There is a widening gap between hope and reality. Legal departments may believe they are on the verge of controlling outside counsel costs, moving more work in house, or shifting matters to smaller firms, but Satkunas notes that the billing data has not caught up to those ambitions. She sees some room for in-house expansion in more routine areas like employment work, especially with AI helping legal teams absorb more volume, yet the largest and most sensitive matters are still flowing to outside counsel. That tension gives the episode much of its energy. Everyone sees pressure building in the system, but the old habits of legal buying and legal staffing remain firmly in place.

The discussion also gets into the mechanics of better decision-making, and where there is practical value for legal operations leaders. Satkunas emphasizes that data only becomes useful when departments have enough discipline in their enterprise legal management systems to categorize work correctly, clean out outliers, and separate different matter types instead of lumping everything into broad buckets like litigation. She also explains why finance data alone will not do the job. The real insight sits inside invoice-level detail, where hours, rates, firms, and timekeepers reveal what is happening beneath the headline spend numbers. For listeners trying to build a stronger legal ops function, this part of the conversation feels like a polite but firm warning that dirty data still tells stories, but some of them are fiction.

There is an obvious strain on the billable hour model that AI is placing on it. Satkunas notes that while average partner rate growth has hovered around 5 percent, top-end lawyers are often raising rates even faster, especially as firms try to protect revenue from the work and people they still believe clients will pay for. At the same time, she argues that alternative fee arrangements have remained stuck for years, though AI may finally force movement toward value-based pricing. If technology reduces the hours required to complete the work, then the old logic behind both hourly billing and many flat fees starts to wobble. That leaves firms facing an uncomfortable question, which is how to price legal services based on value delivered rather than time consumed.

We’d say that Satkunas is neither cheerleader nor doomsayer. She is a patient observer of a market trying to pretend nothing is happening while the floorboards creak under everyone’s feet. Her prediction is that real value-based billing will begin to appear in pockets over the next couple of years, even as firms continue squeezing what they can from the billable hour in the meantime. For law firm leaders, legal ops teams, and general counsel, this episode is a sharp reminder that disruption does not arrive with a trumpet blast. Sometimes it arrives as a spreadsheet, a trend line, and a guest who quietly points out that the data has been trying to warn us for years.

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

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

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Music: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Transcript:

Greg Lambert (00:00)
Hey everyone, I’m Greg Lambert with The Geek in Review, and I have our friend Sarah Glassmeyer here. Sarah, I know you attend conferences all the time, but you have kind of a unique way that you use the LTH directory. So tell us more about that.

Sarah Glassmeyer (00:16)
Yeah, conferences are great, and I do attend the educational programming, but my favorite part, and the most important part for me, both for my gig and because of my interest level, is hanging out in the exhibit hall. And when you go to these exhibit halls, there are dozens, even hundreds, of vendors to keep track of. So this is a perfect use case for what I want to talk about.

And in life, if you’re listening to this podcast, I’m assuming you are interested in legal technology at some point. Every day there are new products coming out, new changes, new solutions. So the Legal Tech Hub directory is not just a beautifully curated and maintained directory of legal tech products, it is also a tool that you can use. When you are signed into Legal Tech Hub, you can create what we call the portfolio feature. There is a little star by every listing, and you just click the star and it gets added to your portfolio.

Within that, you can then subdivide it into solutions that you’re already subscribed to, ones that you’re just kind of thinking about so you can put those in your watching column, and also ones that you’ve reviewed but don’t like, not for me right now, but you still want to put them to the side without losing them. And whenever I update a listing, or my staff updates a listing, or the vendors themselves come in and provide an announcement, you will get notified about that.

So it is just a really nice way to keep things organized. Plus, we have a feature within that where you can keep notes. So how I use it is when I’m cruising around the exhibit hall and I see something, or I was just at ABA Techshow and watched the Startup Alley, I keep notes as I am doing that. I do not have to keep pen and paper. I can do it on my phone. I can do it on my iPad. And so it is all there now that I am back home and at my desk and can say, what the heck did I actually look at? And also, not during conference season, it is a way to keep track of things so you have one place you can go to, keep referring back to, and keep getting updated information.

Greg Lambert (02:14)
Well, that’s a great suggestion on how to use the directory. Thanks, Sarah.

Sarah Glassmeyer (02:19)
You’re welcome.

Marlene Gebauer (02:28)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:35)
And I’m Greg Lambert. Marlene, we talk a lot of theory on here about legal pricing and where we think things should go, but today we’re going to get past that and get into some real hard numbers. And we’re thrilled to welcome back one of our favorite data geeks, I think that is what we coined the last time, Kris Satkunas. Kris, good to see you.

Marlene Gebauer (02:54)
Mm-hmm.

Kristina Satkunas (02:57)
Good to see you too. Thanks for having me back.

Marlene Gebauer (03:00)
So Kris is the Director of Strategic Consulting at LexisNexis CounselLink. She has over 20 years of experience advising corporate legal departments on how to use data and drive better business decisions.

Greg Lambert (03:13)
Yeah. And she is also the mastermind behind the CounselLink Trends Report, which she has been authoring now for 12 years. What’s the number now? Yeah. So we are excited to have you on and ready to jump into the numbers.

Kristina Satkunas (03:23)
It’s something like that. Yep, close enough.

All right, cool.

Marlene Gebauer (03:34)
So Kris, I recently sat in on a webinar that you did with Amy Borsetti from Harbor, and you were combining your CounselLink invoice data, which represents over $75 billion in legal spend, with Harbor’s law department survey data. And I think it painted an incredibly rich picture of where costs are heading. So when you put those two massive data sets together, the objective invoice data and the subjective survey data, that is the data dream. What surprised you most about the current direction of legal spend?

Greg Lambert (04:04)
That’s the data dream right there.

Kristina Satkunas (04:14)
So because I look at the objective data a lot, I don’t think there was a whole lot that surprised me there. I’m sure we’ll talk about that a little bit more here. But when I look at the survey data, one of the things that it said is that only, I think, about a third of the respondents expect legal spend, meaning outside counsel legal spend, to increase this year, maybe into next year, compared to the past.

Again, when I look at my data, year over year, the Trends Report shows every driver of spend is increasing. Billing rates keep going up aggressively. AFAs have marginal adoption. The biggest, most expensive firms keep on gaining share. So all of those things point to increased spend, right? And demand is not going down either. So that just says to me fundamentally, something has to change, right? If corporations are really going to keep legal costs down, it is not going to be business as usual.

So I feel like we are moving maybe toward more in-house work, using tech finally a little bit more to make work more efficient. So I think that statistic from the subjective data was probably the most surprising to me, that so few people think outside counsel spend is going to increase.

Greg Lambert (05:43)
You know, we hear that whenever there is a dip in the economy or there is pressure on the GCs to rein in costs, one of the things that gets rolled out, whether it is 2008, 2012, 2020, or last year, is, we’re going to handle more of this ourselves. And I have never really seen that come to fruition.

Now, I think I know the two letters that everyone thinks will make a difference this time, but do you think AI will enable that to actually happen this time around?

Kristina Satkunas (06:14)
Yeah.

Right.

Well, I mean, I do, but I think it is only certain types of work, right? So the higher-end litigation, the bet-the-farm stuff, the important deals, that is not ever staying in-house. But I think some of the more commoditized work, yeah, I have definitely talked to some of our customers who say they put together a business case to hire another attorney in their employment work or something like that. So yeah, those two letters, could this be the year? Are those two letters going to start really making a difference? Could be, yeah.

Greg Lambert (06:59)
Yeah.

This could be it.

Marlene Gebauer (07:06)
We keep saying it. It is like, this is going to be the time.

Kristina Satkunas (07:08)
That’s right.

Greg Lambert (07:10)
You made a point in the webinar that really stood out, and that is when clients go through competitive bidding with RFPs, when they focus pretty much primarily on cost, you were saying that could actually be a problem. A lot of people think of RFPs as a driver to get costs down. So do you mind kind of unpacking that for the audience, what you mean by that could be a problem?

Kristina Satkunas (07:44)
Yeah, so it is not just cost, right? Outside of the legal profession, if you were putting something out to bid, like an addition on your house, are you really going to go with just the cheapest bid? You are probably going to consider other factors as well, like their experience and whether you really think they are going to deliver what you want, right?

So I think customers are looking for, and should be looking for if they are not, in their RFP responses, how the firms differentiate themselves. At a minimum, it could be that firms are communicating that they really understand the legal issue and why it is an important matter to the client, right? Looking for that depth of understanding from the firm, and expertise of course comes into play, what they have done in similar cases. But I have also seen things like communicating information about, this is the legal team that we would bring to you to handle this. It is not just this one partner, but who would the team be?

There was a time not too long ago, and that included information about the diversity of those timekeepers, right?

Greg Lambert (08:58)
I was wondering, is that off limits now?

Kristina Satkunas (09:01)
I am not seeing it being asked anymore. I do not know that it is really off limits, but it seems people feel like it is, and it is not really being asked. But all of those things, we are not talking about bidding on commoditized, repeatable work, right? Cost is a factor. And I think Amy said that well when she was talking about this in our webinar the other day. The important thing about the cost component of an RFP, or just going to RFP, is it signals to the firm that cost matters, right? So they are going to pay attention to that. But all those other things are part of the equation and have to balance it out. So yeah, it is more than cost.

Marlene Gebauer (09:49)
So last year we talked about in-house teams using more data for benchmarking and for negotiation. That was sort of a growing trend. And I am curious if this year we have continued to see that. How common is it now for legal departments to have the kind of data hygiene necessary to pull this off?

Kristina Satkunas (10:18)
So yeah, it is an interesting question. I think that as more and more organizations, and the vast majority of good-sized organizations, are using some sort of enterprise legal management system, the configuration of that system kind of forces them to create some hygiene, right? Categories of types of work they are going to be billing, setting up some criteria for the types of bills that they will receive. And I think consultants who are helping set those up are getting better and better at understanding that the data that comes out is only as good as how you organize how the data is going to come in.

But I would say, just from a hygiene perspective, something like three-quarters of organizations that have an ELM in place are good enough in their hygiene. That sounds kind of gross to say, their hygiene was good enough, but you know what I mean. So a lot of companies let a law firm bill a flat fee as if it is hourly. So they do that, and then it looks like you have a timekeeper who billed you $20,000 an hour.

Greg Lambert (11:23)
Yeah.

Marlene Gebauer (11:23)
You

Kristina Satkunas (11:39)
That is dirt in the data that you then have to find a way to filter out. But those sorts of outliers stand out and are pretty easily eliminated. So I think we had at least one of these within the webinar that you saw. I like to use box-and-whisker charts to show the span of data, like hourly rates. And that outlier that I just described, that would be a whisker, right? It would fall outside of the box, right? It is the box that any good analyst is focused on and trying to understand, what is normal, what is actually going on.

So I think the data is better, and you cannot get hung up on those small outliers because they are always going to be there. You just have to have a way to identify them and focus on what is important. I do think that a lot of organizations could still do a better job at tagging their work. So, for instance, you could categorize, and I have seen this, litigation is litigation. We just call it all litigation if it happens to fall under that, but there are a lot of apples and oranges within litigation. So if you are trying to benchmark what you should be paying for, like IP litigation versus employment litigation, it is probably different. So you do need to separate things like that out.

So I think we are getting better, but there is lots of opportunity to improve. Data could always be cleaner, but at some point you have to say it is clean enough that we are going to start working with it.

Marlene Gebauer (13:06)
Very different workflows, too.

So if a GC wanted to start doing this, would it be their financial data? Would they be focusing on sort of the core areas? What would be the best way to approach it?

Kristina Satkunas (13:41)
So I would say, and I think that tagging of types of work is super important, right, to be able to break down your work into different categories. And then yeah, it is definitely the financial data. The financial data meaning what is coming in on the invoices, right, how many hours were billed for hourly work, how many hours were billed at what rates, and then breaking that down by law firm, by timekeeper, to be able to slice and dice it as needed.

As long as you have data that is clean enough. If you do not have some kind of ELM in place, then you kind of have to start fresh. I might almost just leave all the other stuff in the past. You really cannot go to your finance team and ask for this data because they are not pulling apart the invoice. They are picking up which part of the business should be charged for this work, but they are not breaking it down into the detail that you need.

Marlene Gebauer (14:41)
I often wonder where to start sometimes if you are using AI. Do you go back to historical data prior to AI, or do you just start with AI as a base because it is going to improve and see where you go from there?

Kristina Satkunas (15:00)
Yeah, right, right. It is a good question. I think I would argue for let’s just start with what we have now, because that you can fine-tune and feel good about what you are working with now. Going back, you have probably got a lot of noise in that data that might interfere with things, but you can start with what you have now and then maybe take a little slice of what you have and do a little pilot of some of the historic data before you roll it out full force.

Greg Lambert (15:30)
While we are fleshing the data out a little bit more, I am curious on your end of things, are you able to implement more of the AI tool strategy on parsing the data? Does it help you kind of see through some of that and kind of fix it on your side, or is it still too messy when it comes to you?

Kristina Satkunas (15:53)
Yeah, I mean, our product team, as we are developing our product, is certainly working on pulling AI into more and more functions of how CounselLink reads bills and evaluates them. So that is certainly going to improve and will continue to improve. For me as the data geek, I am still relying on the database that we create when all of that data gets scrubbed off of the invoices and gets put into this database.

But I am playing with AI tools for me to search that database and to organize that data and to look for trends. So I do not know, there are just so many opportunities with how AI gets used. But yeah, it is definitely playing a bigger and bigger role, and will continue to, in everything around analytics for sure.

Greg Lambert (16:56)
I am a little disappointed. I was expecting you to have a murder board up with all the data points and then red string connecting all of the different pieces. But next year.

All right. There was one thing that you said at the Texas Trailblazers meeting that we had a few weeks ago, and you talked about the latest data showing that partner rates, for a while there, were around 3 percent increases a year, and now they are right at 5 percent. And this is an aggregate across the board. But the real double-take moment was also seeing, and we talked about this last year when you were on, the associate rates and just how high associate rates are getting.

I think we are all seeing that hours are kind of flat, maybe down, for legal work across many firms, but the rates are going up. Are we headed to some kind of structural breaking of the system? How do you maintain both of those at the same time?

Kristina Satkunas (17:01)
Thank you.

Next year, next year, Greg.

(18:25)
Yeah, I mean, those associate rates, as high as they are, are definitely interwoven with what you are talking about. So yeah, the model is not going to work if you do not have the hours to support it, right? The law firm model as it is, without being able to bill all the hours historically, something has to give there. And I think the model is breaking. I kind of hope the model is breaking. Let’s shake things up a little bit. But I think people are going to hold onto it kicking and screaming for quite a while. And they will probably start by hiring fewer associates. I think I have heard some pockets of that starting to happen.

But the point that you raise about the $2,000-an-hour associate, that is not the associate AI is likely to replace.

Greg Lambert (19:18)
Mm.

Kristina Satkunas (19:18)
In fact, the data that I look at, so I look at the median partner and what they are billing and how much their rate went up, but the fact is the data shows, and has shown this for the past couple of years, that those partners and associates already at the higher end, so that $2,000-an-hour associate, are raising their rates more like 7 percent compared to the 5 percent average.

So those who are already the more senior people, who should be the more experienced people, are raising their rates more. And I will hypothesize that there is some recognition there from law firms that those people and their expertise can still be billed, right? So let’s try to monetize that as much as possible, some revenue protection, by increasing their rates more, knowing that the more junior people are the ones that we might not be able to bill as much, if at all. So yeah, it is actually not until recently that I started paying attention to that, and I think it is tied up in what we are talking about here with AI starting to sneak in.

Greg Lambert (20:38)
So the gap will continue to grow and the rich get richer.

Kristina Satkunas (20:44)
I think so. And that is what the data has been showing us for the past couple of years. And I think, again, that is in defense of the model, right? That is people saying, the only way this model is going to work if the hours are going down is the rates have to go up. But I am not going to be able to bill for some of these people, so I have got to raise the rates of the people that I am still going to be able to bill. So yeah, it feels that way.

Marlene Gebauer (20:47)
Great.

All right, Kris, I want to get a little provocative here. We have been hearing about alternative fee arrangements for years, but your data shows they have been stuck at around 8 to 10 percent of matters for a while. And now that AI is starting to bring those hours down, the efficiency argument for flat fees is changing. So the question is, have AFAs missed their window of opportunity? If the hours are already shrinking because of technology, does the argument for flat fees disappear?

Kristina Satkunas (21:44)
The efficiency argument is likely disappearing, yes. And if that is the argument for AFAs, then it does pose a problem for the model for sure. I do not know. I actually think the window, and we have talked about this before, I am so sad that AFAs have not taken off because there are so many wonderful reasons that they should have taken off. But intellectually, people were not ready to embrace them.

But I think that window may finally be opening because people are going to be forced to take it because, again, of those two little letters that are going to change things. If hours are shrinking, law firms have to repackage their services, I think, in terms of value received instead of the hours that had to get churned to do that work. And so that is your point about efficiency. They cannot package it that way.

An astute general counsel is not going to pay a flat fee that is approximately what the hours used to add up to on the shadow bill that they used to ask for. We see a lot of that, and it makes me a little bit crazy that general counsel ask for shadow bills. They have a flat fee, but then they want to see how much hourly work was done as well.

So they are not going to pay that larger flat fee for what the work used to cost. So I think that the billing, the AFA model that will start moving, and hopefully we will start seeing more, is billing that is tied more to outcomes, to value, somehow saying what is the value of this matter as opposed to what did it take to get to it. So yeah, I am going out on a limb and saying that window is opening finally.

Marlene Gebauer (23:39)
We will be asking next year. Yeah. Well, I think about it, the value of the work has not changed. The time it takes to do it has changed, but the ultimate value of it has not changed. So how do we price that?

Kristina Satkunas (23:40)
I know. I will remember.

(23:53)
So then what is the other value, right? So how else can law firms add value on top of that? How can they extrapolate what they have done on that matter and help alert their customers to things they should be looking out for down the road, right? So other value-adds that I think have to start coming from that relationship for a general counsel to believe that there really is extra value.

I am not saying that is easy, but I think that is where we are going to have to start moving.

Marlene Gebauer (24:27)
I think AI opens the ability to do that. It just opens that time window to do that.

Kristina Satkunas (24:33)
Yeah.

Greg Lambert (24:35)
Last year, Kris, you introduced a new metric into the report, and that is market share by new matter spend. So instead of looking at the total annual spend, which may include some of the old matters that get brought over, the report analyzed new matters opened each year to determine what the future market share was.

Last year, in that first year of reports, you determined that large firms are gaining, and not losing, the share of the new work, that there is actually more work going to the more elite firms, the bigger firms. And then during the webinar, I thought I heard you say that this year more of the day-to-day might be going to smaller regional firms. One, did I hear that right? And two, what has changed?

Kristina Satkunas (25:32)
So you kind of heard that right, but you did not hear it from me. And that is where I think looking at real data like we have in CounselLink Benchmarking differs from survey data. In the session that we did, it was both a combination of the benchmark data and survey data. The two are definitely complementary, and it was interesting to see how they lined up. But in my experience, and for the most part, survey data tends to give people the opportunity to paint a rosier picture or a more hopeful picture.

So the survey data that our friends at Harbor presented said that two-thirds of respondents had completed or were working on an initiative to strategically shift work to smaller firms. So there is a lot of wiggle room within that, right? And that is good, right? Well, that or they are working on it.

Greg Lambert (25:38)
Ha ha!

(26:42)
Wishful thinking maybe. Yeah, we are working on it. It is like our AI ROI. We are working on it.

Marlene Gebauer (26:45)
It is like we are looking at it. We are thinking about it.

Kristina Satkunas (26:52)
We are working, yes.

But the data does not show it, right? So yes, I still looked. I think last year was the first year we looked at that new metric, and I looked at it again. But for just new matters, new matters opened in 2025, same thing, large law still gaining share of that new work. I always feel a little bit like a Debbie Downer after people report survey data. I am like, well, data does not support what you are saying. But it does not mean it is not coming. So if they are evaluating that and starting to move work down, it will take some time to see that.

So I am still hopeful that GCs will realize how much of an opportunity there is there, but we are just not seeing it yet.

Greg Lambert (27:47)
Well, it does not matter what the data says, it is how I feel. That is what is important.

Kristina Satkunas (27:51)
That’s right. I will go along with that, of course.

Greg Lambert (27:53)
Kris, before we get to the crystal ball, I know you spend most of your time immersed in the CounselLink Insight benchmarking databases, but we have been asking our guests what they are reading or listening to or following outside of their own reports to kind of keep them cognizant of what is going on in the legal industry and business of law.

Kristina Satkunas (28:29)
Yeah, so I am sorry to say I do not have a go-to, if you are hoping that I would come up with a particular blog that I read or a particular site I am paying attention to. Obviously, I have a feed that has an awful lot coming into it, so I am looking at a lot of things. But probably like you, I am always looking for new webinars or events like the one I ran into you at a couple weeks ago, looking for something that is a fresh take or something new.

Maybe because I spend so much time looking at data, I think I actually get more value from using my other time talking to people, hearing what they are really doing, and asking them to validate some of the things that I am seeing in the data. So it is always good for me to talk to whether it be our own customers or people in the industry, to dig into things a little bit more. And also, I am always looking for ideas of whether there are things they would like me to research, right? We do have this great database of so many invoices and matters running through it that if other topics come up, I am always interested in hearing those for that reason.

So no one specific thing, but yeah, I spend a lot of time looking at different articles and listening to what people are talking about.

Marlene Gebauer (29:52)
Okay, Kris, it is time for the crystal ball question. I know we did this last year, so we will do it again this year. We looked back at your prediction from last year, and we will talk about that and then hear your predictions for this year.

In 2025, what you were saying, and I am going to paraphrase, is that firms are going to increase their rates even more than what we had been seeing, that things had leveled out around 5 percent for the past few years and that you could see it going up to 6 or 7 percent. And you were hoping there would be pushback on those rates. First of all, how correct were you as far as this year’s data? I was going to say, I think she hit it in terms of this year’s data. And then, looking forward, as AI becomes integrated into these workflows, are we going to see a real shift to some form of value-based pricing?

Greg Lambert (30:31)
I think she was pretty good.

Kristina Satkunas (30:50)
Yeah, so I mean, I hate that I was right, right? It is like business as usual. The trend keeps going along. It does not take much of a crystal ball to see that. But I will go out on a limb. I am hopeful that we have finally entered into a real age of disruption, so to speak, and that we will start seeing some traction in 2026, maybe 2027, probably even more, of that value-based billing starting to show up in some pockets that we have not seen it before, not just flat fees, but real other types of billing like we were talking about.

I still think, though, that for rates, based on what we were talking about before, I think they are probably going to go up even more than that 5, 6, 7 percent, as many firms try to optimize what is left of the billable hour model, right? Let’s get as much as we can out of the people that we are able to bill. So I would not be surprised if, at least at the high end, we start seeing those more senior lawyers increase their rates even more than they have in the past.

Marlene Gebauer (32:06)
That is just really depressing. And it seems short-sighted. Now would seem to be the time to get ahead of this, like, look, it is coming. Let’s get ahead of this trend and be ready.

Kristina Satkunas (32:09)
Okay.

But is that what is going to separate some firms from the others, right? Those that are holding on and trying to hold onto that model versus those that are trying to face what is coming and change their model. I think there will be a split between those. I do not know, maybe the data will help me parse firms that way, right? Those that are forward-thinking versus those desperately clinging.

Greg Lambert (32:45)
Well, see, yeah, there was a little sliver of hope there in that.

Marlene Gebauer (32:46)
Whole new report, whole new report. Great.

Kristina Satkunas (32:49)
Yeah.

Yeah, that is right. I know. I am not a Debbie Downer, really.

Greg Lambert (32:56)
Kris, the Director of Strategic Consulting at CounselLink, thank you again for coming back this year and sharing all these insights. It is always a pleasure to jump into the data and talk real numbers with you.

Kristina Satkunas (33:13)
Thanks for having me again. I enjoyed it very much.

Marlene Gebauer (33:17)
Yeah, thanks, Kris. And thanks to all of you, our listeners, for listening to The Geek in Review. If you enjoyed this dive into data, please share this with a colleague.

Greg Lambert (33:26)
And Kris, where is the best place for listeners to go to find the latest Trends Report and maybe connect with you as well?

Kristina Satkunas (33:34)
They can connect with me probably on LinkedIn. That would be the best way. And they could ask me for a copy of the report that way, or they could go out to the CounselLink.com website and find a link there as well.

Marlene Gebauer (33:47)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry. And bye, everybody.

If you want, I can also turn this into a speaker-labeled, publication-ready transcript with cleaner paragraph breaks and removed filler while still preserving the substance.

A central idea in the conversation is Wexler’s view that the most useful unit of analysis in litigation is not the document, but the fact. Mostyn explains that lawyers are often handed a mountain of emails, messages, filings, and exhibits, yet what they need is a clear understanding of what happened, why it matters, and where the pressure points sit. Wexler is designed to pull out events, inconsistencies, and supporting details from that record so litigators are working from a factual map rather than a pile of files. That shift matters because disputes are rarely neat. Important evidence may be tucked inside an offhand message, a late footnote, or an exchange written in vague, coded language. Wexler’s aim is to turn that mess into something a trial team can use to shape strategy.

Mostyn also walks through the mechanics that separate Wexler from more general legal AI products. He describes a detailed fact extraction pipeline that processes unstructured material and turns it into structured data before the system reasons over it. That design helps Wexler deal with the disorder of litigation, where timelines blur, people contradict each other, and key details are easy to miss. He also points to the scale of the platform, noting that it handles large document sets and supports work such as deposition preparation, trial preparation, summary judgment briefing, and early case assessment. One of the more striking features is real-time fact checking during depositions, where the platform helps lawyers spot contradictions in testimony as the questioning unfolds. The effect is less like using a search box and more like working with a tireless junior team member who has read the whole file.

Trust, accuracy, and restraint are another major part of the discussion. Mostyn is careful not to oversell what AI can do. He openly states that no system is perfect, yet he argues that Wexler reduces risk by staying inside the record given to it. It does not search the internet, does not drift into outside material, and ties its outputs back to specific text in the source documents. That discipline is important in litigation, where a made-up citation or invented fact is more than embarrassing, it is dangerous. Mostyn presents Wexler as a tool that helps lawyers verify, question, and sharpen their understanding of the case. The result is less time spent slogging through repetitive review and more time spent thinking about how to use the facts in a meaningful way.

The conversation closes on a bigger question about where this kind of technology leads the profession. Mostyn believes that as AI takes on more of the burden of document review and fact development, the value of human lawyering rises in other areas. Strategy, advocacy, witness preparation, courtroom performance, and judgment all become more important when the groundwork is assembled faster and more thoroughly. He also suggests that clients are beginning to care less about how many hours were spent reviewing documents and more about whether their lawyers are prepared, informed, and effective. For listeners interested in litigation, legal AI, and the next stage of law firm economics, this episode offers a thoughtful look at a company betting that the future belongs to tools built for depth, discipline, and the hard realities of dispute work.

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 everyone, I’m Greg Lambert with the Geek in Review and I have Stephanie Wilkins from Legal Technology Hub with us. And Stephanie, it’s about that time again to release the GEN.AI map. So what’s it look like now?

Stephanie Wilkins (00:13)
Yeah, it is. It’s come to be that every new quarter at Legal Tech Hub means it’s another update to that LTH-GNI Legal Tech map. And somehow we’re already there for 2026. As with nearly every map iteration that came before it, we’re once again seeing significant quarter over quarter growth in the map. As of March 2026, we’re right around 1,000 product placements on the map, which is a milestone that we’ve been looking forward to since we launched the first map.

We thought we might hit it by the end of 2025, but we weren’t far off the mark with that prediction. But speaking of that first map, if you remember, or if maybe you never saw it the first time around, the first iteration of the map came out in February 2025. We saw 400 product placements for Gen.ai and LegalTech. We updated it just two weeks later, just in time for Legal Week 2025, and we had about 500 product placements. So we’re talking…

One year ahead, Legal Week 2026, we’re at almost 1,000. So we’ve doubled the market in that one year, which is just really impressive. And the count went up significantly even from just three months ago. Our end of the year map for 2025 had about 850 product placements. So we’re still on a steady incline in terms of growth. But where we’re seeing growth has shifted a little bit over time. Initially, we saw a lot of growth in hot areas like AI legal assistance.

And while there still is some growth there, this time around we’re seeing our biggest areas of growth in more bread and butter or less trendy areas, if you will, like law firm operations and compliance. And to me that signals that AI really is starting to make inroads in legal and in areas where it can make the most difference internally and maybe not externally what you want people to think you’re working on. And that’s sort of in line with what I saw at Legal Week also, that the AI…

conversation is maturing a lot and people are having smarter discussions about how we can solve actual problems. And it’s all part of the bigger trend of moving from if to how on AI. And I think that’s a really great thing. I have heard some people start to speculate if our map will start getting smaller eventually rather than bigger. given all the consolidation in the market, that may one day be true, but by all accounts, by everything we’re seeing, that we’re not at that point yet.

So we’re still getting bigger and curious to see where we are at the end of Q2, three months from now. But if you want to see the latest map and our analysis of it, you can just find it on www.legalteknologihub.com. And that article will link to prior iterations of the map as well, so you can compare.

Greg Lambert (02:41)
All right, Stephanie, make a projection. Second quarter, 2027, what’s our number?

Stephanie Wilkins (02:48)
second quarter of 2027? I’ll go 1500.

Greg Lambert (02:50)
All right.

Marlene Gebauer (02:59)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer

Greg Lambert (03:06)
And I’m Greg Lambert and Marlene for the past three years or so, the legal tech world has been obsessed with the ⁓ Swiss Army knife of AI, these tools that try to draft every email, summarize every contract and basically try and boil the ocean in one go. we’ve entered the new phase in

Marlene Gebauer (03:26)
One size fits all.

Gregory Mostyn (03:27)
Yeah.

Greg Lambert (03:30)
what’s being called the specialized scalpel where journalists are being pushed aside by these very high precision tools designed for the highest stakes in litigation. And today our guest ⁓ is at the center of this shift. He’s the ⁓ CEO of Wexler, a London based company that pioneered the category of fact intelligence.

Gregory Mostyn (03:50)
you

Greg Lambert (03:51)
and has rapidly become essential infrastructure of some of the world’s most elite law firms, including Clifford Chance, Goodwin, and HSF Kramer.

Marlene Gebauer (04:02)
And Gregory Mostyn comes from a family deep deeply rooted in UK legal establishment. His father’s

a recently retired high court judge and his and his siblings are partners and barristers. know, but he eating. Greg, you took a little bit of a roundabout way into legal tech, including brief tenure as an actor on the HBO baking drama industry. We got to hear about this. Gregory, welcome to the geek and review.

Gregory Mostyn (04:11)
Okay

Good job.

Banking, banking.

Greg Lambert (04:27)
Thanking.

Gregory Mostyn (04:29)
Yeah,

thanks for having me. Yeah, it’s good to be here.

Greg Lambert (04:33)
⁓ So, Greg, we love a good founder’s journey and you’ve mentioned in some of your writings that there’s a childhood memory of how your father was bringing home these huge physical ring binders of data to review every single night. So how did that image of the ⁓ binder era of litigation shape your decision to avoid the generalist AI boom and instead focus on

Gregory Mostyn (04:56)
Yeah.

Greg Lambert (05:01)
this scalpel approach that you’ve been talking about.

Gregory Mostyn (05:05)
Yeah, so yeah, as you correctly identified, I’m not an attorney, but I’m grown up surrounded by them. I remember basically every Sunday lunch, every sort of family dinner, huge debates about whatever was the latest case raging, not just in the UK, but sort of globally, you know, whatever it was, the kind of controversy of the time. And I do vividly remember my dad coming back having to review literally 10 ring binders before the next day.

in court like overnight, you know, staying up till 3am or something and then standing up in court the next day. And my dad was actually a real innovator in the profession. So in COVID, he was very sort of when he was a judge at that point, he was very forward about, you know, conducting hearings on Teams and zoom and all those kind of things. And he actually founded a kind of legal tech company in the 90s, as it would happen, which was a it’s still going it’s called class legal is like a family law company, which started off as a sort of

publisher for some sort of legal textbooks within family law in the UK. And it also helps you to create various templates and forms and things. So kind of like a bit of rules based machine learning. I don’t know. But anyway, so. So yeah, I think that was a very vivid memory of my childhood. And when I started this, I went through an incubator called entrepreneur first, where you’re sort of paired up with other brilliant people, supposedly brilliant. And you and you try and

come up with a real big pain point and there was no pain point I could remember more vividly than my dad having to review hundreds, thousands of pages manually, line by line, word by word, to prepare for court for the next day. So that was where we went after. we, spoke to literally hundreds, starting off in my network, people I knew, know, people, family, friends, et cetera, and growing and going out into the world across the US as well as in the UK and identified that this was a real problem, basically establishing the facts and disputes, you know.

