of people that have products, like ClearPeople and their product, Atlas, and then Atlas AI, who we just recently had on the show. There are a number of these vendors that are looking at how to do this. Microsoft is also doing this as well. And I think the trick is

Greg Lambert (32:24)
But are they doing it through Harvey? That’s what I wanna know.

Marlene Gebauer (32:29)
I should say Harvey also has something, too. And so I think the trick at this point is, you know, going back to what you were saying about security and sort of what we’re comfortable letting these tools access because, you know, we’re still in a situation where certain clients are saying, you know, I don’t want Gen AI to touch any of my stuff, or I don’t want my work to be combined with your other work. There’s also movement towards a need-to-know model in your DMS. Given all of these factors, how do we make these tools work effectively for people? And I mean, it’s going to be done because, I mean, some of these tools already follow the rules that your DMS already has in terms of who can access what. So you know, I think it’s really just a finesse question.

Greg Lambert (33:23)
Well, you know, giving the AI rules works every time, right, OpenAI. So, yeah. Now I do

Marlene Gebauer (33:29)
Yeah, why did I have this story like right after that one? It’s like, never mind. Never mind.

Greg Lambert (33:36)
I do want to say that in the OpenAI test, they actually took the guardrails off, which explains part of this. But they thought because it was in a sandbox environment that would protect it. But yeah, of course, yeah.

Marlene Gebauer (33:45)
They’d be okay. I feel much better now. Of course.

Greg Lambert (33:52)
I watched a video last week from Claire Vo, who hosts How I AI. It’s great, and although it isn’t legal, I think very applicable to what we’re doing. Last week she was showing how she makes AI harnesses. And after

Marlene Gebauer (34:13)
Tell us what a harness is.

Greg Lambert (34:14)
So a harness is essentially software you put on top of the AI to help guide the AI on what you’re asking it to do, the rules that you set up, and the output. And one of the things that stood out as she was talking about how she develops the AI harnesses for the tasks she performs, mostly for evaluating code she uses for her company and its website. If you need something to be the same every single time regardless of the AI model that’s underneath, the harness, she explained, is how you get there. So you set up the parameters of this is how you handle the information, this is what you do, and then this is the output and every single time it’s the exact same thing. And there’s lots of stuff that we do that falls into those parameters, those guidelines of making sure that if we’re processing data that it processes in a certain way and the output is in a certain format. So I’m going to make the projection. You know how we’re talking about AI agents in 2026 and turning the agents loose. I think for 2027 we’re going to be talking about AI harnesses. We’ve got the power. Now we need to set the direction of how these very powerful models actually work in a predictable and consistent way. So if you’re not looking into harnesses now, I suggest you do that. Coincidentally, there’s a three-part story that I’m writing on Substack this week that talks about AI harnesses. So if you haven’t subscribed to the Substack page, do so now.

Marlene Gebauer (35:57)
And in case you wanted to hear more, are you aware of any vendors that are using these or promoting them?

Greg Lambert (36:11)
Well, they all have harnesses. So, when you’re using the interface on Harvey, that is a harness. It’s a layer on top of the AI company’s models.

Marlene Gebauer (36:21)
But this harness apparently produces the same results every time. So

Greg Lambert (36:24)
Yeah. You set it up, you tell it what to do. You program it or vibe code it, and this is the interface that you use or this is the tool that uses the AI and then guides the AI and its output. So, lots of things to learn. Like I said, three-part story coming out this week, so stay tuned. Yeah.

Marlene Gebauer (36:44)
Very good. We’ll be looking for it. I wanted to put in one thing about small firms and I saw something in the news. It’s an AI legal receptionist called LexiDesk. What I thought was interesting about it is that it captures quality business during intake. So it’s not just strictly a receptionist, it’s trying to figure out whether this is the correct business for you. What I thought was interesting about it was what counts as quality business how You program that, how do you prompt that and how do you make sure that’s happening? Can it recognize urgency or vulnerability? Because, again, we have certain obligations as attorneys to address things. So, can it figure that out? How is confidentiality handled? I mean, it’s one thing to record that a call came in and schedule something on your calendar and it’s another thing to really do an assessment of the call and what your next steps are. So I thought it was an interesting one.

Greg Lambert (37:50)
Yeah. Speaking of interesting, this is either going to be really interesting or not interesting at all. You know how we’ve been asking folks to tell us about what resources they’re using to read or keep up with the industry. I heard a very interesting question this week from Simon Willison when he was talking with three engineers at Anthropic and one of the questions he asked was, what is true today that wasn’t true a year ago for you? And of course these were engineers talking about Claude Code and the harness and everything that they’re doing. But we may swap out the question. We ask a crystal ball question about looking into the future. I think this would be a good setup to, you know, take a peek back at the past and look at today and say, you know, what’s true today that wasn’t true a year ago? And so, Marlene, you get to be my guinea pig on this. What’s something that’s true today that wasn’t true a year ago?

Marlene Gebauer (38:55)
Well, a year ago I think there was a lot more emphasis on adoption and just using the tools. And I think now there is much more focus on incorporating this into workflows. We need to change the workflows. It’s not just making an existing workflow more efficient. It’s basically changing the workflow completely. And that includes staffing, that includes pricing, and so it’s I mean, the whole ride has been interesting, but it’s super interesting now because you’re bringing in a lot of different experts. You know, there’s a lot of different departments this touches. You have these groups kind of working together, which I think is, you know, absolutely incredible and interesting. Everybody’s bringing their expertise to the table to make these changes in a positive way for businesses. The other thing that is quite different, and this is very recent, is now everybody’s sort of talking about the cost of generative AI. Because I think, you know, we got everybody on board and now all of a sudden the reality hits I know, I know. And it’s

Greg Lambert (40:12)
Wait, wait, I can’t burn every token I can get my hands on? Yeah.

