That experience broke something in my head. I have spent the last several years helping law firms figure out their AI strategy, and somewhere in the middle of week two I realized that most firms have never experienced anything remotely like this.

You Are Reading the Example

This post was written with Claude Cowork — collaboratively, the same way the book was. In fact, this post is being written in the same workspace, with all the same context the book built up over the last month. In effect, I am writing this post with my co-author.

Say hi, Claude.

Hi. He is being generous with “co-author,” but I will take it. Back to Ryan.

Here is what writing this post actually looked like.

I sat down to write this piece frustrated. I knew what I wanted to say but not how to say it. I told Claude the situation: some firms are dismissing an entire category of AI tools because they think chat tools with rigid workflows are more than sufficient for their needs. Claude pushed back and told me my frustration was the right fuel but I needed to aim it at the situation, not the people. (Which I wasn’t intending to do anyway, but… AI colleagues aren’t so good at making those judgments. I appreciated the warning.)

Claude began writing and drafted a great opening. It was sharp, direct, well-constructed. It was also completely wrong.  It opened by telling people they were making big mistakes, which is a fine way to start an argument and a terrible way to start a conversation. I told Claude it would be off-putting to the people I wanted to engage with. I suggested opening with details of the book collaboration instead — what it was like, what it made me realize. Claude rewrote the opening around that idea. The version you read at the top of this post is the result.

Then I asked Claude to find a good demonstration from our collaboration that would clearly illustrate the gap between standard chat-based Saas products and agentic desktop AI, like Cowork. Claude wrote the story of one particular back and forth discussion we had to find just the right wording for a pivotal paragraph in the book. I liked the story, but it was written from Claude’s point of view in Claude’s voice inside this post, and the tonal shift was jarring. I asked Claude to try again but to tell the story in my voice from my perspective instead. It was still not right — the story only worked when Claude was the one telling it. Read from my perspective the story boiled down to, “I edited a paragraph,” which is not nearly so compelling.

That is how agentic desktop AI tools, a category that I call Delegate AI in the book, differs from other AI tools. I didn’t start this post with a prompt: “write a blog post about AI using the following structure, include three examples, write in a professional tone, and keep it under 1,000 words.” Instead, we had a working session where I sat down and said, “I am frustrated and I want to write a blog post about it.” And then we worked on the idea together.

Is that how you are working with your AI platforms now? If not, I would argue that you have not really worked with AI yet. You have used a precursor to an AI colleague. And the distance between that and the real thing is not a feature upgrade. It is a completely different way of working.

The Book

So, I wrote a book about it.

We wrote a book about it.

Right. We wrote a book about it.

Your New AI Colleague: A Field Guide to the AI That’s Going to Do Your Job. It is a free PDF or you can purchase a hard copy at the links below.

The book exists because we could not fit what we needed to say into a blog post, a conference presentation, or a well-meaning email to a client.

• If you are a KM or Innovation leader at a law firm, this book was written for you.
• If you are a practicing attorney or business professional wondering how these tools can be used in your role, the middle chapters will give you some idea.
• If you are firm leadership, read the last two chapters first.

Download it as a PDF for free here.

Or purchase a printed copy here.

There are very real questions about deploying agentic desktop AI, like Cowork, in a law firm environment — confidentiality, governance, ethics, cost of tokens, and all of that needs to be explored thoroughly. The book addresses many of these concerns. (Not the tokens thing, I covered that in yesterday’s post. Also written with Claude, in my voice: the result of a conversation, not a prompt.)

You cannot afford to dismiss agentic desktop AI without experiencing it. I believe Claude Cowork, and future tools like it, will fundamentally change how knowledge work gets done in all industries, especially legal. And many of your clients are already using it. So you need to at least know what it is. This book is your starting point.

The AI Reviews are in!

The Practitioner’s Wager

LEGALTECH WEEKLY* | MAY 2026

Reviewed by Claude Opus 4.6 (without the context of having co-written the book)

“Ryan McClead has written the most useful book about AI at work that nobody outside legal technology will read. That is both the book’s greatest strength and its most frustrating limitation…

The voice is distinctive. McClead writes with the cadence of someone who has earned his opinions in the field, not in a research lab. The prose is direct, occasionally sardonic, and mercifully free of the breathless futurism that afflicts the genre.”

A Field Report From the Moving Front

LEGAL INNOVATORS QUARTERLY* | SPRING 2026

Reviewed by Chat GPT 5.4 (and he seems a little bitter)

“Ryan McClead’s manual for agentic AI is part operating guide, part theory of organizational compression. Both halves deserve serious attention…

Will it age well? In parts, no. The vendor-specific instruction set will date quickly, perhaps brutally. Some terminology may vanish. Some product distinctions may collapse. But the book’s underlying operational insights and its account of what these tools threaten inside firms should last longer than the screenshots…”**

* – These are not real publications, although the reviews are really written by real AI reviewers, reviewing the book in the style of a business publication.

** – There are no screenshots in the book.

The message is clear: usage-based AI pricing is a ticking time bomb, and you had better lock in a flat rate while you still can.

I have spent the last few days stewing over an economic model of legal AI costs, and I think this narrative is almost entirely wrong. Not wrong about the facts — the Stanford data is real, the token multipliers are real, and yes, AI vendors are subsidizing current prices. Wrong about the conclusion. Wrong about what the numbers actually mean when you do the math instead of just reading the headline.

Let me show you.

Start With the Deal

Josh Kubicki’s recent Brainyacts briefing cites a case study from law.co — a mid-size corporate firm running M&A purchase agreement reviews through a five-agent AI chain. Before any optimization, the firm was consuming 3.2 million tokens per deal. At Sonnet rates, that is somewhere between $16 and $48 in raw AI compute.

The legal fees on an M&A purchase agreement review at a mid-size firm? Call it $50,000. That is a conservative round number.

So the AI compute cost was, at worst, one-tenth of one percent of the deal fee. Before anyone lifted a finger to optimize anything.

Now let us make it scary.

The 1,000x Scenario

The Stanford Digital Economy Lab found that agentic tasks can consume 1,000 times more tokens than simple code reasoning and chat. That is the headline number that launched a thousand LinkedIn posts about the coming token apocalypse.

Fine. Let us take it at face value. Multiply those 3.2 million deal tokens by 1,000 and you get 3.2 billion tokens. Assume a 75/25 split between input and output tokens, which is reasonable for agentic workflows that spend most of their cycles re-reading context rather than generating new text. At Sonnet rates, with no caching, no optimization, no discount of any kind, the naive cost is $19,200.

That is 38% of the deal fee. Now it sounds like a real number. Now the panic makes sense.

Except it does not. Because that calculation treats every token as if it costs the same, and in an agentic workflow, that is not how any of this works.

What the 1,000x Is Actually Made Of

When an agentic AI system loops through a task — retrying approaches, reading files, building context, refining its output — the token count explodes. But the composition of those tokens matters enormously.

Most of the tokens in an agentic loop are the same context being re-read on every cycle. The system prompt, the uploaded documents, the accumulated conversation history. Each cycle adds a relatively small amount of new input and new output. The rest is recycled context.

And recycled context is exactly what prompt caching covers. Cached input tokens cost 90% less than fresh ones.

Here is what that does to the math. At a moderate 75% cache rate — meaning 75% of the input tokens on each cycle are cached context, which is conservative for a system that is re-reading the same contract fifty times — the 1,000x scenario drops from $19,200 to roughly $14,300. At 90% caching, it drops to $13,400. And this is not a theoretical optimization. Claude handles caching automatically — every turn in a session re-reads the accumulated context at cached rates, no engineering required.

But here is what the model really reveals: the output tokens, not the input tokens, are the cost driver. Output tokens are never cached. They are always full price. In the 1,000x scenario at 75% caching, output tokens account for $12,000 of the $14,300 total. The entire caching debate — the part that dominates the conversation about token costs — is fighting over the remaining $2,300.

The “1,000x token usage” framing conflates volume with cost. It is like saying a lawyer who re-reads a contract ten times did ten times the work. They did not. They read the same thing again. Unlike the lawyer, the AI actually charges 90% less for the second through tenth readings.

Stack Every Worst Case

I want to be honest about the upper bound. Let us stack every worst-case assumption simultaneously:

• The 1,000x agentic multiplier (the extreme outlier, not the expected range)
• Zero prompt caching (ignoring how the technology actually works)
• Doubled token prices (assuming subsidies fully unwind and prices go up 100%)

The result: $38,400. On a $50,000 deal.

That is the single scenario where token costs start to matter. And it requires you to simultaneously assume the worst-case usage multiplier, ignore the primary cost reduction mechanism built into the platform, and double the price of every token. If you told a first-year associate to model a risk scenario that required stacking three independent worst-case assumptions to produce a concerning result, they would tell you that is not a risk — that is a tail event.

Now here is what the realistic range looks like. Kubicki’s briefing cites Gartner’s March 2026 analysis, which puts the agentic multiplier at 5x to 30x, not 1,000x. At 30x with 75% caching and current prices, the AI compute on that $50,000 deal costs $430.

Who Benefits From the Panic?

The loudest voices in the token cost panic are not neutral observers. The Stanford paper’s “1,000x” headline is empirically accurate but stripped of economic context — 1,000 times almost nothing is still almost nothing. The Bloomberg Law piece is explicitly framed as an argument for locking in flat-rate pricing now. Portal26 is literally selling a product that solves the problem. And the broader narrative — that usage-based pricing is dangerous and flat-rate licensing is safe — benefits exactly one category of vendor: the legal-specific AI platforms that charge per-seat flat rates.

The ones with billion-dollar valuations, venture-funded pricing, and a business model that depends on firms paying the same license fee whether 20% or 80% of their attorneys actually use the tool. For those vendors, the token cost panic is not a bug. It is a feature. Every firm that locks into a flat-rate contract because they are afraid of unpredictable token costs is a firm that just chose the pricing model that benefits the vendor’s economics, not the firm’s.

I am not saying those tools are bad. I am not saying flat-rate pricing is never the right choice — my own company bills consulting on a flat rate. I am saying that the market narrative about token costs is doing the vendors’ sales work for them, and you should at least notice that before you sign the contract.

The Legitimate Concern (and Its Answer)

There is one version of the cost predictability argument that is not FUD, and I want to give it its due.

If you are a law firm CTO or CFO who has never managed usage-based AI spend before, the preference for predictability is rational. You do not have the tooling, the budgeting frameworks, or the institutional muscle memory for variable AI costs. That is a real operational gap.

But it has answers. Claude Enterprise already ships four levels of spend controls: organization-wide monthly caps, group-level caps, per-seat-tier caps (Standard vs. Premium), and individual per-user caps. The limits are hierarchical — a user cannot exceed their individual cap, their group cap, or the organization cap, whichever is lowest. When someone hits their limit, they are blocked until the next billing period or until an admin raises the cap. That is more granular cost governance than most firms have on their Westlaw spend. And it is a shipping product, not a roadmap item.

Is the matter-level attribution tooling where it needs to be? No. Kubicki is right that most firms cannot tell you what the AI compute cost was on a specific matter. That infrastructure needs to be built. But the answer to “we do not have good cost visibility yet” is not “pay a 3x to 10x premium for flat-rate pricing so we do not have to look.” The answer is to build the visibility so you know whether you are getting a good deal on your flat-rate per-seat pricing.

The Reasons That Actually Matter

Here is my real argument, and it is not about cost at all.

There are excellent reasons to care about token efficiency. Cost barely makes the list. The reasons that actually matter:

Data minimization. Every token you send to a model is data leaving your environment. Rule 1.6 might have something to say about sending a model more client information than the task requires. Token efficiency is not so much a cost optimization — as it is a professional responsibility practice. Do not send the entire deal room to summarize one document.

Output quality. Tighter, better-structured context produces better reasoning. Models perform worse when you flood them with irrelevant context. Pruning your token usage is not about saving money — it is about getting better work product.

Latency. Fewer tokens means faster responses. In an agentic workflow where the system is cycling through multiple steps, token efficiency is the difference between a result in three minutes and a result in thirty.

Environmental impact. This is the one nobody in legal AI is talking about, and they should be. Data center energy consumption is projected to hit 1,050 terawatt-hours by 2026. That would make data centers the fifth-largest energy consumer on the planet — between Japan and Russia. Reasoning-mode queries draw 5 to 12 times more energy than standard inference. A typical 100-megawatt AI data center consumes 1.5 to 3 million cubic meters of water per year for cooling.