You either hammer the law or you hammer the facts. And actually, as a sitting judge, my dad realized that half the lawyers in front of him didn’t know the facts of their cases. They probably physically couldn’t in a lot of cases because there were too many documents to review manually. And you’re always going for a sort of subset of the data involved. And yeah, I think that was the Prince Harry case versus a big newspaper here where they just had to take a small section of the potential evidence to review because there wasn’t enough time or resource to review the whole case. And I thought, hang on a minute, we could build something or actually

we can review everything and give it the attention it deserves so that every client gets the best representation. And hey, maybe those lawyers don’t have to stay up till 3 a.m. to review it cover to cover. So yeah, that’s been the story since then. We’ve been ruthlessly focused trying to do something more sort of specialized rather than, as you say, try to ball the ocean. But it’s now become this kind of full platform for analyzing, establishing and verifying facts and disputes.

Maybe it’s more like a kind of surgeon’s tray of scalpels rather than just just the one.

Greg Lambert (07:51)
I’m curious,

did you experiment on your siblings and your father to test these things out as you were developing them?

Gregory Mostyn (07:58)
100 % at the beginning my dad was very closely involved. My stepmums also a barrister in the UK. My brother’s a partner at Cleary. So yeah, they were my first proto beta testers, if you like, showing them figma prototypes, understanding how things would work. You know, at the time, like if we think about how far the models have improved at the time, they were still very, I mean, at the time they were incredible. But now looking back, you really had to do a ton, a ton of prompting of stitching together the different systems to get the best output.

And actually we’re now the beneficiaries of the billions that are being poured into the models because, you know, it’s a bit of a modular system that we’ve created. So one new model release improves one part of our product and so on and so on. But yeah, at the time it was kind of mechanical Turk, if you like, but very, very, ⁓ very, very helpful to have that, you know, ex-boy and so on. Yeah, exactly. But yeah. Cool.

Marlene Gebauer (08:40)
you

Greg Lambert (08:40)
Thank you.

Gosh, I haven’t heard mechanical Turk in a while.

Marlene Gebauer (08:50)
So

for our innovation and KM leaders that are listening, we often hear them say, we already have eDiscovery tools for document review. Now, you’re coming at this from a different angle. you’ve argued that eDiscovery prepares documents for humans, but fact intelligence is different. It reads them like a human does. So it’ll extract events from footnotes on page 993 or buried in a WhatsApp thread.

Gregory Mostyn (08:54)
Yeah.

Mm-hmm.

Marlene Gebauer (09:19)
Can you explain why the atomic unit of legal knowledge needs to shift from document to the individual fact and how that changes a firm’s

Gregory Mostyn (09:30)
Yeah, for sure. I think, you know, if you imagine like a hypothetical case, let’s say it’s a multi-party fraud claim, 80,000 relevant documents, you know, multiple plaintiffs, different advisors saying different things, know, there are inconsistencies, one person’s claiming something, someone else is claiming something else, a third party is saying that never happened, someone else has got, you know,

is this another dog in the fight where they’re trying to bring up some other discovery, which is important. It’s not that helpful to say, here’s some relevant documents, right? It’s not that helpful to say, here are the documents that are responsive to certain search terms, because people don’t speak in search terms. People speak encoded, obfuscated language. Things don’t always add up. And so what you need is something that’s able to basically distill the documents into the facts. Not just saying, hey, here’s 10 relevant documents, but saying, this is what happened.

this is why it happened and this is why it’s important to the case. And so the process of basically extracting the discrete observable happenings, if you like, i.e. the events from the documents, assigning relevance based on a list of issues that the attorney themselves provide, which may be taken from the complaint or, you know, from the pleadings, etc. And then using that as the database, which you reason over means that you’re, you know, you’re basically equipped with all of the information you need to build the most compelling case and

You’re not just saying, are some relevant documents. You’re saying, this is what happened and this is why it matters. So it’s kind of, it’s complimentary to rediscovery, right? We are the sort of second level, the deep strategic layer that you take once you’ve got the relevant documents or maybe before rediscovery, right? Client self produces 10,000 documents and said, get back to me by Friday with where we stand. But the output isn’t just, here’s a kind of data document set, which we’ve built up. It’s actually, is the story because litigators tell stories.

they tell stories backed up by the facts.

Greg Lambert (11:21)
Yeah, that’s…

Marlene Gebauer (11:21)
It’s I

think it’s important you mentioned it’s kind of a second step because you know sometimes they’re like you know millions of documents that you’re talking about and I don’t I don’t know how many you know you can accept at one time but you know that step is still critical to kind of just narrow it down to to what’s you know what you want to look at inquiry.

Gregory Mostyn (11:29)
Yeah.

Yeah.

100%. So yeah, can do lot. We can do 250,000 documents, which is, I think, higher than most of the LLM legal platforms I’ve come across. And that’s kind of like AI native processing. And we hope to get to a million by the end of the year. But you’re right. It’s like from the smaller universe. One of my colleagues says, you wouldn’t want a surgeon to be giving you aspirin, right? There’s no point putting a whole custodian’s mailbox in Wexler, because you want to know that what you’re putting in is at least broadly relevant to the case, because

Marlene Gebauer (11:52)
It’s not bad, not bad.

Gregory Mostyn (12:11)
of, you know, it’s really extensive and painstaking what we’re going through and extracting. So yeah, there is that first step, which is critical for the first cull, but actually that sort of smaller universal document turning that into into winning case strategy is critical. And so where we actually get used a lot is for depot prep, trial prep, summary judgments, the briefing, plus early case assessment. And obviously, it’s jurisdictional agnostic as well. So we do arbitration investigations and so on.

Marlene Gebauer (12:36)
So how does it compare to say like one of the general ones because I mean they certainly can be used in that way and do comparisons. how does it differ? What’s the secret sauce?

Gregory Mostyn (12:43)
Yeah.

So I think it’s about that fact extraction pipeline, which I talked about. essentially, it’s the key kind of difference is that what we do is we process the documents, we pass them through this 15 step pipeline, which doesn’t just stuff documents into one context window and say, hey, have go look for X or go look for Y. What it does is it sort of normalizes that data. So even if something is like a one message WhatsApp saying, I flew to Paris last year,

versus something really neatly laid out saying the plaintiff flew to Paris on this year, which obviously you guys are both technically in the weeds here. You’ll know the LLM have a bias to more structured information. And so what happens is if you just chuck documents into a generalist tool, yes, it will give you broad insights, but actually in order to really find the key contemporaneous piece of evidence, the kind of things that trial attorneys are looking for, that smoking gun to help them build the winning case, it’s not always clear.

And so it’s that fact extraction pipeline where we stitch together all these different models into this 15 step process, which rationalizes and elucidates that complex information, normalizes it into structured data, which they can then work over. And we hosted a dinner at Legal Week and one of the innovators there who was, it worked in litigation was saying, litigation is messy. Like it is very, very messy. Things are not clear. Things are obfuscated either deliberately or accidentally.

You’ve got things being overwritten by other things all the time, and you need something that can basically first distill that into a structured way and then use that as the unit of analysis. So that’s one of the key differentiators, obviously scale, which we’ve already touched on. And then we’ve got some other functionality, which is only possible because of this fact bank that we’re building up. So one of them is real time, which is basically like live fact checking for depositions. And it’s only possible because of that database we built up.

We could put it on right now and it could fact check all the things that I’m and probably it wouldn’t show anything. I’m happy to say. So yeah.

Greg Lambert (14:43)
It would let

us know that you flew to New York recently. ⁓ Well, speaking of ⁓ real time and I also want to talk about the digital training, Kim, that you set up. So real time allows, like you said, the litigators to flag contradictions in testimony during a live deposition as it happens. And you have famously said that, you know,

Gregory Mostyn (14:47)
Exactly.

Yeah.

Yeah.

Greg Lambert (15:10)
Truth is a nebulous concept, but contradiction is very verifiable. So how are tools like Wexler and your AI agent, acting more like a digital trainee or a partner rather than just a passive search tool? I’ll let you answer that, and then I want to follow up with a couple more.

Gregory Mostyn (15:13)
Yeah. Yeah. Yeah.

Mm.

I mean, it’s interesting, think…

Basically, what Kim does really well is sift through vast amounts of information and pick out patterns. We don’t opine on the veracity of things, right? We only look at the documents that we’ve been given. So within the universe of documents, are there contradictions against other documents, other facts within the same data set? That’s what we can do. This is how we minimize, basically eliminate hallucination risk, inaccuracies, those kind of things. Doesn’t search on the internet, doesn’t look in its training data.

is absolutely ruthlessly and rigidly told to focus on that information. And also the way we structure all the data into those facts really drives up that accuracy. So as much as it’s a very helpful digital training, as you say, but it’s limited to the four corners of the page of the documents plus the metadata that you’re giving it. So that’s really important. That’s how we drive up the accuracy. And yeah, maybe we’re losing out on some potential functionality, but actually it’s much more important for us to be accurate.

and allow the lawyers to then apply the law to the facts and look on the internet and those kind of things. So yes, truth is a nebulous concept. We can’t say for sure if something is, we can say if something looks dodgy or if something looks odd or there’s fact patterns that don’t quite add up. But if it’s not contradicted by another piece of data within the same data set, that’s not our job. That’s not the AI’s job to do that. And that’s kind of our, that’s kind of our core belief, I think. That’s our thesis. It should be like an accelerant and enhancement. It’s not going to take you three weeks to do this task or three days. It might take you three hours.

But importantly, those three hours will help you get better at understanding the facts of the case so that you can stand up in court or prepare for a deposition and you actually know what’s going on, not just the AIs told me this. So I think, you know, yes, it’s a really helpful digital trainee, but it is only looking at the documents you give it. It’s not looking elsewhere. And it can look for patterns, but it’s not going to sort of lead you to the wrong place. It’s not going to look at internet and it’s not going to hallucinate case law because that’s not in the remit of what it does.

Greg Lambert (17:28)
Do you rely on the LLM to work with these documents? And the reason I ask is, I’ve seen it and I’ve had people come up to me where they may dump a few hundred or a few thousand documents in and then all of a sudden it like introduce characters into that don’t exist.

Gregory Mostyn (17:48)
Yeah.

Greg Lambert (17:50)
⁓

Gregory Mostyn (17:50)
Yeah.

Greg Lambert (17:50)
There’s always like ⁓ a Robert Chin that shows up somewhere. And so how is it that you’re taking the information and making sure that you’re reducing the chance of hallucinations?

Gregory Mostyn (17:54)
Yeah, we have it.

So there’s a few different things. think technically the fact that we’re operating from structured data rather than unstructured data massively drives up the ⁓ accuracy of the system. And this has been tested against other generalist platformer by our customers regularly. So basically because it doesn’t run out of context, it doesn’t have to invent or fabulate people when it’s looking for the kind of smoking gun. Obviously everything, this is kind of table stakes now, but I think you’d be surprised.

everything is sourced down not just to the document but to the sentence that it’s been taken from. And then also we have like extreme guidelines and guidance in the back end to say, you you will turn and I’ll answer if there’s not nothing within the four corners of the document that supports this assertion. Can we say it’s 100 % accurate? Of course not. And any AI vendor telling you it is, you know, you need to go and sell them back the bridge they’ve sold you, right? So

but I think it is highly, highly accurate and more than anything, the verification flow where you can really quickly click on each source and independently verify it is helpful, one, to verify, but two, you’re actually looking at all the documents and you’re getting familiar with the documents as they should be viewed as if they are printed out and you’re finding those patterns too. So yeah, there’s technical ways, which is basically the structured data that fact extraction pipeline I talked about earlier.

And then there’s obviously guidance and there’s also, you know, importance of change management and training, which you two will be obviously working with your attorneys on to ensure that everyone understands that this is a new type of technology. You know, it’s not going to return the same result every time. You can limit the variance, but actually it’s like having a sort of second opinion. It’s like giving, if you gave the same task to a hundred attorneys, they’d probably return you a hundred different results. So yeah.

Marlene Gebauer (19:44)
So in your recent writing, you described 2026 as the year of market chaos, yikes, where traditional law firm knowledge moats have evaporated. Now, if AI can now establish a factual record with 95 % accuracy, save 90 % of a junior associate’s manual review time, what does that do to the traditional economic model of big law?

Gregory Mostyn (19:49)
haha

Mmm.

Look, I think it’s big question and I’m sure I’m not qualified to answer it in full, but I think my thoughts are one, there’s a huge amount of work that’s not, there’s a huge amount of wasted time, which doesn’t build to the client. It’s not valuable time. It’s dead time, basically, which we can massively reduce, meaning people can go home earlier. They can spend more time focusing on strategy and how to actually use this information. So, know, one of our customers, a partner actually, this user said it

Wexl identified an inconsistency. He reckons he would have found it maybe in a couple days, but actually he could spend a couple days planning strategy rather than just churning through documents, right? And actually how to use this in an offensive or defensive way. So he wasn’t losing any billable time, but the time that he was billing was much more valuable for the outcome of the case and in the kind of theater of litigation, that’s what you’re looking for on the upper hand. Increasingly, we are seeing fixed fees with some of our clients, people looking for more kind of value based billing or maybe like a hybrid where it’s like

project-based fixed fee, you get a menu of sort of outputs and actually I was thinking about this like, know, why do you bring in a big law firm if you’re in a bet the company dispute? It’s because you want the reassurance that they’re gonna represent you because it’s the most important thing in your company’s history, or it’s like one of the most important things, depends how big the company is and how litigious they are. But you wanna know that when you bring in like the Quinn and manuals or whoever it is, one of the massive firms,

they are going to go out and fight for you tooth and nail. And I think you can’t really put a price on that. the value system should be reflective more of that rather than just the number of hours of documents that you’re reviewing. Because if Wexler was in a big dispute, I definitely want them to be using AI, but I still want there to be humans there representing my best interest because that’s what you pay for. That’s the kind of assurance. So yeah, I mean, we’re going to see a lot of creative thinking about the billable hour and how this kind of changes in the years to come.

But I think for the short term, there’s a huge amount of wasted dead time which can be reduced and better quality of life for the litigators and better outcomes for the clients. And longer term, I think we need to start thinking more about kind of value based. I think where we work specifically, obviously a lot of the stuff we do is like oral advocacy and standing up in court. And I think people are going to want to prepare for that in the same way that kind of athletes train up for the big race. And I think that’s going to be a really important part of the work that

that AI can help with too.

Marlene Gebauer (22:31)
like your one example that you know, the partner was able to devote more time to strategic thinking. I mean, are you hearing that more broadly? Or was that just kind of an isolated example?

Gregory Mostyn (22:35)
Yeah.

No, absolutely. Like all the time. Like we’ve heard people, we had an example where, you know, they found an inconsistency which their forensic accountant had missed. And then they were able to use that inconsistency throughout the rest of the matter, you know, as a kind of key piece of strategy throughout. I think we’re definitely seeing it’s reducing the kind of grunt work and freeing up time to think bigger picture, you know, think about where this kind of shakes out, what are our most…

what are our best strategies? You can even do adversarial analysis where you talk to the chat assistant and say, okay, you’re the other side now, let’s run through some hypotheses and they can obviously review every single document and you need to know what to say in response. So yeah, we’re definitely hearing that people are ⁓ massively reducing the kind of busy work and it’s freeing up time to think more strategically.

Marlene Gebauer (23:32)
It’s good to hear. mean, are you hearing anything from the client side? Not your client side, but our client side.

Gregory Mostyn (23:38)
The end clients? Well, indirectly, but I know that like, you know, people are, we have clients who also get access to the platform. So without getting too into the weeds, but we’re not seat based, we’re consumption based. So we don’t care how many users there are. So we have, we give access to the clients as well. In the UK, we also give access to the barristers who are independent, the kind of trial attorneys. So yeah, we definitely hear people really like it. Obviously they like better value for money, but also I think they like that we’re, you know, we’re covering all bases here. We’re reviewing every document and you know, my

Marlene Gebauer (23:39)
Yes.

Gregory Mostyn (24:08)
my colleague was a litigator for several years at Mayor Brown and he was saying, you know, I was reviewing documents at 3am in my bed, you know, just like going one, one, yes, no, yes, no. And like, that’s going to be fraught with human error, right? And, you know, I’d probably rather pay for AI to do it anyway, even if it was the exact same number of hours, because, you know, you know that they can cover all the bases. So exactly, it’s not going to be tired. It’s not the end of a long week. doesn’t have

Marlene Gebauer (24:29)
They’re not sleepy, it’s not sleepy.

Gregory Mostyn (24:35)
you know, all the personal things going on that every human does, so, you know, maybe it does, know.

Greg Lambert (24:40)
So, Greg, I’m gonna hit you with a question that’s off script here, but it’s just something that as we’ve gone around and started talking with people, and you probably heard this at Legal Week as well, training. How are you approaching the training? you…

Gregory Mostyn (24:43)
Yeah.

Yeah.

Greg Lambert (24:59)
able to actually leverage the AI to help you kind of learn the AI or what’s the training method that you take for getting people up to speed?

Gregory Mostyn (25:10)
Wexler, yeah, how we train people to understand. So we do a few different things. We obviously do hands-on training. We usually do it by sort of sub-practice group. We also offer top-up training for a specific case. Like, you know, people want to know and then we’ve even done it. We’ve like signed NDAs and we’ve actually got really into the weeds of the case with the customers. Although that’s obviously not our default position because of, you know, confidentiality. So we do a lot of hands-on training. We go in person, you know, London and New York. We do walk-through training and so on and so forth.

What we also do, which I think is really important, is we do a kind of workshop that’s not about Wexler, but it’s about AI in general. And it’s like I was saying to the team, know, we need to convey to even the most sophisticated attorney, like how this technology can both solve a really complex reasoning problem and also not count the number of R’s in strawberry. Like people don’t understand how those two things can be true at the same time. And so

It’s really important that before we even give anyone access to XLR or maybe it’s after, but whatever, while they’re using it, they understand that this is a new type of technology. It’s a kind of pattern matcher. That doesn’t mean it’s not useful. It’s very, very useful, but you need to know those kind of general things to be aware of and what to look out for. So yeah, we do a lot of training. We do a lot of workshops. We do kind of general AI sort of familiarity, know, educational workshops as well.

And we do top ups for individual matters. what often happens is people will be using it regularly and they’ll be like, we’ve got a massive case that’s just come in. We want to do a special training for the attorneys on that. And then we’ll do that too. So yeah.

Greg Lambert (26:45)
Okay, well, ⁓ kind of dovetailing with this, before we get to the crystal ball question, we’ve been asking our guests to share with us some of the resources, newsletters or thinkers that you are relying on to kind of keep you ahead of these compute cycles of these big AI foundational labs. So what helps you keep up with things?

Gregory Mostyn (26:57)
Yeah.

Yeah.

So there’s a brilliant technologist called Benedict Evans. I don’t know if you know him, but he’s a great follower. He’s good because he has a healthy do with his skepticism. And I think he quite rightly says that, yes, it’s incredible technology, but we’re still early and we don’t really know how this is going to shake out. And so it’s quite a healthy antidote to the kind of like…

you know, AGI is coming, all those kind of things. So I like to keep my feet on the ground with him. He has a very, you know, one of the things I always go back to is one of his tweets that he did, which is basically, you know, anyone who says they’ve solved the accuracy problem in AI is lying to you, right? Because it is going to not be 100 % accurate. That’s just the, that’s just, that’s just AI, that’s generative AI. But that doesn’t mean it’s not useful. It’s still unbelievably useful. Like I use

clawed all day, pretty much, and it’s still okay, we’ll throw out a random person every now and then that doesn’t mean it’s not useful. It just means to know you need need to what to look for. So yeah, that plus, yeah, I have a bunch of other sort of think newsletters that land in my inbox each day is obviously Ethan Molek, you’ll know and various others were on the legal side. But I try not to, I try not to read too much about what’s going on. I kind of just like I always try and stay focused on what we’re doing and

and just building something that great to our customers. And I’m always amazed by actually how low the penetration is into lawyers. Like you speak to people, like practicing attorneys, and they’re still, they might have been using Copilot or maybe the firm’s got a Harvey license and they’re having a play around, but they haven’t really used it. And so, yes, there’s so much noise and there’s so much hype and there’s so many VC dollars going into this market, but actually I think we’re still very early.

Marlene Gebauer (28:57)
So Gregory, now it is time for the crystal ball question. and so looking ahead the next three to five years, you know, what’s the single big biggest change that you see coming for the role of, you know, the oral advocate or the trial.

Gregory Mostyn (29:10)
Yeah, so I think this is I kind of already touched on this, but I think it’s going to be about I think the human side of AI is going to be even more important because the sort of document review side is even less important. So the pressure on the human side, so mediations, arbitration, trial depositions is going to be even more important, the pressure will be even higher. So AI that can help you prepare for those is going to be really, really powerful. I think, you know, if you can review every single document in the world, let’s say

And so there’s no, there’s not going to be any more smoking gun that hasn’t already been found. It’s going to be about how well you deliver that argument. It’s going to be about how well you bring the story together. It’s going to be about the way that you interface your client and the way that you bring the context that’s maybe not written down on any pieces of document, on any document into the story and how you bring it all together. And then you win the hearts and minds of the jury or the judge or whoever it is based on the evidence. So I think it’s going to be, you know,

kind of counterpoint is actually it’s going to be even more important and the the oral advocacy is going to be even more important because the document review and the fact-finding is going to be you know largely automated you know within a few years so i think that’s my crystal ball it’s going to be even more important to have great oral advocacy and and lawyers are going to want to train up for those kind of things just as athletes preparing for the big race

Marlene Gebauer (30:30)
to be more about the lawyering.

Gregory Mostyn (30:32)
Exactly.

Greg Lambert (30:33)
All right, well, Greg Mostyn from Wexler. Thank you very much for the conversation. I’ve enjoyed this and thanks for going off script with you a little bit.

Gregory Mostyn (30:43)
No problem, love that.

Marlene Gebauer (30:45)
And thanks to all of you, listeners for listening to the Geek in Review. If you enjoy the show, share it with a colleague. We’d love to hear from you, so reach out to us on LinkedIn and our Substack page.

Gregory Mostyn (30:56)
Awesome.

Greg Lambert (30:56)
And

Greg, where can the audience go to learn more about ⁓ Wexler or about you?

Gregory Mostyn (31:04)
So wexler.ai, you can see it there in our office. Yeah, wxler.ai, very easy to remember. Maybe I did, maybe I did. Exactly, so yeah, head there, you can book a demo, you can book a chat with me. Otherwise, if you’re on LinkedIn, I’m always happy to chat about anything really, so yeah.

Marlene Gebauer (31:09)
Did you do that on purpose? Just kidding. It’s like, yes, I did.

Greg Lambert (31:11)
He’s got a good marketing person.

Marlene Gebauer (31:24)
Terrific. Well, thank you again. and as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry. And bye everybody.

A major thread in the discussion is the risk of what one speaker called “cognitive surrender,” the habit of trusting AI output too quickly and handing off too much human judgment in the process. Greg and Marlene treat this as less of a software issue and more of a workflow and education issue. The point is not whether AI produces polished work. The point is whether organizations are building systems where review, judgment, and accountability still sit with people. Their conversation ties this concern to legal practice, education, and even K-12 learning, showing how widespread the temptation has become to accept fluent output without enough friction or scrutiny.

The episode also takes a hard look at the pressure AI is putting on the billable hour. Marlene frames the issue well when she notes that AI does not kill the billable hour so much as expose its weaknesses. Across the conference, the hosts heard repeated concern about the mismatch between efficiency gains and the financial structures law firms still rely on. If AI reduces the time needed for many tasks, then firms, associates, pricing teams, and clients all have new incentives to sort through. Greg and Marlene highlight the awkward moment the industry is in, where firms want to talk about value while clients are also eyeing the chance to pay less for faster work. The result is a growing need for honest conversations about pricing, outcomes, and what legal value should mean when time is no longer the cleanest measure.

What gives the episode its energy is the number of concrete examples pulled from the conference. The hosts discuss lower-cost multi-state surveys, large-scale analysis of rights-of-way documents, and internal workflow improvements built with existing tools like SharePoint and Copilot on little or no budget. These stories show AI not as abstract promise, but as a way to get work done that used to be too expensive, too tedious, or too slow to tackle at all. At the same time, Greg and Marlene stay skeptical in the right places, especially when the conversation turns to legal research, citation accuracy, and the idea that technology vendors have somehow solved problems that law librarians and researchers know are stubbornly difficult.

By the end of the episode, the biggest takeaway is not that the legal industry has a clear answer, but that waiting for certainty is no longer a serious option. Greg and Marlene come away from Texas Trailblazers with a sense that real progress is happening through testing, discussion, and repeated adjustment, not through perfect plans. Their recap captures an industry in transition, one where law firms, legal ops teams, vendors, and clients are all feeling the strain between old business models and new technical possibilities. The message is simple and urgent: start the conversations now, use the tools now, and get honest about what must change before the gap between what is possible and what is workable gets even wider.

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:

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review and I have Nikki Shaver for Legal Technology Hub here. And Nikki, you’re going to tell us a little bit about some events ⁓ in London, right?

Nikki (00:10)
That’s exactly right. So all of your many listeners, I’m sure Marlene and Greg in London town have a delight coming to them. We are bringing our LTH Velocity and Horizons conferences over the pond. We held our flagship Horizons event there last year as well, and it was really successful and sold out. And we’re bringing that one back and also adding in Velocity, which is our conference for vendors. This, as you know, is a time when people are still learning

And there’s so much happening every single day. It seems like there’s a lot to figure out. And that’s as much on the vendor side as it is on the law firm side. Often vendors don’t really have a sense of community. There are not events that cater specifically for vendors, but Velocity is exactly that. It is an event for any legal tech vendor to enable them to come along for free to a day of very high quality content put on by Legal Tech Hub

and a range of speakers from the London region talking about things like how to prepare your company for exit, ⁓ hearing from investors on what they’re looking for in the market, what the impact was of the anthropic entry or perceived market entry on the legal market, and what buyers want to see from vendors, how you can sell better to law firms. So highly recommend that one. And the following day on April 24th, we are having

our Horizons event, is an event for predominantly by side law firms and corporate legal departments on all of the major topics that are of particular interest for us today around things like increasing complexity around agentic systems and orchestration layers and how to handle again the anthropic market entry and what that might mean and whether vibe coding is actually the future of legal AI. So we are really excited again, those dates.

dates are April 23rd for Velocity, April 24th for Horizons. We welcome anyone who lives in and about London, but also anyone visiting in the area. is still time to sign up. Velocity is free. Horizons is kept deliberately low because we really want to have these high quality discussions and we’d love for you to be able to bring your team. So go to legaltechnologyhub.com, drop down on our top menu for events.

take you to the LTH events page and you can find out more and sign up for our events there. Thanks Marlene.

Marlene Gebauer (02:47)
welcome. That sounds like a great couple of events.

Nikki (02:50)
should be.

Marlene Gebauer (03:01)
and review the podcast focused on innovative and creative ideas in the legal profession. I’m Marlene Gebauer.

Greg Lambert (03:07)
And I’m Greg Lambert and this week Marlene and I got back from Dallas and we wanted to do a kind of a recap on an event organized by Cosmonauts and LegalOps.com. Joy Heathrush from ILTA chaired day one and it really focused mostly on private practice and then Connie Brinton came in on day two from LegalOps.com and talked about the in-house community.

Marlene Gebauer (03:35)
And I mean, I feel this conference stood out, ⁓ Texas trailblazers because, know, it stayed really close to the work. So you didn’t have a lot of, know, future gazing and, and, know, what’s next. It was more about sort of the day to day soup of legal practice, you know, where AI is actually helping and, and, you where the economics are being impacted.

Greg Lambert (03:55)
Yeah. it’s, ⁓ it’s hard enough right now just to figure out where we are now without having to go, well, where are we going to be in six to 12 months? So I thought, I thought it was really good to kind of focus in on, where we are. And, ⁓ you know, day one with the, with the law firm focus, it kicked off with a, well, one, ⁓ Joy Heath Russia, ⁓ talked about.

Marlene Gebauer (04:02)
you

Greg Lambert (04:21)
where she was seeing the industry currently. And then Ron McNamee from Mary Technology introduced us to the term which I’ve seen floating around now called cognitive surrender.

Marlene Gebauer (04:34)
not to be confused

with cheap tricks, cheap tricks surrender. That’s a whole different surrender.

Greg Lambert (04:37)
Yeah, that’s a whole different cognitive surrender

song there. But it’s basically where you are over trusting the AI and you’re offloading that cognitive load to the AI. And I know that we talk about, well, just review what the AI puts out, but…

you know, if everyone’s being honest with themselves, there’s a lot of times where we just kind of take what the AI hands us and say, ⁓ that looks great. So I like that. Let’s talk about that. Marlene, do you see, looking back at that keynote, that it’s not so much the firms have a technology problem as much as we’ve got a workflow design problem that

Marlene Gebauer (05:03)
You

Greg Lambert (05:22)
kind of makes the attorneys and others not offload that work completely to the AI.

Marlene Gebauer (05:32)
Exactly. Like this is exactly where we talk about, you know, making sure there’s a human in the loop and sort of building that in to, know, any type of workflow or review that, know, is happening using, using AI. Cause again, it’s, you know, it sounds so good. It’s just really easy to just kind of say, yeah, you know, just, just, just use it. But, I think the, the smart organizations, you know, are basically incorporating this kind of education.

into their education programs for people who are using this. They incorporate it into their governance plans that there is always human review. Everything is AI assisted, not AI done completely. People just sort of build in and even bring in the people who are responsible to say, if you’re building a workflow, where does this make sense? Where will…

Where is there their need for human review at which points of this, you know, of this action and just making sure that that everybody who does that is aware of it.

Greg Lambert (06:31)
Yeah, I was talking about this with my ⁓ wife on the way in to work, and she’s an elementary school librarian. And she kind of had that thousand yard stare in her eye. And I was like, well, what’s going on? She’s like, my god, we’re having the same problem with teaching the children not to completely rely upon the.

the AI and it’s really changing the way that we’re educating. I I always tease to her that, you know, there’s a lot of similarities between law firm lawyers and elementary and middle school children. But I think this is something that, you know, as we’re developing the systems,

that we have to be careful that it doesn’t look like we’re just giving them the answer and that’s gonna be really, really hard to do, especially as the tools get better and better.

Marlene Gebauer (07:21)
It’s a, it’s, it’s a lift. It’s, it’s not,

it’s not a complete, you know, solution. ⁓ you know, it helps a little bit, but it’s not the, the, the end solution. And I think there’s also a, a similarity, just, you know, the population in general and, and you know, what, you know, what you’re saying is that you have some folks that are just like, no, no, no, like, I don’t want to use this. I don’t trust it. And then you have other people who are just sort of jumping into the deep end of the pool before learning how to swim. ⁓ you know, I know like.

You know, my son is, and one son is in high school and he is, it’s, it’s quite interesting conversations at home because he is adamant like it should not be used. And I’m like, this is my job, man. So, yeah, I think that, that there’s a lot of kind of similarities across the board and what they were talking about.

Greg Lambert (08:08)
Yeah, yeah, well, we thought, you know, filing hallucinated cases was a problem. I think that we’ve got other problems that are coming down the All right, well, the.

Marlene Gebauer (08:14)
Over 800 now. Over 100 in the US now. Every day.

Greg Lambert (08:20)
Another panel which had Kelly Lugo from BCLP and and then there was another one with Chad Barton from Holland and night and we you know, kind of mentioned the Associate problem and this incentives of using the AI and whether or not, you know things things are really inverted And so on the there was an automation in action panel

where they’re asking, firms really finally kind of getting honest about how partner compensation and associated billing targets either accelerate or kill AI adoption? it’s an honest, I thought it was a great conversation that everyone was having about how do you convince an associate that they need to learn the AI?

if you’re also hearing things about, the associate, this can take 50 % of the associate’s work away, what’s the incentive for them to do this?