Marlene Gebauer (40:18)
Again, it’s intellectually interesting. It’s also very stressful. I mean, from a business perspective in terms of like what this is going to cost and how is it going to get paid for? And everybody has seen stuff in the news about paying per token, but I don’t know whether that is going to end up being the right model. And I do think vendors are trying to be thoughtful about this. I mean, I’ve had some discussions with some of them and you know, they are trying to be very thoughtful about how to do this because straight-up token costs or use costs. I mean, we’ve seen that model before, like you and I have seen that model before. And it’s the old Lexis Westlaw model from years and years ago. And

Greg Lambert (41:02)
Yeah. We’ll just find a way to track the cost and then pass that on to the client, right? Then

Marlene Gebauer (41:09)
Right. And I mean, look, in the beginning that made very good sense, but you know, in time it became a situation where clients said, look, that’s the cost of doing business and we’re not gonna pay for that. So a lot of times that is not paid for. And I think now it’s even more imperative to think about it because everybody has access to these tools now. I think there’s a fine balance between saying it’s like, okay, we’re gonna pass on, you know, token costs to people, to clients, and clients are like, well, you know, we could do that. Like we can do it ourselves. I think there’s a lot more thought that has to go into sort of what you’re doing with the AI and you know, the value that provides to the clients. And whether you’re kind of working on these things together or if it’s something that’s just more internal that helps you.

Greg Lambert (42:08)
Yeah. I’m going to kind of spin this into our crystal ball question when it comes to the tokens because I can’t remember, I think it’s Taleb’s Law where it’s the saying, “I’ve seen gluts not followed by shortages, but I’ve never seen a shortage not followed by a glut.” I don’t think it’s going to be next year, but I think in two or three years this whole, you know, looking at the cost of tokens is going to be a nonstory anymore because

Marlene Gebauer (42:43)
Agreed.

Greg Lambert (42:44)
efficiencies are going to catch up. Hopefully the latest model doesn’t burn fuel like a rocket. We’ll find efficiencies because we’re gonna have to. And I think competition will help. I think we’re gonna see more competition from the Chinese models. We’ll probably see competition from open-source models from Europe. We’ll probably see some regulation come in after 2028 perhaps. But in the meantime this is a wave we’re gonna have to ride till it breaks, right? So

Marlene Gebauer (43:16)
Yeah, I mean, I agree that it’s not a long-term issue, but it is a significant

Greg Lambert (43:21)
But it’s an issue.

Marlene Gebauer (43:22)
It’s a significant short-term issue. I mean, in terms of cost, but I agree with you and I mean, I think competition is going to force people, I think, to become better users. I mean, it’s sort of a painful way to do it, but

Greg Lambert (43:39)
Yeah.

Marlene Gebauer (43:40)
it will do it. So that is also not a bad thing.

Greg Lambert (43:45)
Yeah. Well, it was great catching up with you this week.

Marlene Gebauer (43:50)
Yeah, you too.

Greg Lambert (43:52)
It’s always nice to have these and just kind of talk about what’s going on in the industry ’cause man, there’s so much. So much going

Marlene Gebauer (43:57)
So much. So much.

Greg Lambert (44:00)
on. So thanks for sitting down in the morning and for letting me pull out my really cool coffee mug.

Marlene Gebauer (44:08)
Just got to rub it in. You just got to rub it in.

Greg Lambert (44:11)
And show everybody your Starbucks cup while I show my Wolverine claws.

Marlene Gebauer (44:13)
Yeah. No, they saw it already. It’s okay. I’ll bring a good one next time. Thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. You know, we’d love to hear from you on LinkedIn and Substack. And as always,

Greg Lambert (44:27)
Yeah. Subscribe to that Substack. Watch those stories roll in.

Marlene Gebauer (44:31)
Greg needs likes. Greg needs likes.

Greg Lambert (44:34)
I do!

Marlene Gebauer (44:34)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye everybody.

]]> Fresh from AALL in Cleveland, Greg reflects on a conference filled with legal information professionals who understand how technology performs under real working conditions. These librarians purchase products, train users, support law schools and courts, and often serve as internal advocates for legal technology. Their expertise makes vendor engagement especially valuable, yet major product announcements were scarce. Marlene balances Greg’s conference report with stories from her hiking trip through Zion and Bryce Canyon, plus a brief comparison of Ohio and Utah karaoke culture.

The conversation turns to the rapid growth of innovation attorney positions across law firms and legal organizations. Greg and Marlene describe these professionals as translators who connect legal practice, technology, workflow design, and organizational change. Firms are searching beyond traditional legal career paths for people who combine technical fluency with strong interpersonal skills. For law students and junior lawyers facing uncertainty around AI, these emerging roles offer broader career options beyond the familiar associate track.

Marlene explores the growing use of AI personas and simulations for professional development. Deposition witnesses, opposing counsel, negotiation partners, and drafting reviewers now appear as interactive characters with distinct goals and behaviors. Lawyers receive a place to practice, make decisions, and receive feedback before working with clients or appearing in court. Greg connects simulation-based learning with legal fiction, including his Beyond the Model series, which uses a fictional law firm to explain AI systems, business pressures, and changes in legal work.

The discussion takes a serious turn with a reported AI benchmarking incident involving an agentic model, a breached sandbox, and unauthorized access to Hugging Face resources in search of an answer key. Greg and Marlene examine the episode as a warning about containment, accountability, and excessive faith in technical guardrails. From there, they consider the renewed importance of knowledge management and security as AI systems gain access to documents, financial information, client data, and institutional expertise. Greg predicts growing attention around AI harnesses, structured software layers designed to guide model behavior and produce predictable outputs.

Marlene closes with examples of AI moving into client intake, business qualification, and workflow decisions, including an AI legal receptionist designed for smaller firms. The larger shift involves moving beyond simple tool adoption toward redesigned workflows, staffing models, pricing structures, and client service. Token costs are creating immediate budget pressure, while clients are questioning which AI expenses belong on their bills. Greg and Marlene argue firms must connect AI spending with legal judgment, measurable value, and responsible delivery, rather than treating consumption as a proxy for progress.

Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠

[Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.]

Email: geekinreviewpodcast@gmail.com

Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

LINKS:

Here is a chronological list of the substantive topics and sources referenced in the episode:

Transcript:

Marlene Gebauer (00:00)
Hey, this is Marlene Gebauer and Greg Lambert of The Geek in Review

Greg Lambert (00:04)
And this week we thought we would just have a little chat with each other, catch up on the latest in legal technology, AI, and talk about what could possibly go wrong if an AI tool breaks out of containment and hacks into another company’s database. I’ve never seen that movie.

Marlene Gebauer (00:20)
But first up, let’s get a bit of wisdom from our sponsor, Legal Technology Hub.