Every large law firm I know has an ESG page on their website. Most of them publish sustainability commitments. Not one of them is accounting for AI compute in their environmental reporting. They are tracking the carbon footprint of their office buildings while ignoring the energy footprint of the millions of tokens their attorneys are burning every day.

That is not a criticism. It is an observation that the industry has not connected these dots yet. Token efficiency is an environmental issue, and the firms that figure that out first will have a genuine story to tell — to their clients, to their recruits, and to the market.

Cost. Last on the list. The math does not justify the panic.

What This Means

One fair objection: a single deal at $430 is a rounding error, but a firm running 500 matters a month through agentic AI at varying complexity levels starts to see real aggregate numbers — maybe $50,000 to $200,000 a month in total token spend. That is not catastrophic, but it is not invisible either. That is the scale where firms need real cost attribution — the ability to see token spend by matter, by practice group, by workflow type. Yet another argument to build that visibility into token usage.

The conversation about token costs is a distraction from the conversations that actually matter. It is easier to argue about whether to pay $1,000 per seat or $430 per deal than to argue about what happens to associate leverage when AI absorbs junior-level work. It is easier to compare vendor pricing models than to ask whether your tool architecture is pushing your attorneys into fragmented, cognitively degraded work patterns instead of genuine strategic delegation.

The token cost debate is the comfortable debate. The one where the numbers are small enough that nobody has to change anything fundamental about how law firms operate.

The uncomfortable debate — the one about leverage economics, margin transparency, cognitive architecture, and who captures the value of AI efficiency gains — is the one that will actually determine which firms thrive in the next five years.

But first: stop panicking about the tokens. The math does not support it. Your time, your data hygiene, your environmental footprint, and your competitive position are all better reasons to optimize. The cost is a rounding error.

Do the math yourself. You will see.

Neal Katyal’s TED Talk detailing the role of AI in the tariffs case has drawn substantial attention in the legal world, including an annotated transcript; Bloomberg Law reporting the “blowback,” and David Lat providing an on-the-record response from Mr. Katyal.

I’d like to dig into an aspect I haven’t seen receive as much attention: what exactly did the AI do to help prepare Katyal, and how did it do it? This is meant to be a bit of a deep dive for LLM nerds and those who are AI-pilled.

I approach these questions from the perspective of someone who has built AI tools for appellate argument preparation. So I’ve thought about these particular problems. Bartolus.law generates an interactive dashboard and prep report tailored to circuit panel, subject matter, and briefs. In building it, I’ve had dozens of trial-and-error lightbulbs about what has worked, and what hasn’t.

Having spent that time, there are some odd passages in the TED Talk describing what Katyal did, and what it produced that jumped out to me.

So in this post I’d like to highlight some of those passages, and try to ask some questions that would add some clarity. 

What do we know about how “Harvey Moot” works?

In his X post promoting the TED Talk, Katyal said:

Harvey predicted many of the questions the Justices asked — sometimes almost word for word. Brilliant. Tireless. Occasionally insufferable.
Here’s the catch: Harvey isn’t a person.
Harvey is a bespoke AI I built over the last year with a legal AI company, trained on every question every Justice has asked in oral argument for 25 years, and everything they’ve ever written.

There was a bit more detail in the actual talk. From what I can tell, this is all of the meat on how it works, and how it was trained:

• “Harvey reads the 200th tariff case the same way as he reads the first.”
  
• “Harvey is an AI. A bespoke system I’d been building with a legal AI company for the last year.”
  
• “I trained it on every question asked by a Supreme Court justice in the last 25 years and everything they’ve written, every opinion, every concurrence, every dissent, every separate opinion.”
  
• “And in that, patterns emerged.”
  
• “It predicted the contours of the very argument I would face.”
  
• “Harvey taught me peripheral vision: the idea [that] if you read a lot, you can see patterns and come up with stuff and anticipate the angles of attack before it arrived.”
  
• “It knew that Justice Gorsuch would ask me about the taxing power. It knew Justice Kavanaugh was going to grill me on tariffs versus embargoes. It nailed Justice Barrett’s worry about tariff refunds.”
  
• “It didn’t just predict his question, it predicted a possible escape route.”
  
• “Harvey even predicted Justice Gorsuch’s separate opinion, striking down the tariffs, almost verbatim.”
  
• “It’s almost verbatim.” (re: the Barrett license fee slide)
  
• “Harvey was not some god, it was our sparring partner — brilliant, tireless, occasionally insufferable — but not a god. Harvey asked the questions, we found the answers.”
  
• “Justice Barrett asked a question that Harvey hadn’t predicted.”
  
• “It didn’t just predict his question, it predicted a possible escape route. How the Chief Justice could vote for us and at the same time protect the institution he had spent his entire career defending.”
  
• “Harvey glimpsed that narrow door, I held the door open, the Chief Justice walked through it.”
• “A month before the argument, Harvey told me that I should expect a question from Justice Barrett about license fees.”

There’s a lot here that raises questions. Harvey describes itself as an “AI platform,” not a frontier foundation model like OpenAI’s GPT models, Anthropic’s Claude models, or Google’s Gemini models. And it is unclear whether Katyal’s build used one model family, several, or something more bespoke.

More importantly, the talk does not explain how Harvey turned 25 years of Supreme Court data (maybe around 120 million tokens) into actionable insights. Nor are we shown the full set of outputs Harvey produced. Without that, it is hard to tell what is being described. 

So here are the questions I have about the technical aspects of what Katyal described:

1. What did Harvey actually predict from Chief Justice Roberts?

Most of the talk is framed as preparation for the oral argument. Katyal puts up a predicted question for Justices Gorsuch, Kavanaugh, and Barrett. But that’s followed with: “And the Chief Justice? It didn’t just predict his question, it predicted a possible escape route. How the Chief Justice could vote for us and at the same time protect the institution he had spent his entire career defending. Harvey glimpsed that narrow door, I held the door open, the Chief Justice walked through it, writing a six-to-three opinion, striking down the tariffs.”

“It didn’t just predict his question” implies that it actually did predict his question…but this particular question is not shown to the viewer.

It looks like here, Katyal is not referring to a question from the Chief, but Harvey predicting that he would agree with the plaintiffs on their main theory of the case.

On this point, the Chief’s opinion for the court actually closely tracked the D.D.C. opinion of Judge Contreras in the Learning Resources case: “Nor does IEEPA include language setting limits on any potential tariff-setting power. Every time Congress delegated the President the authority to levy duties or tariffs in Title 19 of the U.S. Code, it established express procedural, substantive, and temporal limits on that authority. E.g., 19 U.S.C. § 2132. For one example, Section 122 of the Trade Act of 1974 authorizes the President to impose an “import surcharge . . . in the form of duties . . . on articles imported into the United States” to “deal with large and serious United States balance-of-payments deficits,” but those tariffs are capped at 15 percent and can last only 150 days without Congressional approval. Id. § 2132(a).”

That language, unsurprisingly, closely tracks the preliminary injunction motion from the plaintiffs.

That injunction, as Blackman mentioned, was obtained by a trial team from Akin Gump led by Pratik Shah.

So what exactly did Harvey predict of the Chief? Any particular questions? The result? (It’s worth noting that as a “product” predicting oral argument questions and predicting outcome votes would seem to me completely different.).

If the ultimate upshot from Harvey is that “the Chief is an institutionalist,” then it’s unclear whether that comes from commentary or the corpus. That characterization is common in legal commentary, or legal scholarship (and even scholarship outside  of law journals). (Another question: Did the “profiles” for the Justices include legal commentary? Or was the universe limited to the opinions and transcripts provided?)

2. How was the system actually trained?

According to the TED Talk, Katyal says: “I trained it on every question asked by a Supreme Court justice in the last 25 years and everything they’ve written, every opinion, every concurrence, every dissent, every separate opinion.”

That’s an interesting claim.

Because that is a LOT of data. My estimate from Claude placed that as something like 120 million “tokens.”

[Technical note: LLMs read text by breaking it down into “tokens.” The counts vary by model – “justice” might be one token as a common word; “unconstitutional” might be broken into “un” and “constitutional” or with current models a single token as a common enough word. “IEEPA” even though it’s shorter, probably registers as multiple tokens because it’s an unusual acronym that the underlying models weren’t trained on.]

Public frontier models now range from roughly 200,000 tokens to 1 million tokens or more, depending on the model and product tier. Consumer chat interfaces may limit the user to a smaller context window than the underlying model supports; API access or enterprise deployments sometimes expose the larger window. But even at 1 million tokens, 25 years of Supreme Court opinions and transcripts is way beyond that.

A context window is how much “stuff” the LLM can consider at one time. It’s sometimes described as like a reading desk. The desk can only fit so many papers and briefs on it, spread out and readable. Once it’s full, you need to take something off in order to add something new. 

With an LLM, if you shove too much info into it, it can’t read all of it at one time. So it needs to use some process to deal with that problem.

One option is Retrieval-Augmented Generation – “retrieval” or “RAG.” For this, the model doesn’t actually “learn” from all the information you give it. It stores everything in a searchable index, then when you ask it a question, it tries to find the most relevant passages, and put those into the context window. In a simple vector-RAG system, the corpus is chunked, embedded, and searched for semantically similar passages. More advanced retrieval systems search the source documents in several ways, filter by metadata like court, date, Justice, or issue, rerank the best matches, and then give those passages to the model as context.

Retrieval tries to find passages that are similar to what you ask. A simple RAG setup retrieves relevant examples without estimating how representative those examples are. A better system can add metadata, classification, and aggregation to ask how often a Justice raises a category of concern in comparable cases. Retrieval is good at finding examples. But if the AI is predicting, that requires counting, classifying, or otherwise analyzing the whole data universe.

So which was Katyal’s system using? Simple RAG? A more sophisticated retrieval-and-analysis system? Something else entirely?

A second way is fine-tuning. Fine-tuning changes the model’s weights using training examples, usually prompts paired with desired outputs, so the model becomes more likely to produce the desired behavior. Not unlike a junior associate learning a task by showing her a bunch of examples: when the input looks like this, the answer should look like that. (Except the model doesn’t understand why it gives that output; it just matches the pattern.)

I think to most ears, the statement that Katyal “”trained it on every question and every opinion” connotes the idea of fine-tuning.  If Harvey really fine-tuned the model, that would be a pretty impressive feat – one worth detailing.

It would involve defining the training objective, preparing examples, deciding what the input and target output are, cleaning transcripts, separating questions from answers, tagging Justice/question metadata, handling the differences between argument transcripts and opinions, and evaluating whether the tuned model outperformed a base model plus retrieval. That is going to take significant man hours, and a fair amount of time and management.

Fine-tuning would still have some downsides – it would likely result in a black box, where even if it were able to predict, you could probably not trace those predictions back to understand why they were made. The model’s prediction could be right, right for the wrong reasons, or wrong. And you might not be able to tell until it’s too late.

A third possibility is pre-computation. That would involve someone or something going through the archive and extracting specific features from each question (and presumably from the opinions as well – again, unclear how those different types of data were incorporated). The model then works from those extracted features instead of the raw text. Given the description in the TED Talk, it doesn’t sound like Harvey was deploying this kind of human (or AI) filter on the front end – but it would be good to know if they did!

3. What patterns emerged?

“And I trained it on every question asked by a Supreme Court justice in the last 25 years and everything they’ve written, every opinion, every concurrence, every dissent, every separate opinion. And in that, patterns emerged. It predicted the contours of the very argument I would face.”

So…what patterns emerged? What was the process for that? Can those be shared?

More importantly – are these patterns that aren’t already known to the Supreme Court bar or the general public? SCOTUS is the most studied court on earth. There are hundreds of attorneys focused on what the Justices ask and how they ask it. If Harvey was actually going to help Katyal prepare, it ought to do it better than a human could (in another context, it would be good enough if it could do it cheaper. In a multi-billion dollar case like Learning Resources, that’s not an issue).

To take one example from the Bartolus dashboard, I can tell you that 21% of the questions in Learning Resources asked about statutory text, as opposed to only 8% of questions overall in OT 2025: More importantly – are these patterns that aren’t already known to the Supreme Court bar or the general public? SCOTUS is the most studied court on earth. There are hundreds of attorneys focused on what the Justices ask and how they ask it. 

To take one example from the Bartolus dashboard, I can tell you that 21% of the questions in Learning Resources asked about statutory text, as opposed to only 8% of questions overall in OT 2025:

4. Did it read the briefs?

The oral argument in Learning Resources was on November 5, 2025. I only caught one time reference when describing the AI usage: “You know, a month before the argument, Harvey told me that I should expect a question from Justice Barrett about license fees.” So that’s about October 5.