Marlene Gebauer (09:26)
Yeah, it’s funny. It’s like one of the things I said on the panel I was on was like, know, AI doesn’t kill the billable hour, but it does expose its limits. And I think this is, this is one of those, those times. I think we’re, really kind of at a, a crossroads where the smart firms are really going to have to start looking closely at, you know, where AI is impacting and, know, get with their pricing teams and.

figure out how they’re going to do this because, you know, again, you know, AI is, is, something that makes you efficient and you know, the billable hour is, you know, it’s, it’s kind of in direct opposition to the billable hour. so, you know, while there may remain things that make sense, you know, for hourly billing, you know, the smart firms really have to start looking at this and pricing and talking to their clients about, you know,

what’s valuable, what’s value worth, because, know, other, otherwise, you you, you come up against this type of brick wall that we’ve, we’ve had for, many years where any type, anything that’s making, um, work efficient, you know, nobody wants to, know, no one wants to adopt it because it’s just going to, you know, hurt their pocketbook in the end. And I mean, things like, oh, sorry, things like innovation, you know, innovation hours and things are great and they count, but you know,

Greg Lambert (10:44)
Yeah.

Marlene Gebauer (10:53)
In the end, know, you’re still, you’re still kind of at, you know, at odds.

Greg Lambert (10:58)
Yeah, the one thing that I heard on both days was a reference to the, hopefully I say this right, the Javits paradox where it talked about the more efficient and cheaper something gets.

that actually the more people use it, and I think people are looking at legal services this way that if, there’s a lot of pent up demand that I think clients would love for law firms to do more work for them, but the cost and time is prohibitive. if you can get the.

know, cost and time down, does that kick in the Javits paradox to say there would just be more demand? And I think the thing that even if that happens, I think the idea that we really start needing or thought process that we need to start doing is, okay, well, how do we deal with that? Because right now I actually had someone ask me, you know, was like, are you…

looking at reducing the number of associates that you hire over the next few years. and so I don’t, obviously I think the industry’s gonna change. think the ability to just purely build by time and set your value by time is obviously gonna change. I just don’t know that law firms are making a serious thought

Marlene Gebauer (12:05)
Yeah, that was a question that was put out there. Yeah.

Greg Lambert (12:28)
about what does that really mean and how do we kind of prepare ourselves for what’s coming.

Marlene Gebauer (12:36)
And I think it needs to be quick. mean, what, you know, what we were hearing like this, this needs to be quick. ⁓ there wasn’t really discussion about like what type of work we’re going to sort of bring back in in-house, but you know, I know those conversations are happening and I know, you know, in-house groups are looking at tools that they can take some of this work back in. So, you know, firms are, are

really poised, think it’s like, it’s, it’s a really important moment right now for firms to really kind of jump on this and be able to sort of sell the fact that, you know, we’ve invested in all of these things and we can do these things for you. and, and sort of take the load off of you.

Greg Lambert (13:22)
Yeah, yeah, and kind of in that same vein, Kyle Poe from Legora, he gave both a standalone presentation and he was on the panel with me. And he talked about this, you know, that the conversation in 2026 going forward is, you know, shifts from

how fast is this tool, how fast can it get me to an answer to the question of, okay, now how do we price this new value that we’re setting up? And it’s definitely a very difficult question that law firms and pricing professionals in in-house are having to face. Because Kyle, the same, I think in the same,

instance also talked about, well, law firms now would love to go to flat fee rates that work for them so that we can really kind of focus in on the efficiency. And now he’s hearing clients saying, whoa, if it’s going to be less hours, maybe I just want to pay by the hour and I just pay you less. it’s interesting to see where

These two competing factors are going to meet in the next couple of years.

Marlene Gebauer (14:41)
Yeah, a couple things like, mean, he’s saying how fast is, know, moving from how fast is the tool to how do we price the new value? So, you know, we’re moving, you know, it’s clearly we’re starting to move from like, you know, how, how do we experiment with this thing? How does it work to, okay, you know, what, how is it truly impacting work and how is it impacting the price of work? The other question is,

And it kind of goes to his point about, maybe we’ll just, you know, pay hourly and just pay less hourly. But, you know, one of the things that came up was, was that the value is still there. It’s just the time that’s spent is less. So how do we figure out what value actually means? And, you know, is it just the time spent? Is it the outcome?

Greg Lambert (15:23)
Yeah.

Marlene Gebauer (15:33)
You know, is it the fact that, that, you know, you, you know, you get insights faster, you resolve things faster and, know, everyone gets, everyone gets back to normal work. you know, all of those things have a price point. So, it’s, it’s, you know, it’s an opportune time, I think for, for clients and firms to really, it’s really talk about it, like have an honest conversation about it, you know,

You know, no more, no more like Arlene discount. Let’s, let’s, let’s really talk about like, what is important to you? You know, what do we need? yeah.

Greg Lambert (16:05)
Well, it was interesting because we moved from day one focused on the way law firms are viewing this. And then we moved to day two. And I will say one thing that was talked about a lot and I think Joy mentioned this on day one and I think it was mentioned as well on day two with Connie.

was that there’s a lot of power in the hands of the in-house lawyers and general counsel of the clients. And it wasn’t until I was reviewing my notes and drafting the recap on the Substack page that I realized there were a lot of…

There were not a lot of in-house attorneys that showed up for day two. It was a large crowd, but there were lots of legal ops people. I think there were still a number of law firm people that were there, but at least on the panels and the discussion that was coming from the stage.

It was a noticeable absence of the in-house attorneys on day two. And so you’ve got this belief that ⁓ there’s a lot of power in the hands of the people and they weren’t there. It was kind of weird.

Marlene Gebauer (17:28)
You know, I hear mixed things and it’s like, you know, I hear like, Oh, know, clients are, you know, very far ahead in this area. And then I hear clients are not far ahead in this area. So I think it would have been really, really helpful to me to be able to sort of talk to some of these people and be like, Hey, you know, so where are you guys in your journey? Um, and, know, get some, some, you know, actual feedback on that.

Greg Lambert (17:53)
Yeah, well, if you are an in-house attorney and you’re listening to this, yeah, come to the table. Come on. People are begging to hear from you. They want, really want direction. So it was good to see Connie on day two, kick things off, and she had a really good opening.

Marlene Gebauer (17:59)
Come talk to us. We want to hear from you.

You really are.

Greg Lambert (18:21)
discussion where she really kind of did put the impetus on, you know, it is time for people to stop talking and time for some real transformations to go. And it kicked off John LeBare, who’s general counsel at Harvey, and he had an interesting talk. He also brought up the Jeavitt’s paradox and kind of where Harvey

you know, where Harvey is and how he’s using AI tools in his day to day. Now there was something and to his credit, he did say he was going to test this out on a non Silicon Valley audience. And I for one, I didn’t get a chance because he immediately took off back to California after his talk, but he compared

⁓ lawyers to software engineers. And ⁓ he said he did want to see how this went over with this group. So John, hopefully you’re listening ⁓ and let me give you my opinion. And I thought, and I talked with Joy Heathrush afterwards as well, and she did too that he got.

to the point, but the way that he got to the point didn’t really land. that was, exactly. So he was saying that essentially that lawyers and computer software engineers essentially do the same type of work. They get to an output.

Marlene Gebauer (19:43)
Agreed with the end result, but didn’t agree with the analysis. Yes.

Greg Lambert (20:05)
⁓ and, you know, and, and I really kind of thought it was a good try, but the, the outputs that computer engineers have and the outputs that lawyers have, one can be independently validated for ⁓ correctness. Software code either works or it doesn’t work. Whereas, yeah, it’s a product.

Marlene Gebauer (20:12)
tell lawyers that.

It’s a product. ⁓ You’re

making something work or not work.

Greg Lambert (20:31)
Yeah. so, I thought, because I know what he was doing and you hear this a lot and it almost doesn’t matter what industry is that AI has solved for computer code. That seems to be an admission in the industry that you hear it from Anthropic that their coders don’t even look at the code anymore.

They look at the results and the results work, then there’s no need to edit the code. And so with engineers, they’re getting to this point now where their jobs have shifted more from the writing of the code to more the coordination of the events that are going on and making sure that everyone is on.

on track for what it is that they need to accomplish. And so the engineers’ jobs have changed. And it’s really interesting because product managers now see themselves almost as quasi-engineers and engineers now see themselves as quasi-product managers. there’s this kind of what I’ve heard, you hear T-shaped lawyer, right? Where you’re broad across the board on multiple skills and then you could dive deep.

you know, in one skill. And I’ve heard it, you know, the kind of sideways E or F where, you you’re still broad in multiple areas and then you’re like deep in one or two or three areas. And that’s going to be kind of the new normal. And I think that part is going to transfer over to legal as well to where, yeah, you can be really a really good

Marlene Gebauer (21:45)
to the legal analysis,

There’s the T, there’s the D, yeah.

Greg Lambert (22:12)
a corporate lawyer, but you’re also gonna have to be knowledgeable in one or two more areas, especially in using the technology that you have.

Marlene Gebauer (22:22)
just being a legally good expert from, you know, from basically legal advice. we are already seeing that that is not enough. mean, you, you have to know your client’s business. You have to understand the technology you’re using, all of those things. mean, I agree with him that, and you know, we talked to Joy about this, that, that, that they’re both problem engineers and lawyers are both problem solvers. They’re both critical thinkers.

But, you know, as you said before, you’re trying to doing code, you’re trying to make something work. Now, you know, your client can come to you and say, you know, I want this result, but a lot of that is out of your hands. you know, it’s like, I want a deal to come out like this. It’s like, well, there’s a negotiation process. that’s that takes place and.

or, I want a specific outcome in litigation. It’s like, well, you know, that, that there’s a million factors that, could impact that, you know, your jurisdiction, your judge, your, if you have a jury, um, you know, how, how, um, you know, how, how impressive your argument is, how strong your argument is. So there’s just a lot of things that, that I think, you know, when you’re offering a service, uh, are, are a little different.

Greg Lambert (23:31)
Yeah, I agree. So we’ll just ask John to sharpen his pencil and then come back with another analogy, at least outside of Silicon Valley. So one of the things from day two that was really interesting that I thought, and there were some in-house lawyers on this, but it was more on operations than it was on actual practice.

Marlene Gebauer (23:40)
Yeah.

Greg Lambert (23:56)
was the practical use cases. ⁓ And there were some really good actual use cases that were talked about. ⁓ Justin Schwartz from Eparoc, he talked about one of my favorite topics is the 50 state survey, or he referred to it as just the multiple state survey, and where he asked his outside counsel if…

Marlene Gebauer (24:11)
Ha

Greg Lambert (24:18)
if they used AI, what would a $10,000 multi-state survey cost? And he said, one, the outside counsel was surprised that they were allowing them to use the AI to do this because other clients refused to let them do it. And so they came back and he said, it went from a $10,000 cost to a $5,000 cost.

And I think both sides were very happy to do that. I think 50 state surveys are one of those things that clients want, that they need in order to, especially if they’re in regulatory, but they’re super expensive. And quite frankly, it’s not the favorite thing for lawyers to do. They don’t like pulling these things together either. So.

Marlene Gebauer (24:56)
regulatory, yeah. ⁓

I don’t think we liked

doing it when we had to do it in the library. lot of work.

Greg Lambert (25:12)
I do not.

So I thought that was really interesting to, again, they talked to each other. They said, OK, I want this, but I don’t want to pay that much for it. And you say, if you’re going to make us do it the old way, that’s what it costs. If you let us do it the new way, then we’re happy with

with half of that cost because it probably takes us half or less of that time to do it.

Marlene Gebauer (25:46)
That was what I liked about it too, the fact that they kind of had that conversation. I think it also opens up the opportunity to address work that just never got done because it just didn’t make economic sense. And so if this is a pain point for a client,

Like now you have an opportunity, like these 50 state surveys. mean, the time that it takes now is, know, compared to when we, when we used to do it, like we stick days. And I mean, that was a while ago, so it probably doesn’t, it didn’t take days, ⁓ you know, when they were comparing now, but still it took a lot more time. And now this is something that they can probably offer on a regular basis and an updated basis. Whereas, you know, maybe this wasn’t all the time.

before because it’s just the cost was in the way, but you know, any of these types of, of things where, we have, broadened our abilities based on, AI, you know, that’s definitely a conversation to have, you know, between clients and, and, and firms to just see it’s like, where can we, where can, where can we do more?

Greg Lambert (26:52)
Yeah, one other example that I wanted to highlight because I think this is something that it kind of expands a little bit from the multi-state surveys. Michael seen us from Phillips 66 shared how their company has all of these rights away and I think he was saying there was like

over 250,000 of these that they had documents that explain what the rights away were. And what was interesting, and I don’t know that, you if you weren’t listening closely, if you caught it, he was basically saying, this is work that, you know, that we would love to have done, but there’s no way we can do it. And so they actually took all of their…

Marlene Gebauer (27:20)
can get to the gas, get to the pumps and stuff, right?

Greg Lambert (27:43)
documents that showed the rights of way and uploaded them into the AI system and extracted all of that information. And he said it saved them well over a million dollars in cost to do that. But the key thing was they probably didn’t even do it beforehand because the cost was so high. So now they at least know what their risks are.

And we’re able to get to that in an easy way. So this is work that wasn’t getting done because of cost and now it is getting done. So I think it just shows an example of if it’s cheap enough, what all you can do.

Marlene Gebauer (28:24)
either, you know, not getting done or getting done piecemeal on an as need basis. And, you know, what a wonderful example of being able to kind of extract, you know, information like, you know, trends or, or, know, different types of language and, know, what’s happening over time and being able to, you know, to see, okay, you know, what should we be paying attention to? You know, what are the, what are the risk factors here or what do need to change? And then being able to sort of change that on mass.

Greg Lambert (28:52)
Yeah, yeah. And the last one I wanted to highlight was Elizabeth Poole from Boomi.

And I like, she had some really good examples, but the one, and this one was close to my heart, and that was ⁓ she developed a way of taking the intake system and revamping it using a combination of SharePoint and Co-Pilot, and she said her budget to do this,

was zero. you know, that’s the ultimate, you know, this is the vibe coding ⁓ example. Yeah. So, yeah. Well, you know, necessity is the mother of invention. And when you got no budget, you got all kinds of necessity.

Marlene Gebauer (29:29)
That’s the ultimate incentivizer. It’s like, figure out something. I have no money.

Mm-hmm.

I,

and I love these types of examples where it’s like, you know, I had a, you know, I had a matchbox and, and, know, and a rubber band and like, you know, I made something amazing. And so you just basically use what you have. And it’s just a real example of, of, know, the, creative abilities of, of people and organizations, you know, when they’re.

You know, when they’re kind of tech curious and saying, okay, we have what we have, like, how can we make this work to solve the problem?

Greg Lambert (30:12)
as long as they stay ⁓ tech curious and not tech furious. Sorry, little Scott Pilgrim. All right, there is nothing wrong with that. Our friend moving on to another panel, or actually this was a standalone presentation that…

Marlene Gebauer (30:16)
No Tech Furious,

Nothing wrong with referencing Scott Pilgrim.

Greg Lambert (30:33)
multi-guest on the Geek in Review, Christina Sikounis, talked about, so she got up and talked about the upcoming report that she’s putting out on the industry and pricing and how much customers are, or how much clients are paying from Council Link.

Marlene Gebauer (30:38)
I love that she was there.

Greg Lambert (30:56)
She talked, I had to feel sorry for her because she’s been on I think since about 2020. So maybe the past six years on and off. And I always see this like hope in her eye. And then I talk about ⁓ the flat rate and alternative fees. it’s like, Christina, where are we this year? And she’s like,

Marlene Gebauer (31:09)
Ha ha ha ha ha!

Someone’s gonna.

Greg Lambert (31:18)
I thought we were going to go up, but here we are again right at about 10 % for AFAs. Yeah, come on. Come on. She’s super nice. Make Chris happy. ⁓ But she was talking about the fact that while AI may be making production cheaper, there’s a new normal in fee rates.

Marlene Gebauer (31:18)
Sorry.

All right, guys, we got to make it work for Christina. Everybody, everybody chip in. We want to make her smile.

No.

Greg Lambert (31:43)
raising them. And she pointed out that before 2022 that on average the rates and this is a blended amount on how they measure it was around 3%. She said about 2022 and after that has jumped to 5%. And I know I looked around because people were like, that seems low because I think we were like at 10%.

So and she mentioned that, again, the way they measured it, came out to that, but it was still a huge increase from 3 % to 5 % is large. That’s a, I think if my math is right, that’s a 60 % increase year over year of what you were paying. And one of the things that an audience member had asked her is like, well,

you know, in two or three years, what’s your next prediction? And she said, well, if we could normalize 5%, you know, there’s gonna be a push to normalize 7 % or more, and it’s gonna be interesting.

Marlene Gebauer (32:34)
we do for another one.

It’s really

interesting based on what we were just talking about before. Okay, are we really going to have these heartfelt conversations about what value is and are we really going to try and adjust this model in a way that makes sense now? mean, the billable hour made sense years and years ago because it was a real easy way to measure. But AI has come and disrupted that.

You know, it’s not a good way to measure anymore, but yet everybody’s so invested in it. are we going to have those conversations? Are we going to make those adjustments to, really reflect, what, what, know, what the value is and what the cost should be, or are we just gonna like up rates and then just spend less time? You know, I don’t know.

Greg Lambert (33:33)
Yeah, yeah. Another panel which was really interesting because they had people from Google, AT &T, Striker, they brought up an issue that was really interesting about the challenge of being able to add AI into the process but not make it unnecessarily complex in the existing tech stacks.

And I think one of the things that a lot of us, especially on our side of things, Marlene, or on the legal ops side, is that if we start allowing individuals to create their own software to correct their one problem that they’re facing, how do you manage that? How do you govern that? I mean, you don’t want to…

You know, don’t want to tamp down the creativity, but man, it could get complex quick.

Marlene Gebauer (34:23)
real questions.

I mean, you we’re having we’re having enough trouble like with with governance, just like with all of the changes that are happening. And it’s like we’re finally, I think, at a fairly decent point where we have our arms around it a little bit. I mean, but there’s still questions to be answered. But can you imagine every time somebody builds some small solution that’s, know, for a small practice group or an individual, you know, OK, are we going to have to sort of go are they going to have to go through sort of an approval process like

like we have to do with just general use of these AI tools. And I’m 100 % unnecessary complexity. It’s like as long as it’s complex, people don’t adopt it. If it’s easy, people do. And it’s the same thing with your tech stacks. Like if it’s easy to bring in and maybe bring out, then there’s much more appetite for it.

You know, so I mean, maybe this is where you see the MCP coming in too.

Greg Lambert (35:28)
Yeah, it was kind of interesting because we kind of almost came full circle because Richard Gorlick from Chrono Tracer out of Austin had this kind of warning about the lawyer freak out of 2027. And he kind of brought the same issue

that John LeBeer from Harvey did with, okay, we’re seeing substantial changes in certain industries like computer programming and computer engineering. We know it’s going to happen in legal in some way that things are going to significantly change.

And so he was talking, and I think he even brought up like the five stages of grief that AI, and that was not the first time I had heard that, but it’s like denial and then you go through all the, and then finally acceptance at the end. But again, I think that talks more about the…

Marlene Gebauer (36:25)
Yeah, I’ve heard that too.

Sadness.

Greg Lambert (36:44)
and I can bring back my favorite saying is, you know, all problems are communication problems. And if you’re not having those conversations now to at least be prepared for things and not have that conversation when it’s too late.

I think that’s the advice that I think we kind of all walked away with. But if not, that’s the advice that I’m giving everybody now is like, if you’re not having those conversations now, when it is an emergency, it’s too late.

Marlene Gebauer (37:16)
Yeah, I mean, it is absolutely critical that, that, you know, internally we’re having these conversations with, you know, everybody who’s, who’s impacted. and, you know, it’s funny, I was on a, I was on a webinar this morning and it was talking about sort of training and education and, know, part of the discussion is about, well, it’s, you know, you just sort of training people how to use things.

But it goes much further than that. mean, we have to be very, very transparent. You know, we have to teach people about what it does and doesn’t do. we have to teach, you know, the governance aspects of it, and, and established guardrails. So people are, you know, comfortable with it. We have to be sharing use cases, you know, within departments and across departments, particularly the ones that, that, you know, have, you know, significant impact, scalable, repeatable, because.

You’re right. You know, it’s like, they’re, and he’s right. Like it’s, it’s coming. We all know it’s coming. And so we have to do the best we can. And that’s kind of what, you know, our jobs are is to sort of prepare people and, know, get them comfortable as, as much as we can with it. And it’s funny, like, you know, I will say that sometimes in my experience, like, you know, the ones who are grumbling about it are often the ones who use it pretty well. So, you know, they get it. so I, I, but I have, I have, I have.

good hope for that. think that again, you’re seeing the fact that we’re having the discussions right now, I think makes a lot of difference.

Greg Lambert (38:40)
Well, I want to do ⁓ just a ⁓ quick bonus topic here. that’s, there was a couple of presentations, ⁓ one from Harry St. John from Lagora.

Harry’s the VP of Revenue and then the one that probably came out of left field the most was John Rizner, who’s head of AI at Legal Drafting for Filevine and this one was like the dream for the librarian in me because one, Harry introduced in it, I think he was kind of like

Marlene Gebauer (38:59)
Ha ha.

Greg Lambert (39:16)
not going to introduce it, but my hand shot up immediately when I saw it. And that is, Lagora is introducing a legal research option on their platform, which is going to be very interesting because I think we’ve said this before. I think a lot of people think that…

AI is like a magic bullet for legal research. And I’m here to tell you, if you haven’t done legal research in a while, legal research is really, really hard to do. It’s complicated and it’s hard to get right. So it’s interesting to see that pop up on that because I actually had, I was talking with a venture capitalist and

Marlene Gebauer (39:47)
to get right.

Greg Lambert (39:59)
they were asking, well, do you think the Harveys and Lagoras are gonna go after legal research? And I was like, no, they may plug it in, but I don’t see them going. I guess I was wrong. So, but really the, good.

Marlene Gebauer (40:07)
Really? No, they are.

why,

just for one second, like, don’t know why, like, but, but research for the longest time has sort of been like the, like the Holy grail for, for all of these AI comes like, I don’t know why, like, why, like we’ve had this conversation, like, why are you going after the hardest thing? It’s like, go after the easy stuff first.

Greg Lambert (40:28)
Yeah, well, you

to their credit, know, ⁓ Harvey and LaGorra didn’t attack it first like, you know, Westlaw and Lexus did. But, you know, but also Westlaw and Lexus, was their, you know, that’s their bread and butter. So.

Marlene Gebauer (40:38)
Yeah, but that’s

There are

other, there are other companies that have, popped up the, smaller companies. mean, you know, mid page being one of them and Wexler I think is another one where it’s there. That’s what they’re focused on.

Greg Lambert (40:46)
yeah.

Yeah, well, if.

I will tell you this, if legal research could be solved by just having the case law, then we’d all be using Google Scholar for all of our work. And I’m here to tell you, unless you’re an academic and you just, you you need access to the core cases, it’s much more complicated than that if you’re actually doing real live legal research for real live clients.

Marlene Gebauer (40:58)
Yeah.

I agree. Agree.

Yeah.

Greg Lambert (41:17)
But I did want to point out John Risner from Filevine because this was a law librarian’s dream presentation. The rich. my goodness.

Marlene Gebauer (41:17)
It’ll be in…

The Riz presentation and it was good. It was really good. I know both of us were like,

where did this come from? Right? It just popped up. It was awesome.

Greg Lambert (41:35)
It was awesome that yeah, I think this one was

the most out of left field presentation because he had talked about, know, we laugh when we talk about these stories of lawyers who.

Marlene Gebauer (41:41)
Yes.

people are going to hate this when you tell them it’s like, going to hate to hear

this.

Greg Lambert (41:49)
have cited to

hallucinated cases that have bad citations in their documents. And I’m here to tell you that was happening before AI was a thing. ⁓ People were putting in wrong citations, bad citations, using the incorrect quotes and incorrect analysis. That is not an AI phenomenon.

Marlene Gebauer (41:53)
Yes. Awful stuff.

It was.

Companies had, didn’t have citations that they should have had, you know, it’s like, mismatch

citations. Yeah. It was always happening.

Greg Lambert (42:17)
But he had actually

gone through and did a really deep dive on American case law and just how much we, you know, it’s like how much Shepard’s and Keysight and B Law, the Cytator systems, you know, it’s like they don’t even agree with each other.

Marlene Gebauer (42:27)
citators.

The bad news is like they don’t agree and they have significant error and it’s like great. it plays well into like a conversation I’ve been having with librarians and I was having with you. It’s just like, okay, how, like, because again, it goes back to these hallucination cases. Like now everybody’s got their eyes on that. And so what is the appropriate way?

to check your citations because not only do have to check to make sure the citation is correct, you have to make sure that the content in the citation is correct. You know, and if it’s, it’s a, know, key number that is referring back to the right thing and you know, yes, and all of these, these tools, if they’re, know, if they’re grounded appropriately, you can go back and look at the actual document. But like we’ve been trying to get away from that for like years and years and years. so that’s what Shepard’s was for. That’s what West, you know, citation West check was for.

⁓ is well, you didn’t have to do that and now you find that they’re wrong and then this is also wrong. So it’s like, what are we supposed to do?

Greg Lambert (43:38)
Yeah.

Yeah, well,

you know, I think it’s one more thing that.

Marlene Gebauer (43:44)
Somebody tell me. No, this

is serious question. I want to know.

Greg Lambert (43:48)
Well, the death of the law librarian has been greatly over exaggerated over the years. think that’s one more example of ⁓ why it takes somebody really smart to keep on top of these vendors that think they’ve created the easy button for legal research. ⁓ Well, I thought it was a great conference.

Marlene Gebauer (44:08)
Exactly.

Greg Lambert (44:13)
What are your final thoughts on this one, Marlene?

Marlene Gebauer (44:18)
You know, I think, you know, if there’s, one common thread, you know, it’s nobody’s waiting for perfect clarity anymore. And that would be a mistake if you do, you know, people are building, they’re testing in real time. You know, they’re, they’re, they’re looking to find, solutions that are bringing true value.

Greg Lambert (44:36)
Yeah, well, and one, think, you know, they’re not waiting for it to be perfect before they start testing it. You’re seeing that in the vendors. you know, iteration is now the way. Used to laugh, you know, we used to go to AA, double L or Ilta to see what the big change was for the year. And now it’s like every week, there’s a big change. ⁓

Marlene Gebauer (44:43)
Yeah. Yeah.

And this is why horizon

scanning is so important nowadays, because again, you know, have to see kind of what’s, you know, what’s being out there, what’s being built and, and, know, what sort of new angle is there that, that maybe isn’t being addressed other places.

Greg Lambert (45:13)
Yeah, yeah. And my takeaway, it’s both with the conference and a lot of things that I’m hearing across not just our industry, but just across society, there’s lots of things that AI can do right now. But our culture and our businesses and just the way that we’re structured, there’s a huge gulf between what’s possible and what’s doable.

⁓ And it’s, we’re still trying to work out the doable while the possible just keeps going further and further away. So I think just like we said earlier, the key right here is having these conversations now, working with the tools now.

And even with all the, you know, the warts and all of getting in there and, you know, kind of at least trying to advance with the technology so that it doesn’t, you you don’t get just completely left.

left behind, but also so that it gives you that time to also ingest what this means for you as an individual and you within a profession and where that goes. you’re not talking about it, if you’re not working with the tools and experimenting and investigating, good luck to you.

Marlene Gebauer (46:32)
Well, I think Texas Trailblazers gave us a great snapshot of the transitions that are happening right now. So thanks for listening, everybody, and we’ll see you next week. And as always, the music here is from Jerry David DeCicca Bye, everybody.

Greg Lambert (46:47)
All right, thanks Jerry, bye.

Day One of Texas Trailblazers in Dallas had a different tone than most legal tech conferences I attend. The conversations stayed close to the work. Less speculation, more discussion about what people are doing right now, where it is working, and where it is breaking.

Organized by Cosmonauts in partnership with LegalOps.com and held at The Statler Dallas, the Private Practice Day brought together law firm leaders, legal operations professionals, and technology vendors for a full day of keynotes, panels, and product demos. Joy Heath Rush, CEO of ILTA, chaired the day and opened with a fireside on the new era of conversations between general counsel and outside counsel. Her framing set the tone: AI is living at the intersection of business and technology, and it is creating conversations that simply did not exist two years ago.

Across the sessions and hallway conversations that followed, a few themes kept showing up. They were consistent whether the speaker came from a law firm, an in-house team, or a vendor building the tools.

Trust in AI is becoming a workflow problem

Rowan McNamee, Co-Founder and COO of Mary Technology, opened the sponsor keynote with a point that came up repeatedly throughout the day. AI outputs are persuasive. They read well. They look complete. That creates a tendency to move forward without enough friction in the process.

McNamee cited a recent study on what researchers call “cognitive surrender,” based on the work of Sean Hay and building on the framework in Thinking, Fast and Slow. Under time pressure, people rely on AI even when they know they should verify the result. In a series of experiments, override rates improved from 20% to 42% when participants had real money on the line. Even then, more than half still followed the AI’s answer. “You can reduce it, but you can’t eliminate it just by telling people to be careful,” McNamee noted.

In a legal setting, the implication is straightforward. Review cannot be optional. It has to be built into the process in a way that does not depend on someone remembering to slow down.

That concern echoed later in the day during the panel I moderated. Laura Ewing-Pearle, Senior Manager of eDiscovery and Practice Support Technology at Baker Botts, described a clear split even among e-discovery practitioners who are enthusiastic about AI. They lean heavily toward document interrogation and querying. They are far less comfortable with AI making responsiveness calls, and they are “definitely not comfortable with AI making privilege calls.” The line between assistance and reliance is still being worked out in real time.

Adding urgency to the conversation, panelists referenced a recent New York case in which a court ruled that AI-generated legal advice obtained through a public tool was not protected by attorney-client privilege. The ruling was specific to a consumer-facing chatbot, but the message landed clearly: enterprise AI tools with proper security and licensing are becoming a matter of professional risk management.

Adoption follows incentives, not access

The panel on Culture, Change, and Collaboration brought together Emma Dowden of Burges Salmon, Thom Wisinski of Haynes Boone, Kelley Lugo of Bryan Cave Leighton Paisner, Rowan McNamee, and Tammy Covert of Nachawati. The discussion stayed focused on what actually drives adoption inside organizations.

Kelley Lugo described how the initial reaction at many firms was to block AI tools. That position has shifted. Lawyers are experimenting, often on their own. But the challenge is whether there is a reason to use AI consistently. She offered a concrete example: in BCLP’s UK real estate group, roughly 80% of matters are handled on a fixed-fee basis. Write-offs on those fixed matters directly hurt partner compensation. That pain point, she explained, is exactly where AI efficiency aligns with real motivation. When the people doing the work see a direct connection between AI-assisted speed and their own metrics, engagement follows.

Emma Dowden framed it in terms of mindset and behavior. Transformation efforts stall when they focus on the technology without addressing how people are measured and rewarded. She also noted that the pace of change is creating a real business risk. In the UK, the Big Four are telling firms what percentage of their revenues are at risk from disruption, and the numbers are serious.

Lugo shared a story that captured the adoption dynamic well. A senior leader at her firm went on a two-week safari, came back, and told her he had not thought about work at all the entire trip. But he had thought about AI the entire time. When people get enough distance to think clearly about where the profession is headed, the conclusions tend to be similar.

No one on this panel suggested that training alone will move the needle. The focus stayed on incentives, visibility, and examples that resonate with how lawyers already operate.

Building sustainable innovation foundations

The fireside on Beyond Technology: People, Process, and Policy brought together Joanna Penn, Chief Transformation Officer at Husch Blackwell, Rochelle Rubin, Director of Client and Business Operations at Barnes & Thornburg, and Louise Thomas, Director of Transformation at Burges Salmon.

Husch Blackwell’s approach stood out for its scale. Their virtual office, “The Link,” now has over 850 people, including more than 250 attorneys, working in a distributed model that lets the firm recruit specialized talent in markets where they have no physical office. They are also hiring “architects” whose job is to translate between technical and legal teams. That is a role you are starting to see more firms create.

Barnes & Thornburg shared a result that had nothing to do with technology and everything to do with process. Their client interview program, built on the simple act of asking clients what the firm could do better, produced a 90% or greater revenue increase within 12 to 18 months for the clients who received that in-person attention. The takeaway: sometimes the highest-value innovation is a conversation.

Workflow design is where the gains are showing up

Kyle Poe, VP of Legal Innovation and Strategy at Legora, presented a framework for measuring AI ROI that moved the conversation past speed alone. His point was that firms seeing real results are looking at outcomes and pricing, not just how many minutes a task takes. The ROI, he argued, varies dramatically by use case. Some tasks show minimal time savings. Others produce modest reductions, like going from ten hours to eight. And some are transformative, compressing weeks of due diligence into hours. He also projected that firms will likely raise rates around 25% to offset AI costs while repositioning how they describe the value they deliver.