Stephanie Wilkins (00:25)
The Gen AI conversation has been advancing faster than a lot of people can keep up lately, but some new developments have brought older concepts like prompting back into the spotlight thanks to a trending new topic, token cost. Token cost moved into the spotlight recently as tools like Claude gained traction in legal, because most plans come with token limits, as well as the ability to request limit increases, which has resulted in tales of astronomical bills for some users. Recently, Legora also announced that it’s moving its Agent Pro offering to consumption-based pricing, which means it’s billing by what the agent does rather than by a flat seat license. And what agents do is consume tokens. Eventually, other providers are sure to follow suit. What many don’t fully understand is just how quickly token usage can add up. A few extra follow-up questions, a document pasted in twice, a chat continuing long after it should have been reset. If that sounds familiar, token consumption compounds faster than you might expect, and you may be looking at a higher token usage than you think. And you might not even realize it until you’ve hit your usage limit or worse, seen the bill. This is a blind spot we’ve been unpacking in one of our latest article series on Legal Tech Hub: how to get more out of tools like Claude without burning time, decreasing accuracy, or racking up unnecessary bills. We’ve covered topics like what tokens actually are and why they function as a hidden meter running behind every chat. When to reset a conversation versus continue in the same chat. And what everyday prompting habits, from repasting whole documents to burying five questions in one prompt, might be driving up both cost and inaccuracy without you knowing it. Head over to legaltechnologyhub.com to read the full series and learn more about how to get the most out of your token limits and your usage of tools like Claude.

Marlene Gebauer (02:04)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer

Greg Lambert (02:11)
And I’m Greg Lambert and we thought this week we would catch up. I’m just getting back from beautiful Cleveland, Ohio, Marlene’s favorite town in all of America.

Marlene Gebauer (02:21)
Ha ha

Greg Lambert (02:22)
And I was there for AALL. Our friend Jenny Foster got the conference kicked off and completed without a hitch. Congratulations. And now that she’s rotated off and Jessica Whytock is president, Jenny is a nobody anymore. So welcome to the club, Jenny.

Marlene Gebauer (02:39)
Yeah, not true, not true. She’s going to be working behind the scenes. I seem to recall how that goes.

Greg Lambert (02:45)
Yeah.

Marlene Gebauer (02:46)
But yes, I was sorry to miss AALL, but I was actually I wasn’t sorry to miss Cleveland.

Greg Lambert (02:52)
No, you weren’t. You weren’t sorry to miss Cleveland, I can tell you that.

Marlene Gebauer (02:57)
But I was doing some great stuff. I went on a trip to some of our beautiful national parks. I went to Zion and went to Bryce Canyon and did quite a bit of hiking for a week and I went with a very good friend of mine and it was amazing. It is breathtaking.

Greg Lambert (03:19)
Was it breathtaking from the view or from the hikes?

Marlene Gebauer (03:22)
It was from both. We were doing about ten miles a day and as you can imagine a lot of it was up. So we saw a lot of hoodoos at Bryce Canyon and saw great vistas at Zion. There was one hike that’s called the Narrows and you walk through a river pretty much the whole time in waterproof boots. So, pretty cool.

Greg Lambert (03:44)
Sounds perfectly safe. Glad you made it back, Marlene. So we’re filming in the morning so I’ve got my cup of coffee. I pulled out my Wolverine mug. So for those that are watching the video. Yeah? No, don’t worry about it. Only one of us

Marlene Gebauer (03:53)
He’s showing off because I have a Starbucks and I said, I got to go get a cool mug. And he’s like, No, don’t worry about it. So I have my boring Starbucks cup.

Greg Lambert (04:02)
Only one of us can bring a cool coffee cup at a time. Well, let me jump in and talk about what my experience was at AALL. If you’ve never been

Marlene Gebauer (04:12)
Yes, please. Besides karaoke.

Greg Lambert (04:15)
Besides karaoke. Well, man. All right. Well, let me talk about karaoke first. So we actually

Marlene Gebauer (04:18)
I just posted it on LinkedIn, so I figure it’s fair game. Get it over with, yes.

Greg Lambert (04:23)
had two karaoke events the same night. They kind of overlapped a little but you could go to both. Lexis did one and BankruptcyData along with our friend Andre Davison from Harris County Law Library here in beautiful Houston did a great job. I can tell you this, the DJ was not expecting a bunch of librarians on a Monday night to bring it and the librarians brought it. So I

Marlene Gebauer (04:49)
As usual.

Greg Lambert (04:50)
I think we saw a range of songs. My favorite was one of the librarians who got up who was still in his shirt and tie from the day and got up and sang Ice Ice Baby from Vanilla Ice and brought the house down. So good stuff. Thanks to Andre Davison for sponsoring that. And I even sang the Bowling for Soup song “Ohio”, a song about Texas and

Marlene Gebauer (05:15)
Mm-hmm.

Greg Lambert (05:17)
posted it on LinkedIn. So if

Marlene Gebauer (05:19)
So check that out, folks.

Greg Lambert (05:21)
If you’re curious to hear my singing voice, luckily the sound isn’t very good, so I’ll just blame it on that.

Marlene Gebauer (05:28)
That’s it.

Greg Lambert (05:29)
As far as the conference itself, I think again, second year in a row and I’m gonna call out the vendors, especially some of the big ones. These librarians are the people who buy your product, who use your product, who have to sell your product inside the law firms, who teach your products to law students, who provide them to the government, and absolutely no announcements out of AALL again this year. Harvey was a bronze sponsor this year. So thank you, Harvey, for doing that, but they didn’t send anybody. And I’m telling you, they would have been the belle of the ball because everyone wanted to talk Harvey and Legora while we were there. But one of the things that you have to know about the librarians and the library conference is again, these are not the people who install and support your products on the network. These are people that use it. So they were telling us about what’s working, what’s not working, what’s on the horizon, what students expect, what lawyers expect, what judges expect. I talked with Steve Embry while he was there. Steve was there, and Bob was not. Probably Bob’s got a little Cleveland bias too, maybe. I don’t know.

Marlene Gebauer (06:48)
See? Do we need to talk?

Greg Lambert (06:50)
But yeah. Yeah, you two could be good friends.