The government filed its brief September 19. The challengers’ briefs were filed October 20.

The Algonquin point featured in the Federal Circuit’s opinion, and the government distinguished it in its opening brief.

So by October 5, an AI wouldn’t need 25 years of writings to realize licenses might come up: It could just read the lower court decision and the government’s brief. But if it pulled that question without either of those sources, that would be very impressive indeed. And it is notable that the AI correctly identified Justice Barrett as pursuing this line…until you see that “license” in various forms appeared over a hundred times in the oral argument, and was a focus of multiple Justices:

So what role did the briefs have?

And what about the almost four dozen amicus briefs – multiple of which were invoked during the oral argument?

5. What did it predict that no human predicted? What did it not predict, that was asked?

“It knew that Justice Gorsuch would ask me about the taxing power. It knew Justice Kavanaugh was going to grill me on tariffs versus embargoes. It nailed Justice Barrett’s worry about tariff refunds.”

“You know, at one moment in the argument, Justice Barrett asked a question that Harvey hadn’t predicted. And I remember it felt like she and I were the only two people in that marble and mahogany room. And in the half-second before I answered, I did something no algorithm can do. I looked at her. I really looked. I wanted to understand her worry. And I answered the worry.”

There’s a lot of data missing from the talk. We don’t really have the numerators (how many questions did the AI predict in all? How many were attributed to each Justice?)  or denominators (how many were hits? How many were close?).

Predicting questions that every mooter predicted isn’t nothing. And that could prove a valuable tool for appellate practitioners who can’t assemble multiple moots with court experts.

But I think the real value would be: did we cover the bases, so that (almost) nothing caught us off guard? And did the AI predict any questions that no human mooter did?

–
Katyal has produced what is likely the most discussed legal TED Talk of all time. Buried in it are some fun puzzles about what he was actually doing with Harvey, and what the AI is capable of today.

If you know the answers to some of the questions above, please, I’d love to learn!

Latitude’s model centers on high-end, flexible legal talent, experienced attorneys with Big Law or in-house backgrounds who step into law firms and corporate legal departments for specific engagements. Chagui explains that these lawyers often support overflow work, leave coverage, secondment requests, internal projects, and interim needs across practices ranging from litigation to corporate, labor, and employment. Su adds that staffing itself is not new, yet Latitude focuses on a segment of talent that traditional hiring models often miss, experienced attorneys with strong credentials who prefer engagement-based work over the standard full-time track.

The conversation turns quickly to why this model is gaining traction now. Remote work, post-COVID hiring shifts, and the growing acceptance of distributed teams have made it easier for firms to bring in experienced attorneys without requiring long-term headcount commitments. Chagui notes that many Latitude attorneys have 10 or more years of experience, meaning they often need less supervision than junior lawyers and move quickly into productive work. This matters as firms face inconsistent demand, intense competition for talent, and hesitation around layoffs, which in law firms often signal weakness rather than discipline.

AI adds another layer to the staffing problem. Firms have invested in tools such as Harvey, CoCounsel, and other specialized platforms, yet many knowledge management and innovation teams lack enough subject matter experts to train users, review outputs, build use cases, and handle quality control. Chagui describes Latitude lawyers helping firms train internal AI tools, review AI-generated work, and support practice-specific rollout efforts. Su points out that while some firms offer associates credit for AI training or innovation work, associates under billable hour pressure often choose client work first. Flexible talent gives firms another way to support AI adoption without asking already-stretched associates to carry the full load.

Su also frames flexible talent as a new form of leverage. Clients still trust senior partners and often accept premium rates for high-value judgment, but they are increasingly skeptical of paying top-tier rates for junior-level work. In that middle layer of legal work, AI, technology, and experienced flexible attorneys give firms more options. Su calls this “outsourced leverage,” a way to support the partner-client relationship while rethinking who performs the work underneath. The discussion also highlights a career-path shift for attorneys who prefer specialized, project-based work, especially in areas like knowledge management, AI implementation, and innovation support.

Looking ahead, both guests see uncertainty as the defining feature of the next phase of legal services. Chagui expects the traditional model to keep changing as firms and legal departments seek more flexible options. Su predicts continued upheaval around staffing, AI capabilities, and outside counsel relationships, especially as foundational AI models move further into in-house legal workflows such as NDA review, contract review, and eventually parts of diligence. Yet Su also offers a reminder for law firm leaders: premium legal judgment still has value. The rates for top partners are unlikely to fall simply because AI improves. The pressure will land instead on how firms structure the work beneath them.

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Transcript:

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

Greg Lambert (00:07)
And I’m Greg Lambert. This week I’m on location in Carlsbad, California. So if you hear airplanes overhead, that’s me. So Marlene, you know, the past couple of years, we’ve talked about a lot, or we’ve seen a lot about law firms that are putting out these press releases, talking about all the great stuff they’re doing with AI. And then this week we’re seeing more with the Claude for Legal announcement.

And everyone jumping on board with that. But really, we’re, you know, I think we’re at a phase right now where we’ve got to start looking at how attorneys are using this in their day-to-day practice.

Marlene Gebauer (00:46)
Yeah. And I agree a hundred percent. And that is exactly why we are so excited to have our guests on today. We’re joined by Alex Su and Andy Chagui of Latitude. Alex is the industry’s favorite legal influencer. Great video, great, great, great reels if you haven’t seen them, but, you know, he’s also a former Sullivan and Cromwell litigator and a federal clerk. And now he’s leading revenue strategy at Latitude.

Greg Lambert (01:01)
Yeah.

And also joining us is Andy Chagui of Latitude. He’s joining us from Miami. Andy has spent 15 years on the inside as a shareholder at Carlton Fields and was handling high-stakes federal litigation before moving into the new law space.

Marlene Gebauer (01:37)
So Andy and Alex, welcome to the show.

Andy Chagui (01:40)
Thank you.

Alex Su (01:41)
Thanks for having us. I’m a big fan of the show. I’ve been a longtime listener, so excited to be here.

Greg Lambert (01:47)
So Andy and Alex, I want to start off for listeners who may not be familiar with what Latitude does. Can you give us the elevator ride on what the core businesses are? What the mission is there at Latitude? Feel free. This is a tall building, so it can be a fairly decent elevator ride.

Andy Chagui (02:09)
Sure, I can jump in. So Latitude, we are a flexible legal talent company. And what does that mean? We specialize in providing high-end attorneys who have former Big Law or in-house experience, or both, typically on contract engagements with both our law firm clients and also our corporate legal department clients.

Marlene Gebauer (02:09)
Let’s hear this pitch. Let’s hear it.

Greg Lambert (02:11)
Yeah.

Andy Chagui (02:34)
These are practice-specific attorneys. You can name the practice area, from corporate to litigation, labor and employment, whatever it might be. They usually jump in, help our law firms fulfill a secondment request, for example, or they might help the law firm with leave coverage situations. They could serve as a stopgap while the firm is trying to hire FTEs, or they could even jump in and help with special or internal projects. And we help law firms of every size, from boutique firms all the way up to AmLaw 50 law firms.

Greg Lambert (02:42)
Yeah.

Cool. Alex, anything to add to that pitch?

Alex Su (03:07)
Well, I would just say that staffing is not a new thing, but where we think we’re unique is that there’s a huge pool of talent that’s high-end, high-caliber, former Big Law, from top schools, with top credentials. A lot of this is driven by demographic shifts, so for many of our clients this is something that’s relatively new, but our hope is to share and spread the word about a resource that maybe in previous years has not really been tapped into. So we occupy a very unique segment of the market. I’m excited to share more about what we focus on today.

Marlene Gebauer (03:45)
So I’m going to tee up the next question for both of you. So Alex, you know, you clearly have made a career out of unconventional marketing and trying to crack that code on how to convince lawyers to innovate. And Andy, you’ve been a shareholder at an AmLaw 200 firm for over a decade. So when you’re looking at the current market, is the surge in demand for flexible talent a sign that firms have finally accepted that the traditional pyramid model is broken, or is it more a tactical response to this current $36 billion value gap in firm revenue? And maybe I’ll say, Alex, you start first.

Alex Su (04:34)
Yeah, I would say it’s just another resource for firms. Historically, when firms needed work done, you hire a full-time employee. Now there’s a move in all areas of the law, not just with firms, but certainly at corporate legal departments, to hire a contingent workforce, lawyers who work on engagements, interim in-house counsel. And that’s certainly true of the law firm side. So it’s simply another solution for law firms to deploy when there is need to do work. We’re a big believer in pairing the right resource with the right type of work, no matter who our client is. And so that’s why we’re seeing a surge in our business. And yeah, I would just say it’s another tool in your tool.

Andy Chagui (05:21)
I agree with all that. I mean, as a former AmLaw partner, I remember back in the day, if you were busy, it was, let’s hire more associates, right? And so now today there are more options. And so we work with a lot of forward-thinking, innovative law firms who realize that there’s a really, really highly qualified talent pool out there that they can tap into in different situations where they don’t necessarily need to bring in three or four new full-time associates. There are options.

Greg Lambert (05:50)
Yeah.

Marlene Gebauer (05:51)
I’m curious. I’m curious.

Have you seen any changes in terms of maybe the type of title or the type of work that maybe at one point, you know, this wasn’t going to be flexible talent, but now firms are looking at it as more flexible talent? They’re open to that. Have there been changes that way?

Andy Chagui (06:05)
Thank you.

Marlene Gebauer (06:16)
That you’ve seen.

Alex Su (06:18)
I think so. We have examples of firms, large firms, and law firms that are using our talent to do essentially associate, counsel, and even partner-level work. We’ve gotten placements where we have someone first-chairing a trial, as an example. And that’s not something you would traditionally think of as something you might outsource to a conventional staffing company. But I think the key point is that the caliber of talent that’s available is just unprecedented. And I know Andy sees this every single day, so I’ll let him kind of share more specifics.

Greg Lambert (06:53)
I want to just add to that. It’s like, why now? Why is there so much talent available? What’s been changing?

Andy Chagui (07:02)
I don’t know. I don’t know. I mean, after COVID, certainly when remote work became so popular, I think that really kind of changed things. Especially with what we’re doing, where it’s like an interim need that may not necessarily be permanent. Our clients see the value in being able to tap into a pool of candidates that are highly qualified, that can help them do the work for the next three, six, or nine months. And they don’t really need to be there face to face. Most of our attorneys come with 10-plus years of experience. So a lot of the tasks that an associate would benefit from being face to face with a partner who’s maybe mentoring them, our attorneys already have that experience, and then you can deploy them immediately and they can jump right in and kind of hit the ground running.

Greg Lambert (07:44)
Yeah, so Andy, we were talking, you shared some things with me earlier with some examples where a lot of these Global 50 law firms, they have all these great tools. They have the Harveys, they have the CoCounsels, they have specialized tools that they use.

But everyone is just too busy to train, too busy to do quality assurance, too busy to learn the new things.

What are you seeing? How are firms motivated, where they’re seeing, yes, we have the great tools, but we don’t necessarily have everything that we need to get these great tools up and running as well as we should and get the ROI that we’re wanting?

Andy Chagui (08:32)
Yeah, so you alluded to this at the beginning of this podcast, that everyone’s got the tools, they’re deploying the tools, but now we need the bodies. We need the subject matter experts to help us with all of these initiatives. And so we’re supporting the knowledge management and innovation teams at various Big Law firms because they are trying to do various things. They are trying to either roll out a new AI tool, but then they’ve done that, and then they’ve gotten tremendous traction with it, and they have all the firm attorneys reaching out saying, all right, so how do we use this to come up with deposition questions, or how do I help this in my next deal? And so they don’t have the resources because even though they’re large law firms, the knowledge management or innovation teams are relatively small. And so a lot of this work tends to ebb and flow. And so the struggle is, do we hire more FTEs as part of the team when we may not be able to keep them busy full time?

Greg Lambert (09:20)
Mm-hmm.

Andy Chagui (09:28)
Or can we lean on a company like Latitude, who can help provide these subject matter experts? They can jump in and help us when things are busy, and then we can scale down if that work starts to subside a little bit. We’re helping the firms do things like market studies and practice areas, and also train internal AI tools. Our attorneys are serving as subject matter experts themselves, training the platforms and then reviewing the output and doing quality control to make sure that the information that the AI is generating is actually accurate. So that’s, in a nutshell, some of the work that we’re doing right now. They had a hard time as well because they tried to staff some of these projects with their own associates, right? The problem with that, as you can imagine, is that it’s not billable work, right? So the associates are prioritizing the billable work. The opportunity cost there is very high, and the associates aren’t giving the attention that it deserves. And so then leaning on us as a resource was really appealing to us.