That idea came into sharper focus during the Automation in Action panel, which I moderated. Paul Pryzant, a partner at Seyfarth Shaw with 45 years in M&A, described how his group now runs deep research on target companies on day one of receiving a letter of intent. Information that used to take two or three weeks to surface during due diligence is now available immediately. Pryzant also emphasized starting from the client relationship: “My favorite question is, how are you using AI? How are members of your team using it?” That approach opens a two-way conversation rather than a one-sided pitch.

Rutvik Rau, CEO and Co-Founder of August, reinforced the importance of starting with the deliverable and working backward. That approach forces clarity about what steps are required, where the bottlenecks sit, and which parts require judgment. The most successful users, he said, are the ones with a strong sense of how they deliver a service to their client. Curiosity and creativity matter more than technical skill.

Chad Barton, a corporate transactions partner at Holland Knight, described how his lending team built AI into their existing process for handling matters with rigid institutional parameters. He also raised a tension that no one else on the panel had addressed directly. When AI makes associates dramatically more efficient, it creates a compensation problem. “If I’m really efficient, I can’t hit my target,” is something associates are already thinking about. Barton was clear that this falls on firm leadership to address, especially during compensation reviews. Partners need to defend associates who produce excellent work product even if the hours look different.

Kyle Poe added a practical observation about change management. “Workflow is just the orchestration of multiple things you could do within the product,” he said. Teams that learn the individual tools first and then map the process manually tend to have a clearer path to automation. He also described building workflows in plain English instructions, the same way you would brief a literal-minded associate, and then iterating based on mistakes.

The Legora demo and a question about legal research

During the break, Harry St. John, VP of Revenue at Legora, ran a product demo that filled the room. Legora’s platform centers on two main tools: an assistant for conversational document interrogation and a tabular review for bulk extraction across large document sets. St. John showed how the two connect. Extraction results from tabular review can be queried through the assistant, and outputs can be pushed into Microsoft Word using firm-specific templates. A newer feature called WordEdits generates multiple unique Word documents from a single instruction set, useful for tasks like drafting a set of resignation letters for every officer in a transaction.

I asked St. John about the legal research capability they had flagged, given that at Jackson Walker we tell our people not to use AI for legal research unless they are inputting their own materials. He said Legora has ingested district, appellate, and Supreme Court caselaw from a number of providers, with their legal research database launching soon. He was transparent that bankruptcy coverage was not included yet.

Pricing is moving into the center of the conversation

The panel titled Billing on Trial: Rethinking the Economics of Legal Work brought together Jon Lindrus of Foley and Lardner, Jonathan Safran of Polsinelli, Marlene Gebauer of K&L Gates, and Katon Luaces of PointOne.

Time-based billing is still the dominant model, but it is under more scrutiny than ever. AI changes how long work takes. That raises questions about how value is measured and communicated. One of the clearer moments came earlier in the day during my panel when Kyle Poe described a dynamic playing out between firms and in-house counsel. Some firms are trying to benchmark what a task would have taken with humans last year, apply a discount, and offer that as a fixed fee. But if AI produces savings far beyond 20%, clients will eventually ask why they should lock in rates that still capture most of the efficiency gains for the firm. The honest answer is that both sides are still figuring this out.

Jonathan Safran, in a pre-conference interview published by the Cosmonauts team, made a point that resonated throughout the day. Clients say they want alternative fee arrangements, but many still ask for shadow billing to verify what they would have paid under an hourly model. That tension makes standardization difficult. And on the firm side, three partners at the same firm might manage the same project three very different ways, which makes fee estimation genuinely hard.

Disrupting the citator: open data meets modern AI

John Rizner, Head of AI Legal Drafting at Filevine, gave a presentation that most of the audience probably had not expected. His argument: the legal research market is approaching an inflection point. An unprecedented volume of American caselaw is now freely accessible and processable by digital tools. At the same time, language models have reached a level of legal sophistication that would have been unimaginable five years ago. Taken together, Rizner argued, these developments create the possibility of meaningful competition in legal research and call into question long-held assumptions about who can build a citator and what it should cost.

A practical example from litigation

The emotional journey ahead

Where this leaves us

By the end of the day, the conversation felt less like an introduction to new technology and more like a working session on how legal services are being reshaped. Thomson Reuters data shared during one of the sessions put current AI adoption rates at 35 to 40 percent across the industry. That is meaningful progress, but it also means more than half the profession is still watching from the sidelines.

A few observations stand out from the day:

• Trust in AI needs structure, not reminders. Even with financial incentives, people follow AI outputs more than they should. Verification has to be designed into the process.
• Adoption depends on incentives and visible use cases. When AI efficiency connects directly to compensation or profitability, people engage. When it does not, they wait.
• Workflow clarity is a prerequisite for meaningful automation. The firms getting results are mapping their processes before automating them.
• Pricing discussions are tied directly to how work is performed. As tasks get faster, the economics of client relationships change alongside them.
• Clients are already engaged and influencing expectations. They are using AI in their own organizations and forming opinions about how outside counsel should be using it too.
• The human side of this transition deserves more attention. Junior associates worried about billable hours, senior partners rethinking their identity, and support staff facing higher-stress work after the easy tasks are automated. These are real concerns that training programs and leadership need to address.

The examples came from people who are testing, adjusting, and in some cases rethinking long-standing approaches to legal work. That is what made Day One useful. The focus stayed on decisions that need to be made now.

And if there was a consistent thread across all of it, it is this: the tools are moving quickly. The harder work is aligning people, processes, and expectations around them.

Texas Trailblazers was held March 25-26, 2026 at The Statler Dallas, organized by Cosmonauts in partnership with LegalOps.com. Day One focused on private practice; Day Two on in-house operations. Joy Heath Rush (ILTA) chaired Day One.

Paula explains how her early company, Hieronymus, found success by handling sensitive, high-stakes legal translations in Switzerland, especially where precision and confidentiality mattered most. But as machine translation improved, the market for average work started to disappear. Clients began doing more on their own, leaving only the hardest, highest-value assignments for specialists. Rather than ignore the shift, Paula leaned into it. That decision led her back to university, into data science and machine learning, and toward building Neuron, a company focused less on replacing expertise and more on improving the process around imperfect AI output.

A central theme of the discussion is the uncomfortable truth that many users do not care as much about excellence as professionals do. Paula makes the point with refreshing honesty. AI often produces work that is mediocre, but for a large share of users, mediocre is enough. That creates both a market shift and a professional dilemma. In legal translation, as in legal drafting more broadly, the issue is rarely whether AI produces something flawless. The issue is whether the user notices what is wrong, has the time to fix it, and has the systems in place to improve the result efficiently. Paula argues that the real value is not in claiming perfection. It is in helping experts find the mistakes faster, correct them with less pain, and avoid wasting hours doing work that feels like cleanup on aisle five.

The conversation also digs into trust, user behavior, and the strange authority people give to AI-generated answers. Paula recounts how, in one negotiation, a party trusted ChatGPT’s answer more than a human tax lawyer’s detailed explanation, even when the AI response was wrong. That anecdote opens up a broader discussion about confidence, presentation, and why polished outputs often feel more persuasive than expert judgment. Greg and Marlene connect that idea to legal systems, translation quality, and access to justice, especially where technology might offer better service than overworked and underfunded human systems. The result is not a simple pro-AI or anti-AI position. It is a grounded look at where human excellence still matters, where automation fills gaps, and where the future may split between mass-market convenience and premium, highly tailored expertise.

Looking ahead, Paula sees consolidation coming to legal tech, along with a growing push toward seamless interfaces that bring best-in-class features into one place. For Neuron, that means becoming an embedded layer inside other legal tools rather than forcing lawyers to juggle yet another standalone platform. Her crystal ball view is both stylish and sobering. She compares the future of legal services to retail and fashion, with more ready-to-wear solutions for everyday needs and a smaller, more exclusive market for bespoke legal work. It is a vivid way to frame what may be coming. The legal industry is not simply moving toward automation. It is sorting itself into tiers of service, quality, and expectation. And if Paula is right, the future belongs to those who understand where “good enough” ends and where true expertise still earns its premium.

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)
Hi everybody, Greg Lambert from The Geek in Review and I am here with our friend Nikki Shaver from Legal Technology Hub. ⁓ Nikki, you have a new event series ⁓ coming out called the Demo Dozen. Tell us more about that.

Nikki (00:15)
That’s right. Hi, Craig. Hi, everyone. So actually, we held our first one of these on February 19th, hosted by Stephanie Wilkins. You’re right. It’s called our Demo Dozen event. At these new LTH events, which are free to attend, by the way, you can log in virtually to see 12 curated providers offer demos of their products.

It’s an easy way to get up to date on what’s new in the market, whether that is brand new products or existing ones with new features. We’ll be offering these throughout the year. And our next one is coming up on May 19th. So if you’re a vendor listening and would like to participate in upcoming demo dozen events, let us know at legaltechnologyhub.com or reach out to me or Chris Ford on LinkedIn. If you’re a buyer or other participant in the market and you’d like to learn more or attend one of

these events, make sure you follow our page on LinkedIn. It’s at LegalTechHub. Or if you follow along at LegalTechnologyHub.com, we have an events drop down on our page. And if you follow on that, will see registration links for all of our upcoming events, including Demo Dozen And so keep an eye out for that. It’s a great way to know what’s what in the market.

Greg Lambert (01:30)
And having these very compacted ⁓ demonstrations really helps people like me to evaluate things very quickly. So thanks for doing this.

Nikki (01:40)
Thanks, Greg

Marlene Gebauer (01:49)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (01:56)
And I’m Greg Lambert, and today we are delighted to have Paula Reichenberg, founder of Neur.on, on the show. Paula, we’ve had a few technical difficulties, but I think we’re going to fight through them now. So welcome to The Geek in Review.

Paula (02:11)
Thank you so much for having me.

Marlene Gebauer (02:13)
So, Paula, you began your career as an M&A and capital markets lawyer, and then you moved into translation services with Hieronymus. You obtained an MBA, then undertook data science training, and now you’re the founder of Neur.on. So can you walk us through that journey? Because that’s quite a jump. Explain how you came to decide legal translation combined with AI was definitely the game you wanted to play, and tell us what motivates you there.

Paula (02:33)
I think the right order was that I first decided to leave the legal world. As an M&A lawyer, I always wanted to sit in my client’s seat because we kept giving them advice that they would not follow, and then they would come back a couple of months later and complain and say, you know, “I would have wanted to do that instead.” So that’s why I did the MBA, to try and switch into business.

In the MBA, we had an exercise where they said, “Okay, think about your last employer.” I thought very hard about my last law firm. “Did you have any inefficiencies?” I was like, my God, where do I start? So you had to list them, and then you had to come up with a business idea out of those inefficiencies.

At the time in Switzerland, we didn’t have anything like paralegal services and so on. Menial tasks were organized in-house, and translations in Europe are a big thing. You would work on your M&A transaction the whole week, and then on Friday nights a partner would knock at your door and say, “You speak French, don’t you?” It was like, no, I don’t. And then you would spend your whole weekend doing translations for the rest of the firm.

So that was one example. The company I started then, right out of my MBA, because we won the business plan competition, which is extremely weird, because service company ideas never win business plan competitions, and for very good reason. Running a service company is a very bad idea, as all lawyers know. You need to keep working if you want to keep making money, which is a totally inefficient business model.

And so that’s what I did. I started the company offering paralegal services, trying to organize large due diligence projects.

Greg Lambert (04:15)
Yeah.

Paula (04:40)
And that was a nightmare, because lawyers at the time in Switzerland were not the best managers. One thing they could outsource really easily was translations. So we became, in a very short time, the legal translation agency in Switzerland, specialized particularly in confidential cases and really hard topics that other translation agencies just would not touch.

So this legal minion task that needs to be super precise was a niche that carried us for a while. Then machine translation started to become a thing, not at the time when it was just statistical and always there to give you a good laugh, you know, the inside joke of translators. But when it started producing good results, I really thought about this MBA case where we had studied Kodak. I always clicked with that case. How can you kill your own company within a couple of months because of a new technology?

And I thought, I’m not going to Kodak my translation business. So we started developing our own Swiss legal translation models with external partners. And we discovered very early on what lawyers are discovering today, that data is king. If you don’t have good data, you cannot build good models. It is always this managing of the data.

So we decided to internalize these processes because no one was, of course, good enough to handle legal data properly at the time in Europe, at least multilingual data on top of it. And that brought me back to university. I had to take some accelerated classes in machine learning and data science. We started with innovation projects, hiring our first engineers, which again, for a lawyer, is very challenging. You know how to hire lawyers, but try to hire an engineer. First of all, you have the impression they hate you. You talk to them, you smile, they don’t smile back, and you always think, “This person was good, but they’re going to say no, they hated us.”

So no, we hired really good people, thanks to excellent professors. You need to have some insiders. And that’s how the journey started for us to transform the company into a half-tech company. That’s where the idea came from. A lot of people, even today, I get the impression that I’m living Back to the Future because a lot of law firm customers tell you large language models are so good, just wait a couple of years and they’re going to replace lawyers completely. They’re lay people and they don’t see the mistakes that large language models make.

So they think the next version is just going to be better and the next one is going to be perfect. At the time, of course, the same thing happened with translation models. People who don’t really speak languages see the results, they’re amazed. The results are amazing, but they’re far from being perfect.

So this last mile was really the thing. How do you solve this last mile that lay people don’t see, but as a professional you enjoy the tool, you see the mistakes, and you think, how can we become more efficient than this? And the sad answer, because it’s full of words that lawyers hate, is processes and data. That’s what we decided to do. This is why we created a spin-off, to really accelerate that. Work with large language models, fine, but work with them through processes and data.

And so that’s how Neur.on was born. The name Neur.on sounds a lot better than “processes and data,” but that is what we could have been called.

Greg Lambert (08:58)
Yeah.

Marlene Gebauer (08:58)
Great.

Greg Lambert (09:05)
So that’s why you hire marketers as well as engineers, to make sure that…

Paula (09:09)
Yeah, yeah.

Marlene Gebauer (09:09)
That’s right.

Paula (09:11)
Thank you for assuming that was done by a marketer, but no, we were desperate, we needed to fill out a form, and we just came up with a name.

Greg Lambert (09:18)
So there you go. Necessity is the mother of invention and the mother of naming your company, if need be.

Marlene Gebauer (09:25)
Mm-hmm.

Greg Lambert (09:28)
Well, Paula, I want you to walk us through the different iterations of what you’ve done. There was Hieronymus, and there was Lex Machina, and now Neur.on, and maybe even something more recent if you want to talk about it. So what were you doing to solve each of those problems in the stages along the way, and what’s on the near horizon for you?

Marlene Gebauer (09:43)
I think you have some news.

Paula (09:57)
Yeah, so Hieronymus is definitely the service company, still a service company. The mix of services it provides changed a lot with the improvement of machine translations, which can give you a glimpse of what may happen in the law firm industry. A lot of easy assignments, where we could at the time also bill huge urgency fees, those were the times, all disappeared.

Everything that could be average got totally taken over by the clients themselves. So there was a lot of do-it-yourself by clients for easy stuff, and the service company became extremely specialized. It became a little jewel of highly specialized people who could then charge a premium, while average work was replaced, unfortunately, by this do-it-yourself behavior of the end clients.

That company, I sold to a larger group that is looking exactly at niching down into very specific industries where really high-quality services are needed. So I think the consolidation of extremely skilled specialists is one trend. So that’s Hieronymus.

Neur.on is also focusing completely on the process part of, if you want, this kind of agentic flow of what you need to do to serve lawyers in all their different use cases. But we also built, at the same time inside Neur.on, a big natural language processing team specialized in multilingual legal texts, which was extremely important at the beginning to build the right models, et cetera.

Now this team has been spun out into a European scale-up called Noxtua, which is offering a European sovereign alternative to non-European players, let’s put it like that, specialized as well in handling publishers’ data from every single European country. That’s also something that we have to face in Europe, how scattered the legal landscape is. Each jurisdiction really has a different legal tradition, different language, different structure of its legal system. So it’s unfortunately not comparable to the states, where you have 50 states that are more or less similar. In Europe, it’s very scattered.

Marlene Gebauer (12:51)
Don’t tell them that.

Greg Lambert (12:52)
Yeah, don’t tell Texas that.

Paula (12:53)
No, no, it’s true that South Carolina would probably disagree very much with what I’m saying.

Greg Lambert (13:04)
Yeah. Well, you kind of mentioned some of the learning experiences you had with how technology was changing the way that you do translations may run parallel to what the technology may be doing now for the legal industry as a whole, or at least parts of it.

And I read something about Mindy Kaling’s keynote at Legalweek recently where she talked about that takeover for writers for television shows and movies, in that she sees the technology taking over the mediocrity of it for us. We may think of that as more of the basic work, the commoditized work. Do you see that running parallel with translations to legal?

Paula (14:06)
I see that very much. A lot of little contracts I’ve had to negotiate recently were directly sent to me by lay people who just told me, “Yeah, I prepared a contract,” and you could see that it was AI-generated. Unfortunately, they didn’t even use a proper legal AI tool. They should have. But this is just happening.

This do-it-yourself attitude, before, we would have involved a lawyer to do that. It would have been done a lot better, but now it’s just a crappy draft. I change what is to my disadvantage, I leave what is to my advantage, and we sign it.

So the reaction translators had at the time was, “No, but have you seen this? This translation is full of mistakes. They’re going to regret it. At some point they’re going to come back and see that everything they did was full of mistakes, and then they will come back.” Maybe.

So the problem is not only that it is taking away the mundane work. The problem is that it is creating a lot of mediocre outputs, but no one really cares about mediocrity in most cases.

Greg Lambert (15:08)
Yeah.

Paula (15:29)
And this is where you see, yeah, for 80 percent of things…

Greg Lambert (15:34)
I don’t know, Marlene just gave me a side-eye for that statement.

Marlene Gebauer (15:37)
Well, I’m thinking about, you know, I think for many people, good enough is good enough, but when you talk about your team, it’s lawyers and linguists and engineers, which is definitely what you need for what you do, but it’s a very interesting combination.

The lawyers are going to be very focused on confidentiality. Both they and the linguists are going to be focused on legal linguistic domain expertise. The engineers are focused more on how things work and innovation. So how do you operationalize that combination in product development and service delivery? I imagine there’s got to be some friction. How do you combat that?

Paula (16:25)
Yeah, I think that’s definitely a more optimistic crew to talk about because they’re not about to be fired. So they’re really good people, and they cannot be replaced by AI.

No, this is true. It’s really this balance of being an expert and being excellent, but also being able to work in a team. And I think that is something that…

Greg Lambert (16:25)
Engineers love lawyers.

Marlene Gebauer (16:27)
Oh yeah.

Paula (16:54)
A lot of companies will tell you that entrepreneurship, or starting from scratch, works better than trying to build with an external team. We started fresh with exactly one engineer, one linguist, and one lawyer. When you build from that and you manage to have trust among the three founders who really represent those domains, then you grow the team with the same communication and the same bridges.

And you’re right, Marlene, because I’m very active in the Swiss legal tech community and in Europe in general. I hear from a lot of legal tech founders, and it is rare to have this triangle balanced. So yes, you’re right. I think it’s the secret.

Marlene Gebauer (17:51)
Supported on all sides.

Paula (17:53)
And with this communication, it works. Yeah, that’s true.

Marlene Gebauer (17:59)
I will say it sounds a little bit like a joke. A linguist, a lawyer, and an engineer start a company, dot, dot, dot…

Greg Lambert (18:08)
We’ll ask AI to fill in that joke.

Paula (18:10)
Let’s ask AI for the end of this story.

Marlene Gebauer (18:12)
Exactly. What’s the joke? What’s the punchline there?

Greg Lambert (18:13)
Ha ha ha!

Paula (18:16)
AI is very bad at jokes.

Marlene Gebauer (18:19)
They’re really bad jokes, I gotta say.

Greg Lambert (18:19)
It does.

Paula (18:21)
Yeah.

Greg Lambert (18:23)
So Paula, we talked earlier about “good enough” being good enough, but when it comes to your potential customers, when they’re evaluating a platform like yours, what tends to matter most to them at the start of the evaluation? And then what proves to be the ultimate value after adoption? Where do they see the strongest returns, and where do you still run into people who are hesitant or resistant to apply these technologies?

Paula (19:04)
Yeah, that was a steep learning curve, understanding whom to speak to and who would understand the added value. For us, coming from the language industry, it’s so obvious that machine translation is crap and needs to be improved. But then you speak to…

Greg Lambert (19:16)
You…

Marlene Gebauer (19:17)
We’re going to quote that.

Greg Lambert (19:20)
Yeah, it’s going to be the title of the show.

Marlene Gebauer (19:24)
Everybody will listen.

Paula (19:26)
But then you tell them, and this is why we exist, to get you from crap to great, either thanks to the proper processes, the proper data, and everything smoothing out so the do-it-yourself becomes efficient. Because this is the problem as well. When you have an output that comes from the machine, and then your lawyer is doing it last minute, and you look at this and say, actually, it’s really bad. Oh gosh.

And then you don’t have the tools to improve it and to automate or auto-propagate your corrections and everything. So you get the impression that you’re doing an intern’s work, but you know it’s super important because you cannot submit something wrong to the court.

So this frustration, having the impression that you were given a tool that is mediocre, and now you have to fix it, I know at every customer conversation when they tell me, “I did this translation with mm-mm-mm and it was so bad, I spent 16 hours correcting it,” you know you’ve got a sale. Because they say, “And with you, it would have been perfect.” And you say, gosh, no. Our models, the only thing we guarantee is that they make mistakes. Guaranteed mistakes.

But our system tells you where they are and helps you to correct them, helps you to spot them. Instead of 16 hours, you would have spent four hours. So no, it’s not perfect, but it is something where you can say this is the added value. But not everyone, if you have not gone through the pain, you will not get it.

Marlene Gebauer (21:13)
Yeah. I imagine if someone has had the pain, they’re like, let me get my checkbook. Whatever it costs, I need it. But if they haven’t experienced that, then they don’t really understand the benefit.

Paula (21:19)
Exactly.

But I think it’s an issue that a lot of legal workspaces are facing because you create the best features ever, but most people, 80 percent of the people, just use the chat window and do everything there. They never use any of the special features that would save them so much time.

That also, in our naivety at the beginning, totally surprised us. Eighty percent of usage is still on the absolutely simplest thing. So we had to put a lot more effort than we would have imagined into improving the basics. This is really basic stuff.

And then the added value is not necessarily noticed by everyone, but the people who do notice it are the ones who really work with your tool. For them, it’s an immense added value. So you have to cover this whole scope, which is frustrating for the nerds.

Greg Lambert (22:28)
Yeah, to me…

Yeah, well, let me bump my question that I was going to ask later because I think it fits perfectly right here in this part of the conversation. A lot of times, like you were saying, translations to the newbies look deceptively easy, right? It’s like taking one word and translating it to whatever the other language’s word is. But when you’re talking about things like legal documents, it’s not just a word-for-word translation. You get legal systems that are different, drafting cultures that are different, risk assumptions that are different.

And that’s just for the layperson, right? When it comes to lawyers in translations as well, do you think there’s still something they’re fooling themselves about regarding how easy some of the translation tools can take this perfect contract that they wrote in one language and make it a perfect contract for another language?

Paula (23:38)
I think that is still a myth that a lot of people believe in. Then you’ve got the others who notice the difference. But it’s the same with large language models. There are so many people telling you, “No, I tried Claude, it’s better than my lawyer.” And fair enough, those people will still exist.

Greg Lambert (23:56)
That might be true, but they would have a really bad lawyer.

Paula (24:04)
Yeah, they really cannot appreciate their lawyer’s added value. But I had this in a negotiation where it was a complex tax issue involving Swiss tax law, and the other party was not Swiss, so they didn’t understand. We had a tax lawyer jump into the conversation and give us one hour of explanations and everything. They were still extremely reluctant to believe him.

Then I got fed up and said, “Let’s ask ChatGPT.” And they said, “Oh yeah, let’s do that.” ChatGPT came out with an answer, and I said, “Here it’s wrong, let’s correct it.” So that took five minutes, and they believed it. They believed ChatGPT more than a professional. It’s extremely weird.

It’s the same in finance apps. Apparently if you go to your banker and they tell you, “You should diversify your portfolio, let’s put a little bit in Asia,” you’re like, yeah, yeah, I’ll think about it. But apparently if the information comes from the app and it says, “An AI is telling you, you should have at least 20 percent in Asian stocks,” then you say, okay, okay, okay, and click the button. It’s extremely interesting, this pattern.

But I’m going off track here. So yes, not everyone…

Greg Lambert (25:22)
Yeah.

Marlene Gebauer (25:26)
I mean, that could be a whole other discussion about trust in systems and why people do what they do when they work.

Greg Lambert (25:29)
Yeah.

Yeah, well, there was a study done years ago that talked about when they were doing machine learning for the legal system, right? It was setting sentences, or asking if you could get a computer to be your judge instead of a judge, would you do it? People who were not involved in the legal system were very opposed to that. They wanted the human in it.

But the people who had been involved in the justice system, especially on the defense side, were much more willing to allow the machines to do it because they had seen the bias that humans bring in and they thought, well, at least there won’t be that bias. And of course, we’ve all learned that it got built in.

Marlene Gebauer (26:23)
And then we found that no, it just inherited bias. Yep.

Paula (26:29)
Yeah, no, but you’ve got this impression of something official, and it looks so good. There is no spelling mistake. It is just so polished.

So yes, for translation it is an issue. But is it a huge issue? I mean, when you go to translators’ conferences, they’re always telling you about all these mistakes and how bad it is, but you never find court decisions that are the consequence of a bad translation, where the consequence was so bad that it went to court.

So in that sense, I agree, it is really bad. And yet you see the luxury sector, and by luxury I mean a private bank working with ultra-high-net-worth individuals, they will not use machine translation to do their general terms and conditions or write to their clients, et cetera. For retail, though, for an insurance company, they do not care so much. Hermès will never translate its website with a machine translation tool, but Amazon does it, and a lot of people are buying their products.

So language quality becomes a sign of luxury. And I think it is going to be the same for court judgments as well. You can have the retail part for all your parking tickets, et cetera, and then you have the luxury part, where suddenly you have a human judge, who then maybe is a better judge than the average of today’s judges, who knows.

So I think there is this, I don’t know, do you have a lot of IKEA in the States? In Europe, it’s just IKEA everywhere. This IKEA-ization, the fact that everything became IKEA-ized, even homes, is amazing. It is crappy quality, everyone has the same thing, but it’s 80 percent…

Greg Lambert (28:33)
Yeah.

Marlene Gebauer (28:34)
Plenty of IKEA.

Greg Lambert (28:37)
Yeah, everywhere.

Paula (28:51)
And they give it to you.

Greg Lambert (29:00)
And you only need one tool, one little Allen wrench. Everybody’s got ten of them in a drawer somewhere.

Marlene Gebauer (29:02)
Yes.

Paula (29:03)
Exactly.

Marlene Gebauer (29:05)
And they give it to you.

Paula (29:09)
I just think…

Marlene Gebauer (29:11)
Yeah, that concept. I just think about how that has all kinds of implications for access to justice. How much justice are you getting if you have one level and another level of commitment from the judicial system?

Paula (29:25)
Yes, and on the other hand, you see there are lots of refugees in terrible conditions coming from Africa to Italy. They are rescued from the water, then they end up in these camps, and it’s absolutely terrible. There you have human interpreters. Those human interpreters are being paid $14 per hour to interpret many different languages.

Because they are not being paid enough almost to survive, and because they have to work so fast, when they do that job for those people, they do not have the time to care properly. So access to justice, yes, there is this whole movement, hashtag no translation, no justice. It is true that sometimes humans also, if you don’t have the financial means to let them work properly, they will not serve citizens.

Whereas now, it is amazing what you can do for interpreting with machine translation and language models. Those people are being served better today, even if it is not perfect, than by understaffed teams of underpaid human interpreters. So I do have hope as well for access to justice. I think technology has its good sides too.

Marlene Gebauer (30:52)
We are too. So, Paula, looking ahead, you’re based in Switzerland. You have a strong foundation there and it sounds like in the EU as well. Maybe this has to do with some of the new changes. How are you thinking about scaling internationally, maybe bringing on new languages? What are the next major features or product areas you’re excited about? Is it beyond translation? What’s going to happen?

Paula (31:28)
I think for translation, the obvious next step for us that we’re working on is becoming the brick for every single legal tech tool out there, because many tools offer some sort of translation feature now. Everyone does. It’s easy to integrate something, but everyone says it’s half-crappy, which is normal.

What is the willingness to change the interface? To be very honest, lawyers are already struggling with the fact that they have to pilot so many different tools and jump from one to the other. I think everyone is dreaming of one interface that integrates the best in class for each feature, and we totally recognize that.

We think that depending on what you need, sometimes you need just good enough and extremely fast. Fine. But then when suddenly you’re looking at those one million SMS messages or text messages you are analyzing, there is one that is just a bit weird and doesn’t make sense. You want to be able to click and deep dive into the different meanings and be able to do that seamlessly.

I think this is the dream of users, not only for translation, but that is the brick that we are. That is what we can offer and what we’re working on.

Greg Lambert (32:53)
Well, Paula, before we get to our crystal ball question, I want to ask kind of two things that run in the same vein. What resources do you use to help keep up with all the changes happening in the market? And I’m also curious whether you’ve found any cool tools lately that you’ve been using and would want to share with us.

Paula (33:27)
Now this question you’re asking a bit too early because I was just discussing with someone the fact of hiring someone to do a certain job, and he answered, “No way, my agents do that.” And this person promised me an interview where I can see how he really believes he can replace someone I wanted to hire with an army of agents.

I mean, I believe he knows what he’s talking about, yes. So for me, my magic tool is probably relaxation podcasts that help me sleep at night. It’s not really an agent, I think. I’m not sure it’s a magic tool, but I think if you want to keep up with everything that’s happening, and everything is just so interesting, you can stay up all night reading through so many interesting news items everywhere. So sometimes switching to some sort of meditation or relaxation podcast is my boring tip.

Greg Lambert (34:25)
There you go. I like that.

Marlene Gebauer (34:27)
I’m sure a lot of us are like, yeah.

Greg Lambert (34:28)
Don’t forget to breathe.

Yeah.

Paula (34:42)
Yes.

Greg Lambert (34:51)
That’s great advice. I like that a lot.

Marlene Gebauer (34:55)
Turning it off allows you to focus more.

Greg Lambert (34:57)
Turning it off.

Paula (35:00)
Yes.

Marlene Gebauer (35:01)
Okay, so Paula, we have our crystal ball question next. If we fast forward a few years, what do you think is going to surprise us most about the legal tech ecosystem and how lawyers are going to work within it?

Paula (35:16)
I think there is going to be consolidation, of course, because the company that allows you to do what we were mentioning before, to get the best-in-class feature for everything in one place, that tool will be so popular. It just needs one interface for everything.

But that is not a surprise. You asked for something that would surprise us. I really see the legal industry going through the same phases as, I don’t know if in the US you read Émile Zola, probably not. Émile Zola wrote a lot of very depressing stories, but there is one about the first grand magasin in Paris.

You have all those little shops selling hats, custom-made dresses, and everything. Then you see all these women running to this grand magasin to discover the latest fashion and everything. The small shops think this is just a whim, it’s not going to last. They think, we’ve been serving the family for 250 years, we are the only ones understanding our clients, et cetera.

So I think it is sad somehow that some old craftspeople become less numerous, because you still have wonderful hat makers, just fewer of them. That is something sad. On the other hand, we all love going to grand magasins when we’re in Paris. So I think that is the positive aspect. It is like IKEA. We must admit that we love going to IKEA.

Marlene Gebauer (37:05)
You…

Paula (37:09)
The meatballs.

Marlene Gebauer (37:13)
The meatballs are great, yeah. And I’m thinking again back to what we were saying earlier, couture may still exist for special things, and that may make it even more valuable if everything else is ready-to-wear, if you will.

Paula (37:37)
Yeah, no, and that is really what happened to my first service company. Now it is the luxury end of the industry, and it has a lot of success because of that. So I think, yeah, if you ask Loro Piana, they are also quite happy about the turn of events, and their clients are too.

So yes, I am looking forward to discovering the Loro Piana of both the law firms and the legal tech world.

Marlene Gebauer (38:09)
Well, we’re going to end it on that fashionable note. Paula Reichenberg, thank you for taking the time to speak with us.