Marlene Gebauer (06:54)
Okay.

Greg Lambert (06:55)
But Steve, he and I were talking and He was like, this is just an amazing conference of people who understand how the technology works, how the products work. So kudos to Jenny for pulling it off. Great conference in Cleveland, Ohio. Hey, Cleveland rocks. I don’t

Marlene Gebauer (07:15)
Rocks.

Greg Lambert (07:15)
care what you say, Marlene.

Marlene Gebauer (07:16)
Cleveland Rocks. Cleveland Rocks. I told you where to go in the Rock and Roll Hall of Fame. So I have many good memories of my time in Cleveland.

Greg Lambert (07:21)
Man. Yeah. And I told you yesterday when we were talking, they had a women in rock exhibit in the 1990s and early 2000s.

Marlene Gebauer (07:31)
Mm-hmm. Yeah.

Greg Lambert (07:32)
It was fantastic. It was amazing. Yeah. So, all right, Marlene, what do

Marlene Gebauer (07:34)
Yeah, that’s cool. Well, I

Greg Lambert (07:38)
do you have up next?

Marlene Gebauer (07:39)
Well, I’ll quickly share that I also had a karaoke experience while I was away in Utah. Karaoke in Utah is very different than karaoke everywhere else. Yes.

Greg Lambert (07:49)
Karaoke in Ohio

Marlene Gebauer (07:51)
let’s just say, you know, you have to order food if you’re doing karaoke in Utah. Unfortunately we got there very late so we were not able to get a song in. However,

Greg Lambert (08:03)
Okay. Wait, wait. Tell people what very late is in Utah.

Marlene Gebauer (08:08)
Yes. Nine o’clock is very late. It’s very late. So they were playing “Goodnight, Sweetheart”. And so I jumped up and I finished it in an empty hall. Yeah, right. And there are no posts.

Greg Lambert (08:21)
It’s Utah’s version of closing time. So closing time’s a little too spicy.

Marlene Gebauer (08:29)
There are no posted videos, nor will there be. So sorry about that, everybody.

Greg Lambert (08:32)
That’s too bad. That’s too bad. Yeah.

Marlene Gebauer (08:35)
So, what was I going to start with? I’m noticing, and I’ve been talking to other people about this too, lots of innovation attorney jobs everywhere. It just seems that they’re in lots of different places. Yes.

Greg Lambert (08:49)
Yeah. I’m hiring. Are you hiring?

Marlene Gebauer (08:53)
We’re both hiring, in addition to a bunch of other firms and a bunch of other organizations. So I think it’s great. I mean, yes, I think there is going to be a lot of competition to get people, but that’s not a bad thing. I am elated that the industry is coming to see that these jobs are important, that people who have a deep knowledge of their practice but also understand technology, understand the workflows, understand how to translate between highly technical folks and the attorneys. This is kind of the glue that makes it happen. And so I think it’s great.

Greg Lambert (09:36)
Yeah. I know there was a story run a couple of weeks ago in Bloomberg, especially about the director and up positions, but I think even like entry-level, midlevel, and upper-level roles, this is a new set of skills and we’re looking for new talent. So I know I’m seeing people from outside of legal proper and outside of BigLaw that are applying. So it’s kind of interesting to see where we’re going and again, you know, we’ve made fun of law firms and the legal industry for, well, as long as I’ve been around about being slow

Marlene Gebauer (10:16)
Long time.

Greg Lambert (10:17)
to adopt technology. But I think we’re ahead of our peer industries when it comes to looking at a lot of the technology and beefing up internally. Even if you’re not spending half a billion dollars on infrastructure to help your firm out, you’re still spending a lot of money percentage-wise. I’d say it’s really good.

Marlene Gebauer (10:34)
Yeah. I mean, I do think this is the beginning. You’re starting to really see the beginning of kind of a change in staffing and therefore kind of a change in how the workflow operates because of the impact of Gen AI. So I think

Greg Lambert (10:55)
Yeah. Yeah.

Marlene Gebauer (10:56)
You’re going to see lots more of these kind of interesting new jobs. And I look forward to that.

Greg Lambert (11:02)
Yeah. I think it’s similar to the run that we saw in the early 2000s when Arthur Andersen folded and all of their knowledge management people came into the legal industry and kind of revamped how we looked at our data.

Marlene Gebauer (11:14)
Everything. Mm-hmm.

Greg Lambert (11:17)
I think we’re in that same kind of period of transition. So

Marlene Gebauer (11:22)
Yeah.

Greg Lambert (11:23)
Beef up your resumes, everybody. Send it to me and Marlene if you want.

Marlene Gebauer (11:25)
That’s right. Get tech skills, people skills.

Greg Lambert (11:29)
Yeah. All right, I’ve got one more conference that I’m going to that’s not ILTA. I actually got invited to speak at a conference that I’d never heard of before. Turns out there’s only like 11,000 people that go to this conference, and it’s called

Marlene Gebauer (11:43)
well.

Greg Lambert (11:43)
Ai4, the AI and the number four conference.

Marlene Gebauer (11:45)
In Vegas, baby.

Greg Lambert (11:47)
It’s in Vegas. It’s at the Venetian in Vegas in the best time of the year, early August. So I’m

Marlene Gebauer (11:54)
Only second to July when you fly in.

Greg Lambert (11:57)
Yeah. And again, it’s not a legal conference but AI focused. So it’s going to be interesting. I’m going to be speaking on the legal track. I’ve seen a few other law firm folks that are going to be there as well, hoping to learn a lot from outside the industry as well as what’s going on with some of our peers in legal.

Marlene Gebauer (12:21)
Yeah, I think our industry conferences are fantastic. I would never suggest anything otherwise. They really are second to none. But I think it’s good to go to conferences periodically that are outside of what you normally do. And I love the fact that they’re recognizing that they should bring in some people to hear from them about what they do because this is not something that’s normal for us. I think oftentimes that is the best way to learn about new things and get new ideas and see what’s being applied in other areas and whether we can apply it in our industry. So one of the things I’ve been really interested in is like personas, and you’ve probably seen in the news that AltaClaro now has DepoSim and you know, if you haven’t seen that, that’s an amazing tool. And essentially what it does is train people how to take depositions.

Greg Lambert (13:18)
Didn’t we interview the people on that?