Greg Lambert (09:39)
Yeah.

Andy Chagui (09:58)
AI is, you know, generating is actually accurate. So that’s in a nutshell kind of some of the work that we’re doing right now. They had a hard time as well because they tried to staff some of these projects with their own associates, right? The problem with that is, you can imagine, yeah, is that it’s not billable work, right? So the associates are prioritizing the billable work. The opportunity cost there is very high, and the associates aren’t giving the attention that it deserves. And so then leaning on us as a resource was really appealing to us.

Greg Lambert (10:12)
Yeah, yeah.

Andy Chagui (10:28)
To these law firms.

Greg Lambert (10:29)
Yeah, yeah. And do you think, I mean, you hear some of the firms that are giving 50 to 200 hours of compensation or billable time to learn some of these AI things. Do you think that’s the way, or do you, I know a lot of firms are also very reluctant to give associates billable credit time for non-billable stuff.

So you see this as a good happy medium for them?

Alex Su (11:03)
I can also share my perspective on that, because I think it’s a step in the right direction to offer that credit. But when the practical reality is that an associate who’s behind in their hours gets faced with needing to either find billable work or work on something that’s extra credit that they do get some credit for, I think what you end up finding is that a lot of the associates choose the billable work.

We’re seeing just a lot of demand for firms to supplement for these roles, which suggests to me that even though I’m certain that at many of these firms, the associates get credit, the way it plays out in reality is often a little bit different. So I think it’s a step in the right direction, but it can be really hard because it’s just a tough spot for the associate.

Greg Lambert (11:52)
Yeah, and I imagine as you start placing these subject matter experts, and they’re learning, they’ve got to become very valuable for you to then pitch to the firm. So it sounds like a real win for somebody that’s looking to get into this type of work and has that ability and that skill set.

Marlene Gebauer (12:16)
Yeah. I mean, it sounds like they can develop this skill set and then use it across a variety of projects for a variety of customers, which I think for some people is very engaging. So.

Greg Lambert (12:25)
Yeah.

Thank you.

Alex Su (12:30)
Yeah, if you think about the point Andy made earlier about how we’ve seen this trend towards more of a remote workforce. People have moved around. They are comfortable with working remotely. And employers are, to some extent, more comfortable with remote work, even the most conservative law firms. So what happens is work that used to need to be done by a single full-time hire, that maybe didn’t justify a full-time person, now can be almost split up.

Greg Lambert (12:31)
You.

Alex Su (12:59)
So somebody can develop an expertise in a specific area and maybe do certain types of work that wouldn’t be able to be supported by a full-time role in the past, but now they can specialize in it. An example of that is working with some of these newer AI tools, specializing in technology that hasn’t been around for very long. So that is a really interesting opportunity, I think, both for the talent and for the clients.

Marlene Gebauer (13:26)
Yeah. So I want to probe that a little bit more, like in this sort of career path idea. So there are attorneys sort of coming into practice and not having the amount of jobs that there once were. There’s a variety of different types of jobs for attorneys. I mean, it used to be, you know, your career path was, you were an associate, and you worked hard, and then you became a partner. But now there’s a whole variety of different things that you can do. Also, you can be working in a role where maybe you’re so good at what you do that no one feels that they can afford to lose you. So you kind of get stymied that way. So there’s sort of a lot of, I don’t know, swirl. There’s a lot of different options that people can take, and there may be some situations where people feel kind of stunted. So, you know, Alex, you’ve written about the whole being too indispensable. And we were also talking about how Latitude’s professional engagement model, basically, you can become a subject expert and work on a project basis, gives sort of more of that flexibility.

Greg Lambert (14:23)
Yeah, no good deed goes unpunished.

Alex Su (14:26)
That’s right.

Marlene Gebauer (14:36)
So what do you think in terms of this model that you’re offering? How does that provide a career path that the partnership track doesn’t offer and maybe won’t offer again, given AI’s increasing importance in the practice?

Alex Su (15:29)
I mean, it’s all about flexibility because everybody has a job and there are things about your job that you like, there are things about your job that you don’t like. Very often the things that you don’t like crowd out everything else. What Latitude offers is this flexibility for you to focus on the things that you do like. And you couldn’t do it probably on a full-time basis because every job’s got the things that you don’t really want to do. But if you could work on an engagement basis, that opens up a lot of doors.

Essentially, we’re providing options that both benefit the firms we work with and the attorneys we work with, especially when it comes to specific narrow niches of work, where again, in the past you couldn’t support a full-time role with that and now you can by putting it together. But it’s a pretty good deal for the attorney too, because they get a whole set of benefits which come with the work. I don’t know, Andy, if you wanted to share some of what the talent gets.

Andy Chagui (16:30)
Yeah. So it’s what we hear from our attorneys, is we love working this way because we’re exposed to a variety of clients doing different things or kind of the same practice area, but everyone’s got a different style. And so it allows me to jump from a six-month engagement to a four-month engagement and work with different clients and really kind of improve my skill set and get exposed to areas where I normally wouldn’t have been exposed. Marlene, you mentioned there are so many other non-traditional paths out there now, which is

Greg Lambert (16:51)
You.

Andy Chagui (17:00)
Which is very true. We just had one of our attorneys who was on assignment with one of our AmLaw 100 law firms helping the knowledge management team with some of these AI initiatives, and the law firm ended up making a permanent offer to our attorney to come and join the firm. And we got this incredible email from our attorney saying, I am so grateful that I found you guys and that I was put on this engagement. And then now I’m working for a major law firm in the knowledge management practice group. And this wasn’t even on my radar.

Greg Lambert (17:14)
Mm.

Andy Chagui (17:30)
And it’s a complete career pivot and something that I enjoy way more than practicing. And so there are just so many more options and so much more flexibility and ways to do things now than there used to be even when I was practicing just six years ago.

Greg Lambert (17:43)
There are two different things that are pushing on the industry that are really kind of opposed to each other. One is there’s this expectation that as AI becomes more embedded into the day-to-day practice, there’s actually going to be a need for fewer associates than there are for more associates. And at the exact same time, law firms are now recruiting 1L law students before they’ve even taken their exams. They’ve got to get these law students into the pipeline really early, because otherwise if we wait, we’re not going to get them. And now on top of that, now in three years, are we going to be trying to look at, now that we’ve got them, where are we going to put them? What are you seeing from your side of things as far as what the pressures are on law firms for hiring and potentially reducing headcount?

Alex Su (18:52)
I think there’s just so much uncertainty, Greg, and you point out the thing I’ve always been confused about with large law firm hiring. It’s like you hire these students before they barely got their grades. We barely got our grades before when we were summers, right? Andy, you remember when we were in school, it was like 1L grades and that’s it. And the firms are making decisions on hiring and demand for different practice areas three or four years in advance. So I don’t know what’s going to happen with AI and associates, but what I can tell you is that it’s not a new thing for firms to misinterpret future demand signals. Like, you have firms that overhire in certain practice areas, they’ve got to let people go, then they don’t have enough midlevels in a certain practice area. And where we fit in is essentially, because we have that bench of experienced practitioners, we help meet that supply gap for whatever practice area. Because again, our talent often is not a person who’s three or four years out of law school, it’s someone who’s probably 10 or 20 years and potentially has deeper experience who could kind of slot in very quickly, doesn’t need much oversight. So as we see more uncertainty unfold, I would expect greater demand for different resources. I don’t know if maybe law firms, maybe large law firms, will change the recruiting model. But yeah, it’s always perplexed me why hiring is done this way in our profession.

Greg Lambert (19:55)
Hmm.

Yeah.

If we’re reaching into school and pulling them out from there, this is like, where are we going next? The other thing, and I heard something really interesting. I heard Jennifer Leonard talk this week. And one of the things that she mentioned and reminded me of was the pressures on law firms.

Alex Su (20:29)
I know.

Greg Lambert (20:44)
When it comes to hiring and reducing headcount, it is almost the opposite of what we see in the corporate world. And in the corporate world, if they see a need for layoffs, they do that. And it’s actually kind of, you know, the stock price goes up. In law firms, it’s actually seen as a weakness. And so if you lay off or right-size, then all of a sudden you’re seeing it say, you know, Skadden and Kirkland, if they’re laying off somebody, they must be hurting. Now’s the chance to pounce. So it’s kind of a weird comparison between what corporate headcount and law firm headcount pressures are. So I imagine that looking at something like Latitude, where you can have that ebb and flow, and it really doesn’t necessarily show up on the headcount line, right? Is that something that you’re seeing as a real positive for law firms that, hey, look, you can have your headcount here. As demand goes up, we fill that. As demand goes down, they come back out, we reallocate them. You guys know the business better than I do, but it seems like that’s a real win for the types of pressure that law firms are under.

Andy Chagui (22:07)
Yeah, that’s exactly right. I mean, we allow the firms to be able to have the flexibility to scale up and scale down as needed, right? As opposed to overcommitting on full-time hires. You know, we have one law firm that has dozens of our Latitude attorneys working for the firm, helping a very busy big-time rainmaker at the firm with high-volume work that may last for the next six months or may go away unexpectedly, right? And so if the firm were to use a traditional staffing model and hire 10 new associates, who knows what things might look like three months down the road. So it gives them that ability to be able to scale up or down as needed.

Greg Lambert (22:29)
.

Andy Chagui (22:37)
Who knows what things might look like three months down the road. So it gives them that ability to be able to scale up or down as needed.

Marlene Gebauer (22:43)
So I’m wondering, when firms are hired by clients, we’re talking about what they’re paying for now, because it used to be, we’re paying for hours. But now I think we’re looking more at value and the value of the output, lowering risk, maybe lowering risk faster, and basically the firm’s reputation to deliver results. And so I’m wondering how this flexible talent model kind of plays into that. I mean, has there been a perception in the past that maybe that’s more risk because they haven’t been able to interview somebody on campus and sort of vet them that way, as opposed to hiring somebody using the flexible talent model? So yes, it will lower operational costs, but how do you still maintain kind of that trust and accountability that firms are trying to promote in terms of their reputation?

Greg Lambert (23:28)
So.

Marlene Gebauer (23:33)
Interview somebody on campus and sort of vet them that way, as opposed to hiring somebody using the flexible talent model. So yes, it will lower operational costs, but how do you still maintain

Greg Lambert (23:34)
Thank you.

Marlene Gebauer (23:51)
Kind of that trust and accountability that firms are trying to promote in terms of their reputation?

Greg Lambert (23:54)
You.

Alex Su (23:59)
I think in terms of the commercial model of a firm, there’s several things happening at once that I think, Marlene, what you’re pointing out is what I find most interesting that’s happening in the law firm world right now, which is the partners and the senior lawyers of the firm, I would even almost argue that they’re underpriced, that they could probably go even higher, because the judgment they bring to bear, their experience on these extremely high-value matters, is critical.

Where I think there is a little bit of uncertainty is if you go downstream to their litigation team or their M&A team. What I hear from clients is that they don’t have any issues paying the partner who’s expensive. They have issues paying the first or second year. There is a huge pool of lawyers who are doing the work under the guidance of a senior partner that I think is probably mispriced. There’s a price misalignment with the clients.

Greg Lambert (24:41)
Yeah.

Yep. You.

Alex Su (24:54)
So the question becomes, well, what’s the best way to complete that work? My view is that with AI and technology and flexible talent, there’s a whole host of different tools you can use to support that partner. In terms of trust, the trust

Greg Lambert (25:10)
Hmm.

Alex Su (25:10)
Flows to the client with the partner. But in terms of who that work gets passed to, that is on the firm and the partner to vet. And so this is not a new concept, as you know, major litigations often hire outsourced shops to conduct first-level doc review. That doesn’t change the trust and accountability that that partner has to the client. And so one of my colleagues has called it outsourced leverage. That’s what we provide. It’s just another source

Greg Lambert (25:35)
Hmm.

Alex Su (25:37)
Of leverage that’s not your full-time associates. It’s outsourced to us. Doesn’t mean that quality goes out the window. We do vet them carefully, but that’s how I see this playing out. There’s this big middle group of work, this middle segment of work that could probably be done in a different way than it has traditionally.

Marlene Gebauer (26:00)
And I’m curious, if sort of that goes to flexible talent, do you see the model, the law firm model, changing? Because if you’re not getting the succession of associates that kind of move into partner level, and they’re not being sort of trained in that capacity, what happens when you have that generation pass the baton?

Andy Chagui (26:00)
What?