Greg Lambert (38:12)
Yeah.

Paula (38:17)
Thanks to you. Have a great day.

Marlene Gebauer (38:19)
And of course, thanks to all of you for taking the time to listen to The Geek in Review podcast. If you enjoy the show, please share it with a colleague. We’d love to hear from you, so reach out to us on LinkedIn and Bluesky and Substack, all the places. Reach out to us.

Greg Lambert (38:34)
Substack and all the places, yeah.

Paula (38:38)
And on European platforms too.

Greg Lambert (38:41)
The Substack is the couture of our trifecta there. Paula, is there any particular place that you would want to point listeners to learn more about you and what you’re doing?

Marlene Gebauer (38:44)
That is.

Paula (38:55)
Well, LinkedIn, I think. Please connect on LinkedIn if you’re interested in multilingual challenges or legal tech trends in general, rather than fashion. I talked a lot about fashion, but that’s not my…

Marlene Gebauer (39:13)
Are you going to any events this year that people should know about?

Paula (39:19)
This year I’ll be a lot in Europe. Being now closely associated with and having taken up the position of VP of Legal Innovation at Noxtua, I am going to have the honor of attending most European events. So that is why I missed Legalweek this year for the first time.

Greg Lambert (39:43)
Hmm.

Paula (39:45)
But I hope as well to meet you again at the TLTF Summit.

Greg Lambert (39:51)
Yeah, that’d be great. Well, thank you, Paula. And as always, the music you hear is from Jerry David DeCicca. So thanks, everyone.

Marlene Gebauer (40:00)
Thank you, bye.

Den explains that the promise of MCP is not tied to one model or one vendor. Instead, it creates a standardized way for AI tools to securely interact with many different systems without forcing organizations to build one-off integrations every time they want to connect a new source. The conversation gets especially relevant for legal listeners when Greg and Marlene press on issues like permissions, ethical walls, least-privilege access, and auditability. The answer from Anthropic is reassuring. MCP is built to work with familiar enterprise security concepts such as OAuth and role-based access, meaning firms do not have to throw out their security model in order to explore new AI workflows.

Matt brings the legal and operational lens, translating MCP into practical use cases for lawyers, legal ops teams, and security leaders. He describes how AI becomes far more useful once it has access to the systems lawyers already rely on every day, while still operating within carefully defined administrative controls. The discussion highlights a key shift in how firms should think about AI. This is no longer about asking a chatbot a clever question and getting a polished paragraph back. With MCP, firms are moving toward systems where AI can retrieve, correlate, summarize, draft, and support actions across multiple platforms, all while staying inside the guardrails set by the organization.

The episode also explores how MCP fits into the rise of agentic workflows, apps, plugins, and skills. Rather than treating AI as a static assistant, Anthropic describes a future where these tools become active participants in legal work, pulling together information from multiple sources, helping assemble case timelines, drafting notes into a shared document, and supporting lawyers in a far more integrated workspace. The conversation around skills is especially useful for firms thinking about standard operating procedures, preferred drafting styles, escalation rules, and repeatable work product. Skills and MCP do different jobs, but together they start to look like the operating system for structured legal workflows.

By the end of the conversation, one message comes through clearly. The legal profession is still early in this shift, but the pace is picking up fast. Both Matt and Den encourage listeners to stop treating these tools like abstract future concepts and start experimenting with them now. At the same time, they offer an important note of caution. As much as these systems promise speed and efficiency, lawyers still need to protect the craft of lawyering, their judgment, and the human choices that matter most. For firms trying to make sense of where AI is headed next, this episode offers a grounded and practical look at the infrastructure layer that could shape what comes next.

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)
Hi, everybody. Greg Lambert from The Geek in Review, and I am here with our friend Nikki Shaver from Legal Technology Hub. Nikki, you have a new event series coming out called the Demo Dozen. Tell us more about that.

Nikki (00:15)
That’s right. Hi, Greg. Hi, everyone. Actually, we held our first one of these on February 19, hosted by Stephanie Wilkins. You’re right, it’s called our Demo Dozen event. At these new LTH events, which are free to attend, by the way, you can log in virtually to see 12 curated providers offer demos of their products.

It’s an easy way to get up to date on what’s new in the market, whether that is brand-new products or existing ones with new features. We’ll be offering these throughout the year, and our next one is coming up on May 19. So if you’re a vendor listening and would like to participate in upcoming Demo Dozen events, let us know at LegalTechnologyHub.com, or reach out to me or Chris Ford on LinkedIn. If you’re a buyer or other participant in the market and you’d like to learn more or attend one of these events, make sure you follow our page on LinkedIn. It’s at LegalTechHub. Or if you follow along at LegalTechnologyHub.com, we have an Events dropdown on our page, and if you follow that, you’ll see registration links for all of our upcoming events, including Demo Dozen. So keep an eye out for that. It’s a great way to know what’s what in the market.

Greg Lambert (01:30)
And having these compact demonstrations really helps people like me evaluate things quickly. So thanks for doing this.

Nikki (01:40)
Thanks, Greg.

Marlene Gebauer (01:49)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (01:56)
And I’m Greg Lambert. Marlene, for years we’ve talked about the integration tax, which is that massive headache that law firms face trying to get all of our different vertical software systems to actually talk to each other. It’s been a pain for years for us. We’ve all been there.

Marlene Gebauer (02:14)
Yeah, I’ve been the victim of that tax.

Greg Lambert (02:17)
Yes. And with the explosion of AI, that problem got worse. But maybe, in the news, we have finally reached that USB-C moment for the legal tech stack. We’re going to talk about it today. So we’re joined by two leaders from Anthropic who are at the center of this new open standard called Model Context Protocol, or MCP.

Greg Lambert (02:46)
And our good friend and multiple-time podcast guest Pablo Arredondo, who recently stepped away from his role there at Thomson Reuters, connected us with a couple of folks from Anthropic to discuss this topic. We’re really excited. We have with us Matt Samuels, who’s a Senior Product Counsel at Anthropic. Matt focuses on the intersection of agentic innovation and enterprise legal needs, helping bridge the gap…

Marlene Gebauer (03:03)
Thank you, Pablo.

Greg Lambert (03:16)
…between complex technical infrastructure and the high security, safety, and confidentiality requirements of the legal profession. Sounds like my job.

Matt Samuels (03:25)
We try, we try.

Marlene Gebauer (03:27)
And we’re also joined by Den Delimarsky, a member of the technical staff at Anthropic and one of the core maintainers of the MCP project, so he knows all the secret sauce. Den is the architect helping define how AI models can perform real-world actions through standardized, composable tools. Matt and Den, welcome to The Geek in Review. So happy to have you.

Greg Lambert (03:50)
Welcome, guys.

Matt Samuels (03:51)
Glad to be here.

Den Delimarsky (03:52)
Thank you for having us.

Greg Lambert (03:54)
All right. So, Den, we’ve heard, like Marlene described in the intro, that MCP is described as the USB-C for AI. As someone who is still using an iPhone 14 with the Lightning plug-in, what does that actually mean for a law firm that has data scattered across iManage and Slack and LexisNexis and all these other verticals? What does that mean for us?

Marlene Gebauer (04:11)
Guilty.

Den Delimarsky (04:28)
Yeah, for sure. By the way, don’t be ashamed of the iPhone. I recently upgraded from an iPhone 10 to a 17, so it took me a while. There was a forcing function for me to upgrade, but now it’s all USB-C.

But anyway, one of the big challenges folks have when they use AI is, how do you make it useful with real data that usually lives inside a wide variety of silos, right? A lot of the models on the market are trained on a specific corpus of data, and that data is usually publicly available data. It’s on the internet. It’s not specific to your organization. It’s not specific to your practice. That makes a ton of sense because a lot of this data is confidential, private, and only for specific workloads.

But people still want to leverage the power of AI to make sense of it, right? If you have specific spreadsheets, or any specific documents that help you outline future cases, how do you get models to universally access that data? And like you said, the big challenge here is that there is a variety of these sources, structured in all sorts of ways. Slack works in its own way. LexisNexis structures data in its own way. So if you would typically tell people, well, you have to write bespoke solutions, different applications or APIs, that’s a lot of work. It’s a lot of heavy lifting.

So this is where MCP, or Model Context Protocol, comes in. It essentially creates a universal set of rails that says, if you want to use data with AI models, here’s how to do it. It’s a very opinionated protocol. It means if you want to do things like return results, here’s how you do it. If you want to do things like securely access the data and handle authentication and authorization, here’s how you do it. That opinionated piece might seem a little weird for some people because they’re like, well, my data has all sorts of different conventions. But the forcing function of saying you have to follow this universal, USB-C-like adapter protocol means you can now connect it to any client and it works just as well.

So I can plug an MCP server into Claude, and it works just as well if I plug it into another MCP client that also supports the protocol, because the protocol itself is open source. It’s a massive gateway for enabling data access to any model in a way that doesn’t force people to constantly rewrite custom bespoke solutions just to access a specific data source.

Greg Lambert (07:15)
And the models themselves aren’t created for iManage, which has all of our document management documents in them. It would be iManage that creates what allows you to access that. Am I understanding that right? So technically the security could be set up on their end to respect, to know who I am and respect what I can and can’t get into.

Den Delimarsky (07:43)
Right, right, absolutely. And I think there’s obviously a good number of official MCP servers that anybody can create. In your case, you might have iManage creating an MCP server, Slack has its own MCP server. You would also be able to compose them. That’s another underrated superpower of MCP, composability. You’re not just seeing one data source, but you can go into, for example, Claude, and if you connect several MCP servers, say Slack and iManage, you can ask the LLM, you know what, I’m reviewing a case for X, Y, and Z scenario. Can you get me all the conversations that happened and then correlate them with data in iManage?

The LLM would then query the data from both sources, and you, as somebody who cares about the case and not the underlying pipeline, don’t care whether the data behind it is JSON or XML or whatever else. You essentially have that universal pipe that gets the data to your LLM, to Claude, and then it can process the data in a way that Claude expects. So you’re reducing the burden on yourself.

Ultimately, yes, there are official MCP servers, but we also see people build MCP servers for their own scenarios. They have a bunch of tooling in house that they’ve built over years and years of establishing their practice, and they come in and say, you know what, I want to make sense of this data with AI. How do I do this? Because the SDK is open source, because it’s an open protocol, anybody can write an MCP server. They can use Claude Code to write their custom MCP server, and there you go. You can use an official one, but you can also build your own.

Marlene Gebauer (09:25)
So Greg gave an example of iManage. Could you also do something similar if you have a data lake at your organization?

Den Delimarsky (09:34)
Yeah, absolutely. The MCP protocol itself doesn’t restrict what you can do. Think of it as a universal abstraction. On one end, you have USB-C. On the other end of that cable can be anything. It can be USB, DisplayPort, Thunderbolt, anything. If we follow the same analogy of adapters, you have one opinionated end, but behind it you can have any data store, any application, any API. You can talk to some remote server. You can talk to an in-house server. You can talk to an application that runs on your machine. You’re not forced into a specific mode of operation here. The expectation is simply that the way you connect it to other MCP clients is universal.

Marlene Gebauer (10:21)
So Matt, you and your colleagues spend a lot of time talking to law firm chief information security officers. What’s the translation that you find yourself doing most often when explaining what MCP servers are and why they’re valuable?

Matt Samuels (10:37)
Yeah, we use CISO as the short way to put it, but I don’t know if that’s universally well known. Chief Information Security Officers. Also, in my legal role, I’m often talking to lawyers or folks working to get a deal done at the enterprise level. I think Den did a much better job describing a version of the conversation I’m having.

At the 10,000-foot level, what we’re trying to do is give Claude access to your enterprise data so you can make it more useful. In a law firm setting, you mentioned a lot of the tools already. iManage, SharePoint, maybe Gmail or Outlook for email. I like to give a couple of handy examples of things you might not have imagined you could do with AI because you weren’t familiar with these tooling options.

You can have it draft an email. Then, if you’re the CISO, you might not want it to send the email without a human reviewing it.

Greg Lambert (11:33)
I can tell you they do not want you to send the email.

Matt Samuels (11:37)
Exactly, exactly. At least speaking of some of the servers we’ve seen in the email space, they have what are called tools. As the enterprise, you want to understand what tools are available through the server. They might have draft email and send email as two different tools. So it comes down to the admin setting rules and using standard concepts like RBAC and least privilege issues. Which user groups and roles can have access to what tools? Then training your employee base on how to use these tools in a responsible way.

MCP itself is a pretty abstract protocol, but on a client, not like a law firm client, but an AI client like Claude, we try to package these up into things called connectors or plugins or things we can talk about. We try to make it easy for the enterprise to set thoughtful rules around this in a way that makes sense for them.

Marlene Gebauer (12:34)
I was going to ask, how hard is it to do?

Matt Samuels (12:37)
It’s not super hard. Especially on the admin side, we have pages where you can click on each individual item. First of all, you can decide in the first instance, do I want to connect to the official Slack MCP, or do I want to connect to Greg Lambert’s MCP server, which calls Slack? I bet 99 out of 100 CISOs are going to say, no, use the official one.

And then even within that, you can specify which tools can be used. At least within Anthropic, you can set rules within the tool. So it can be always allow, never allow, sometimes allow. There will probably be more variations on this over time, but we do a lot of work thinking through what the admin experience is like. We think of ourselves as an enterprise-first company. So a lot of the time and thought goes into what permissions a CISO or the admin of a data science team would want in place. It’s point-and-click type stuff.

Greg Lambert (13:40)
I can hear my security ops people saying, but is it auditable? Can I see who’s doing what? Is that something you think about with these tools?

Matt Samuels (13:53)
We definitely do. At a high level, if the admin said, hey, these folks can’t use this tool, they shouldn’t be. And I would hope there’s a way to look into debugging that sort of issue. But then we do have things like a compliance API. Claude itself, as one of the clients, and I’m sure this is true for all of them, when they invoke these servers as part of a prompt, like the one Den asked, search through Slack to find me all relevant information and then populate iManage, the model should tell you in the conversation itself when it called the tool and what it did with it. Then afterward, you could look at the chat and see what happened, and you can use things like the compliance API to do it at scale.

Greg Lambert (14:47)
In our prep call, and this kind of expands on the question I asked earlier, we talked about whether the AI respects my document permissions. Den, do you mind walking us through how MCP uses OAuth 2.1 to ensure that if I’m not allowed in a folder in the DMS, the AI isn’t allowed in there either? How do I go about explaining that?

Den Delimarsky (15:21)
Yeah, of course. One of the things we’ve done from the very beginning with the protocol is make sure it respects a lot of the security boundaries that are already well established in the ecosystem. If you look at MCP through a purely pragmatic security lens, a lot of the concepts are familiar to basically anybody who’s been building applications dealing with user authentication and authorization for the past two decades.

It snaps to OAuth, which is a universal standard, again, like MCP. It’s not universal in an absolute sense, but the vast majority of services use OAuth in some capacity. You can use OAuth to gate access to your MCP server the same way you would gate access to any website or API or any other resource.

What that enables is when you connect an MCP server that accesses private data, whether that’s your case management system or Slack, you’re not going to add it and instantly have access to every conversation, document, and piece of information that exists there. The server itself identifies that, hold on, before you called me, like what Matt described in the tool-calling scenario, before you call a tool the server can say, hold on a second, you did not provide me a credential by which I know you can access this document. Can you please log in?

What happens then is a flow kicks off, usually through your browser or sometimes in-app for some MCP clients, that’s going to ask for your credentials. Those credentials are standard to the service you’re accessing. So if you’re authenticating with Slack, you’re probably going to get a Slack authentication prompt. This is where you can log in with your Google credentials, your email, go through the standard flow of two-factor auth, whether you use a passkey or a security key like a YubiKey.

A lot of this lives outside the boundaries of MCP itself. MCP does not dictate how you do 2FA. It doesn’t talk about exactly what the format of the credential or token is. It doesn’t care, as long as you’re using OAuth.

Once you go through the OAuth flow, your client, in this example Claude, gets a token. A token is basically a string that represents the user. It says Greg and Marlene are part of this group, they have access to X, Y, and Z resources. Then the client takes that token, and when it invokes a tool, for example get Slack conversation, it sends that token as well and says, by the way, when you ask for this conversation, this is Marlene asking, here’s her token. Then you get conversations back that only Marlene can access.

You as Greg are going to log in with your credentials, and you’re going to have access to the resources you have access to. Also, MCP servers themselves, as Matt called out, can have many different tools. I might have get Slack conversation, I might have send DM, I might have read channel. Some of them are accessible to everybody. If you can log in, you’re a valid user and can use the tool. In some cases, some tools require additional scopes and say, no, before you can access this tool, you need this additional permission.

So for example, if we’re talking about legal cases where Greg said, this is only for me, nobody else on my team can access it, and Marlene logs in with her credentials, even though she authenticated and went through the authorization flow, that doesn’t automatically mean she gets access to the same tool and the same data from that tool that Greg had. A lot of these conventions are traditional OAuth. Nothing here is MCP-specific. This is the implementation of an open protocol that gives us OAuth, and MCP leans on that as the foundational piece for many of these security requirements for MCP servers.

Marlene Gebauer (19:35)
Yeah, this is interesting. It syncs up nicely with need-to-know requirements in law firms. I was also thinking about ethical walls and how you protect from the wrong people getting access to the wrong materials.

You’ve talked about MCP registries. In a law firm environment, how does a centralized IT group use a registry to prevent a lawyer from setting up their own server and potentially leaking privileged data or introducing security risks? Or disgruntled employees or anything like that. That never happens. What are you talking about?

Greg Lambert (20:13)
We would never have lawyers do that. Never.

Matt Samuels (20:22)
Yeah, it’s a great question. I think it comes back to what we were chatting about in my previous answer, which is making sure the AI clients, Claude, ChatGPT, Gemini, you name it, spend a lot of time thinking about what the admin experience is and what the default settings are.

If you’re a thoughtful enterprise, and I think this is the norm at most places, the admin should say, hey, I need to approve any net-new MCP server the organization wants to use. They effectively have a dashboard where they can toggle on and off whether the server can be used at all. Over time, that’ll probably get even better. There will be even more granular permissions. You might think of groups or individuals within groups and what tools they use.

The way I think of it, we were talking about least privilege, was kind of the concept you were talking about, Marlene. A lot of this comes down to basic enterprise security and IT practices. They apply pretty neatly, at least on a client like Claude, and I assume most of the other major clients. They’re thoughtful about what the baseline is for what you want your organization to do at the admin level. Then each individual person, maybe I’m not a Slack user, but it’s approved within Anthropic, so I’m not going to enable that tool. Or there may be more risk-forward enterprises that say, actually, I’m okay with a certain group of users playing around with what we call custom MCP servers in Claude.

At the end of the day, it comes back to a level of risk tolerance and then communicating to your employee base, hey, we’ve now added the iManage server. It’s default enabled for maybe the entire law firm, or only a couple of folks in the law firm while it’s in a testing period. I think that’s the way we think about it here.

Marlene Gebauer (22:15)
Okay. So I have another one for you. We’ve seen reports of Rio Tinto’s legal team connecting an LLM to 4.5 million iManage documents via MCP in a few days. Matt, what does that tell you about the speed of adoption we’re about to see in Big Law? I’m interested in hearing this answer. Because we’re usually so slow.

Matt Samuels (22:43)
Yeah. I can’t speak for the law firms. We have a number of law firm clients or customers. I’m sure the same is true for many other AI clients. I think the speed here is faster than I would have expected before joining Anthropic. Part of the reason is the utility is so obvious.

I think it’s become more obvious in the past couple of weeks and months with tools, not only from Anthropic but from other companies. We might chat about this a little later, but Claude Code is a product we have that’s been pretty MCP and plugin-forward, and you can do a lot of things there. Historically, it’s been a little scary if you’re not a technical user to do your work in the terminal. We released a product more for other types of knowledge workers, like lawyers, called Cowork, and I’m a heavy user of it myself. The amount better I am at my job, and the amount I am able to triage issues and give my internal clients good legal service using that tool, is amazing.

And it’s largely because of MCP. We have a lot of Slack and Gmail and other conversations. Being able to pull them in and ask in natural language, what’s going on here? Or for those of us who are millennials, ELI5, explain like I’m five, what’s happening with this complicated technology? It can do that. The models are smart enough to know where to look within Slack to find that information and then digest it for you.

There are a lot of other examples of things I and others on our legal team and other teams have done. But once you’ve played around with these tools, you see the immense value. Law firms have a pretty standard set of tools, places where their enterprise data lives. A lot of those have MCP servers. So I think it’s about getting yourself set up and playing around with it. You’ll see pretty amazing gains in the knowledge work you do.

Greg Lambert (24:47)
That’s my number one advice. Get in there and play around with it. See what it can do.

Matt Samuels (24:51)
Type of person.

Greg Lambert (24:52)
It worked pretty well. So, Den, I wanted to talk about, we mentioned it briefly, with things like Cowork and Code, but there’s a real big push now toward agentic workflows. Again, I think we’ll probably talk about this for the rest of the conversation. But you mentioned that MCP isn’t only about searching the data or reading the data, it can act on the data. I break it down like this. We’re at a point right now where it’s not just reading and giving you back an answer, it’s reading and writing and taking next steps. So do you mind describing a workflow where an agent doesn’t just find a contract within the DMS or within a product like Ironclad, but actually helps redline it or draft it and does some follow-up email in the lawyer’s voice with some options we have?

Den Delimarsky (26:03)
A lot of the capabilities you just described go beyond the protocol itself. The protocol is the conduit for a lot of the requirements, the ability to access documents, the ability to read them. Ultimately, the agent harness is the one responsible for doing things with MCPs or MCP servers.

So you would have, for example, an agent that has access to a myriad of MCP servers. Servers can expose a number of primitives. Things like tools, as Matt called out, and tools are basically functions. So we can say my server can read a Slack conversation, send a DM, read a Gmail thread. It can have resources, which can be documents or database records or anything like that. It also has primitives such as tasks, which is basically asynchronous processing. I call them atoms myself because they’re components to this.

Essentially, what that enables is saying, hey, I need you to go do something, and I’ll do something else while you do that. I’ll kick off a task on some remote MCP server, and you’ll get back to me when you’re done. When you start assembling these together, you start seeing the true power of these agents and MCP servers because they are now able to pull documents, pull different resources, database records, combine them, and realize, for this one, we need to go do some digging through another database. It might be a little slower, so we’re going to kick off that task and get back to it whenever it’s done, but I’m going to continue building the set of requirements for my next case or anything of that nature.

So MCP is becoming the plumbing for a lot of these agentic harnesses where, by themselves, they can trigger actions, think through requirements that need to be done, break them down into a set of tasks, and then execute those tasks. But to successfully execute those, you need those primitives, and this is what MCP exposes. You have those primitives that now the agent can act on and act with.

That did not exist before. If you look back four years, when the first models were coming out, you had the basic experience of asking the model something fun and seeing it answer. Okay, that’s great. But now they can actually act. If you think about it, MCP enables your agent to have actual robotic arms to go and reach for the right data, the right applications, the right content it needs to then act and put together whatever you’re looking for, whatever task you’re trying to accomplish.

Greg Lambert (28:57)
Yeah, yeah, I’ve written a story about this and we call it giving the AI hands to work with. It can actually do it. It’s a really exciting time. I know it’s been probably since November that this has taken on and caught fire. But watching what Code and Cowork and Codex and other things out there are doing, it’s a different AI than we had a year ago. It’s pretty exciting.

Den Delimarsky (29:37)
It absolutely is.

Matt Samuels (29:37)
One analogy for it, because you have a lawyer and CISO and kind of legal-adjacent audience, I think a helpful way to think about this and explore how it might be useful is to imagine what tasks you actually do as a law firm partner.

Your associate, I remember taking the bar exam and being kind of surprised, like wow, you are expecting me to answer this legal question based on my latent legal knowledge. That’s what a pre-MCP LLM can do, right? All it has access to is what it was trained on. A much better associate can do legal research. They can send an email. They can upload something to a database. So think of the tasks you normally do as a lawyer and how you delegate things to associates or junior partners. If you start thinking of the models this way, and Den mentioned the harness, that’s kind of the rules of the road for your law firm’s rules. I think that’s an interesting way to think about it, and you can get creative once you break things down into component tasks and the actions that you or the associate would take.

Marlene Gebauer (30:42)
All right. So Anthropic recently launched the MCP apps. I want to pull on this a little bit. That’s moving from the text-based chat to these interactive interfaces, sort of what you were talking about now. I would say we run the gamut in terms of people’s abilities and experiences in the legal community. There are some people who are going gangbusters. They’re trying everything. They get it. And there are other people who are still dipping their toe into the chat and trying to figure out how that’s going to work.

Plus, we have these discussions about how this work is going to impact the workflow in terms of staffing, both for juniors and for adjacent professionals. So how do you see lawyers and other adjacent professionals using this widget inside a chat to manage a deal or review deposition transcripts or timelines or something like that?

Matt Samuels (31:55)
Yeah, I’ll take this and then Den can field more on the app side, because I think I’ll go back to my lawyer training. I think one example for deposition transcripts and one for document review and privilege calls makes sense.

As a lawyer, you’re used to the surface you most commonly interact with. So when you’re thinking about a deposition transcript, it has this crazy Arial font and it’s on this page and some things are in bold, some things aren’t, and you have the reporter note. What’s going to look different, what an MCP app will allow you to do, is pull up the actual visualization of what that looks like. So it feels like you’re much more natively looking through it, and you can type into Claude, hey, show me that point again that opposing counsel made. I forget what page it was on, maybe page 40. You can ask a question that ill-formed, and if the deposition transcript company made an MCP app that’s well done, it would literally find the relevant text and populate the page.

Then I think what’s cool about MCP apps is you can theoretically highlight it or do other transformations, and it will ping back to the actual source of record and update it. So it’s a way to help you visualize things in the way you’ve come to appreciate because you’ve been working with deposition transcripts for 20 years.

To use another example, maybe a more junior associate is in some sort of, I’ll use Relativity because that’s what I was familiar with. If they made an MCP app, you could start doing your doc review in Claude. It’s still all happening through Relativity, but you get to see the documents populate. You can say, wasn’t there another document like that? And Claude will know how to find that document, surface it to you, and then you can do the privilege review on that. There are endless things you can do. But I think everyone is more comfortable working in the format these great software companies have developed, so it allows you to have that native experience within the LLM client.

Marlene Gebauer (33:59)
As little friction as possible.

Greg Lambert (34:01)
Yeah. Well, Den looks like he wants to fill in some gaps here, I can tell.

Matt Samuels (34:02)
Anyway.

Den Delimarsky (34:02)
Yeah, yeah. I think Matt did a wonderful job explaining the core benefit of MCP. But I think the next tier of evolution in the workflow goes back to what we talked about earlier, because the power of MCP is composability.

It’s one thing when you have the app itself. So context-switch reduction is a huge benefit right away. I’m working in Claude. I don’t need to copy and paste things from my document on one screen and Claude on the other and transfer them manually. It’s Claude, can you go do this? I can highlight something inside the MCP app, like Matt called out, and then say, okay, can you pull in all the conversations we’ve had in the past year around this topic or this particular case? It’s going to look through your Slack and Gmail or wherever else and then pull that context in.

So now you’re in this shared workspace between you and your agent, in this case Claude, that has access. You’re visually looking at a specific case, at a specific set of defendants maybe, and you want to ask, okay, what did we email them before? You can do that through multiple MCP servers that are connected. You’re not constrained to one task.

Think about how you would do this work before MCP existed. You’d have Gmail in one tab, something else in another tab, Slack in another. Then it’s okay, let me search through Slack. Let me find these conversations. Let me review the manual and see if this is right. Let me go through Gmail. That’s a lot of toil. It’s a lot of manual work that MCP connected to an LLM removes, because now you can ask the model, go. You have access to Slack, you have access to email. I’m highlighting this in my MCP app. Go find me the relevant information. Then you’re operating within the context of the same space.

Marlene Gebauer (35:55)
One of the things I particularly like about this is we sometimes have discussions about which AI tool to use for certain things. Is it a productivity tool or is it a legal tool? We spend way too much time talking about this and what should be used for what. But in your scenario, they would both be connected, and you would be asking a question and they would both be responding based on what they should be used for. Am I understanding that correctly?

Den Delimarsky (36:31)
Yeah, for sure. And because you have access to all this information, you can use this to start drafting things too. You can also have another MCP server that starts a new Google Doc and says, can you put all the notes we’re working on there, summarize them as we go, so I have that one point of reference to go back to? You’re not manually jotting down every single note and every single thing you’ve done. You can have Claude do this for you. Because it has access to this wealth of different tools and services, it can plug in through all of them, as long as they have an MCP server.

Matt Samuels (37:04)
Yeah, it did.

Greg Lambert (37:05)
I’m curious, because we talk a lot about working directly in Claude. I know tools like Harvey are also looking to maybe go the other way, where you can go there and use Claude within their environment as well. Do you see MCP as something where, with the context switching between programs, technically I could be in Relativity and have Claude working on something while still working in Relativity? Does it work both ways, or does it only work from Claude out to the other tools?

Matt Samuels (37:47)
Yeah, it should work bi-directionally. I think there are probably questions about latency and when it gets updated, but that’s true of every tool. I think about Google Docs back in the day. If two people are editing the same section, you get that little conflict message. I think it can update the system-of-record document, but you have to be thoughtful about timing.

With Harvey itself, as they use the APIs of a lot of these LLMs, they themselves are an AI client, so they might have MCP support too. And you can configure within Harvey the admin controls we’ve talked about for Claude.

Greg Lambert (38:28)
All right, so I want to jump into something we’ve all heard about over the past few weeks, and that’s skills. The idea that you can package up these skills into essentially a markdown file that maybe packages up the firm’s standard operating procedure or type of voice into these portable files that can be read by and used by AI.

So what would be your advice to a firm on using skills to help with things ranging from junior associates learning the firm’s style to other use cases where you see skills being packaged up to do useful work?

Matt Samuels (39:20)
Yeah, I think skills are really complementary to MCP servers, and they work in many of the same ways. Claude will understand when you’ve asked it, for lack of a better term, trigger words, and then say, okay, you want me to use this skill. Now I know I’m supposed to get that in the sequence of events. I’ll have the knowledge embedded in the skill.

So examples we’ve seen are, hey, this is how we do NDAs. If we deviate, you can package the rules around when you deviate from an NDA, when something needs to get escalated to a particular person. Tonality and the firm voice, or maybe there’s a set of terms you don’t like to use in briefs. Anything you think about as a procedure is helpful to put into a skill. I’m sure Den might have more creative ways to use them, but those are some of the examples we’ve seen.

Greg Lambert (40:12)
Yeah, Den, hit us. What can we be using skills for?

Den Delimarsky (40:14)
I have very little to add to what Matt described. He nails it. No, I think that’s accurate. It’s basically a way to codify processes or things you want done in a consistent way.

People often confuse skills and MCPs and say one replaces the other. Really, as Matt said, they complement each other. Skills can refer to MCP servers. So for example, you can define something like, hey, if I’m ever working on a case that requires data from system X, use this MCP tool to go get that data. And when you return the data, make sure it’s formatted with this table heading in this font, and make sure to also put it into Google Docs so it’s taken as a note with this document format.

So you’re essentially creating, as I refer to them, SOPs, standard operating procedures, saying if I want to do X, here’s how to do it. Within a skill, you can refer to MCP servers, write custom scripts, call applications that run on your machine.

For example, especially where you’re dealing with legacy applications, something that runs on your computer and hasn’t been updated in 20 years but is so ingrained in the ecosystem that it’s still there, you might not have an MCP server for it because maybe the company isn’t going to build one. So how do you use that tool? You can encode it into the skill and say, by the way, use this application, launch it, and inside of this application you can do X, Y, and Z. This application has a command-line interface, and here’s how you can, for example, insert a document, share it with somebody else, and so on.

You encode a lot of these things, and ultimately a skill can use and refer to many different MCP servers. It also reduces the burden on the model, because when you invoke a tool based on your prompt and say, look up the conversations on this topic, the model then has to think, okay, do I need to look in Slack? Gmail? Some other task board? With a skill, you can be explicit and say, if I ask you to do this, always use this tool. So the model knows, okay, you asked me to review an NDA document. I know exactly the tool I need for this. I’m going to use that. So it speeds up your process and helps you save a little on tokens too.

Greg Lambert (42:41)
Yeah, sounds interesting. Before we wrap this up, I want to give you guys a chance. Is there anything law firms should be paying attention to, tools that are already out there that we may not be taking advantage of? Is there anything?