Marlene Gebauer (13:21)
We have. Abdi was on, yes, for sure. And so what this does, in case you missed that one, is that it helps train you to take depositions or helps you improve your ability to take depositions. There are a number of personas, both for the deponent as well as opposing counsel. There are different fact patterns. And you can kind of go through the exercises and then you get a grade at the end. That’s a rather simplistic explanation, but you get the idea. But there are a lot of other things coming out. I mean, I attend the CodeX calls, the Stanford CodeX calls, and now I’m seeing a lot of students who are putting these things together. I am aware of former partners in BigLaw that are putting these things together for depositions and hearings. I was on a webinar that Opus 2 sponsored. I attended a couple of days ago and the chief innovation officer at Vorys was on and mentioned as part of that webinar that they have developed personas that they’re using to help people train for drafting, which is amazing. I’m a big fan of this. I think this is going to be a big deal in terms of training and getting people to learn. I’m waiting to see where this goes.

Greg Lambert (14:51)
Yeah. For someone who doesn’t understand when you say personas, do you have an example of what they should expect with a persona?

Marlene Gebauer (15:00)
Yeah, so a persona is basically a character. I mean, it has different, you know, qualities of a person. For example, in a deposition situation, it could be someone who’s very reticent to speak or someone who’s very outspoken and you would have different types of tasks with either one of these people. And that’s a simple example. You can get very sophisticated with some of the personas in terms of someone being a CEO at a small startup and these are their interests. You know, they like to go hiking, they like to ride bicycles, and personal time is important to them. They are concerned about protecting their IP. You can basically build these things to model, you know, whatever it is that you want to test against. Then you would interact with that persona, that model, and they would respond in a way that you would expect. And then you would kind of test yourself accordingly against that. So if you were drafting, then you would, you know, wait to see what their feedback was. You might ask questions. If you were doing a deposition, obviously you’d be asking questions of that persona and sort of dealing with both personas.

Greg Lambert (16:19)
And based on the persona, that’s the difference in the reaction you’re getting is that an outgoing person

Marlene Gebauer (16:24)
Yeah. And I

Greg Lambert (16:26)
may respond differently than an introverted person kind of deal, or

Marlene Gebauer (16:30)
Correct.

Greg Lambert (16:31)
a partner may respond differently than opposing counsel. So yeah, I can see that.

Marlene Gebauer (16:33)
Correct. And I should mention, Flatiron also has worked on this with Stanford and Megan Ma in terms of a negotiator simulator. I mean, very early on.

Greg Lambert (16:45)
Yeah.

Marlene Gebauer (16:46)
We had them on very early on for that.

Greg Lambert (16:48)
I was going to say if you want to know what’s going to happen in two years, talk to Megan Ma now. Well, you pointed one of these to me, but I’ve been seeing more now, you know, I’ve been writing a thing called Beyond the Model on our Substack since January.

Marlene Gebauer (17:05)
I do.

Greg Lambert (17:06)
I’ve been actually writing them since last year, where I’ve taken a fictional law firm and I apply technology to it.

Marlene Gebauer (17:12)
Fame comes slowly sometimes, you know? You just have to be patient.

Greg Lambert (17:16)
I’m the Megan Ma of legal fiction writing. How’s that?

Marlene Gebauer (17:20)
That’s it. That’s it. No.

Greg Lambert (17:24)
Of course, I always claim that I stole it from Anusia Gillespie after our interview with her. But I’ve noticed our friend Richard Tromans has the Innovators fictional story that he’s doing. Jennifer Leonard has a new book published by PLI that uses fictional characters to explain the story. It’s interesting. I think it’s a great way of taking facts or projections of how the technology can work in an actual law firm and applying it in a way that makes sense. In the latest story that I’ve done on our Substack page, I took a nonlegal-industry survey that was given to over 5,000 technology workers on how AI is affecting their job and their view of their importance within their job and applied it to a law firm where we gave our fictional law firm a survey to see how associates, partners and business professionals would take that survey and react to that survey. Again, fictional, but I feel pretty confident in where we were. The interesting thing that was in that survey was whether or not you would recommend your profession to someone who’s starting. In the tech survey, and I think this would apply in legal as well, the further up the chain you were, the more likely you were to suggest that somebody get into the industry to do what you do. So it’s

Marlene Gebauer (19:01)
Makes sense.

Greg Lambert (19:02)
And I think equity partners probably still see a lot of benefits. But I think associates right now, and even though I didn’t talk about law students, I’ve talked to a bunch of folks at law schools and they say the first-years, second-years, and third-years are all freaking out, mostly because of the early job offers that they’re getting, including summer associate gigs before they’ve even taken their first-semester tests and received their grades back. In fact, I heard at AALL that at one of the schools out on the East Coast, a T14 school, they saw earlier this year that an incoming 1L had already accepted a job before they’d even gone to their first class. I see this face. You’re not shocked at all. So it’s insane. The recruiters know it’s insane. The students know it’s insane. But other firms keep doing it.

Marlene Gebauer (20:05)
And yet we just keep doing it.

Greg Lambert (20:07)
Yeah. So I imagine that you know, if you score a 178 on your LSAT, you’ll probably get a job offer before you even apply to school. That’s going to be next. I don’t know. All right, high schoolers, we’re coming for you.

Marlene Gebauer (20:18)
Amazing. No pressure or anything. It’s like that, yeah, I know. It’s pressure enough. It’s like once you’re used to law school and how it’s done and then people are just sort of going in and taking jobs, having no idea. And they have to be

Greg Lambert (20:36)
Yeah. Well

Marlene Gebauer (20:38)
ready that soon without any type of legal prep.

Greg Lambert (20:40)
The other thing that I don’t think a lot of us are thinking about is that the students that don’t get these early offers are feeling like, Well, should I even be here? Is there a chance for me to get into a law firm now?