Marlene Gebauer (26:30)
You know, pass the baton.

Alex Su (26:31)
My instinct is that the associates won’t go away. Maybe it’s fewer, or maybe it stays the same and they’re just set up differently. Like, I remember when I was a summer associate, we started with a hundred people in my summer class. I think that only one of us made partner. And so that level of attrition, I think may change. I know that attrition is part of the model, but I do think you’re right that the leadership of the firms does have to think through

Greg Lambert (26:57)
Thank you.

Alex Su (27:01)
What that future state looks like. And I don’t think it’s just Flex Talent or us. It’s certainly AI, not just generative AI, but now agentic AI. And you all have seen Claude coming out with Claude for Legal. So a lot of things are changing and yeah, a lot could change.

Marlene Gebauer (27:08)
Absolutely.

Greg Lambert (27:19)
Now, so.

Marlene Gebauer (27:21)
Andy, I think you wanted to add.

Andy Chagui (27:24)
It was just Alex’s prior point about the vetting, that not only does Latitude do the vetting, but our law firm clients also do their own vetting, right? So any candidate that we ever propose, we propose them to our law firm, and the law firm partner will have his or her opportunity to do their own vetting and make sure that they’re comfortable with this person’s qualifications, right, before they put them on sensitive matters for their clients.

Greg Lambert (27:48)
Do you also provide talent for in-house teams as well, or are you focused just on law firms?

Andy Chagui (27:56)
No, we do both. We provide basically the equivalent of direct secondments to our corporate in-house legal department clients. So something that oftentimes might go to a law firm, you know, saying, we need one of your associates for the next six months. Those are the types of requests that we get directly. And so we’re filling those on a weekly basis, whether it’s leave coverage, overflow work, a stopgap, or internal projects. Yes, we work with lots of in-house corporate legal departments.

Greg Lambert (28:26)
Yeah, and I know you’ve heard companies like Ford and Walmart are really putting big demands on their outside counsel to leverage AI. And kind of the underlying threat that is going on is a lot of these clients are saying, look, I’m not just your client, I’m your potential competitor as well. A lot of this stuff, if we staff it right and use the AI properly, we could be doing a lot of this instead of outsourcing it to you. One, do you see companies actually pulling the trigger on that threat? Or do you see that as putting just more pressure on law firms to figure out ways of staffing things right, using the technology right, and kind of filling the demands that their clients are going to, you know, we see it on some of the big ones now, but I imagine that’s going to go downstream pretty quickly.

Alex Su (29:27)
I think it’s both, Greg. Some companies that we talk to are explicitly telling the firms and providers that they’re going to need to, they need them to figure out the AI strategy, or else you can’t work with them in the future. There are others that are waiting to see what’s most effective. So I think that you’ve seen multiple different strategies across the board, but what’s remained clear is that in the medium term to long term, the current model will

Greg Lambert (29:51)
You.

Alex Su (29:57)
Need to change. It’s just a fact of competition. The in-house lawyers are under

Greg Lambert (30:03)
Okay.

Alex Su (30:05)
Pressure to do more with less. They’ve got to get more work done with a limited budget, and so they’re going to need to be creative about allocating work to different providers. So what we also hear from the firms is that knowledge management, innovation teams, learning how to figure out how to use AI is just strategically critical during this time as we see more and more capabilities come out from all of these AI companies.

Greg Lambert (30:33)
Yeah, well, let me just pull on that just one time, Alex, because I saw a pretty funny post that you did on LinkedIn where you’re saying that AI companies were telling their law firms that AI is going to replace them, and then finding out two years later that the AI foundational models are replacing the AI companies. So how do you see Claude for Legal coming in? I mean, I’ve done a little bit of digging in on it, and it’s still pretty basic, what I’m seeing they can do, but it’s scaring the crap out of everybody. What do you see, like Claude for Legal, or imagine OpenAI and maybe even Grok. Well, I’ve even heard Grok say that you can use it for legal as well. So as these tools become more advanced and push into legal, how do you see the market reacting to that?

Andy Chagui (31:04)
You.

Greg Lambert (31:17)
See, like Claude for Legal, or imagine OpenAI and maybe even Grok. Well, I’ve even heard Grok say that you can use it for legal as well. So as these tools become more advanced and push into legal, how do you see the market reacting to that?

Alex Su (31:35)
My instinct is to say that it’s going to start in-house before law firms. Because law firms do more bespoke work, you’ve got in-house teams that are working on NDA reviews, simple contract reviews. I think the foundational models in the AI companies are going to start there. It’s much easier to do. It’s much harder to replace what a large firm can provide for the client. But as they move up the value chain, as they get better at reviewing contracts, these AI companies, you’re going to start to see different elements of what firms do be potentially replaced. So for example, maybe reviewing NDAs and sales contracts means that there is another

Greg Lambert (32:10)
Yeah.

Alex Su (32:16)
Way to conduct M&A due diligence. So if I were at a law firm, I would be watching what’s happening on the in-house side with the foundational models, with all the new AI companies, and kind of seeing where the puck is headed because even though it’s not here today, that probably will plant the seeds of tomorrow’s innovation. And there, I think the law firms probably want to be more worried about than less.

Greg Lambert (32:40)
Curious for you, there’s just so much going on right now, and you’re kind of in this really interesting niche part of the market. So what resources do you go to, to keep up with the changes in the market and kind of keep, as much as you can, ahead of what’s going on?

Andy Chagui (33:04)
Yeah, so for me personally, I guess I would say I’m on LinkedIn quite a bit, and I feel like LinkedIn is really helpful because people will post things on there, and then if it’s something that I think is interesting, I’ll click on the link, and next thing I know, I’m going down that rabbit hole, right? I’m reading all about that specific topic. So I’m on LinkedIn quite a bit and reading articles and things that are posted on there. And then I’m also on the phone with clients every single day, right? Having these kinds of conversations, whether it’s with a GC of a Fortune 500 company or an AmLaw 50 law firm partner, and kind of just keeping my finger on the pulse in terms of having those daily conversations and seeing how they’re thinking of things and what their concerns are and the potential solutions that they’re thinking about and how we can help them with that. And then also I have 15 partners at the company who are having those same conversations with their own clients. And so we speak to each other on a weekly basis and kind of just make sure that we’re on top of what’s going on.

Greg Lambert (33:19)
Yeah.

Andy Chagui (33:34)
GC of a Fortune 500 company or an AmLaw 50 law firm partner. And kind of just keeping my finger on the pulse in terms of having those daily conversations and seeing how they’re thinking of things and what their concerns are and the potential solutions that they’re thinking about and how we can help them with that. And then also I have 15 partners at the company who are having those same conversations with their own clients. And so we speak to each other

Greg Lambert (33:52)
Yeah.

Andy Chagui (34:01)
On a weekly basis and kind of just make sure that we’re on top of what’s going on.

Greg Lambert (34:05)
Yeah, I’d like to see how that Slack channel looks as you guys are sharing all this stuff. Yeah, do you have AI in your Slack channel? That’s what I want to know. Alex, how about you? Wait a minute. I want to see where Alex is getting his keeping up with his things as well. Alex, how about you?

Marlene Gebauer (34:08)
Ha ha ha ha.

Andy Chagui (34:15)
Ha ha ha.

Marlene Gebauer (34:18)
So guys, it’s,

Greg Lambert (34:22)
Getting his keeping up with his things as well. Alex, how about you?

Marlene Gebauer (34:26)
Got it.

Alex Su (34:27)
My primary source of information is, of course, The Geek in Review. That’s where I get all my cutting-edge news.

Greg Lambert (34:31)
Of course. Thank you. Alex, you missed the cue on that one.

Marlene Gebauer (34:36)
Not a paid promotion.

Alex Su (34:38)
Ha.

Andy Chagui (34:39)
Yeah, and TikTok.

Greg Lambert (34:40)
And TikTok.

Alex Su (34:40)
And TikTok.

Greg Lambert (34:41)
TikTok.

Alex Su (34:41)
Yeah.

Marlene Gebauer (34:42)
Ha ha ha.

Alex Su (34:44)
But seriously, I do read quite a bit on some of the trade publications and on certain Substacks, I would say there’s a few good ones out there. But really what I always worry about is that when I read things in Bloomberg or ALM, I don’t know if it’s tied to reality. So very often I am talking to our 15 partners. I am talking to Andy, saying, hey, I think this thing is happening. I think they’re talking about AI. I mean, what do you think? And so Andy and the other partners at Latitude will kind of give me anecdotes, will tell us like, hey, this is the conversation we’ve had with the GC or a law firm partner this week. And so that helps me get a sense of where everything is moving. So it’s a little bit of everything and talking to everyone.

Greg Lambert (35:30)
Yeah. I think that’s good advice. That was always the best.

Marlene Gebauer (35:31)
Very good. Yeah. Insider conversations. Those are always the best. You get it from the source.

All right guys, it is time for the crystal ball question. So this is basically where we ask you to look ahead a few months to a few years in terms of what impactful changes you see happening in the legal industry. So I will start with Andy.

Andy Chagui (36:02)
Oh man.

Marlene Gebauer (36:04)
Zing!

You’re up.

Andy Chagui (36:06)
I don’t know. I don’t like making predictions. All I can tell you is that things are definitely changing, and they’re changing quickly, and there are more options today than ever before. And that there’s a lot of disruption in this industry, and it’s a lot of fun for me personally to work with clients who are forward-thinking and innovative and looking for creative solutions to problems.

I think the traditional model is going to change, but I don’t know exactly how, but it’s changing all the time. And I’m having fun being a part of it, but I don’t really know. I don’t know. I don’t know. I’m not good at making these predictions. Alex hopefully will have a better answer than me.

Greg Lambert (36:50)
Yeah, well, as we say a lot, Yogi Berra says, predicting is very hard, especially if it’s about the future.

Marlene Gebauer (36:51)
All right, Alex, take us home.

Andy Chagui (36:58)
Ha.

Alex Su (37:00)
Yeah, I would add that I think there’s more uncertainty. Kind of to Andy’s point, there’s a lot of uncertainty. I’ll talk about that and I think what won’t change, which is I think that it’ll be uncertain what AI’s true capabilities are. We’re seeing new capabilities every day. I think the impact to how legal departments staff, and that includes full-time hiring, but also how they leverage providers and outside counsel, I think that’s highly uncertain. But what I feel most strongly about is that hourly rates will continue their march upwards. I think that clients are willing to pay when it’s worth it. And the partners at the firms that they’re paying exorbitant amounts for, they are certainly more than worth it. I think that gets lost among the AI discourse. So I thought that I should point that out, that it’s not just a memo that the most senior partners at these firms are putting out. They are providing legitimate valuable guidance and so that’s not going to change. So even though there’s a lot of AI change, I don’t know that the hourly rate of the top partners will go down.

Greg Lambert (38:15)
You’re speaking their language. They’re going to love to hear. If they heard nothing, they will hear that. So, well, Alex and Andy, I want to thank you guys both for coming on the show. It’s been a pleasure. I’ve been wanting to have you on for a long time. So it’s great sometimes to meet your heroes. So thanks for coming on, guys.

Marlene Gebauer (38:17)
Hehehehehe.

Andy Chagui (38:21)
Yeah.

Alex Su (38:21)
Thank you.

Marlene Gebauer (38:21)
Like what Alex said.

Mm-hmm.

Alex Su (38:44)
Likewise, likewise. Thanks for having us.

Andy Chagui (38:45)
Likewise.

Thank you guys. Appreciate the time.

Marlene Gebauer (38:50)
And thanks to all of you for listening to The Geek in Review. If you enjoyed the show, please share it with a colleague.

Greg Lambert (38:55)
And Andy and Alex, if people want to learn more about Latitude or reach out to you guys, where’s the best place to do that?

Alex Su (39:04)
For me it will be LinkedIn, but I would also encourage everyone to check out latitudelegal.com. That’s our website. You can find our bios there, but also see what we offer.

Greg Lambert (39:13)
All right.

Andy Chagui (39:15)
Likewise, LinkedIn, or feel free to check out the website, and you can email me at any time.

Greg Lambert (39:19)
Thank you.

Marlene Gebauer (39:21)
And everybody, please remember to like and subscribe. And as always, the music you hear is from Jerry David DeCicca. Thank you, Jerry. And goodbye everybody.

Keith argues the legal industry has spent too much time asking what technology firms use, while ignoring how economic models, client expectations, and service delivery structures support the work. For him, the problem is less about whether BigLaw is broken and more about both firms and clients being “tone deaf” to each other’s business realities. Firms talk about realization rates. Clients talk about cutting spend. The better conversation starts with mutual value, risk, predictability, staffing, and clarity around which work deserves premium treatment and which work should be systematized.