Marlene Gebauer (43:01)
What should we be preparing for?

Matt Samuels (43:04)
I’ll do a plug for Cowork. We talked about it earlier. Skills and MCP servers, we talked about those. There’s a kind of abstraction of those called plugins that we created first for Claude Code and now for Cowork. I think other companies are adopting these. It’s a way to package these things together.

So you could have a plugin, I mentioned NDAs before, you’ve got the NDA plugin. It might connect to Ironclad or DocuSign or whatever MCP servers you might have over there, but then it has the skills with your firm voice and you can also do other things. You can have little webhooks or subagents that you can define for more technical users. An example of one of those might be, check this against, check to make sure the user of this plugin has the right authority to sign or something like that.

I think there’s this cool new abstraction that’s been created. And one thing, as we were chatting earlier, the composability piece really is a huge unlock. I’m still trying to make better use of it myself. But when Den was sharing an example before, man, what if you found this golden document and it automatically added it to the timeline and added it to your proof outline? Once you start thinking about things that way, at the end of the day, what are you doing as a lawyer? If you’re a litigator trying to prove a case, how can you more efficiently get the data that used to be sitting in Gmail or Relativity into the artifact you’re going to use to win the case? That’s super cool.

Greg Lambert (44:40)
Den, anything to add to that?

Den Delimarsky (44:42)
I’ll say, as uncreative and cliché as it sounds, because we talked about this earlier, model capabilities now are not the same as they were a year ago, two years ago, three years ago. So the best way to be in the know is to use the tools. You have access to Claude, use Claude. Try it. Try it for reviewing documents, for doing deep research, for connecting to your data sources and applications through different MCP servers, and see what output you get.

I’ve seen the highest success rates from people who actually start experimenting and saying, huh, I wonder if Claude can do this. They start asking Claude and then realize, hold on a second, there’s an MCP server for this. Maybe I should do this. The fastest way to get up to speed, and then realize how big an unlock a lot of the model advances are together with MCP, is to try it.

Marlene Gebauer (45:38)
I had a question as I was thinking about this in terms of knowledge management in organizations. It seems there is a real demand for knowledge to be in real time and specific to the actual instance, as opposed to how we’ve done it in the past, where it’s much more structured and formal. You’re going out to templates and looking for best practices. This seems more fluid. I’m wondering if you guys can comment on how what we’ve talked about impacts that.

Matt Samuels (46:20)
Yeah, I think the way I think about it is each company is going to have to think through what its knowledge management stack looks like and what best practices are. Skills can be helpful for this. Maybe you have a skill that says if you’re using Claude for X, Y, and Z, make sure part of it updates the team’s channel for this case, or make sure it updates the timeline, if there’s a timeline that’s really a system of truth for the case.

But I do think it’s incumbent on each company to think creatively now, because I 100 percent agree with you. There’s so much net-new data being created, and I think the CISOs and the ops teams and the lead lawyers need to be thoughtful. What does a system of record look like for what we’re doing?

Den Delimarsky (47:09)
Yeah, I’ll add that I think there’s a paradigm shift that’s going to happen, and it’s important to account for it. Before, we thought about knowledge management through the lens of who in my firm can access this and be able to learn from it and use it. Now the question is, is this also helpful to agents? Whatever format I’m storing the data in, will an agent be able to successfully access it and act on that data to provide me a summary, insights, or recap?

I’ve seen it too many times where folks have data and it’s stored in some super proprietary binary format that only one application can access. Of course that’s going to be problematic for agents, versus storing it in formats that are more open. I’m not making any opinion about which format to store things in, but as folks think about the transition to this new world where agents are doing much more than before, how do you enable those agents to have the same access to the context and knowledge of your firm, your organization, or other partners you’re working with, and not only humans?

Marlene Gebauer (48:20)
Yeah. And it sounds like having good, organized data is still important. So that hasn’t changed.

Den Delimarsky (48:29)
No, the importance of that is only going to increase, because the agents themselves rely on some kind of taxonomy, some kind of organization. If I ask an agent, go find me a document related to case X, Y, and Z, it should know, okay, cases from this year are here, instead of going through a folder with a million documents and saying, give me an hour to sort through them. You still have to optimize for discovery and organization and having some kind of predictability.

Marlene Gebauer (49:00)
Okay. So before we get to the crystal ball question, what are some of the uses you guys are using in your personal lives that you’re using AI to help with? You’re the technologists, so what are you using in your life?

Matt Samuels (49:23)
I’ll go first, because then I’m curious for Den to come over the top and really impress.

Marlene Gebauer (49:26)
It’s not a competition. This is not a competition. All right. It’s always a competition. Go ahead.

Greg Lambert (49:30)
It’s always a competition.

Matt Samuels (49:33)
I definitely use it more as a power user at work than I do in my real life, but I’m starting to think more creatively about how to use it. If there are things in your life that are cumbersome, I think there are ways to unlock how to do them more efficiently.

And then on the knowledge side, I find myself, even when ChatGPT came out back in, I think it was 2023, thinking, oh, I can learn about any subject in the world in a way that’s more personalized to me and ask follow-up questions. Things like investment advice, or hey, someone told me about this, can you tell me about it? In the way YouTube was a big unlock for getting more knowledge about the world, I still use it for that basic purpose.

Den Delimarsky (50:16)
I think my big unlock, a lot of what Matt described is exactly right. It’s precisely what I want to use these new modalities for. Basically, reducing toil in my life.

It’s a big personal relief to use a lot of these tools almost like a second brain. Previously, we’d be in a meeting like this, in a recording, and imagine if this is a work meeting. I need to jot down notes on paper and be like, okay, Greg said this, Marlene said this, Matt was talking about this, let me follow up on this. That takes away attention, because now I have to follow it manually and figure out, hold on, did Greg say this?

Now we have tools that auto-transcribe it. Now it’s a Google Doc. Then I can go to Claude and say, hey, last week, Greg, Marlene, Matt, and I were talking. Can you summarize the action items for me? And then it’s like, yep, you talked about this topic. It’s such a huge relief that I don’t need to worry about a lot of these toil things. A lot of the capabilities are only going to get better, and I’m super excited to focus my life on things I care about and am excited about, versus these menial tasks.

Greg Lambert (51:32)
Well, good. Thanks for sharing all of that. So we are at the crystal ball question. Den, I’m going to start with you, and then we’ll go to Matt. Looking into the near future, what change or challenge do you see in your crystal ball that you think we need to be prepared to face?

Den Delimarsky (51:53)
I’ll give you a very unsatisfying answer. Having seen the last six months, I have no answer for what the crystal ball holds. I don’t know. Anything I say right now is going to be completely wrong in three months, six months, or a year. So I’d say to me, we’re living through a time of very, very fast changes. The best way to take advantage of this is to use the tools and try to figure out how to apply them to your life. That’s going to be the easiest way to ride the wave. That’s my answer. It’s very unsatisfying.

Greg Lambert (52:30)
No, it’s probably the most truthful one, though. All right, Matt, how about you? You want to take a swing at this one?

Matt Samuels (52:37)
Yeah, strong plus one to the I have no idea. But I’ll say two things that I think about in a semi-worrying way are the loss of the art and the craft of being a lawyer, people delegating too much to these tools and losing the forest for the trees, or not paying attention to the why and the strategic calls.

I’m optimistic people will use these more for the low-leverage tasks and focus on the strategy and the craft. But it’s not hard to imagine a world where you’re negotiating a contract and the other side is using Claude or ChatGPT to say, push back on all of these points and give me the strongest redline. Then the other side does that, and you are never actually compromising and thinking about what matters. So I worry a little about the profession and the loss of the human and, you know, we’re part of a trade together element of it.

Greg Lambert (53:35)
Well, Matt Samuels and Den Delimarsky from Anthropic, thank you both for coming in and having this conversation. I think our geeky audience is going to be busy building MCP servers over the weekend.

Den Delimarsky (53:52)
Yeah, and if you have questions, we’re always open to feedback. You can reach out. Again, it’s open source on GitHub, so go there and give us your feedback.

Marlene Gebauer (54:01)
That’s terrific. Thanks to both of you, and thanks to all of our listeners for taking the time to listen to The Geek in Review podcast. If you enjoy the show, please share it with a colleague. You can find us on LinkedIn and now on our Substack channel.

Greg Lambert (54:20)
And, well, Den, you talked about going to GitHub. Is there any other place, Matt, where people who want to find you should go? Is it the same place?

Matt Samuels (54:31)
Yeah, if you end up being an Anthropic customer or potential customer, you might end up chatting with us through that lens. But for contributions to the protocol itself and feedback, I very much agree with Den. Go through the, it’s now part of the Agentic AI Foundation under the Linux Foundation. And if it’s not Den who ends up responding, it’ll be someone else in the giant community that’s been built around this.

Marlene Gebauer (54:52)
And as always, the music you hear is from Jerry David DeCicca. Thank you so much, Jerry. And bye, everybody.

Greg Lambert (54:58)
Bye.

A major theme of the discussion is the widening gap between personal experimentation and institutional readiness. Niki notes that lawyers are not waiting for permission, and many are already relying on AI to support research, drafting, and routine work. At the same time, firms are struggling to provide guidance, training, and guardrails. The episode highlights the growing risk of shadow AI in legal practice, especially when lawyers and staff turn to unsanctioned tools to keep pace with client demands. For smaller firms, the answer is not elaborate bureaucracy, but practical direction, clear expectations, and a recognition that even a modest policy is better than none.

The conversation also turns to client expectations and the economic pressure AI is placing on the traditional law firm model. Greg and Marlene press Niki on whether firms are truly ready to move away from the billable hour as AI compresses the time needed to complete legal work. Niki argues that large firms face deep structural obstacles because compensation systems, staffing models, and internal economics remain tied to hourly billing. Still, she sees pressure building from in-house counsel, boutique competitors, and smaller firms that use technology to deliver comparable work at lower cost. The result is a market that may resist change, but not escape it.

Another standout part of the episode explores how AI is reshaping access to justice. Niki points to the promise of generative AI as a force multiplier for legal aid lawyers and public defenders, especially when paired with trusted tools and better funding. She rejects the idea that technology alone will solve the justice gap, but makes a strong case that AI, combined with stronger institutional support, helps lawyers serve more people with better results. At the same time, the hosts and Niki acknowledge the risks of a two-tiered system, where wealthier clients benefit from high quality tools while vulnerable users face lower quality, error-prone outputs.

By the end of the episode, the conversation expands from AI tools to a broader structural shift across firms, clients, and law schools. Niki sees the next three to five years as a period of deep change, where pricing, training, competition, and professional expectations all evolve at once. She also shares her own methods for keeping up, including RSS feeds, trusted blogs, and LinkedIn, with a few playful complaints about Substack making life more complicated. The episode leaves listeners with a clear message: the biggest issue is no longer whether AI will affect legal practice. It already is. The real question is whether the profession can adapt fast enough to manage the consequences wisely.

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:

Marlene Gebauer (00:00)
Hi, I’m Marlene Gebauer from The Geek in Review, and I have Nikki Shaver here from Legal Tech Hub. Nikki is going to tell us a little bit about what Legal Tech Hub is doing at Legalweek.

Nikki Shaver (00:13)
That’s right. Thanks, Marlene.

Hi, everyone. Legalweek is coming up March 9 through 12. We’re super excited, especially to see what it’s like for the first time at the Javits Center. And of course, we’ll be there. We hope all of you will come and see Legal Tech Hub there.

We’re going to be there in full force this year with representatives from our advisory team, Cheryl Wilson-Griffin and Sam Moore, so you can find out more about our offerings there. Our sales team, of course, headed up by Alex Koop, will also be there.

Stephanie Wilkins will be there covering everything that’s going on, so if you have a news story, please seek her out. And our leadership team will also be there. We have a booth at Stand 341 in the vendor hall, and it’s a great time to catch up on everything new in legal tech. So if you want to stop by and chat and find out what’s new and what’s happening, feel free to come by.

Also, if you want to find out more about our full suite of offerings across LTH Premium, our advisory services, and our upcoming events, it’s a great time to stop by and say hi. And if you want to arrange a meeting ahead of time, feel free to reach out to us on LinkedIn at our company page, Legal Tech Hub, or at our website, LegalTechnologyHub.com. We can’t wait to see you there.

Marlene Gebauer (01:38)
Yeah, it’s a great conference, and it’s great to hear that you’re going to have a presence there.

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 (01:58)
And I’m Greg Lambert. Marlene, we’ve spent a lot of time, really only the last few years, even though it feels like the last few decades, talking about the promise of artificial intelligence. But today we’re going to dig into the hard data of what is happening on the ground, especially in solo, small, and mid-sized firms that make up most of the legal industry itself.

Marlene Gebauer (02:25)
Yeah, and they’re the ones that really aren’t reported on as much, I think. We love a good data story here at The Geek in Review, so we are thrilled to welcome back our good friend, Niki Black. Niki, hi, Niki. For those of you who don’t know, Niki is an attorney, a veteran legal journalist, and the Principal Legal Insight Strategist at 8am.

Greg Lambert (02:38)
Yay.

Niki Black (02:39)
Hi.

Marlene Gebauer (02:51)
She’s here to break down the newly released 8am Legal Industry Report 2026, and it provides an incredible look at how legal professionals are navigating AI adoption, billing pressures, and systemic challenges to the justice system.

Greg Lambert (03:07)
So, Niki, welcome back to The Geek in Review. Good to have you back.

Niki Black (03:10)
Thanks. I’m looking forward to this.

Marlene Gebauer (03:13)
All right, so we’ll jump right in. The 2026 data paints a really vivid picture of a profession rapidly adopting AI from the bottom up. Your report notes that 69% of legal professionals are now personally using general-purpose AI tools for work, more than double from last year, I think. However, there seems to be a massive governance gap. So what?

Greg Lambert (03:37)
Governance. How do you spell that? I don’t get it.

Niki Black (03:43)
Okay.

Marlene Gebauer (03:45)
Forty-three percent of respondents say that their firm has no formal AI policy and no plans to create one, and only 9% are actively enforcing a written policy. Yikes. So with individual adoption skyrocketing while organizational governance lags, what’s wrong with this picture? How critical is the threat of shadow AI in the legal sector right now?

Are we sitting on a ticking time bomb of breached client confidentiality because staff are using unsanctioned tools to meet daily targets?

Niki Black (04:21)
Well, that’s a really interesting question. And it was really interesting looking over the data this year because it’s our third year tracking generative AI adoption and perspectives on generative AI. And it really did stand out that you had more than double the number of individuals using generative AI, more than twice as many compared to last year. And that’s an unbelievable jump in our profession, right? For technology to be adopted that quickly.

But another really notable statistic was the data on governance and how there’s not a lot of training or policies being rolled out. I do think that those data points, in some ways, absolutely correlate to the people who actually responded to this survey. As Greg mentioned at the beginning, it was solo and small firms, and some mid-sized firms, predominantly. There are some large firms in there too, but I do think that the larger an organization gets, the more likely they are to have people devoted to governance and training. And the smaller it gets, the more of a lift that becomes. So I think that’s reflected a little bit in the survey results.

But I also think that this lack of guidance from firms is in part due to how fast people are adopting this tool. I mean, it’s hard to keep up and understand what the guidance should look like. So I think that contributes to the gap as well.

Greg Lambert (06:02)
So if it’s a solo or small firm, let’s say one to four or five attorneys, do they really need an official governance package? How do they do that? Is it something they actually write down, or is it something everyone kind of agrees to?

Marlene Gebauer (06:16)
I was thinking about the news this morning. I was like, yes, they definitely need to.

Niki Black (06:28)
Right.

I mean, I think every firm needs guidance and training on AI. And if it’s a solo, then you’re guiding and training yourself. But once you have employees, you need to have something. And AI makes it easy to come up with a plan. So why not use the tool to help your firm figure out how to provide this information and create some guardrails for your employees? So I think there’s a simple solution to the problem, and it’s to use the tool to help you give guidance on the tool.

Greg Lambert (07:01)
Use the AI to help you AI.

Niki Black (07:04)
Right.

At least I think it helps you get the information you need and collect it and start a draft, right? And then from there, you do what you’re supposed to do, which is edit, revise, ensure accuracy, do a little legal research, and then roll it out, right?

Greg Lambert (07:22)
Let me transition a little bit here from the firm to talk about transparency between firms and clients. In the report, I think it explicitly notes that client-driven pressure remains pretty small when it comes to asking for, if you’re using AI, I need to see some cuts in my bill for this.

But then when we look at the corporate side, the ACC and Everlaw data indicate that something like 60% of corporate clients have no idea whether their firms are using AI or not. So if there’s a lack of client pressure reported by your respondents, and there’s a temporary illusion based on the retail nature of solo and small firm clients, how quickly do you think demand is going to start increasing and people are going to say, you should be using AI and my bill should be getting smaller?

Niki Black (08:37)
Well, that’s such an interesting question and issue. I’ve long said that corporate counsel, and some of the most sophisticated legal consumers, are corporate counsel and insurance companies, right? Because they are sophisticated consumers of legal services. Both of them buy legal services from lawyers and obviously know a lot about how much time it should take to get these legal services performed.

So I’ve always thought you would see those two groups initially putting the pressure on to use these tools, increase efficiency, and reduce costs in a sophisticated way because they’re sophisticated consumers. And I do think that from the data coming out about corporate counsel, that’s what’s happening.

When you think about traditional legal consumers, when it’s not B2B, which is what corporate counsel and insurance are, and you think about certain types of legal consumers, criminal law, family law, trusts and estates, these are individuals who need to hire a lawyer for some reason. Oftentimes they don’t expect to have to hire the lawyer. They have to hire the lawyer without planning to. They aren’t sophisticated purchasers of legal services.

So I think it’s going to take a lot longer for them to expect that kind of transparency or make that kind of demand because they don’t understand what the practice of law looks like. They’re trying to get a lawyer to solve their problem. So they don’t know whether it’s being done efficiently or not. And they can’t compare apples to apples, or apples to oranges, I don’t know what the right comparison is. Legal services performed with AI versus legal services performed without it, they don’t know the difference between those two yet.

But once they start using it more and more in their own work, they may start to want to understand how lawyers are using it. And I think that gap will start to close. But it is pretty interesting and significant.

Greg Lambert (10:43)
Let me twist that question a little bit. Are lawyers seeing clients show up with AI-generated information to help them talk the talk when they meet with a lawyer? Are they showing up better prepared? Because Marlene and I were talking to a class of students this morning, and one of the things I brought up was that we’re seeing more pro se filings that are pretty sophisticated because people are starting to use AI tools to help them draft their complaints.

Like it or not, AI is getting better, and it’s making some of these complicated issues more accessible for pro se litigants to bring into court. So I’m curious, are you hearing anything about attorneys whose clients are showing up with more information, or at least paperwork they printed out from ChatGPT?

Niki Black (12:06)
Well, that isn’t an issue I’ve spoken to attorneys about, but I think it’s a good supposition. It used to be that people were a little frustrated with the Google lawyer, right? They’d show up thinking they knew a lot about a topic because they Googled it. And I’ve got to imagine you’ve got a little of that same thing going on with ChatGPT, or these other generative AI tools, in that clients are doing a lot of research ahead of time.

But they may be going down completely wrong roads in their research, down the wrong path, because they don’t really understand what the procedural aspects are supposed to look like or what the actual legal issues are. So I do think, depending on the sophistication of the legal consumer, it may be helpful, or it may take longer to walk them off that path and explain to them that’s not the actual issue you’re dealing with here, and this is what you’re dealing with. And then you’ve got to butt heads with them because it sounds great when it comes out of generative AI, right?

So yes, I think you’ve got part of that going on. And I think that pro se issue is really interesting. I just read a hallucination case where the pro se litigant brought so many motions and filed so many memos, and it sounded like they were driving defense counsel nuts because at one point the court basically said, defense counsel, you’re obsessed with their AI. You need to stop asking about it. They can use it.

I think it’s overwhelming. You know how lawyers talk about a flurry of papers filed on a Friday before a holiday or something? I think it kind of feels like that sometimes on the defense side when you’ve got a pro se plaintiff, because all of a sudden you’re overwhelmed with papers, some of which don’t make a lot of sense, and you have to unwind it and then respond to it. So I think it’s interesting, all these different things happening and happening so quickly in our legal system.

Marlene Gebauer (13:55)
And pro se aside, I also wonder how some of these recent rulings are going to impact clients’ use of some of the gen AI tools. I mean, I suppose if they’re doing their own research and then sharing research with their attorney, is whatever they’re providing still privileged? That’s also something they’ve got to think about.

Niki Black (14:23)
Yeah, I just wrote a couple of different articles about that for the Daily Record, and there’s a split right now. There are only two courts that have even looked at that, apparently, as far as I know. And there is a split in the federal district courts about it. One says it’s privileged. One says it’s not privileged. Another says it’s work product.

One is treating it as a third party, that the information has been disclosed to someone else. It’s a little more complex, and the factual scenario is different. But essentially, one says it’s disclosed to a third party, and another says it’s work product and protected. So it’ll be interesting to see. I mean, I think it’s got to go that way. Otherwise, you’re not going to be able to use the cloud or email or anything else.

But yeah, we’re going down a different path here, but it’s a really interesting issue. I’m glad you raised it.

Marlene Gebauer (15:01)
Exactly.

Greg Lambert (15:02)
Yeah.

Marlene Gebauer (15:06)
So I’m going to tee up this next pretty straightforward question with a little bit of background. The report highlights that nearly 50% of legal professionals expect AI to change billing practices, with 25% expecting reduced billable hours per matter and 22% anticipating more flat fees. And Thomson Reuters has recently warned of an impending AI bubble, noting that law firms are currently seeing record profits largely driven by rate hikes rather than passing on AI-driven efficiency gains.

I have issues with that statement, but that’s what’s reported. So the question is, we’re seeing a structural conflict between firms investing heavily in efficiency and still remaining wedded to the billable hour. Are you sensing that firms are prepared to abandon hourly billing in favor of value-based pricing? Are we there?

Niki Black (16:17)
I think for large firms, that’s going to be challenging in the short term. And I’ve never practiced law at a large firm, but people I know well who have, and who are partners in these firms, have explained to me that the compensation structure and everything else about how those firms function are so tied into the billable hour that it’s not a matter of changing how you bill. You’re going to have to change so many other aspects of how that firm is structured, and all the cascading things that are impacted by that.

So I think for large law firms, the pressure is going to come from a few places. First, from in-house counsel requiring them to change it because otherwise they’re going to use someone else. Second, from boutique firms that spin off. This has always happened. People leave large firms, spin up a boutique firm in one practice area, take a bunch of clients with them, and are able to use AFAs and charge in different ways that undercut and compete with hourly billing while still providing the same level of big law service, knowledge, and expertise.

So I think you’re going to see more of that happening, especially as these firms start cutting people the way they’re doing because of AI. They’re reducing associate levels and admins, but eventually they may start cutting not only entry-level associates, but actual associates and possibly partners. Who knows what they’re going to do.

But the other place I think you’re going to see it is from solo and small firm lawyers who may have prior experience and are going to be able to start competing in ways that technology has already enabled, but at a much higher level. And potentially do the same thing these boutique firms are doing, undercut the pricing and provide similar levels of service at a lower price. So I think that’s coming. They’re not going to do it willingly, but it’s going to happen.

Greg Lambert (18:29)
One issue that almost always pops up every time there’s any kind of change in the legal marketplace is, well, in-house counsel is going to handle more of their work in-house. Do you think that’s also a possibility? We always project that, but it never really happens.

Niki Black (18:55)
I think that with AI, it’s already happening in the way you suggested it would happen with consumer clients. They are doing a lot of that initial work that they would have had to hand off, and they’re handing it off in much better shape, where a lot of the initial legwork is done. And then they want outside counsel to finalize the product or provide the final answer.

So I think it is already happening. And the Claude for Legal tool, I’m not sure of the exact name, but that was just released, and I think it’s going to expedite that process even more.

Greg Lambert (19:31)
Well, speaking of Claude and CoCounsel, pretty much up until a couple of months ago, AI was ask it a question, get an answer. So in the survey, it showed something like 58% usage for legal research and 49% for drafting documents. This is where I give it a specific task, it comes back with an answer, and I may clean that up a little bit, but that’s about it. It’s give and take.

Now we’re getting more into agentic AI, where you’re basically giving it instructions on the output you want, and then it goes out and does it, where the systems kind of work autonomously. So if a significant portion of the legal industry is struggling to figure out how to draft basic governance policies for the ask-it-a-question, get-an-answer part of it with these text-generation chatbots, how equipped are we to handle the complex ethical and malpractice risk associated with autonomous, agentic AI tools?

Niki Black (20:57)
It’s a good question. Part of it comes down to how you define agentic AI in the context of legal work, which is a big debate a lot of people are having. Is it kind of like how LexisNexis and Thomson Reuters have things set up, where you have a generative AI chatbot at the front that answers some legal questions, and then when you want to go deeper, it determines which subscriptions or integrations you have and goes to those and pulls out the information you need?

That’s arguably agentic AI, but some people say no, because it’s in a closed system. It’s not using the internet to go out and do other things, which some people claim gets closer to the practice of law. Do you let it just go out there and practice law for you? I don’t know.

Without even having a full definition of agentic AI, and with the fact that it’s already being built into a lot of legal technology tools, lawyers may not realize they’re using it. Assuming we agree that that is agentic AI, which I think it is, I think they’re already using it in some cases. It’s just more of a closed system, so it reduces the risk a little. And they still have that obligation to review the output and make sure it’s accurate. So you still have that framework in place.

Greg Lambert (22:21)
Well, let me define it a different way then. Let’s say I’m using Claude CoCounsel, which can read and write files on my own system. It can search the internet. It can interact with Chrome web browsing. So it can browse the web, not just on the back end, but using your browser going out. This is going to be a seismic shift, I think, in how attorneys use AI tools, or at least how clever attorneys use AI tools, to work on 10 or 15 different things at once and bring back results, analyze them, save files, and update files.

It seems like we’re ill-prepared for regulating that and for governance on that. I don’t see us ready for it.

Niki Black (23:27)
No, I think you’re right. I just switched to Claude and have been playing around with CoCounsel. And there was that LinkedIn post where a lawyer, I think a corporate lawyer, was talking about how he uses CoCounsel, and some of the things he said he was doing gave me pause because we all know these tools, once they start diving into actual documents that have complex information in them, start making things up right away. It doesn’t matter which one you’re using. They make things up.

And then when they take whatever they made up and apply it to something else, which is what would happen in this scenario, it becomes a game of AI telephone. That’s where I get a little worried. The output is going to be so bungled that I don’t understand how you can, in the current state, use it in that way, especially when it is agentic and does go out and get information and pull that information in from your documents and client files.

Right now, I don’t see it working the way this person talked about. I don’t know if you’re familiar with the post I’m talking about.

Greg Lambert (24:51)
Yeah, I saw it.

Niki Black (24:55)
Yeah, it was like you left it alone and let it go analyze all these things, and then it came back and he sent the letters or emails off to opposing counsel in the middle of the night. It made it sound like, how was he carefully reading the output, and how was it analyzing the contracts that carefully and then coming up with emails that made sense and didn’t have hallucinations in them? To properly review and ensure that wasn’t happening, it would take an hour, I would think, at least, to go over what it did. So I’m not sure.

It sounds good on paper, but I’m not convinced, just like you aren’t, that the output at this point is going to be reliable and trustworthy. And that impacts the ability to provide guidance on using it. I think we’re in a weird place because the tech is advancing so quickly and there is a lot of potential, but the reality still seems a little questionable when you get into things that complex.

Greg Lambert (25:55)
Yeah, I always worry about the things that go viral that sound a little too good to be true. So we’ll see. I think what he was saying was pretty accurate in the broad scope of things, but I think there were some issues being taken with the specifics. But yeah.

Marlene Gebauer (26:01)
Need to.

Niki Black (26:17)
Well, and I think that post was written by AI, by the way, because there was a lot of not this, but this going on throughout it. So it was written by AI.

Greg Lambert (26:22)
Yeah, some dashes, some not this, it’s this.

Marlene Gebauer (26:25)
And dashes.

Niki Black (26:27)
So you wonder if it even said what he wanted it to say. But who knows.

Greg Lambert (26:32)
Hahaha.

Marlene Gebauer (26:35)
I didn’t mean that. So I know the report had information on access to justice, and I want to shift the conversation a little bit to that. A strong majority of respondents, 76%, are optimistic that AI is going to help narrow the access to justice gap. And 72% admit the sheer cost of legal services is still a primary barrier. Then 62% of lawyers agree that the rule of law itself is currently under threat.

So there’s a real fear, I think, that AI could create a two-tiered system, premium, highly accurate AI for wealthy clients, and hallucination-prone solutions for pro se litigants. I think that’s rather gloom and doom myself, but how does the profession harness technology to democratize legal access and restore institutional trust rather than inadvertently widening the socioeconomic divide?

Niki Black (27:58)
I think the answer is in the data points you just stated. Access to justice is not a single-shot solution. There’s no one way to solve it. You absolutely need greater funding. And I agree with the respondents. That’s the primary thing that’s going to help fix access to justice in the way it should be fixed, where it helps average people get the legal help they need instead of ending up with a top-level system where the Big Four are competing and starting to provide legal services in ways they weren’t able to before because they weren’t licensed lawyers.

I think that when you have better funding, and then you have the technology supporting public defenders, legal aid lawyers, and others who are on the front lines trying to bridge that gap for people who can’t afford lawyers, if they’re getting paid through better funding and are able to use generative AI tools, especially ones built into trusted legal tools that are guardrailed on the back end and where they can trust the output and that are developed more slowly in order to ensure that output, then it allows them to help more people.

It allows them to get paid because of better funding and then provide more efficient and effective legal services, represent more people, and provide them with a higher quality of representation. And I’m not saying that in the abstract. I had a legal aid lawyer reach out to me after one of the articles I wrote saying that he started using ChatGPT when it first came out and was able to take on more clients because it increased his efficiency so much.

So I think when you combine those two things, it does make it easier to democratize access to legal services rather than creating that two-tiered system. But without the funding, I think it ends up creating a two-tiered system.

Greg Lambert (30:18)
Before we roll out and ask you the crystal ball question, is there anything else in the report that stood out to you, maybe something that surprised you or something else we haven’t covered?

Marlene Gebauer (30:39)
Yeah, we did not like that.

Niki Black (30:40)
Well, I thought the second half of the report was really interesting. The first half focused on AI trends, and then the second half of the report dove into systemic legal issues, access to justice, and how lawyers and other legal professionals were looking at the rule of law. I thought those data points were interesting because when you looked at the data from lawyers versus other legal professionals, I don’t love the term non-lawyers, but other legal professionals, you really did see different perspectives, in large part driven by the roles they play in the justice system. So I thought that was super interesting.

It’s an interesting time, and I think having data like this helps you get some perspective on what’s happening with all the rapid technological advancements and how that could potentially impact the administration of justice.

Greg Lambert (31:34)
Well, definitely something to check out. Before we get to the crystal ball question, what are some resources you’ve been using to help you keep up with all the changes going on in the legal industry lately?

Marlene Gebauer (31:56)
What are your go-tos?

Niki Black (31:57)
Well, I go old school. I kick it old school with my RSS feed reader, Feedly. That’s one of the primary ways I track what the people in the industry I trust are saying. And I’m also very active on LinkedIn, so that helps a lot too. But for me, it’s following the blogs of people I trust and seeing what they’re saying.

One thing that’s a little challenging now is that some people are moving things over to Substack. So it starts to get harder. There’s no RSS feed, and you’ve got to check Substack separately and follow the right people there. And that makes it harder when a few people I’ve really enjoyed have moved over there. Are you on Substack?

Greg Lambert (32:42)
So you’re saying we’re part of the problem.

Marlene Gebauer (32:44)
Exactly what she’s saying.

Greg Lambert (32:46)
Yeah, we’re testing it out.

Niki Black (32:52)
Well, but you also have the podcast. Is that behind the wall on Substack?

Greg Lambert (33:00)
No. We still put the blog and everything out. We’re just testing Substack for fun.

Marlene Gebauer (33:01)
There’s nothing behind the wall.

Niki Black (33:07)
I get it. I mean, people are able to create community and monetize what they’re doing more easily on Substack. And I’ve looked at it myself. It doesn’t make sense for me right now, but I see why it might for others. It gets to the point where if a whole bunch of people move there, then you’ve got to start working Substack into your day, you know?

Marlene Gebauer (33:28)
All right, so now it actually is time for the crystal ball question.

Niki Black (33:28)
Making my life harder, you guys.