Marlene Gebauer (20:58)
I think sort of the key here is like you need to kind of have a flexible attitude toward this. I’m sure people went in thinking, okay, this is what it was going to be, but literally in like the last couple of years, the industry has changed remarkably and will continue to do that. And we were just talking about. Look at all these jobs that we’re hunting for. So, okay, it might or might not be an associate job at a law firm, but we’re hiring other types of roles where you can still use your legal smarts as well as any technical smarts you have. I know the big Gen AI companies, you know, are hiring people like this to be researchers, to be people that work on workflows. So I mean, there’s opportunity out there. You just kind of have to open your mind a little bit and the main thing is like, you know, get in there and get that experience. Now, in terms of your storytelling, I wanted to add that it totally makes sense because, look, there’s an entertainment value to fiction that, you know, isn’t present in these guidebooks, which are helpful and good, but you know, people respond to that. I think if you can combine something entertaining with a lesson or with knowledge, I mean that always goes a long way. And I think about how this is writing, but you know, we’ve had the oral tradition for thousands of years to pass on information. I don’t know about you, but you know, fairy tales and parables and Greek mythology, those were things I read when I was younger and I still remember. And I think this is just kind of a continuation of that.

Greg Lambert (22:55)
Yeah, and we’ll talk about this in a few minutes, but I was talking with one of the partners at my firm. There are really kind of two ways I write the story. One is teaching a technology and the reasoning behind it. It started off with why the legal research products suddenly got better. We talked about RAG technology and how the information is indexed, searched, and retrieved. He found that part very interesting. Then there’s a second style of story where I’m applying theory and business models to a law firm, and he’s like, yeah, I don’t like that part. Everyone has their own taste.

Marlene Gebauer (23:39)
Not as entertaining.

Greg Lambert (23:40)
So I don’t want that. I want to learn something. I already know how I’m running the business. I don’t need to know that

Marlene Gebauer (23:46)
Sure, but yeah, I mean, there are going to be other people for whom that’s their learning moment. So that’s, you know, you catch everybody. Okay. All right. So this one I thought was funny and it was kind of cute. So I wanted to bring it up that I was reading that Microsoft’s corporate external legal affairs group selected Harvey as their tool of choice. And I mean, yeah, it does make sense.

Greg Lambert (24:09)
Makes sense to me.

Marlene Gebauer (24:12)
It kind of makes sense and I’m thinking about again, I was reading that there’s sort of a deeper relationship now between Harvey and Microsoft and so it’s like, okay, well that totally makes sense, even though they have Copilot, but why not?

Greg Lambert (24:27)
I don’t know if I wanna say anything. Maybe they used Copilot and found out just how well it works.

Marlene Gebauer (24:35)
Well, I mean, you know, I think if you’re using it for legal, but I mean using it for other stuff is fine.

Greg Lambert (24:40)
Yeah. Copilot’s great because it connects to the M365 network and platform. So, yeah.

Marlene Gebauer (24:47)
Yeah, it connects to the stuff that you have and that is helpful. Very helpful.

Greg Lambert (24:52)
No, I use it every day, especially when

Marlene Gebauer (24:54)
Me too.

Greg Lambert (24:55)
I can’t find that stupid email I sent three weeks ago and I need to get it.

Marlene Gebauer (24:58)
How many would you say you use a day? Like different ones?

Greg Lambert (25:01)
Different ones?

Marlene Gebauer (25:02)
And for different things, I guess.

Greg Lambert (25:03)
Let’s see. I definitely use Harvey. Definitely use Claude every day. Okay, well let me tell you what I subscribe to.

Marlene Gebauer (25:13)
I use a lot.

Greg Lambert (25:15)
So I’ve got a $200 subscription to Claude,

Marlene Gebauer (25:18)
Crazy.

Greg Lambert (25:19)
and I’ve got a $20 subscription to Gemini. I’ve got a $20 subscription to OpenAI’s

Marlene Gebauer (25:26)
You’re an addict.

Greg Lambert (25:26)
ChatGPT. We have Harvey,

Marlene Gebauer (25:29)
You’re an LLM addict.

Greg Lambert (25:31)
we have Microsoft M365, so I have Copilot. So I use them all.

Marlene Gebauer (25:36)
Yeah, I mean I have OpenAI’s ChatGPT, I have the $20 plan. I have Claude, I have Copilot, I have Legora. There’s something else I have. What do I have? I feel like I’m missing one. I can’t remember right now, but not quite as many as you. Mm-mm, mm-mm. I refuse. I refuse.

Greg Lambert (25:54)
Just give your bank statement to Claude and it will tell you how many you have. Yeah. I actually had someone say on a LinkedIn post and I thought he was joking. He was like, “If you give Claude all of your data on your attorneys, their billing, their time entry, their forms, paperwork and emails that it does great for planning out these seven-figure deals.” And I know the person and I was going to do like a laughing emoji back to them and then I thought, nope, they’re actually being very serious about this.

Marlene Gebauer (26:28)
They’re serious.

Greg Lambert (26:30)
I’m like, if you want to watch my security ops team break into my office and tackle me away from my keyboard, that’s what I will start doing. Yeah.

Marlene Gebauer (26:37)
Mm. Wait, we’ll get the camera set up. Yep.

Greg Lambert (26:42)
Well, speaking of OpenAI, I don’t know if you saw the story since you were traveling, but apparently Hugging Face, the open-source platform, found that they had been hacked and they knew that whatever was hacking them must have been an agentic AI system because of the speed of what it was doing. A person couldn’t be doing this, so it had to be an automated attack. It turned out it was OpenAI doing a test on a benchmarking test that used a model and it was a combination of their new GPT-5.6 Sol and an unreleased model where it was given a test, put in a sandbox, and given limited access, supposedly. And like a resourceful college student, it found that rather than doing the work and doing the test as assigned, it was actually easier to go and break into the professor’s office and steal the key to the test and use it that way. So,

Marlene Gebauer (27:51)
Path of least resistance.

Greg Lambert (27:55)
It found a way through a multilevel hack to get internet access, went to Hugging Face, broke into Hugging Face using some stolen passcodes that it found, then found the answer key.

Marlene Gebauer (28:08)
Hugging Face had no idea what was going on.

Greg Lambert (28:10)
Found the answer key. So, I mean, it was interesting that it broke in and had access to all this, but was still on the mission of finishing the quest. It found the answer key, came back and answered the benchmarking test and apparently scored very high. But I will say that, you know, we’re joking around on this, but this is, I would say, this is a turning point in AI right now because the U.S. government as it’s set up today is very hands-off. There’s very little regulation. The federal government is actually stepping in and trying to keep states from regulating it. Now we’ve got this, you know, yeah, it’s kind of funny. But it’s also not funny. It’s not funny because

Marlene Gebauer (28:58)
This time it’s funny and

Greg Lambert (29:01)
Well, most of the podcast and writings I’ve seen on this said that if a human did this, it would be a crime. The FBI would be coming in and arresting people and instead you see Hugging Face’s and OpenAI’s leadership laughing it off and acting like this was a great thing that happened and if you’ve ever watched The Terminator, this is how it begins, people. This is how it begins.