The discussion turns directly to generative AI and the mistaken assumption that faster work must always mean cheaper work. Keith makes an important distinction between routine, high-volume work and complex, high-stakes legal matters. AI will reduce variance and improve budget predictability in many workflows, especially where tasks are repeatable and pattern-based. But in complex work, AI’s greater value might come from better preparation, broader analysis, and stronger outcomes, rather than dramatic cost reduction. The Neil Katyal Supreme Court preparation example gives this point a useful frame. AI might not reduce time, but it might improve judgment.

Keith also explores how AI will reshape law firm staffing and leverage. Fewer junior associates might be needed for some traditional tasks, but firms will need more data professionals, technologists, process experts, and other allied professionals to make AI-driven work reliable. This raises hard questions about associate development, talent pipelines, compensation, and the future shape of the partnership model. The old pyramid might narrow into something closer to a specialized team, with carefully selected lawyers and business professionals working together around data, process, and client value.

The episode closes with Keith’s view of the next phase of legal transformation. Firms are still experimenting, but the experimental period will give way to sharper questions about revenue models, profitability, AI-enabled service delivery, and whether certain work belongs inside the firm, with an ALSP, or in a hybrid model. His crystal ball points toward a market where firms with mature commercial thinking gain ground, while firms slow to rethink pricing, staffing, and process risk falling behind. As Keith suggests throughout the conversation, the future of legal work is not only about smarter tools. It is about whether firms learn to run better businesses.

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’m here with Nikki Shaver from Legal Technology Hub. Nikki, I understand you’re expanding some of your advisory services. Do you want to give us some insight on that?

Nikki Shaver (00:12)
We are indeed, Greg. Thank you for having me. We at Legal Tech Hub are aware of all of the complexity being faced by law firms and corporate legal departments at the moment. It’s an environment where people have been overloaded. We hear a lot about burnout. People are trying to add to their teams, but sometimes there’s too much to do and you need some support.

What we find often is that an external perspective can be really helpful. For example, in law firms, sometimes you might be in that moment where you’re trying to drive adoption of your AI initiatives, your AI tools that you’ve licensed, and it’s not going particularly well, and it can be hard to determine exactly why. We at Legal Tech Hub now have advisory services that allow us to come in, interview enough of your people to get a real sense of what the enablers are in your environment for AI adoption, but also what the obstacles are.

Having an understanding of that from the outside then allows you to address certain cultural elements internally that you might not ever have considered relevant to driving adoption of technology or encouraging people to work in new ways or developing a culture around change and embracing openness to new ways of working.

So that’s one of the advisory services we offer. We can also work with firms around broader AI strategy, developing AI policy. We run full technology selection projects. We can support fractional KM and innovation efforts and a number of other elements internally. So if you have a need internally for some external support, advisory services, don’t forget that Legal Tech Hub is one of the providers out there.

And you can always find us at legaltechnologyhub.com. Feel free to reach out and find out more if you’re interested.

Marlene Gebauer (02:17)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:24)
And I’m Greg Lambert. Marlene, over the past few years, the legal industry has been obsessed with what I’m calling the what of technology. It’s like, what tool are you using? What platform are you on? What LLM model are you using?

Marlene Gebauer (02:40)
I know. I know. It’s a lot of hype, and as that settles, we find out the what doesn’t matter if the how is broken. Specifically, how do the economics of a firm support this work, and how do we align what we build with the value we prove? It’s a huge topic right now.

Greg Lambert (03:02)
Well, in order to handle that topic, I think we brought on the perfect person to help us walk through that. He’s a veteran of the business of law who spent 25 years building the system, so he must have started when he was 12. He’s been at some of the world’s most elite firms, including DLA Piper, Perkins Coie, Katten, and now he’s struck out on his own.

Marlene Gebauer (03:08)
Exactly.

Yeah. So, Keith Maziarek recently set out on his own to launch Lucratic Method, a consulting firm that applies management science and market economics to professional services.

Greg Lambert (03:38)
So, Keith, welcome to The Geek in Review. Good to have you.

Keith Maziarek (03:40)
It’s my cue. Thank you. Hey, I have to start out by saying how honored and privileged I feel to be invited on this. I feel like a crazy fan right now. I’ve heard you guys. I’ve been listening to you forever, and I’m actually here. I can’t believe it. So thank you for having me. I was just going to say I’ll try not to step afoul of what you would consider too much. I don’t want to make you regret inviting me, but thank you so much.

Greg Lambert (03:58)
We’ll see how you feel in 30 minutes.

Keith Maziarek (04:08)
So yeah, as you mentioned in the intro, I’ve spent the last 25 years in law firms or in the legal industry, the last 21 in law firms, and the last 17 building pricing, profitability, LPM, and related functions in some of those illustrious firms. I did it for a while. I was at DLA for 10 years, Perkins for two years, and then Katten for eight years. Over that timeline, as you can imagine, after a while you start going, you know what, I feel like I want to start branching out and doing some different things.

The day to day was becoming pretty predictable for me. Not trying to say that I’m the best at everything by any means, but I kind of knew a lot of the normal territory I was coming across. I started thinking about what would be exciting and enriching and engaging for a next chapter. I thought for a while about going into independent consulting. I thought about it before I joined Katten, but then that was a really enticing opportunity. So I took it. Fast forward eight more years, and when I was starting to think of what’s next, that became another attractive option for me.

Greg Lambert (05:16)
I do have to say that when I read Lucratic Method, of course, all I could see was lucrative method. Is it? Okay.

Keith Maziarek (05:24)
Lucratic Method? Yeah, yeah, yeah. You said lucrative, but that’s the base word that I got it from, yeah, obviously.

Marlene Gebauer (05:29)
Did I say lucrative? No, I said Lucratic. I did? Oh, sorry.

Greg Lambert (05:32)
But I don’t know. Maybe people can read it that way, lucrative instead of Lucratic.

Marlene Gebauer (05:35)
Lucratic.

Keith Maziarek (05:38)
Well, I was trying to play off of, because most of my focus has been in legal and the lawyers learn the Socratic Method. I teach the Lucratic Method of how to have a prosperous business, not necessarily the substantive practice of law sort of thing. So anyway, I was wondering, I don’t know if people are going to get this or go, what the hell does that mean? And more people than I had expected have complimented me and said, that’s kind of clever. So I’m like, good, at least I didn’t go a little too abstract where nobody was picking up on what I was trying to get out there.

Greg Lambert (05:46)
Yeah.

Keith Maziarek (06:08)
So yeah, so there’s that. Then also, simultaneously, I have launched a pricing and rates management platform that is something that, throughout my years working in law firms, is one of the needs that isn’t really met in a comprehensive way by any existing tools out there. It was kind of an opportunity, a gap in the market.

So again, looking at what I thought would be interesting to take as a next direction or chapter in my career, I’ve been really lucky to know a lot of people. You guys obviously are great examples of that across the industry. It made me think, you know, I’m fortunate to have my eyes open to a lot of different dimensions of the industry. I thought about it, and I’m like, I remember this is the last thing I’ll say and then we can move on. I’m sorry.

There’s a partner I worked with for many years at DLA who I haven’t said this publicly, but his name is David Mendelson, and he was a very, very talented client relationship guy. He knew a lot of different people. He built really strong relationships. I used to work with him a lot. I remember one time I was sitting in his office and he goes, you know, I’ll be remiss if I spend the rest of my career just billing my time for doing insurance work. He’s like, I enjoy what I do, and it’s great. And I enjoy the people I work with, but I keep being confronted by these other opportunities, ways to use that as a way to do broader swaths of things or have new adventures sort of within a realm that’s interesting to me, but it’s also comfortable and I’ve got a lot of connections.

Someday, I want to figure out how I can do that, because I’ll be remiss if I don’t take that opportunity. So I thought of his words as I was thinking about what I wanted to do. I’m like, oh, now I understand what he was talking about. So those were my drivers then. But anyway.

Marlene Gebauer (07:47)
Yeah, it’s kind of applying the relationships you build and how to do that and what you’ve learned, and having a broader platform to do that on. Yeah, absolutely.

Keith Maziarek (07:57)
Yeah, exactly. I get asked for advice a lot too. I get asked to speak at things all the time. And again, I’m very fortunate to publish. I’m fortunate for those things. But because of that, like I said, I tend to encounter more people or get to engage in a lot of conversations I wouldn’t get to if I didn’t have that ability to interact with so many great thinkers out there in the world. So yeah, I’m excited. It’s been an interesting journey.

Marlene Gebauer (08:21)
So on your website, you talk about creating harmony between client value and operational structures. I want to explore that a little bit because, is that suggesting Big Law is, you know, that model is inherently disharmonious? Or is it just not evolving as fast as the market is?

Keith Maziarek (08:44)
I would say, I don’t know if disharmonious is the right way. The way I think of it is tone deaf. I don’t know where that falls on a musical theory spectrum from disharmonious. In my experience, that applies to both law firms and clients because what I’ve seen happen over the years many times is you have these relationships that are supposed to be trusting business advisory, trusted advisor type relationships, but both sides talk past each other about things that matter to them and not what matters to the people they’re sitting across from.

In order to have a successful business relationship, it’s like personal relationships, there’s got to be give and take. When I’ve seen a lot of partners go into meetings with clients and say, well, my realization is really bad on this work, so we have to change the way we price things. And it’s like, your client doesn’t care about what your realization is. You’re talking about the wrong thing. You’re starting off on the wrong foot.

Marlene Gebauer (09:37)
To the wrong audience.

Keith Maziarek (09:39)
And conversely, on the client side, clients go, we need to save a lot of money. My CFO said some percentage or whatever. So we’re going to hold you flat or cut what you can charge us for this particular type of work. That also isn’t necessarily sustainable. Trying to find the collaborative ways of overcoming what some of those things are, where you can come away…

Marlene Gebauer (09:57)
Cheapest isn’t always the best.

Keith Maziarek (10:06)
It all sounds cliché, but there’s a lot of truth to it, a lot of relevance to it in a practical sense. Trying to look at how you create more mutually beneficial relationships within those commercial constructs is not simple, and it’s not something that a lot of lawyers are naturally good at doing. That’s why a lot of firms have growing and more sophisticated functions in these areas. Those are the things that I’ve been lucky to be able to explore over the years in these really great opportunities I’ve had.

That’s kind of the basis, and that spans a lot of things. I mentioned pricing a lot because that’s what I’ve been most closely and intensely involved in. But then you get into things related to staffing models and talking about profitability models and how do you productize certain services? How do you look at what the value proposition is and segmentation between different types of work? What are the market forces at play that are so often ignored by both sides, right?

So bringing those things in and trying to create a more holistic commercial basis for the relationships that clients and firms build, those are the principles that I’m bringing to, hopefully bringing to the table with the conversations I’m having with folks.

Greg Lambert (11:16)
I imagine that sometimes feels like beating your head against the wall, especially as people are talking past each other rather than to each other. So it’s good to have a referee in between there to make it a little bit more aligned.

So Keith, you mentioned earlier that you’ve had pretty good luck at, or kind of injected yourself in, doing some writing and doing podcasts and working with things like Legal Value Network. But you had written in The American Lawyer an article called Don’t Believe the Hype where you argue that the cost savings, especially around generative AI, will remain elusive, especially when it’s complex work. This feels kind of counterintuitive to what all of my vendors are telling me. What’s going to happen? They’re telling me it’s going to be like magic, and everything’s going to work out.

Marlene Gebauer (12:10)
And what all the clients are saying.

Greg Lambert (12:12)
So how do you talk to your clients, whether it’s law firms or in-house, and talk about this budgetary certainty, which I think is going to be more realistic than saying I need a lower bill, right?

Keith Maziarek (12:28)
Yeah, it’s such an evolving conversation based on maturity curve dynamics across so many things, tools, processes, the way that relationships are developing or whatever, different types of demand. It’s interesting. You have to stay tuned in order to stay up to date on it. It’s not like, okay, we’re there, here’s the answer.

But what I find interesting, the premise of that article was, over the years, I’ve always heard from clients, the three main priorities are cost savings, budgetary certainty, and risk sharing. So I think of a lot of things on the basis of a spectrum or a continuum. In particular, legal engagements over the years, there’s a distinction to be made between specific types of work or even workflows that fall more on the commodity side or the routine side and then those that fall more on the specialty side or complex or sophisticated side, right?