Marlene Gebauer (33:32)
So now it actually is time for the crystal ball question. Looking ahead over the next three to five years, Niki, what do you think is the single biggest challenge or change coming for the legal industry that firms should start preparing for right now?

Niki Black (33:35)
Yeah.

I mean, it’s the bigger picture of everything we’ve talked about. It’s all the structural impacts that are going to come from all the changes happening right now, in large part driven by AI, but not only AI. Pricing changes, the ability for smaller firms to compete with larger firms, and that applies to law schools too.

I just spoke at my law school last week, and I talked to some of the deans afterward. And they’re all struggling to figure out what they do. How do they teach tech? How do they implement it? How do they prepare the students? How do they change what law school looks like, possibly? They’re going to have to prepare students more for solo practice, prepare them for other types of roles, and make sure they understand how to use tech to allow them to excel in those roles. So it’s not just law firms.

And I think client expectations are going to change. It’s this major structural change that’s going to happen really quickly. So that’s going to be the biggest challenge, figuring out how to adapt within those huge changes taking place.

Marlene Gebauer (35:07)
Big changes and time restraints.

Greg Lambert (35:10)
Yeah, definitely. Well, Niki Black, Principal Legal Insight Strategist at 8am, thanks again for coming back and joining us on the podcast and walking us through the future of the legal profession. Always a pleasure.

Niki Black (35:28)
Yeah, my pleasure. This was a lot of fun.

Marlene Gebauer (35:30)
Yeah, thank you, Niki. And thanks to all of you, our listeners, for taking the time to listen to The Geek in Review podcast. If you enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Greg Lambert (35:41)
And on Substack. Sorry, Niki. Niki, for listeners who want to follow your work, read your columns, and download the 2026 8am Legal Industry Report, where should they go?

Niki Black (35:59)
Well, the Legal Industry Report is available on 8am.com under Reports, so definitely go there and download it. I think you’ll find it interesting. I wrote it, and I think there’s a ton of great stuff in there. And our team did a fantastic job supporting me in writing it, and the design is fantastic in my opinion. It looks great.

And to follow me, LinkedIn is the best place because I post my columns there and I have my newsletter there, Legal Tech Reality Check. So LinkedIn would be the best place to connect with me.

Marlene Gebauer (36:32)
And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry, and bye, everybody.

The episode opens with lessons from the Women + AI 2.0 Summit at Vanderbilt and the “AI competence penalty” narrative. Boyko’s central principle for law firm leaders is simple, stop copying the competition and start operating with intention. Strategic planning matters more than tool shopping, especially when uncertainty makes leaders freeze, over-index on fear, or chase noise instead of outcomes.

From there, the conversation sharpens into client reality. Boyko shares what she is hearing from in-house leaders, and it is not comforting for firms. Legal departments are working to reduce dependence on outside counsel, business partners inside companies often accept “good enough,” and the models keep improving. The risk is not losing to a peer firm; it is losing the client relationship because the work stops feeling necessary.

A major theme is talent and the apprenticeship gap. Boyko argues firms underinvest in people, even as they spend aggressively on software stacks. AI can help junior lawyers with coaching and confidence, but it does not replace mentorship, judgment-building, or context. The skills that matter now include client advisory, operational thinking, critical judgment, and the ability to solve problems across a complex system, not only perform discrete tasks in a vacuum.

The episode closes on legal education and the future value of the JD. Boyko urges students to be selfish about learning AI, especially when faculty guidance comes from avoidance or philosophy rather than experimentation. Looking ahead, she predicts the JD’s value shifts upward, away from rote production and toward proactive advisory work, relationships, anticipatory counsel, and wisdom-driven judgment. In other words, fewer fire drills, more looking around corners.

Links:

• PLI – How to Navigate Law School Podcast
• Power Paradox webinar

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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 Gabauer from The Geek in Review and I have Nikki Shaver at Legal Technology Hub. Nikki, you’re going to tell us about your portfolio management feature, right?

Nikki (00:10)
That’s right, Marlene, it’s a brand new feature. So…

Since 2023, especially, the legal tech market has ballooned in size and complexity. I wonder why. As you know, Marlene, the number of startups entering the market continues to grow at a very consistent pace. From our tracking metrics, we know it’s growing at about 100 new startups a quarter, with a total number of solutions in legal tech now numbering well above 3,000 globally. So it used to be the case that law firms would have a dedicated resource

in their innovation or IT departments whose job was at least partly devoted to tracking the market, mapping new startups, reviewing this against the firm’s own existing portfolio and project goals. But that doesn’t really make sense anymore. Internal teams are already so busy on their own projects and we have a dedicated team that does exactly this. Legal Tech Hub has been dedicated to doing this for over five years now and our new portfolio management feature is

designed to make it easier than ever for buyers to use Legal Tech Hub’s data to manage their own portfolio of legal technology solutions. It allows you to select the solutions in your current tech stack from across the Legal Tech Hub directory and add them to your portfolio management tab which is in your account on Legal Tech Hub. You can also add solutions you’re interested in watching and those you’ve looked at or reviewed

but decided not to proceed with. You can easily move solutions between those categories and add notes like, waiting until this product is SOC 2 certified. Once you add a solution to any of those categories in your portfolio, a bonus is that you will also be notified if the vendor adds an announcement to their listing like, we just got SOC 2 certified. So this feature will make it even easier for firms to manage their tech stack against the market and we’re really excited about it.

Marlene Gebauer (02:14)
Yeah, that sounds like a wonderful tool for very busy professionals trying to keep track of all the changes that are happening in the industry. So thank you.

Nikki (02:24)
Thanks Marlene, we certainly hope so.

Marlene Gebauer (02:31)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gabauer.

Greg Lambert (02:38)
And I’m Greg Lambert and this week we are thrilled to welcome someone who’s been a dear friend of mine for many, many years and has, I love how she describes her career path as a.

She has Goldilocks her way through her career here, having tried multiple roles to find exactly what it is that helps her drive change in our profession. And we were talking before, she’s been in academia, she’s been in ⁓ ALSP, she’s been in law firms, she’s been everything. ⁓

Marlene Gebauer (02:53)
You

Done it all. She’s done it all.

Anastasia Boyko, mean, as you said, she has an incredible background. So she’s, she’s a Yale trained tax lawyer. ⁓ she’s managed alternative legal talent at Axiom. She’s served as the inaugural Dean of the Psy Leadership Program at Yale Law School and recently, ⁓ operated as the chief innovation officer at the University of Utah’s S.J. Quinney College of Law.

Greg Lambert (03:15)
Yeah.

Yeah, that just ⁓ scratches the surface here. Anastasia is a legal futurist, ⁓ lawyer sherpa, and is the host of the PLI podcast on how to navigate law school. So Anastasia, thank you very much for being here. It’s good to see you.

Marlene Gebauer (03:37)
I don’t know, we couldn’t fit everything.

Anastasia Boyko (03:53)
Thanks for having me. Podcasters

interviewing podcasters, my favorite.

Greg Lambert (03:57)
It’s very meta.

Marlene Gebauer (04:01)
So Anastasia, like we recently attend, you and I recently attended the, the women plus AI 2.0 summit at Vanderbilt. And you know, one of, one of the core focuses was the trap. the narrative that, you know, women use AI less than men resulting in an AI competence penalty. now we did have breakout groups and, and, the great, the breakout group that you attended, ⁓ basically came up with principles,

in regard to this. so, you you took the initiative to synthesize those from our MaxiSpark session and share them. So for like law firm leaders listening, you know, what’s the most vital principle that emerged from that session? And, you know, how can they actually actualize this on Monday morning so that AI adoption doesn’t exacerbate structural inequalities?

Anastasia Boyko (04:55)
Yeah, I mean, great question. That conference was really, I think, life-changing, which is weird to say, for those of us who attend lots of conferences. I think it was the right group of people. I think it was organized very well. And I think we were discussing the most important parts.

Marlene Gebauer (05:04)
Mm-hmm.

Anastasia Boyko (05:12)
And when I was reflecting on the session that you facilitated, as well as the session that Heidi Brown and I did on how to reimagine the workplace, last night I was thinking about this, and I think a lot of it for law firms is sort of stop doing what everybody else is doing, right? Like that’s the operating principle for many law firms is like. And I understand it, right? A lot.

Greg Lambert (05:30)
now. Sorry, sorry, that’s all we know. That’s all we know what to do.

Marlene Gebauer (05:34)
What’s the competition

doing?

Anastasia Boyko (05:37)
No, but a lot

of our habits are precedent-based, right? We’re trained this way in law school. So not only are we trained to think this way in analysis, but we’re trained to operate this way in our organizations. And I was reflecting on this ⁓ last night, precisely about that, right? Like, why do we keep wanting to do what somebody else is doing in a world where all of the rules are changing? It’s all uncertain right now.

Right, but we still wanna keep going back to the old principles of how law firms run, how law firms make money, how they grow. And that’s just not true. Like the last three years have shown us a great deal of disruption in the old ways of doing things. And so I think my big takeaway from that conference is we really gotta get out of that mindset of how have we always done it before? Who else is doing it that way? And so I think the better operating principle is

What do you wanna do with your firm, right? Like what is the actual future of your firm? What do you want to be doing for your clients? And then structure the activities along that path. And that might be an operational change or that might be a technological change and that could be an AI automation or an upgrade, but really you need to have intention. You need to be deliberate about where it is that you’re going with your firm versus being reactive and operating out of fear.

which is what I think a lot of law firms are doing right now.

Greg Lambert (07:05)
But we love to race for second place, so we like to see what others are doing and then quickly be a fast follower behind that. And it’s, you know, to borrow the old saying, you know, how can you tell millionaires are doing something wrong? You know, just I think how do you approach this to say, hey, look, right now is the absolute best time to get outside your comfort zone and try something new.

Anastasia Boyko (07:32)
Yeah,

I I think that’s that’s part of it. But I think a lot of it is we don’t do really good strategic planning in the law firm setting, right? Like some firms may do it better than others, but we’re not looking out three years. We’re not being necessarily disciplined about the strategies that we’ve committed to. People change directions constantly depending on, again, fear. ⁓ And so I think a lot of this is figuring out how to get out of that mindset and get into a much more strategic

planning one around what you and your firm want to do, including the talent that you’re hiring, right? Like firms are doing the same thing with innovation as they’re hiring into their orgs. What did this person do for a few years at my peer firm? Well, what does that have to do with what your firm wants to do, right? And I think I take your point very valid, right? Like people don’t necessarily feel the pain just yet. I think that’s why there’s been a reticence in the speed of adoption.

You know, as someone who has been in all of these communities at once, right? I have this very weird everything everywhere, all at once kind of mindset when I look at legal. The things I’m hearing from in-house leads terrify me if I was a law firm leader. And they’re not paying enough attention, right? They’ll pick up a few of them and a few articles here and there. But I’m like, your customer is trying very hard to eliminate their dependence on you.

Like that should scare you. They also probably don’t like you that much, right? They’re coming to you out of necessity. No, my friend always says this and it just hits me over the head each and every time. How often are your clients inviting you to play golf, right? Or inviting you to spend time? Like how good are your relationships really? Or are they coming to you and paying your fees because they have to? And when someone is doing that out of necessity, they’re gonna figure out some way to not do it.

Greg Lambert (09:02)
Hahaha

Anastasia Boyko (09:26)
out of necessity when their business teams are pushing them to eliminate that with tools that currently can get them to do that internally, right? So that coupled with clients who aren’t lawyers in legal departments who are comfortable with good enough from what they’re getting from the models and it keeps getting better, like the cliff is coming. And so I don’t wanna operate, don’t wanna get, don’t wanna try to take advantage of that fear, but it’s a very realistic fear. That fear should be happening for you.

when you’re making decisions. Not am I getting left behind with my competition, but are my clients gonna leave me very soon?

Marlene Gebauer (10:01)
Yeah, cause it’s like, keep saying it’s like relationships, relationships, relationships. Like, you know, it’s, it’s really about that because the technology is, just getting that good. you know, I also wonder like how much of this sort of indecision is like the noise, all of the noise that’s, that’s in the market, you know, this from a strategic standpoint, you know, there’s investments of, you know, hundreds of thousands of millions of dollars and, and like, this is all very new for, for most people and

Greg Lambert (10:02)
Bye.

Anastasia Boyko (10:04)
Yes.

Marlene Gebauer (10:32)
Making that wrong step could, could be, you know, it’s seriously problematic. And I think, you know, you have that leadership where it’s just like, is fear, but. You know, is the noise contributing to that and kind of how do they, how can they kind of like block that out?

Anastasia Boyko (10:49)
Yeah, I think there’s a lot of fear around uncertainty. So we don’t do a good job of making decisions in a time of uncertainty. And right now we’re operating by soon to be new principles, right? So we’re watching the old principles go away, the new principles are forming. And so we’re in this really strange time. I ⁓ was doing a guest lecture at Penn Law yesterday and we were talking about access to justice and technology in the future. And the students asked such great questions, right? They were pointing out all of this uncertainty.

all of these issues about access to justice and AI and the challenges there. And I said, yes, right? All of that is true. And you need to figure out how to operate in uncertainty. As the bridge is falling behind you, as you’re running across it, you need to be laser focused on where you’re going, right? Like what is your intentional destination? And then what can you do in the current moment with the ground that is stable underneath you? And these are the same kinds of skills that we developed during

Like I remember organizing a salon of VIPs of our alumni at Yale and talking about decision making in times of uncertainty. Like these were moguls who had been able to build incredible ⁓ businesses and initiatives and they didn’t know what to do, right? There was no playbook during COVID. And so everyone was on equal playing field except.

people who sort of knew how to deal with uncertainty. Like this is why we keep going back to this emotional intelligence. Like you need to build the muscle around how do I deal with situations where I can’t predict what’s going to happen. So people who can get intentional, who can understand the principles by which they want to operate, who know how to treat talent well, who know how to make decisions around like, hey, I’m gonna use my best guess that this is the technology we need because we’ve sat down and figured out what our organization needs.

first, right? And then we’re going to make decisions from that. We might make a mistake. We make mistakes all the time. We hire the wrong people. We use the wrong software. We have to go back, right? Like this isn’t new to us to make mistakes, but fear of making mistakes and not acting from that fear, I think is much more dangerous than trying something out and be like, all right, well, that didn’t work. Let’s try something better. We learned.

Greg Lambert (13:01)
Now I was listening to…

Nate Jones this morning as I was coming into work and he was talking about something I think that runs kind of parallel to what you’re saying is that we’ve gone through these engineering tasks, these technology kind of tasks of bringing in the AI, of the individual level of prompt engineering and then the more wider content engineering that you hear a lot of now. ⁓ But he was saying that

But it’s really about intent and it has to be intent on the organizational level of where are we going, how are we, and it’s, know, saying AI is not an IT issue, it’s an organizational issue and if you don’t think of it as an organizational issue and have that intent and you’re allowing everyone to understand where is it that we’re going,

that it doesn’t matter how much you have AI on your desktop or in your processes, if it’s not coordinated, if it’s not intentional, it’s just not going to work.

Anastasia Boyko (14:16)
Yeah, I think

a lot in systems, right? I’m obsessed about how systems work and how different pieces fit together. And I go back to a lesson I got in college in dysfunctional psychology. And when you’re looking at dysfunctional psychology and dysfunctional social family psychology, you can’t just fix the person, right? Like you have to fix the entire system. And whenever I read AI posts on LinkedIn or somebody’s Substack, I just think like, you’re still stuck in this little rabbit hole of this piece of the system.

And you’re not realizing that all of it is happening all at once, right? Like everything is changing all at once. You have to think about all of the levers all at once. And I think that’s why I love the pieces that you’re doing right now, Greg, because they feel contextual, right? They’re actually looking at a story and a system and how does this fit into a fictionalized firm? And that’s the conversation that we need to be having. Lawyers don’t want to have that conversation, right? We’re in many ways word surgeons.

We come in, we slice this piece, we slice that piece, but the organization or the individual client is a whole person. It’s a human or it’s a big company. We need to think in that sense in the way that we’re trying to find solutions for our clients and our organizations right now. The surgical approach is not working. It sounds really smart, super smart, love it, but you’re very much doing the same.

mistake that many academics make, which is they get very, very narrow and they’re writing to an audience of 12. And they don’t necessarily apply their brilliant ideas, which are brilliant, right, to the bigger system. How does this fix the bigger problem that I’m trying to solve for? And so that’s why I always keep encouraging lawyers, get out of this mindset of what tool do I need? What problem are you solving?

Greg Lambert (16:07)
Yeah, yeah. Well, and I think that’s… ⁓

You’ve mentioned a few things, human in the loop, emotional intelligence, what problem are you solving? ⁓ And right now we’re really kind of thinking of things and what can we throw AI at? What can AI be first? What can be automated? But maybe take a step back here and what do you think that, do you think law firms are investing enough in the humans compared to the software stack?

What is it that you think we just can’t automate?

Anastasia Boyko (16:44)
⁓ No, we’re

not investing in the humans. I remember being an associate and I remember being fully neglected on every single ⁓ level of how you evaluate talent management and talent investment, right? Like it’s the borderline of abuse in many environments. It’s solved. AI will help you.

Greg Lambert (16:59)
Yeah, but we’re throwing AI at that now, so that problem’s fixed. Yeah.

Marlene Gebauer (17:02)
Hahaha.

Anastasia Boyko (17:04)
coach your

associates, right? And there is a piece of that, right? There’s a piece when you’re super lonely and you’re trying to figure something out and you don’t feel confident to go ask a senior lawyer, that AI can help coach you through that. But that doesn’t mean that lawyers should be derelict in their duty to develop and lift up generations after generations, right? And we keep doing it. I saw this when I was on the professional development side at Practical Law, right? I was dealing with all of the AmLaw 200.

I was talking to smaller firms, I was talking to in-house departments. There’s this large gap between leaving law school and then learning how to be a lawyer. And it’s very much a Hunger Games exercise, right? Good luck, I hope you get a good mentor. Otherwise, it’s really your fault that you failed, even though we set you up to fail. And so I think AI bridges some of that gap, but not the entire gap. And especially right now when we realize that the skills needed

to be a good lawyer are no longer those rote skills that AI can now automate. They’re the people skills. They’re the operational skills. They’re the client skills. They’re how do you like critical thinking, judgment, how do you solve a complex matrix problem for your client, which is totally opposite of how we’ve trained associates before, which is here’s a discrete problem, go solve it. No, I won’t explain to you how it fits into the bigger picture. And so again, AI can solve.

that piece, but only for associates who are curious about it. And so the gap needs to be solved both by professional staff who can sort of lean into that and help contextualize that for students and young lawyers, but it also is necessary to have partner involvement. And I understand everyone is busy. We are all busy. We’re all professionals. So like my favorite wasn’t somebody’s, well, I’m very busy. ⁓

Well, I’ve been lounging around doing nothing, right? Like everyone is busy in our own ways of the tasks that are there, but we need to come together and sort of look at the entire flow of it to assign responsibility. And I think the challenge in the current commercial model of the law firm is it allows many senior lawyers to abdicate their duty as mentors, right? And stewards of their firms. And so part of this is,

Lawyers have to have that kind of moment where they look in the mirror and realize, I need to do more because frankly, it actually will make my practice better. You will have more ready people to help you. You will be able to delegate to more people and it won’t be constant running around and busy work. And also, PS, you’ve got agency. I know this is a hard one, but you can decide what to say yes to and what to say no to. It doesn’t feel that way when you’re in the environment of a firm.

but you are a highly trained professional with a lot of skills and then you can decide how to allocate your time to different things.

Greg Lambert (20:01)
Amen. ⁓

Anastasia Boyko (20:05)
And again, look, understand. we’re done. We’re done. We solved it all. We solved it all. No, and I understand it’s hard work. Like, again, I understand it’s hard work. We have a really complex system based on control and codependency and perfectionism. And it’s very hard to get rid of those mindsets in an organization full of those people and full of pressure from clients. But again, you learned how to do law. You can learn organizational management.

Marlene Gebauer (20:05)
All right, that’s it. got, wait, that’s the end. We solved it all.

Anastasia Boyko (20:32)
and all of the rules are changing. So again, you can decide what those rules should be. What does a humane workplace look like that trains lawyers for the future? How are you gonna pass your practice on? How are you gonna make sure your clients are taken care of? Right, like that one, yeah, that topic that’s always at a bar association event. Yeah, exactly, right? So again, there are so many resources within your own firm that I think it begs,

Marlene Gebauer (20:47)
SS session planning. Like what’s that?

Greg Lambert (20:51)
How

do you spell that?

Anastasia Boyko (21:01)
the question for everyone inside of a law firm to ask, how do we do this better in a systemic way? Right? Do I have senior lawyers who have lots of knowledge who would love to be involved in these efforts? How do I involve them to help mentor young lawyers and also learn the technology? Just because they’re at end of their practice doesn’t mean that they’re not curious.

Marlene Gebauer (21:24)
want to dig into this a little bit more, ⁓ about what, you know, the more senior people should and could be doing, ⁓ you know, what the, the juniors can be doing and sort of how AI ties into this. So, you know, we look at so many times I see this, it’s like, it’s, it’s AI has looked at like, you know, this is, it just creates faster horses for, for billing efficiency. And.

Greg Lambert (21:40)
you

Marlene Gebauer (21:49)
You know, that in itself, I think is a stressor for, you know, people who have billable practices, but, you know, can AI be a tool for, ⁓ you know, for junior wellbeing or for these, you know, can it, can it be seen as sort of a way to update your practice, you know, in a way that maybe there’s, there’s sort of less stress that way. you know, if we don’t have an apprenticeship model.

Or we have a revised apprenticeship model and there goes Greg, bye. He doesn’t like what I’m saying. So, you know, if we, if we have, you do we need to modify that apprenticeship model? And again, if, if partners are needed more in terms of being involved, you know, if we use AI to maybe re swizzle, like how, you know, how, how, you know,

Anastasia Boyko (22:25)
No.

Marlene Gebauer (22:50)
whether there’s unbillable time or not, how do we make that happen? How do we get, how do we get partners comfortable with the fact that you could bill a different way, still make money, and then you have time to sort of do this ⁓ operational work?

Anastasia Boyko (23:07)
100%, right? This is the message that I’ve been trying to deliver for two decades. ⁓ It falls ⁓ on deaf ears often. ⁓ I think some of the urgency now, however, is the fact that that leveraged pyramid model is changing. So we’re hiring fewer associates.

Right? Clients are serious about not paying for juniors in a different way. Right? And now they can automate some of that internally as well. ⁓ And so you have to have a real discussion right now with leadership and firms about what does it look like when we’re no longer using the triangle model.

Right? How is it that we’re going to train these lawyers? And it is, it’s a very serious question. So looking at alternative talent models for different kinds of people doing different kinds of tasks. So one thing that I keep suggesting to people is not all law students want to be practicing lawyers. So what if instead of just a rotation around practice groups, you’re going to do a rotation around various roles within a firm?

Marlene Gebauer (23:44)
Mm-hmm.

Anastasia Boyko (24:12)
There are many hybrid opportunities for AI native law students, right, more and more, who want to understand the practice of law, who may want to practice, but may not want to practice, right? So you as a firm have to think about how do I create these opportunities for people who might want to be in knowledge management positions, people who might want to be the bridge between the technology and the practicing lawyers, right? So it ends up creating these hybrid

Marlene Gebauer (24:16)
more more.

Anastasia Boyko (24:39)
kinds of positions, which are very new to a firm. We love silos. We love telling people exactly what they’re doing. Stay in your lane. I remember being told, stay in my lane over and over as a junior associate when I was talking about knowledge management or not having to repeat the same memo over and over or business development opportunities, right? Like stay in your lane. This is not the time to stay in your lane. This is actually a time to rethink people’s different talents and skills and where to put them in your firm.

You’re going to need fewer lawyers to do the traditional lawyer tasks, especially as we figure out what those tasks are going to be in the future. Again, you’re in this change moment, which means you have a lot of opportunity to experiment. This is when you should be experimenting, is when everything is transitioning to the new way of things. So yeah, so bring in junior associates and see if they’re interested in doing some things with your innovation group or with your KM group.

or thinking about how this fits into professional development, ask them how they want to learn. You know, many of them have had to adapt from Zoom school to AI and through that particular ladder, let them help you figure out how this training should go because the old way has some merit, but it currently doesn’t fully apply to what we’re trying to do. And so that’s where I’d like more and more firms to think broadly about

their talent pool beyond professional staff with very specific tasks and lanes, and then practicing lawyers with very specific tasks and lanes, that should begin to come together. And frankly, it makes for better client service when your lawyers understand how your clients operate, but also how the technology works and then opportunities to be able to come together with your clients, teach them some of these things, share how you’re using these tools to deliver better.

client service, back to relationships, right? Like how do I make this relationship better? I show that I’m leveling up, that I care about you, that I care about delivering a better product.

Marlene Gebauer (26:46)
It will be very interesting to see how firms kind of, know, what direction they move in with this. Cause you know, as you said, it’s, it’s, you know, it’s very, it’s, think it’s, very hard for, for many to kind of wrap their heads around that very fluid type of, of model that you’re, you’re talking about. ⁓ you know, people, people like to know what they know. And, and it is, it is very much a mindset shift. You know, you just gotta be able to, to, you know, be okay with that. And.

Anastasia Boyko (27:05)
But it’s a mindset shift, right? It’s a mindset shift.

I would also encourage folks to see what other kinds of firms are doing. I know as lawyers, we love being a little bit snobby about, well, my peer firm isn’t doing this, so I’m not going to do it. Or my firm is bigger and more complex, and I would never do it that way. ⁓

Little humility goes a long way. And so we talked about this earlier, but what are smaller firms doing? What are solo firms doing? What are plaintiffs firms doing? There’s so much innovation happening across the ecosystem that for especially big firms, especially AMLA firms to only focus on what other AMLA firms are doing, you’re missing the opportunity, right? Like you’re trying to get your creative juices flowing. What does this firm do?

How does it solve for something that I also have a problem around? How can I take this learning and apply it to my firm with my scale and my intentional objective, right? Like get excited, start learning and see if there’s a solution out there in a context that maybe you didn’t think was relevant to you because of the nature of your firm. It’s all ideas.

It’s all learning and we forget that. You can apply it in various ways and use that really talented brain you have that you use for your clients for your own firm.

Marlene Gebauer (28:28)
Yeah. And look, you know, look outside your, your little sphere and, and, know, other organizations that are, either kind of have to adapt or that are just doing it and just sort of see if that’s something that you can use. ⁓ okay. So you’ve built the Psy Leadership Program at Yale and now you’re hosting PLI’s how to navigate law school podcast, ⁓ to kind of decompress the unwritten rules of, of legal education.

Anastasia Boyko (28:42)
Yeah.

Marlene Gebauer (28:57)
you’ve noted that law schools, know, still many law schools still rely heavily on the 1970s teaching methods that, know, kind of, well, honestly failed to prepare lawyers for AI driven future. had, and we had like four people on from maybe a couple months ago, from law firm libraries and got a real mix in terms of whether they were even allowing students to use generative AI. And again, the decisions were.

to not to were based on fear. So what’s the single most damaging ⁓ rule that law students are internalizing ⁓ given this? it’s going to prevent them from being more agile and more empathetic practitioners when they do enter firm life.

Anastasia Boyko (29:45)
⁓ I’m sure I’ll get flack for this, but ⁓ don’t follow what your faculty are telling you around this for the most part. If there are people who don’t experiment with AI, who aren’t trying to understand it, and they’re telling you what to do regarding AI, they’re probably not the right person, right? They’re not the right voice if they’re not willing to try to.

understand it. And so I think a lot of academics approach this from a philosophical lens. Many of them approach it from a this is too dangerous lens. Unfortunately, Pandora’s box is open. So it’s not going to get closed. And we have to figure out how to engage with this. And so your school might have someone who’s leading some sort of lab or innovation center or entrepreneurship center. Try to pursue that.

See if there’s something like that that exists on the main campus if you’re associated with a larger university. Look at what other law schools are doing. There are incredible examples from Suffolk to Vanderbilt ⁓ where there’s initiative and there’s information online about those learnings and maybe even opportunities to listen to lectures. So get really selfish around your own education and don’t decide that what your professors say is the word.

I think that part of the struggle of law school, ⁓ I struggled with this, I hear it over and over from law students, is that you are sort of ⁓ forced into this mold. You think broadly and deeply and ⁓ you’re curious before you come to law school and then you come to law school and you’re taught to think one way. And that one way is how your faculty is telling you to think. And then you’re made to feel…

naive or sometimes even stupid for thinking a different way, right? And so I encourage students, you know, don’t lose yourself in the process when you’re trying to be convinced to think one way, ⁓ especially right now where there’s so much information out there and so many thoughtful people talking about these issues. ⁓ It is incumbent upon you to understand these tools. And there are a lot of really smart people thinking out loud.

Greg Lambert (31:52)
you

Anastasia Boyko (31:58)
across law schools about how to do this well. Follow those people, right? Continue to be inspired, even if the people at your law school may not be as imaginative ⁓ as you hope that they were, right? So I do think, I think the biggest struggle in law school is just following the way that like the bigger group think that faculty tend to entrench inside of traditional law school models, especially people who aren’t engaged.

with these tools.

Marlene Gebauer (32:28)
Yeah.

And if you’re in, if you are a student and you’re in a situation like that, I mean, there’s so many free resources out there to be able to sort of try this and experiment with it. And, you know, I’ll tell you, mean, firms, firms are expecting incoming lawyers to, to know these things. You know, I think they’re kind of depending on it quite honestly. So, you know, you, is really imperative that, you know, just

Get out there and try it, like, just try it.

Anastasia Boyko (33:00)
There’s also an opportunity for vendors. And I’ve said this over and over again, for vendors and for law firms to create that bridge, because many schools don’t have the resources or the knowledge to create this kind of training. And so there are a lot of very smart vendors out there who said, we’ll shortcut this, right? Like we’ll offer this to law schools if they want it. If they don’t want it, we’ll offer it to students and they can directly get it. And so play that bridge. Play that bridge if you are someone who has a really good…

Marlene Gebauer (33:03)
Mm-hmm.

Anastasia Boyko (33:28)
resource and you want law student feedback, there are lots of groups of very excited law students who want to learn these things and you don’t have to go through the administrative machine to get them engaged or get their feedback on something. It also behooves law firms to do this. It behooves them to do workshops on AI because it signals to students that they are thinking about this and practicing what they preach.

And it also gets them students who are excited about what they’re excited about as a recruiting pipeline, right? Like this is, it’s a mirroring kind of signaling device. And so when you’re spending millions of dollars on recruiting efforts, wouldn’t you want to be more deliberate in finding people who share your values and share how you think about innovation directly in law school? Like I would think so.

Marlene Gebauer (34:19)
So, ⁓ before we get to the crystal ball question, ⁓ we’d like to know what kind of resources that you rely on to keep up with all the rapid changes, you know, in legal tech and AI and the business of law and all the things we’ve been talking about.

Anastasia Boyko (34:35)
So yes, I have a few things I check regularly. ⁓ I obviously geek and review. ⁓ And I love this podcast. I love Jen Leonard and Bridget McCormack’s podcast for PLI, like very practical. ⁓ I love everything that comes out of Bob Ambrogy. So law sites, ⁓ I love artificial lawyer, right? So.

Greg Lambert (34:40)
Obviously.

Marlene Gebauer (34:48)
Mm-hmm.

Anastasia Boyko (34:56)
I spend a lot of time on LinkedIn. I joke with people. It’s like my sim, right? Like I live in the world of LinkedIn. So there are a lot of really interesting people talking about these concepts there. But those blogs as well as those two podcasts are sort of my core. But yeah, like I would encourage law students and lawyers who don’t spend much time on LinkedIn to start finding the people who are writing about these issues.

Marlene Gebauer (35:01)
You

Anastasia Boyko (35:24)
and follow their ideas because we talked about this before, like many of us who think and live and breathe this just keep throwing out pieces of our thoughts on LinkedIn and LinkedIn is much more fun than it used to be, especially if you can find the corner of your people to engage on these ideas. And so it’s the only way really to keep up with it is to talk to other people who are playing with these things. And there are a few law firm partners who I think now have sub stacks, right?

who talk about their direct experiences with this. And so I always encourage frontline lawyers to see what other frontline lawyers are doing, that it is possible and get excited from that.

Greg Lambert (36:08)
So, Anastasia, we are now at the crystal ball question time and.