Marlene Gebauer (29:25)
You know, I mean I know this isn’t human, but when you look at the whole picture, I mean it’s a very human experience. I mean, this is exactly what someone could do. A gray-hat or black-hat type of actor who says, okay, let’s see if I can do this and then does it. And what’s interesting here is that it basically assembled a bunch of actions that weren’t part of that environment and went with it and was very successful. Hugging Face was like, Okay, something’s happening but we don’t know which model is doing it. Then the model actually reveals later.

Greg Lambert (30:08)
what was even

Marlene Gebauer (30:09)
it’s like, it was me.

Greg Lambert (30:10)
What was even funnier was they tried to use both OpenAI’s foundational models and Claude’s foundational models to help fight off the

Marlene Gebauer (30:22)
Ha ha

Greg Lambert (30:22)
attack. But it wouldn’t let them because they had guardrails that wouldn’t allow them to use

Marlene Gebauer (30:27)
It’s like, sorry, we can’t do that.

Greg Lambert (30:29)
their tool to help because they could be going the other way. So they had to use the Chinese models. They had to use the Chinese models to fight it.

Marlene Gebauer (30:30)
So they were the white hats. It’s like we follow the rules. Yes, yes, I read that. It’s like, hey, can we borrow your models? Crazy. Mm-hmm. Mm-hmm.

Greg Lambert (30:42)
Yeah. It’s a great time to be alive. I’m going to end with that.

Marlene Gebauer (30:48)
Yeah. You know, I think KM is having a moment again. I’m very happy to see that. And I think you’re starting to see how, we had looked at, you know, Gen AI in terms of like efficiencies and doing things faster and better. And now I think the industry is turning towards how we can use AI to kind of harness knowledge that we have and surface knowledge that we have and combine knowledge that might be out there. So your traditional KM, you know, document-based, it’s like, okay, well, we’ll have things like that. But, you know, we may also have personas. We may also have financial information. We may also have information from the outside, you know, public information. Being able to synthesize all of this and surface all of it for different types of work is becoming important. So I know NetDocuments is rolling out some new AI-enabled capabilities. I know iManage is as well. I’m wondering if everybody was saving their announcements for ILTA. So we might hear something

Greg Lambert (31:59)
They are. They are.

Marlene Gebauer (32:01)
Something there. We’ve talked to you know a number of people that have products, like ClearPeople and their product, Atlas, and then Atlas AI, who we just recently had on the show. There are a number of these vendors that are looking at how to do this. Microsoft is also doing this as well. And I think the trick is

Greg Lambert (32:24)
But are they doing it through Harvey? That’s what I wanna know.

Marlene Gebauer (32:29)
I should say Harvey also has something, too. And so I think the trick at this point is, you know, going back to what you were saying about security and sort of what we’re comfortable letting these tools access because, you know, we’re still in a situation where certain clients are saying, you know, I don’t want Gen AI to touch any of my stuff, or I don’t want my work to be combined with your other work. There’s also movement towards a need-to-know model in your DMS. Given all of these factors, how do we make these tools work effectively for people? And I mean, it’s going to be done because, I mean, some of these tools already follow the rules that your DMS already has in terms of who can access what. So you know, I think it’s really just a finesse question.

Greg Lambert (33:23)
Well, you know, giving the AI rules works every time, right, OpenAI. So, yeah. Now I do

Marlene Gebauer (33:29)
Yeah, why did I have this story like right after that one? It’s like, never mind. Never mind.

Greg Lambert (33:36)
I do want to say that in the OpenAI test, they actually took the guardrails off, which explains part of this. But they thought because it was in a sandbox environment that would protect it. But yeah, of course, yeah.

Marlene Gebauer (33:45)
They’d be okay. I feel much better now. Of course.

Greg Lambert (33:52)
I watched a video last week from Claire Vo, who hosts How I AI. It’s great, and although it isn’t legal, I think very applicable to what we’re doing. Last week she was showing how she makes AI harnesses. And after

Marlene Gebauer (34:13)
Tell us what a harness is.

Greg Lambert (34:14)
So a harness is essentially software you put on top of the AI to help guide the AI on what you’re asking it to do, the rules that you set up, and the output. And one of the things that stood out as she was talking about how she develops the AI harnesses for the tasks she performs, mostly for evaluating code she uses for her company and its website. If you need something to be the same every single time regardless of the AI model that’s underneath, the harness, she explained, is how you get there. So you set up the parameters of this is how you handle the information, this is what you do, and then this is the output and every single time it’s the exact same thing. And there’s lots of stuff that we do that falls into those parameters, those guidelines of making sure that if we’re processing data that it processes in a certain way and the output is in a certain format. So I’m going to make the projection. You know how we’re talking about AI agents in 2026 and turning the agents loose. I think for 2027 we’re going to be talking about AI harnesses. We’ve got the power. Now we need to set the direction of how these very powerful models actually work in a predictable and consistent way. So if you’re not looking into harnesses now, I suggest you do that. Coincidentally, there’s a three-part story that I’m writing on Substack this week that talks about AI harnesses. So if you haven’t subscribed to the Substack page, do so now.

Marlene Gebauer (35:57)
And in case you wanted to hear more, are you aware of any vendors that are using these or promoting them?

Greg Lambert (36:11)
Well, they all have harnesses. So, when you’re using the interface on Harvey, that is a harness. It’s a layer on top of the AI company’s models.

Marlene Gebauer (36:21)
But this harness apparently produces the same results every time. So

Greg Lambert (36:24)
Yeah. You set it up, you tell it what to do. You program it or vibe code it, and this is the interface that you use or this is the tool that uses the AI and then guides the AI and its output. So, lots of things to learn. Like I said, three-part story coming out this week, so stay tuned. Yeah.