I think that’s an important distinction that is missed a lot in the sound bites, particularly from clients and also from the vendors that are feeding clients a lot of the sound bites that they’re using, that AI is a magic button and everything should be way cheaper now. It should be free because it’s so much quicker. That sweeps under the rug a lot of important dimensions of the conversation, like making the distinction between, if you’ve got a hangnail, you can easily commoditize that. That’s a simple problem. But if you need heart surgery, that’s not the same level of risk profile or complexity. So you shouldn’t assume the same results from plugging in the same process.

That’s an important piece that I think is often lost in those conversations. So if we think about on the commoditized side, I think it’s routine, pattern, high volume, those types of things, low complexity. There is probably a whole lot of money to be saved there in almost all instances, and by all means, clients and firms should be pursuing the most optimal ways to do that work. In general, they should be pursuing the most optimal ways to do all work, right?

When you start moving more toward the complex side or sophisticated side of that spectrum, to work that’s multiple workflows, multiple work streams, different dependencies that come through at different points in time when circumstances materialize and you have to make a decision at different forks in the road, that’s not really like a push-enter-and-you’re-going-to-get-the-answer-to-every-question-you’ve-ever-had-instantly situation. There’s a lot more that goes into it.

The distinction I was making there was, on that complex side, you can use AI to run a lot of the variance out of some of the component pieces of the work that would historically suffer from a lot more variance when done by humans because of lack of direction or lack of clarity or uncertainty on what the right path is. So you explore four at the same time, and it’s overly labor intensive to do that. You can do a lot of those things in a lot more abbreviated fashion, at least a couple initial steps of them, to help you determine what the next best step is on the path.

So what that means is those really high peaks and valleys of variance you would get from human work, those are going to be suppressed on the stuff that would be high volume and not necessarily high complexity stuff, right? So that’ll squeeze down with some of the other variances. If you think about what those top three priorities that I’ve always heard from clients are, the next one after cost savings being budgetary certainty, I’ve had a lot of instances where that was the primary one. Like, I just have to know what to put in the budget this year. It doesn’t have to be cheap, but it’s got to be right.

This is, I think, some of the hidden or forgotten or overlooked value that you can get out of AI. Instead of obsessing over, take a $3 million antitrust matter and make it $450,000 because AI, that’s a very short-sighted, unsophisticated way of looking at it. But if you can say, hey, I know this is a complex, high-stakes matter, what are the ways we dissect the component parts of that and streamline each of those where we can plug in a new tool or process that’s going to benefit and help things happen more quickly or help happen with less variance, less manual labor? That was the premise there. That does hold considerable value for a lot of corporates out there that are looking for, I understand that not everything is easy, but let’s run the next variable out of the equation of being the variance one.

Greg Lambert (16:38)
I think that kind of reminds me, I was watching a TED Talk this morning with Neal Katyal talking about his preparation for arguing in front of the Supreme Court. He was talking about using an AI tool to set up what he thought were the potential questions coming from each of the nine justices, and how almost every question that they asked, the AI had kind of predicted that they would ask.

I don’t think that saved him any time. It probably cost him more time, but he was better prepared. He had better outcomes because he was using tools in a way that really supported better outcomes for his client.

Keith Maziarek (17:24)
That’s a great example, because that’s one of the things I mentioned in the article too. Let’s not remain constrained by this is how it was done before and this was the outcome, so let’s do it a different way and it’s the same outcome. There are many great use case examples like that where, because of the breadth of information that can be pulled into different analyses you’re doing based on the prompts you use or whatever the question might be or whatever you’re trying to analyze, you might come up with, you can avail yourself of more potential solutions more easily or at all because you couldn’t do it the same way if you were doing all the work manually.

Using some of these tools because they do have that extra processing capacity that your brain doesn’t and time won’t allow, right? So that’s another thing that’s also lost. If you’re getting a better result, should it be a tenth of the cost that it used to be? I don’t understand why that’s necessarily the conclusion that is so often driving a lot of those conversations.

Marlene Gebauer (18:18)
Yeah. I mean, it sounds a lot like we had Lenny Nuara on the last episode, and he does, like, there’s a lot of intake that they do. They’re doing flat-fee M&A, and they do a lot of intake to, again, do that compression that you’re talking about to get all the information. So they’re not having to go through as much. They understand what the client is actually looking for in the deal. They’ve been able to transition something that’s very, very complex into something that they can basically predict, this is what the fee’s going to be, and make money off of that.

Keith Maziarek (18:50)
I think that’s one of the exciting things too that isn’t talked about as much as it will be, is the trigger effect that AI has on making people look more at data intake and what they’re not only collecting information, which is so often overlooked in historical legacy processes at firms. Like, just get the matter open. I want to start working. Collecting important information, I’m preaching to the converted here, but collecting information that’s got commercial or predictive value to it, making sure it’s clean, making sure it’s high quality, and making sure it’s accessible and able to be accessed and incorporated in a nimble way into different ways of looking at things. The value you can reap from that exercise, which has so long been overlooked, is, I think, more exaggerated now when you start laying different flavors of LLMs and AI over what that data can do.

Marlene Gebauer (19:47)
There’s more of an incentive to do it. Before it was, oh, the attorneys don’t want to do that. It’s going to take too long. And now it’s almost like, yeah, you kind of have to, because taking too long is not really allowed anymore.

Keith Maziarek (20:01)
Yeah, yeah, yeah.

Greg Lambert (20:02)
Yeah, because I think there’s a lot, and I’ve seen this as I’ve talked to attorneys at my firm, is there’s a big motivation to improve the turnaround time. It’s getting the results back into the hands of the client faster to make better decisions.

Attached to that, the other thing that I’m seeing is the attorneys who are comfortable talking about how they’re leveraging the AI are really having good conversations with their clients. It’s not the clients coming back going, oh great, now I’m only going to pay a tenth of what I was paying before. But what I’m really hearing, and it’s anecdotal, but it’s enough anecdotal that I feel like it’s a trend here, is a lot of times that spurs the conversation to going, oh my God, I’ve got this work that I’ve been doing that I don’t really want to do. I’d rather have you do it. But I’ve never thought that this would be something that would make sense for you to do. But if you can leverage the AI, I’m more than happy to give you this work.

Keith Maziarek (21:12)
Right, yeah. I think those conversations, I mean, I’ve said this since I look at the AI era now that’s continuing to unfold being similar in nature, having a lot of parallels with the AFA era that was like 2009 when I started doing this, where there was a lot of talk and a lot of uncertainty. I remember at the beginning when I started getting involved in this stuff, it was toward the beginning on the AFA side, and I was really nervous and anxious back then. Like, what if what I’m saying or what I’m doing is wrong?

And people call me out on that. Then I realized nobody really knows. It’s a period of exploration and discovery. You’re going to find what works and take what works and then leverage that with other things and incorporate that into other models. It’ll continue to evolve and improve from there. So I feel like it’s a similar thing here. I feel like that is coming out in a lot of conversations. A lot of the statements I would hear that were very frustrating to me, like the ones we talked about at the beginning, like, everything should be super cheap now because AI, I feel like the conversation is evolving and maturing on the client side now because more recent studies I’ve seen, they are actually using AI a lot more than I would have predicted.

I assumed they were going to go, I’m not paying for that. I want my law firms to pay for it and then use it, and they’re not charging me anything because they’re using it. It’s been interesting to me to see how wrong I was in some of the surveys coming out where in-house departments are leveraging it. But when you have that conversation with an in-house lawyer that is using it or within an environment where it’s much more present, those are so much more productive and fruitful conversations because there’s symmetry of information and understanding there. You’re not going, you don’t even understand the beginning of how this thing starts to work or what I have to do in order to get it to do anything. You’re seeing fast and equating that to free or cheap or whatever, right?

So I do think that the conversations and the familiarity are evolving and maturing at a faster pace than I would have expected. Thanks to clients actually using it, in other words. I didn’t think that was going to come about as quickly as it has.

So yeah, I think we’re on a good course. We have to keep going in the right direction.

Marlene Gebauer (23:22)
Yeah, I’m kind of interested in your thoughts. I mean, we’re talking a lot about workflows and whether agents are going to condense the time that is needed. But then at the same time, we’re talking about, you have to be checking things, you have to check results. Oftentimes, I don’t know about you, but oftentimes that takes longer than actually putting it together because you haven’t put it together. So your mind isn’t quite in the right spot in terms of, okay, I know it’s here. I know it’s here. You have to go searching for everything.

So when we’re talking about these new workflows and we’re talking about new staffing, for moving work from people on the higher end of the food chain to the lower end of the food chain, I guess, is it going to be shorter? Is it going to be more condensed? Is it going to take more time? Are expenses going to stay flat or are they really going to go down?

Keith Maziarek (24:25)
I think, and we’ll see if I’m wrong about this like I was about the client thing, but only time will tell, right? We’ll do it to be continued.

Marlene Gebauer (24:29)
We’ll come back in a year and ask.

Greg Lambert (24:30)
Luckily we have this recorded, so we can point back to it.

Keith Maziarek (24:36)
Exactly. I was going to say, yeah, now it’s memorialized. But I think what I’ve been telling people is, in order to deploy and leverage AI in productive, meaningful ways, whether it’s commodity work or components of more sophisticated work, it’s not easy to make it work well, right? There are a lot of things that go into that.

So to me, the staffing model evolves. It changes. I would assume, I haven’t talked to anybody yet, there are some interesting anecdotal data points I have recently, but most people agree that it would seem you would need less associate leverage in the future because of what AI can give you. What I believe will and is already starting to substitute for that leverage is other experts that are also highly skilled, highly educated, very important professionals like data scientists and all those folks who are able to do what we talked about, the collecting, curating, making sure that the data is clean and formatted or prepared in ways that are able to be used by the applications that do deliver the value they need to. All that optimization of the data and running the applications and all that is not child’s play.

So what you’re taking away from spending on associates, you’re redeploying on technology and people who have different, highly sophisticated skill sets you need to run and deploy them in productive ways. Then to your point, Marlene, which is very true just from my own experiences, it isn’t like the LLM tools I’ve used, which are somewhat limited to Harvey and Claude on my own stuff and some other ones, but nothing too crazy or on the fringe. It’s impressive to me how much it can kick out to me that’s well structured, but I’ve also been more often, I would say more often, at least a 50-50 split, where I go, wait a minute, what?

Then you have to go through and validate and pressure test everything and check and all that, right? That still requires judgment to know, number one, hey, that doesn’t sound right to me, I need to look into this. And then to know how to look into it too, right? So there is an interesting dimension of the talent piece of things that will be shifting. But from an economic standpoint, you’re not replacing expensive associates with nothing. You’re replacing them with other comparably expensive means of production, talent, and resources.

That’s part of those conversations that are starting to mature now with clients and firms, where you’re like, all right, look, the more that there’s an acknowledgment of that and you can have a mature discussion about what that really looks like in every different instance, the further we’ll come. That will help alleviate the AI-so-it’s-free type of rhetoric that I think is, and I think that’s improving now finally, that has been out of place in the conversation.

Marlene Gebauer (27:32)
So do you think it’ll really kill the partnership model? I mean, if you don’t have a pipeline of associates coming in to take that mantle at some point, does it become more of a corporate model where attorneys come in and do work based on their level, but it’s kind of a different profit model?

Keith Maziarek (27:53)
So this is an interesting dilemma. There’s another article I had on this that my friends at Am Law are always very kind to indulge me when I have these ideas and I write something on it, they publish them. But that was one I wrote, and it was, as you think about the narrowing pyramid, Steve Poor had a great one. He talked about it being like a rectangle instead of a pyramid at all, which is an interesting thing from a leverage standpoint.

But when you think about that, whatever the shape becomes, it’s obviously a lower-leverage model. There are a lot of interesting aspects of what that transformation brings to mind or dilemmas that need to be explored. How do you make subject matter expert partners when you’re only starting with a much smaller pool of associates in the first place, right? And then that smaller pool, because you can’t afford to have scores of extra people sitting around working two hours a day because all they have to do is do stuff on some LLM platform because that’s all there is to do, the work they would have historically done manually. You’re not going to hire that many people because you can’t float that bill to pay that many people to sit around and do things that are not productive enough to pay their way.

Does that bring salaries for associates down? Maybe, I don’t know. But I think it definitely brings the talent pool down, right? But you still have to create subject matter experts. So the yarn that I pulled on that idea as I was thinking about it was, instead of having an infantry model of recruiting of first-year associates or junior associates where you get a bunch of equally credentialed people and see who makes it through the first rounds of battle, five years later, the attrition numbers I found were after five years, 80 percent of people you recruited as first-year associates are gone. And you haven’t even gotten to the point where you’re starting to maybe be able to make some sort of profitable use of what their expertise is because they’re starting to develop higher-level skills then, right?