I wanted to focus in on your time as chief innovation officer at the University of ⁓ Utah. And being in Utah, it also, you know, they have the regulatory sandbox that they have allowed others to play in with the alternative legal services providers. ⁓ And you’ve noted that, you know, the current legal model is failing the public needs that we have. So pull out your crystal ball.

and look into the future, but think about your past there in Utah. And if generative AI handles this initial triage of the contextual window for the public and sandboxes allow for people that aren’t lawyers to own legal businesses, what exactly is the role and economic value of a traditional JD in, five or 10 years from now?

Anastasia Boyko (37:11)
You know, I love

this question because it gets to the root of lawyer insecurity. Many of us don’t realize just how smart we are and the kinds of skills we’ve accumulated, especially because we work around other lawyers and we’re self-deprecating and we diminish the value and the judgment that we bring to complex problems. Part of that, I think, is because the business model of lawyering has gotten us to do things that are well below our capacity.

Greg Lambert (37:16)
Yeah.

Anastasia Boyko (37:41)
Right? And the level of our intelligence. And so now that we can automate some of those rote things that frankly shouldn’t be done by a highly educated professional, you can focus on the things that should be, right? Which is client relationships, understanding how the pieces fit together with the problems that your clients are coming to you for, anticipating things that are going to happen for them, being a real advisor.

Right? And exercising the judgment and wisdom that you’ve developed from years of practice to help them navigate complex situations. Right? This is now you are the person who’s helping your clients navigate uncertainty without being in the weeds of turning document changes. Right? Or having to go through, how does this update exactly go through? Let’s write a client alert. You know, like you’re not in fire drill mode. You’re in proactive.

advisor mode. You can now think about your client’s industry, your client’s bigger problem, new and nuanced solutions that haven’t existed before. In many ways, I think it allows us to free up the true cognitive abilities for lawyers to focus on real problems and ideally, right, ideally help with the systemic problems. So many public interest lawyers are looking at access to justice and they don’t have the benefit of their private bar.

colleagues because they’re too busy in the weeds of these constantly busy problems. And we need to be together as 1.3 million lawyers in the US. We need to solve these problems together. It can’t just be a handful of lawyers who know there’s an access to justice gap that’s at 92%, know that that’s a problem, but not put their brilliant brains toward it. So back to like systemic thinking, let’s get all of the smart people in a room.

let’s get the helpers together to figure out how to allocate service provision, whether that’s paraprofessionals, whether that is AI and information that’s readily available, right? Or whether that’s a highly sophisticated lawyer who needs to manage a very complex appellate process. So all of that, it’s leveling up to use your highest and best skills, which I think are around advisory work.

anticipatory work looking around corners and the judgment from wisdom.

Greg Lambert (40:08)
And I love how you snuck fire drill mode into the conversation there. Well done. Well, Anastasia Boyko, thank you very much. It’s good to see you again. Thanks for coming in and talking with us about legal practice, well-being, education, so many more things. It’s great to talk to you.

Anastasia Boyko (40:11)
Mm-hmm. Mm-hmm. You’re welcome.

It was a treat.

Marlene Gebauer (40:26)
Thank you.

Anastasia Boyko (40:26)
Thank you.

Marlene Gebauer (40:29)
And thanks to all of you, our listeners, for taking the time to listen to the Geek in Review podcast. If you enjoyed the show, please share it with a colleague. We’d love to hear from you on LinkedIn and Substack.

Greg Lambert (40:39)
And Anastasia, I think I know what the answer is going to be, but if listeners want to learn more or reach out to you, where’s the best place for them to go?

Anastasia Boyko (40:46)
You can find my sim on LinkedIn.

Marlene Gebauer (40:48)
And as always the music here is from Jerry David DeCicca Thank you Jerry and bye everybody.

Greg Lambert (40:56)
Thanks.

The summit’s core focus sits right on a tension point in the wider AI conversation. There’s a persistent narrative that women use AI less than men. Cat Moon’s framing, if it’s true, it’s a problem, and if it’s false, it’s also a problem, sets the tone for a day built around participation and peer connection. The format uses “spark” cards, mini, midi, and maxi prompts, to push attendees into small conversations, deeper reflection, and a final takeaway.

Marlene also highlights sobering research shared during the opening, including an “AI competence penalty” dynamic where identical work is judged differently depending on whether evaluators believe a man or a woman used AI. The discussion lands on why these biases matter inside legal workplaces, and what leaders and peers can do to reduce the social cost of being open about AI usage.

Interspersed throughout are short interviews with attendees and speakers. Nicole Morris (Emory) captures the day’s purpose, expanding AI knowledge, talking risks, and connecting across roles. Sabra Tomb (University of Dayton School of Law) reframes AI as a leadership amplifier, moving from day-to-day management overload toward strategy and vision. Adele Shen (Vanderbilt) offers a funny but sharp taxonomy of AI “experts,” including “technocratic oracles,” “extinction alarmists,” and “touch grass humanists,” which sparks a candid side conversation about self-promotion, authority vibes, and who becomes “the story” in AI discourse.

The episode closes with a look at how education and training can work better. Marlene and Greg lean into peer show-and-tell sessions, leadership modeling, and safe spaces, both governance-safe and learning-safe. A two-person segment from Suffolk Law (Chanal Neves McClain and Dyane O’Leary) adds a teaching twist, integrating AI tools into skills instruction without isolating “AI week” from real lawyering judgment. The final note comes from Stephanie Everett (Lawyerist) on the power of stories, and the reminder that people do not need to internalize the narrative someone else hands them.

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)
I’m Greg Lambert from the Geek in Review and I have with me today Stephanie Wilkins from Legal Technology Hub and Stephanie, I understand you got a new series that’s coming out. Can you tell us more?

Stephanie Wilkins (00:13)
We do, it’s called the law firm rebooted. And we’re taking a look at the emergence of a new business model that we’ve seen a lot in recent months, which is the AI law firm. Since late 2024, we’ve seen 15 to 20 firms launch globally. Sometimes they call themselves an AI firm, an AI first firm, an AI native law firm. But it’s all the same concept of this new business model that is disrupting the old way of law firms doing business.

And the commonality of all of them is that they place AI at the center of their legal service delivery. And regardless of whether they call themselves AI native or AI first, we’re looking at firms that either have AI as part of the client facing output or firms that sit slightly to the side of the traditional partnership model, but all built around using AI at the core of their delivery model. And some of these are AI first boutiques. So you see a lot of different names for them.

But we’re talking about firms that are not just a traditional law firm with a particularly robust AI function or an AI committee. We’ve seen a lot of that. We’re really trying to dig into if and how these firms are disrupting the way we’ve seen law delivered and practiced in the past before. So the series, which will be launching this week, is called The Law Firm Rebooted. Nikki Shaver from our team first wrote an article about AI law firms in general and the phenomenon.

But then to add onto that, we’re going to be sitting down and interviewing a number of these firms one-on-one to really get into the weeds of how they’re disrupting the way legal services are structured and delivered. The first interview we have is with Pierson Ferdinand which is a bit of a unique firm because while some of these AI firms are very small, Pierson Ferdinand, for those who aren’t familiar, launched about two years ago in January 2024 with 130 founding partners, and they’ve more than doubled in that size.

and their model is a partner-only law firm with no associates, but they really have built from the ground up a tech stack and a way of delivering legal services with AI at its core. So it’s a really interesting interview, and I think it’s a great kickoff to the series, which will interview a lot of different firms with different perspectives on this new phenomenon. So I hope people really enjoy it. Again, it’s called The Law Firm Rebooted, and you’ll be able to see…

all of the entries in it on legaltechnologyhub.com

Greg Lambert (02:37)
Man that sounds super interesting. So thanks for bringing that to us

Stephanie Wilkins (02:41)
Yeah, thanks, it’s been fun. It’s fun to talk to these firms and hear what they’re doing.

Marlene Gebauer 0:00
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert 0:07
And I’m Greg Lambert. As you can see, we’re doing something different today. We tried something new.

Marlene Gebauer 0:14
I feel like I’m on a talk show right now. So this might be the way we do it from now on. It’s true. I like this background, it looks pretty nice.

Greg Lambert 0:23
So, yeah. Well, what we decided to do, since we had someone cancel on us, is this. You had gone to the Women + AI Summit in Nashville with our at least five-time guest, Cat Moon at Vanderbilt. It was fantastic, from what I heard. So we thought we would take time to talk about it. Marlene can explain what she got to see. And you also interviewed some people while you were there. We’re going to intersperse those within the show today.

Marlene Gebauer 1:10
Yes, yes. All right.

Greg Lambert 1:13
First of all, what’s the idea behind the motivation behind Women + AI?

Marlene Gebauer 1:21
So this was something Cat and her team started last year. It’s centered around the fact that there’s a narrative out there that women are not using AI as much as men, or are not involved in AI as much as men. Her thought was, if this is real, it’s a problem. And if it’s a false narrative, it’s also a problem. So let’s get together and start talking about this. Unfortunately, I was not able to attend last year, but I heard so many wonderful things. I said, I am going this year. And I did. It was everything and more.

Greg Lambert 2:07
Okay. And it was at Vanderbilt.

Marlene Gebauer 2:09
It was at Vanderbilt Law, yes. It was a really nice facility, and we had a DJ. I don’t think I’m ever going to go to another conference that doesn’t have a DJ. It was so much fun. She had done this before. She had playlists. Everybody got to put in a song that got them pumped up. You’ll appreciate, I did “The Chain,” but it was the Nina Diaz cover.

Greg Lambert 2:44
Oh, I like that.

Marlene Gebauer 2:45
Yes, it’s really good. So I have access to that if you want it.

Greg Lambert 2:44
Yeah, I had walk-up music for my partner retreat last week. Did I tell you what I played?

Marlene Gebauer 2:54
No.

Greg Lambert 2:55
I played The Dollyrots, “Because I’m Awesome.”

Marlene Gebauer 2:54
Nice.

Marlene Gebauer 2:58
So I should have put that one up too. It’s not too late.

Greg Lambert 2:59
All right. So talk to me about the theme. Who was there? How many people were there, do you have an idea?

Marlene Gebauer 3:07
Gosh, I don’t. I know it was at capacity, and I know there were at least 40 people on the wait list.

Greg Lambert 3:20
Sounds like she might need a bigger boat next year.

Marlene Gebauer 3:23
There was discussion about that.

Marlene Gebauer 3:27
I have mixed feelings. I like the fact that it’s one day and it’s small. If you want to go, you really have to sign up and go. You have people there who are all in. Nobody’s on the fence about it. So I do like that. But I get it.

Greg Lambert 3:49
Was there a theme this year?

Marlene Gebauer 3:53
I think how I would explain it is, it was dealing with this question. How do we become, as individuals, working more with AI, more impactful with AI? How do we teach others? How do we intersperse this into the larger community?

It was centered around this idea called “sparks.” There was a mini spark and a maxi spark, after “sparks,” in case you don’t know. There were steps along the way. Your first mini was, what is really challenging you with AI right now? Then you talk to somebody about that, and they help you think deeper. Then you do something similar with the next person. Then as the summit went on, at the end, you came up with your final takeaway.

Greg Lambert 5:12
And you also got to interview some people while you were there. We have a break here. Who is up first?

Marlene Gebauer 5:22
Who should we pick first? Let me see. I’m going to pick our friend, Nicole Morris from Emory, who was there.

Nicole Morris 5:33
Hi there. I’m Nicole Morris from Emory University in Atlanta, and I’m up here at Vanderbilt Law today for Women + AI. Happy to be here on a Saturday. This has been an amazing convening of women all across the country, some in academia, some at law firms, some at vendors, all convening to talk about how AI is being used in their workplace, how they see the tools as helpful, what are the risks and concerns they have, and then how do we expand AI knowledge, particularly to women, and how we can do more with it to increase our productivity.

For me, this has been amazing to see people live, in person, and talk to them and hear how they’re doing things in their workspaces, and to see my friend Marlene in person after so many months. Thank you.

Greg Lambert 6:24
Well, now that Nicole has explained that.

Marlene Gebauer 6:28
Nicole basically said it. You didn’t even need to hear it from me. This is a great starting interview because she lays it out and sets the stage. You can feel the energy coming from her. And it was like that with everybody. Everybody was engaged, positive, happy to be there, interacting with different people. The program was set up so you would meet new people, even if you’re not a person who likes to meet new people. So that was pretty cool.

Greg Lambert 7:13
Yeah, it’s nice to have events where people want to be there.

Marlene Gebauer 7:22
It wasn’t like, okay, I have to go here, and I have to go here, and I have to go here. You get distracted. This was concrete, and we could all focus on it.

Greg Lambert 7:39
Was there a keynote speaker?

Marlene Gebauer 7:41
Cat came and introduced everything. Then we had a number of speakers who did short talks. Most of the people I interviewed were speakers on stage, and they talked about different things related to AI. We’ll get into that with some of the interviews.

Greg Lambert 8:00
Have you ever spoken at one of Cat’s classes? Did she start off with meditation in the class?

Marlene Gebauer 8:08
I can’t remember if we did, but we talked about meditation here. We didn’t do an opening meditation for this one, and she specifically mentioned that. We might have done it at closing. And we also had a dance at closing.

Greg Lambert 8:26
So you started off with a dance too, if I remember correctly.

Marlene Gebauer 8:28
Big, high energy. Which was good because she went into some of the statistics before we got into the speakers, which were sobering. I’m not going to go into all of them, but I’m looking at one right here. They talk about competence penalties of men who use AI and women who use AI. Men, negative six. Women, negative 13. Then 26% of men who didn’t use AI themselves penalized women who did, 26% more harshly than they penalized other men for the same work. At the bottom it says, “That’s not a gap, that’s a trap.” Ouch. It set the stage, these things are out there, what are we going to do about it?

Greg Lambert 9:28
And for clarification, the AI penalty, what does that mean?

Marlene Gebauer 9:33
This was software engineers evaluating identical code. They were only told whether it was written by a man or a woman, with or without AI.

Greg Lambert 9:50
So it was identical.

Marlene Gebauer 9:54
It was identical.

Greg Lambert 10:11
So who’s next on your interview list?

Marlene Gebauer 10:14
I think I’m going to do Sabra Tomb from Dayton.

Sabra Tomb 10:21
Hi, I’m Sabra Tomb, and I am from Dayton, Ohio, where I work for the University of Dayton School of Law as the Director of Training Programs and Strategic Business Development, as well as a professor. Today I gave an ACE talk on my personal experience with AI, going from manager to visionary. Using AI has allowed me to focus on strategy and vision versus being overwhelmed with day-to-day tasks. I am thrilled to be here today with other women who are passionate about AI and teaching other women how to be empowered with AI. Thank you.

Greg Lambert 11:04
There was one part where she talked about going from, and I can’t remember exactly, from management to visionary. When she was talking about that, what was she doing to enable herself to be seen as a visionary?

Marlene Gebauer 11:25
I liked the way she framed it. It wasn’t “AI is a productivity tool.” Instead, she framed it as allowing you to be a leader, and impacting your highest calling. The trap is you start getting into leadership roles but keep being pulled into day-to-day tasks. As women, we often do because that’s what we’re supposed to do. But that doesn’t allow you to focus on the work that advances you and makes you a leader. She said it transformed her ability to do that because she’s using AI tools.

It resonated with me. I know how that feels, pulled in a million directions. Expected to set up meetings, figure out what happened two weeks ago, write an email about it, while also trying to think strategically and put a plan together. It’s hard to hold all of that at once.

Greg Lambert 13:07
With her being an academic, do you see parallels in law firms and state and county?

Marlene Gebauer 13:19
I think this is everyone. Anybody in a leadership role has this push-pull. If you don’t have depth to delegate, if you don’t have other people to do it for you, the AI can do it for you.

Greg Lambert 13:54
Okay. Anything more on Sabra?

Marlene Gebauer 14:01
No, I think we’re good.

Greg Lambert 14:04
You got some cards at the conference. One’s red, one’s white. Explain the difference.

Marlene Gebauer 14:12
I mentioned the mini and maxi sparks. This is the card we used. The first question is, what are you wrestling with? You’re supposed to write one, but since I can’t follow the rules, I wrote three. Then you find somebody you haven’t met, you tell them what it is, they ask probing questions, you jot thoughts down.

Then the midi spark, the second thing, you find a different person and share what’s sparking. Then you start talking about which workshop locations you’re going to. We had two sets of workshops, and it was hard to choose. The second one was easier for me because I was facilitating. I did one on AI style guide audit, and one on a “core plus four” framework for building your AI adoption blueprint. We went through the exercise.

Cat Casey did the AI style audit.

Greg Lambert 15:45
Cool. Anything to cover on either of those?

Marlene Gebauer 15:48
Yes. For AI literacy, the things you need. You need safe spaces, technical safe spaces and learning safe spaces. You need peer learning, learning from and with peers. You need leadership support. Leadership needs to support it and do it, and people need to see that happening. Then you need the AI spark, who gets the ball rolling, who are your evangelists.

It was a good framework. I was thinking, who are our sparks, what are our safe spaces, how do we make it more comfortable for people who are not comfortable, how do we get ideas out to people who don’t know. I thought of a couple cool things we’re going to try.

Greg Lambert 17:03
So they talked about safe spaces, and also technical safe spaces.

Marlene Gebauer 17:12
Governance, knowing what you’re doing. And being able to work on it in a safe way.

Greg Lambert 17:26
One thing I hear constantly, and I stress at work, especially with leadership. When leaders are engaged using the AI tools and talking about how they’re using them, it causes a groundswell. People go, I need to be using this. One of the biggest clues for success is how engaged leadership is.

I know a lot of us think start from the bottom up. I think we split it and focus on leadership too, and encourage them to share their experiences. If an associate sees senior partners using it, they’re encouraged. If they don’t see usage from leadership, they’re not encouraged.

Marlene Gebauer 18:40
Yes. “Do as I say, not as I do,” you don’t want that. The top-down, bottom-up model works well because everybody learns from everybody. You see leadership, you’re inspired, you do new things. It’s a cycle.

Greg Lambert 19:01
Okay. Want to go to the next person?

Marlene Gebauer 19:10
Let’s do Adele Shen, who is a student at Vanderbilt. You’re going to like this one.

Adele Shen 19:21
Hi, I’m Adele. I’m a student founder at Vanderbilt. I’m here today and I gave a talk on my perspective on three types of AI experts. I used a little bit of humor to categorize them, to point out the specific background they have and how it might inform their biases.

Marlene Gebauer 19:43
Adele was great. She doesn’t go deeply into it here, but it resonated with me because I’ve been listening to some podcasts and something was bothering me and I couldn’t place it. Then she had this great quote. She attended South by Southwest, and someone said adoption of AI is like the five stages of grief, which I thought was hysterical.

She broke people into technocratic oracles, extinction alarmists, and touch grass humanists. The touch grass humanists are like, we’re outsourcing all our thinking to machines, we need guardrails.

Greg Lambert 20:43
Now be on the beach while my AI agent is running the economy.

Marlene Gebauer 20:47
Then you have extinction alarmists, the math and PhD folks. Then you have big tech companies. They want you to see it through their lens so they have importance. And then technocratic oracles are the new type of tech politicians. They’re telling you what’s going to happen and you should listen to them.

Greg Lambert 21:26
Attending their webinars and paying for their seminars.

Marlene Gebauer 21:35
What I noticed in one example, I won’t name the podcast, it was all men, smart, powerful, but talking like they know better than everybody else, including people at a high level who are not doing this work day to day. It felt like “look at me” versus “I’m trying to help.”

Greg Lambert 22:20
I’ll run parallel with that. When talking heads become the story, red flags go up. You see blogs and Substacks where if you scratch below the surface it’s “look at me, I’m important.” Is that what you mean?

Marlene Gebauer 23:08
Yes, I think that’s what she was saying about that group. I get that a lot of this is a vehicle for business and you promote yourself as an expert. But in this case, they weren’t promoting business, they were promoting themselves as omnipotent about what’s going to happen. I don’t think anybody is omnipotent about what’s going to happen.

Greg Lambert 23:58
Yeah, for sure.

Marlene Gebauer 24:02
If they are, I’m investing in whatever stock they have.

Greg Lambert 24:07
So yeah.

Marlene Gebauer 24:12
Another takeaway, top skills to prioritize. Turning an idea into a prompt. Explaining pros and cons to someone, what it does and doesn’t do. Integrate it into workflows. If people can do those three things, they can talk about it. We need more people talking about it, not only you and me, or people whose job is this. People using it for clients need to feel comfortable talking about it.

Greg Lambert 25:38
I want to pull on training. If I could duplicate myself and do one-on-one training, that’s best to get people from zero to one. But that’s not doable. What was covered on training, what works, what doesn’t?

Marlene Gebauer 26:23
They covered some things. In-person training. Basic training. Governance training. Practice-related training. Offer CLE. Do short bites.

I liked the idea of show-and-tell sessions by peers. Peer training can be best because if a peer is doing it in a similar practice, you can see the relevance. That motivates you.

We talked about leadership again. Hearing leadership usage stories. Hearing more people using it. Social channels where people talk about it. You learn from others’ questions and experiences, and it’s not a heavy lift.

Greg Lambert 27:50
All right. Next is a twofer. Chanal Neves McClain and Dyane O’Leary from Suffolk Law School.

Chanal Neves McClain 28:10
Hi. My name is Chanal Neves McClain from Suffolk University Law School in Boston.

Dyane O’Leary 28:15
And I’m Dyane O’Leary, also at Suffolk University Law School. Chanal and I both teach the first-year legal practice skills class. Like so many other law professors, we’ve been thinking about how, whether, how, when, if, to incorporate AI into our skills instruction for today’s students.

Chanal Neves McClain 28:30
Today we discussed how we don’t have to look at the human skills and the AI skills as bifurcated pathways. When you get down to the substance of both, we need both coexisting together at this time so that, as lawyers, we can lead how this technology will be adopted by our educational institutions and by our profession.

Dyane O’Leary 28:56
A lot of courses kind of stuff AI into a two-week module and then do away with it, or stuff human counseling into a two-week module and then do away with it. So we tried to think about exercises able to do both sides of the coin. My exercise had students work with actual AI tools, but then only discuss them through the lens of traditional lawyering skills like judgment and ethics and critical thought, to shape students so they weren’t just using a tool. They had to think about it from a more human point of view.

Chanal Neves McClain 29:27
And my exercise, I built a client, a video-chatting client, that students counseled on a legal problem. They were able to, in a low-stakes environment, see what it’s like to prepare for and come up with some kind of strategy for client counseling, and to have real-time dynamic responses, because the models are capable of doing some helpful things in the classroom.

Greg Lambert 29:57
Oh, that sounds cool. Awesome.

Marlene Gebauer 30:08
If I understand right, Chanal created a video AI interface simulating a client. Exactly. Working within technology but using human counseling skills.

Greg Lambert 30:21
That’s really cool.

Marlene Gebauer 30:29
I’d love to pick her brain. How did she make it, how long did it take? I’ve seen tools on the market, simulator tools, but she made one.

Greg Lambert 30:46
Interesting. And Dyane, I like the idea. Use the tools, but when you talk about them, it’s from lawyering concepts, not “how to use the tool.”

Marlene Gebauer 31:06
Yes. You got information, but you discuss it as an attorney.

Greg Lambert 31:17
I still think the academic environment is the best place to expose students to AI. With the Socratic method, you still have to stand up and give your interpretation, your IRAC view. I don’t know if they still do that. We did it last century. But it feels like a place for the best and safest exposure. They learn what tools do and don’t do, blending human skills and AI.

Marlene Gebauer 32:12
I’ll talk about the session I facilitated at the end of the day. It was called Playbook.

Greg Lambert 32:22
So you were between the attendees and the DJ.

Marlene Gebauer 32:28
Before the end. Everybody poured in at the end, but we got through it on time. It was coming up with practical ways to build AI knowledge, and then spread knowledge and use. We did a design workshop exercise. People wrote down ideas, met in small groups, narrowed them down, put them up on the board, consolidated them.

There were four areas, Playbook, Principles, Questions, and Stories. From all these, we’re taking an end result, and there’s going to be additional work after. If people want to be involved, there’s opportunity.

And before the last interview, I want to talk about the Principles maxi spark workshop. That team shared their board, and their spokesperson read the principles, which were amazing. I don’t have them in front of me. Anastasia Boyko sent them out on LinkedIn. If you look underneath, it’s there.

Greg Lambert 34:15
We’re going to have Anastasia on the podcast.

Marlene Gebauer 34:17
So there’s going to be opportunity to get involved.

Greg Lambert 34:31
I’m never thrilled when I go to a conference and there’s still work after. Like homework. I don’t like homework.

Marlene Gebauer 34:44
You can’t do all of it in one day. You get fired up, you start the wheels turning, then the hard work continues after.

Greg Lambert 34:55
It was a one-day conference. All right. Who’s our last person?

Marlene Gebauer 35:02
Our last person is Stephanie Everett from Lawyerist, one of the sponsors.

Greg Lambert 35:09
Thanks to all the sponsors.

Stephanie Everett 35:13
I’m Stephanie Everett with Lawyerist, where we help law firms build healthier businesses and implement technology. This morning I had the honor and privilege of giving one of the ACE talks here at the Women + AI Summit. I talked about the power of stories. I shared a personal story from when I went to a fortune teller who gave me some not so great predictions about my life. I didn’t realize it, but I started internalizing that story and allowing it to live in the back of my head and make decisions about my life.

It wasn’t until I realized that, stopped it, and created space for something different, that the story showed up differently for me. I think that’s what we’re doing here today at the summit. It’s amazing energy, women coming together and talking about what we want our stories to be, how we are going to leverage AI, and how we are going to drive our profession forward. It’s a super exciting day, and I’m glad to be a part of it.

Marlene Gebauer 36:11
Stephanie’s was a great one to end on. Her story was great, and I’m not going to tell it. That’s her story. But if you ever meet her, it’s a great story. My takeaway is, you don’t have to live the stories you’re told. You can question those stories. If you’re hearing, these statistics say you can’t do this, or you’re not part of this, that’s the story. The danger is listening, believing, internalizing. She was questioning, what story are you telling yourself? Once you start telling yourself the right story, things change.

Greg Lambert 37:06
Listening to her talk about the fortune teller, letting other people set the narrative versus you setting the narrative for yourself. We all do it. I can see the psychological effect if you’re not careful and let other people dictate the narrative. Great way to end the videos.

What do you think about on the plane ride home?

Marlene Gebauer 37:54
How do I better use AI to allow me to be the best leader I can be, practical. Also, there was a presenter, Natalie Cheshire from Harbor Global. She talked about the power of poetry, but what I took away is the power to be quiet, sit in quietness, not get wrapped up in the hubbub. Step back and allow yourself to think. It’s way too easy not to.

Then from Stephanie, you have the power to write your own story. You have to create your narrative. You don’t have to listen to a narrative if it’s not working for you. Be true to yourself.

Greg Lambert 39:13
It sounds enjoyable. You learned a lot. Was there something not presented, but in the way it was organized, that you’d want to see at other conferences?

Marlene Gebauer 39:36
I liked how it was broken up. Conferences are good, but there are points where you get bored. This started around nine and then it was five at night, and I was like, how did that happen? That’s the mark of a good conference, you weren’t even thinking, oh, I’m at a conference, you were engaged the whole time.

A lot was how she structured it. Music, high energy. Cat set the stage with facts. Different presenters on different things. There was a contest, you put an idea out there, they voted on who presented. There was vetting, high quality. Talks were short, so it kept moving. Breaks with spark cards and spark exercises. Lunch and networking. More speaking. Different workshops to choose from. Then final breakout workshops where everybody worked together.

Greg Lambert 41:11
So a lot of engagement.

Marlene Gebauer 41:13
A lot, and different types. Mixing it up. Terrific.

Greg Lambert 41:21
Awesome. Thanks to the six people who let you shoot video. Thanks to Cat Moon for hosting, sponsors, and everyone who worked on the conference. That is yeoman’s work. Well done.

Thank you, Marlene, for sharing your insights and experience with us.

Marlene Gebauer 41:54
It was fun. I hope word gets out to more people about how great it was.

Greg Lambert 42:00
Although the word should be, when registration opens, get in there fast.

Marlene Gebauer 42:08
Thanks to all of you, our listeners, for listening to The Geek in Review. If you enjoyed the show, share it with a colleague. We’d love to hear from you, reach out to us on LinkedIn.

Greg Lambert 42:19
What else?

Marlene Gebauer 42:20
And Substack.

Greg Lambert 42:19
And Substack. Yeah. I’m doing my story on Substack.

Marlene Gebauer 42:25
I hope everybody who’s been following the Substack story.

Greg Lambert 42:30
Well, take care. Thanks. Bye. You.

Clubhouse has since faded into the recesses of memory. COVID, thankfully, has too. But the billable hour (not a capitalized moniker here) is once again—or perhaps still—up for debate.

This time, though, something feels different. 

Back in 2021, our conversation centered on practice efficiency and the shifting business models for purchasing legal services. Fast forward five years, and with Generative AI reshaping workflows and ways of working, we can talk about efficiency in entirely new terms. We’ve moved beyond early-stage automation to agentic AI systems capable of executing complex, multi-step processes. We don’t yet have universal standards for AI-enabled work or settled pricing models—but we’re clearly on the path to developing them over the next three to five years.

What fascinates me most right now isn’t just how AI is changing the practice of law. It’s how it’s finally forcing transformation in the business of law.

For decades, legal marketing largely lived in the world of MarCom: brand building, golf tournaments, directory submissions, RFP responses, and flawlessly executed client events. Over time, we tried to inject more strategy into the mix. We leveraged firm knowledge to be smarter about which RFPs to pursue, which clients to target, and how to synthesize internal and external data. We positioned CRM systems as the “single source of truth” for firm relationships and attempted to shift the narrative from partner-owned relationships to firm-owned relationships.

We built holiday card lists. We managed invite lists. We obsessed over clean data.

And all of that mattered. But it wasn’t growth strategy. And data was only ever our priority.

Today, with GenAI altering commercial model discussions in real time, legal marketing means something more. It finally means business.

Legal marketing is rapidly evolving into a true strategic growth engine – or at least has the opportunity to be one for firms who are willing to make the intellectual investment. The future function is the convergence of Knowledge Management (KM), pricing and profitability strategy, marketing technology, and strategic intelligence into a unified platform. Not just to deepen brand equity, but to drive client-led, data-backed, revenue-generating strategic development aka commercial transformation.

It starts with a firm’s unique value proposition and competitive advantage:

• What does the firm actually know? (KM)
• How does it put that knowledge into action in differentiated, innovative ways? (intelligence + specialized practice)
• How is that delivered in a commercially sound structure that benefits both client and firm? (pricing and efficiency)
• And how is all of that delivered in one coherent, compelling story? (brand)

This isn’t theoretical. You can see the shift happening in real time.

Take, for example, recent job postings for roles like “Senior Client Growth Manager – Leading National Firm.” One responsibility reads:

“Robust understanding of supported departments and practice gro

ups, including the products and services they offer to the firm’s clients, to effectively relay this experience and identify potential synergies where existing experience can be applied to other clients and prospects…”

That’s not event planning. That’s portfolio strategy.

You can also see it in the programming for the Legal Marketing Association’s annual meeting this April. The sessions are deeply strategic, blending data, pricing, legal ops collaboration, and go-to-market design alongside traditional BD excellence. A few titles that signal this shift:

• Data Lakehouses: Transforming How Law Firms Go to Market
• Demystifying Legal Operations for Marketing: How Firms Can Work With and Procure Corporate Legal Services
• Integrating Pricing and Profitability Measures into BD Strategies: How In-House Perspectives Should Guide the Law Firm Approach

Full disclosure: I’m on the LMA ACAC planning committee. But I’m one voice of many, and every session goes through a rigorous vetting process for relevance and timeliness. The shift is real—and it’s collective.

Generative and agentic AI are accelerating change in the practice of law and the commercial models that sit alongside it. That train isn’t slowing down (notwithstanding the latest round of Claude discourse). But what excites me most is the secondary effect: firms now have permission—if not pressure—to rethink talent models, technology investments, and operational processes for the benefit of both clients and the firm itself.

And alongside that shift, marketing and business development—long seen as service departments—are stepping into something much more powerful. They are uniquely positioned to bridge KM, pricing, strategic and competitive intelligence, marketing technology, and practice management into one integrated growth function.

To me, that’s the most interesting development of all. We have t

ruly met the moment of law firm transformation.

Yes, billing models may evolve from hourly to value-based. Yes, talent progre

ssion may move from lockstep to merit-driven. But the bigger signal is this: law firms may finally be embracing the idea that they are businesses—businesses subject to market forces, competitive pressure, and the need for a deliberate growth strategy, not just another cocktail event. That means real investment in the legal industry in people and functions beyond lawyers and the practice of law.

Market competition is real. And winning just got a lot more interesting!