Marlene Gebauer (36:44)
Very good. We’ll be looking for it. I wanted to put in one thing about small firms and I saw something in the news. It’s an AI legal receptionist called LexiDesk. What I thought was interesting about it is that it captures quality business during intake. So it’s not just strictly a receptionist, it’s trying to figure out whether this is the correct business for you. What I thought was interesting about it was what counts as quality business how You program that, how do you prompt that and how do you make sure that’s happening? Can it recognize urgency or vulnerability? Because, again, we have certain obligations as attorneys to address things. So, can it figure that out? How is confidentiality handled? I mean, it’s one thing to record that a call came in and schedule something on your calendar and it’s another thing to really do an assessment of the call and what your next steps are. So I thought it was an interesting one.

Greg Lambert (37:50)
Yeah. Speaking of interesting, this is either going to be really interesting or not interesting at all. You know how we’ve been asking folks to tell us about what resources they’re using to read or keep up with the industry. I heard a very interesting question this week from Simon Willison when he was talking with three engineers at Anthropic and one of the questions he asked was, what is true today that wasn’t true a year ago for you? And of course these were engineers talking about Claude Code and the harness and everything that they’re doing. But we may swap out the question. We ask a crystal ball question about looking into the future. I think this would be a good setup to, you know, take a peek back at the past and look at today and say, you know, what’s true today that wasn’t true a year ago? And so, Marlene, you get to be my guinea pig on this. What’s something that’s true today that wasn’t true a year ago?

Marlene Gebauer (38:55)
Well, a year ago I think there was a lot more emphasis on adoption and just using the tools. And I think now there is much more focus on incorporating this into workflows. We need to change the workflows. It’s not just making an existing workflow more efficient. It’s basically changing the workflow completely. And that includes staffing, that includes pricing, and so it’s I mean, the whole ride has been interesting, but it’s super interesting now because you’re bringing in a lot of different experts. You know, there’s a lot of different departments this touches. You have these groups kind of working together, which I think is, you know, absolutely incredible and interesting. Everybody’s bringing their expertise to the table to make these changes in a positive way for businesses. The other thing that is quite different, and this is very recent, is now everybody’s sort of talking about the cost of generative AI. Because I think, you know, we got everybody on board and now all of a sudden the reality hits I know, I know. And it’s

Greg Lambert (40:12)
Wait, wait, I can’t burn every token I can get my hands on? Yeah.

Marlene Gebauer (40:18)
Again, it’s intellectually interesting. It’s also very stressful. I mean, from a business perspective in terms of like what this is going to cost and how is it going to get paid for? And everybody has seen stuff in the news about paying per token, but I don’t know whether that is going to end up being the right model. And I do think vendors are trying to be thoughtful about this. I mean, I’ve had some discussions with some of them and you know, they are trying to be very thoughtful about how to do this because straight-up token costs or use costs. I mean, we’ve seen that model before, like you and I have seen that model before. And it’s the old Lexis Westlaw model from years and years ago. And

Greg Lambert (41:02)
Yeah. We’ll just find a way to track the cost and then pass that on to the client, right? Then

Marlene Gebauer (41:09)
Right. And I mean, look, in the beginning that made very good sense, but you know, in time it became a situation where clients said, look, that’s the cost of doing business and we’re not gonna pay for that. So a lot of times that is not paid for. And I think now it’s even more imperative to think about it because everybody has access to these tools now. I think there’s a fine balance between saying it’s like, okay, we’re gonna pass on, you know, token costs to people, to clients, and clients are like, well, you know, we could do that. Like we can do it ourselves. I think there’s a lot more thought that has to go into sort of what you’re doing with the AI and you know, the value that provides to the clients. And whether you’re kind of working on these things together or if it’s something that’s just more internal that helps you.

Greg Lambert (42:08)
Yeah. I’m going to kind of spin this into our crystal ball question when it comes to the tokens because I can’t remember, I think it’s Taleb’s Law where it’s the saying, “I’ve seen gluts not followed by shortages, but I’ve never seen a shortage not followed by a glut.” I don’t think it’s going to be next year, but I think in two or three years this whole, you know, looking at the cost of tokens is going to be a nonstory anymore because

Marlene Gebauer (42:43)
Agreed.

Greg Lambert (42:44)
efficiencies are going to catch up. Hopefully the latest model doesn’t burn fuel like a rocket. We’ll find efficiencies because we’re gonna have to. And I think competition will help. I think we’re gonna see more competition from the Chinese models. We’ll probably see competition from open-source models from Europe. We’ll probably see some regulation come in after 2028 perhaps. But in the meantime this is a wave we’re gonna have to ride till it breaks, right? So

Marlene Gebauer (43:16)
Yeah, I mean, I agree that it’s not a long-term issue, but it is a significant

Greg Lambert (43:21)
But it’s an issue.

Marlene Gebauer (43:22)
It’s a significant short-term issue. I mean, in terms of cost, but I agree with you and I mean, I think competition is going to force people, I think, to become better users. I mean, it’s sort of a painful way to do it, but

Greg Lambert (43:39)
Yeah.

Marlene Gebauer (43:40)
it will do it. So that is also not a bad thing.

Greg Lambert (43:45)
Yeah. Well, it was great catching up with you this week.

Marlene Gebauer (43:50)
Yeah, you too.

Greg Lambert (43:52)
It’s always nice to have these and just kind of talk about what’s going on in the industry ’cause man, there’s so much. So much going

Marlene Gebauer (43:57)
So much. So much.

Greg Lambert (44:00)
on. So thanks for sitting down in the morning and for letting me pull out my really cool coffee mug.

Marlene Gebauer (44:08)
Just got to rub it in. You just got to rub it in.

Greg Lambert (44:11)
And show everybody your Starbucks cup while I show my Wolverine claws.

Marlene Gebauer (44:13)
Yeah. No, they saw it already. It’s okay. I’ll bring a good one next time. Thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague. You know, we’d love to hear from you on LinkedIn and Substack. And as always,

Greg Lambert (44:27)
Yeah. Subscribe to that Substack. Watch those stories roll in.

Marlene Gebauer (44:31)
Greg needs likes. Greg needs likes.

Greg Lambert (44:34)
I do!

Marlene Gebauer (44:34)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and goodbye everybody.

]]> xlambert@gmail.com (Greg Lambert)