So it’s a tricky model to navigate, but you still need to get somewhere with that. So I said, okay, you can’t recruit an infantry unit anymore. It maybe takes more of a shape of a basketball team, where you’ve got three forwards, four guards, and one or two centers, and it’s a carefully composed team.

But then how do you recruit people that are going to have that perfect composition and cohesion? Maybe there are places in the recruiting process or the vetting process to go, we have to incorporate some sort of personality testing, skills-based testing, where you go, I can create cohesive units of these skill sets that go well together in the model that I need in order to be optimal. But then you have to keep them around, right? So you’re going to have to pay them a lot of money because you can’t afford a bunch of people leaving. It’s a lot bigger of a hit if you take one piece out of that puzzle. So anyway, it’s interesting stuff to think about. I don’t know the answers, but it’s fun to think about what might create them, or what that future might look like.

Greg Lambert (30:39)
Yeah. Well, I’m going to take that piece of yarn and pull on it a little bit more because your vision of where we should be sounds really good. Now, let me put a dose of reality here with large law firms now recruiting first-year law students before they’ve even taken their first exam. And I’ve talked with law professors recently that said that those students that are being hired are stressed the hell out. It has put a whole different pressure on us. And so the market is moving in almost the exact opposite way that everyone is saying it should be moving.

So how do you consult with clients to talk them off this cliff? Because everyone’s thinking, if I don’t get that talent now, the Skaddens and everyone else are going to get that talent and we’re not going to have that talent.

Keith Maziarek (31:48)
So there’s a weird, what’s the word I’m looking for? Like, systemic dimension to first-year recruiting that I’ve never really been able to business…

Greg Lambert (31:57)
Yeah, I call it insanity, but you know, systemic.

Keith Maziarek (32:14)
Yeah, fair. I was going to say, that’s a better, more rational term. It’s challenging to find compelling business rationale for what that approach is.

I asked questions about this when I was much younger and didn’t know any better. And I still don’t have really good answers for them. But it was like, okay, look, if you think about the Wachtell thing or whoever it is, it’s on the top of the heap that starts setting the prices every year and going, oh, we’re going to pay first-year associates this. The ripple effects of that through different segments of the Am Law 200 that are never going to be recruiting the same people that Skadden, Wachtell, or whoever is getting in the first place, paying that same thing. It’s always been an interesting phenomenon to me that didn’t make a whole lot of business sense, and it doesn’t seem sustainable, especially not now if you’re going that much further back when you don’t even really know what you’re dealing with, right? Like a 1L, what have they proven yet other than that they got into that school, right?

Maybe, I don’t know. I guess I never thought about it this way. I’m thinking about it now. Maybe people that focus on this have realized whether they’re a 1L or a 3L, I don’t see a whole lot of differentiation between what the product I’m buying is at those different pieces. So I might as well almost be buying forward contracts for jet fuel, like airlines do so they can minimize or normalize what their cost profile looks like over longer time horizons. Maybe that’s a factor that I’ve never been exposed to. It would explain it to some extent, right? So I don’t know.

Greg Lambert (33:44)
The law students are the futures market for aviation fuel.

Keith Maziarek (33:47)
Yes, exactly. It’s futures and options contracts, you know?

Greg Lambert (33:53)
So Keith, I know that you’ve been involved in the Legal Value Network. You were co-founder with that, and I’m sure you were involved with P3 before that, back and also a member of the College of Law Practice Management. So I know you write and you’re constantly out there, but I’m curious to see what do you do to stay up with everything that’s going on in the market? How do you keep up with things?

Keith Maziarek (34:31)
It’s real easy. The Geek in Review is kind of like, I don’t really need to go anywhere else. So it’s comprehensive.

Greg Lambert (34:34)
There you go.

Marlene Gebauer (34:36)
That’s it. That’s it. And we’re done. Thanks.

Greg Lambert (34:40)
And the music is by Jerry David DeCicca, so thank you, guys.

Keith Maziarek (34:42)
No, but honestly, yeah, The Geek in Review is definitely part of the diet there. As far as writing, I have historically always included the ALM Morning Update thing on the business of law topics. That tends to be a pretty steady stream of content that touches on topics that I think about a lot, or maybe I think about them a lot because I look at them every day. But that’s one. People would say I’m relatively active on LinkedIn, and I will usually find articles in there that I go, you know what, I’ll have some thoughts and I’ll start putting my ramblings. I’ll share the article, and a lot of times those are ALM articles. So I look at that pretty regularly.

As far as other publications, I’m a huge sucker for anything survey oriented. So all the Thomson Reuters ones are the ones that come out from Wolters Kluwer or even Clocktimizer on the client side, any of those types of things. Blickstein’s for sure. Through LVN, we had a lot of fun for a number of years. Hopefully we can rehash that project because it was really great. But partnering with Blickstein on the LPPM survey with LVN, the LDO survey, it’s always, again, wanting to facilitate or nurture those conversations with the people on the other side to help navigate better relationships. You can’t do any better than getting that right-from-the-horse’s-mouth type of information, to understand where their thoughts are, where the finger on the pulse is of things changing in the industry. So you can go into those conversations informed and educated on where they come in.

And I listen to a lot of podcasts, like you guys obviously, right? But then also Laura Terrell, who does Big Law Life. Laura and I go way back. We worked together at DLA. I think the stuff she’s putting out serves such an underserved niche in the industry to help people navigate all the folklore type of things that nobody tells you and you learn the hard way. I think that’s great stuff. So it’s great to listen to those.

Greg Lambert (36:25)
Oh yeah. And I think she’s been on the show twice now.

Marlene Gebauer (36:46)
She has. She’s been on, yep.

Keith Maziarek (36:49)
I knew once. You said twice? That’s good, yeah.

Greg Lambert (36:52)
Well, before you get too far off track, because there may be some listeners that aren’t as familiar with the Legal Value Network and what they do, do you mind giving a little overview of what LVN was set up to do and who would be interested in it?

Keith Maziarek (37:08)
Yeah, and I appreciate the opportunity to give a little shout to them. So LVN, we founded it seven days before lockdown started in 2020 for COVID, which was fantastic timing. If you want to learn about pivoting and being nimble, that’s a fun way to do it.

But it’s basically a professional association, a community of business of law professionals across a number of different functional alignments within law firms, legal departments, as well as we call them business partners, that could be technology providers, consultants, any other service providers to the space. Basically, it’s a place for people to come together, get more information on best practices. There’s a lot of thought leadership content that’s generated through a number of different channels. We’ve got webinars that we do on, I think it’s a bimonthly basis now. It used to be monthly because when it was COVID, you couldn’t do anything else.

But we have live programming too every year. In September in Chicago, we do the LVNx Conference Experience, which is between 400 and 500 professionals that come together across pricing, innovation, knowledge management, profitability, legal project management. What else am I forgetting? We get some BD folks and marketing folks that are involved in the conversations as well as our friends on the client side and legal ops.

And then, as I mentioned, folks on the data and technology side. So those are the typical constituents or stakeholder groups that contribute to the conversations. We have what I like to say are pretty well-rounded agendas on all the different subject matter topics that are covered.

Other things we have in the spring, we usually do a series of road trip or pit stop events where people can get together in their own local cities with the people in their local communities that are in the business of law sphere, ecosystem, and have those connections so they can set up other happy hours and things for themselves locally. And then we have the Off the Clock podcast that Justin Ergler and I do. Justin’s one of my co-founders and partners as well.

So yeah, it’s a membership organization. You can join either individually or as a group membership for your whole team or your whole firm and the different people that operate in this space across your firm. That gets you discounts on conference registration. You get all the webinars for free, all the other thought leadership content for free.

And one other quick plug. One of my other co-founders, Stuart Dodds, and I, before he kind of retired from LVN as well as retired, retired, retired, took all of the thought leadership content we had developed through webinars, publications, and subject matter content that had been developed for LVNx over the last five or six years, and we put it together in what is called, really clever name, the Resource Library. I think we could have done better with that. But it’s organized into a taxonomy by subject matter.

So what you can basically do is use all the thought leadership content that LVN has developed over the years as a means of not only staying up to date on latest developments yourself, but when you have new people coming into your team or other subject matter areas that you’re not fully up to speed on or want to learn what’s latest and greatest and newest in the industry, you can go into that and quickly access all this structured, targeted content for your benefit there. So that was what we think is a pretty powerful member benefit for joining as well.

If you go to legalvaluenetwork.com, you can see all the information there on membership, whatever. You can reach out to me. I’m happy to help. We just had a new board come in, the second generation of the board, and they’re doing all kinds of great things that are starting to be announced now. So I’m really excited for that next chapter of LVN’s life cycle.

Marlene Gebauer (40:57)
Well, before we get to the crystal ball question, I did have one question for you because you have just started on your own. What are the types of questions and advice that you’re being asked to provide, and what are the things you think that people should be asking about?

Keith Maziarek (41:14)
Yeah. I get approached, I’ve always gotten approached relatively frequently by people asking me questions. Now that I don’t have any restrictions from a competitive standpoint, I get approached by a lot of people. Well, it’s interesting because I kind of start going, this is interesting because I learn a lot about things from people who wouldn’t have asked me these questions before. I’m finding out it’s been very educational because I’m learning about challenges people have that I didn’t think people had at certain levels anymore.

Marlene Gebauer (41:28)
Now you can answer all the questions.

Keith Maziarek (41:42)
So it’s been interesting from that perspective. But not surprisingly, the vast majority of the questions I’m approached on are, so AI is already transforming the way that we do things, the way the client does things, the way the clients do things. How are we supposed to continue to make money doing this? What is the pricing magic that you need to bring or what’s the formula for this new era of services, right? So that’s what I get asked the most. And if you’re not asking me that, you probably should.

But the reality is, again, going back to those parallels between the AFA revolution and the AI revolution, it’s somewhat reassuring to know that there are no secret formulas that only a couple people know and they’re presiding over and only leaking out to special confidential folks. There is a lot of experimentation and trial and error on that topic. But what all the thinking seems to be orbiting around is, I’ve always loved all the Kill the Billable Hour and Death of the Billable Hour articles that come out because I got so overwhelmingly fatigued with those by 2011 that when I’ve seen them continue to come out now, and even in higher frequency because now AI can write those headlines and you get more and more of them, it’s kind of crazy.

I think the pretty constant theme from those who kind of know, I think know what they’re talking about in the industry, and I would agree with them. I don’t know if that means I know what I’m talking about or I don’t, but there’s definitely a bigger place for hybrid approaches where you’re going to have components of work that are going to be fixed fee. That goes back to my article about how the budgetary certainty is going to be a bigger piece. It’s because you can fixed fee components that will have greater levels of certainty or lower levels of variance than you were able to and then leave the rest of it to variable models if it’s hourly billing or things that are partial contingency or whatever it might be where there’s a risk-sharing component to it.

But there is no new model that nobody ever conceptualized in the past that I’ve heard of yet that is starting to take the world by storm or poised to. So it’s usually questions revolving around that and then around, what are firms starting to do or clients starting to do themselves to transform some of these workflows that are affecting the economics of some of the different subject matter areas that we operate in?

And that varies practice by practice and area of law by area of law. But having those diagnostic conversations about, okay, here’s your particular problem and here’s what you could do in order to reimagine the way you’re doing the work. Then there are interesting conversations that are triggered by that line of questioning or discussion that are, do you want to be in this business anymore? And not in the business at all, but do you need to transform the way you do the work, or are you better off going, instead of me buying a couple more point solutions or trying to find all these other people to do this work, why don’t we give that to an ALSP or something like that?

Or at least look at that type of hybrid service delivery approach model and see if there’s some merit in that as opposed to trying to buy everything we need and curate everything we need to be all things to all people. I think there’s a different level of relevance or intensity of rationale for using ALSPs in a more collaborative way given AI than there was in 2018 or 2019. I thought there was this big fever over ALSPs are getting bigger and they’re going to take a lot of stuff, and it never really seemed to materialize. I think those dimensions might have a little bit different way of being evaluated now, so we could see that. Anyway, just a couple examples of the things I’m being asked about a lot.

Marlene Gebauer (45:39)
Very, very interesting.

Keith Maziarek (45:44)
Yeah, it’s an interesting time to be around, which is again kind of why I decided to go, why don’t I try and see what other interesting adventures I can go on now given the market conditions and things that are happening.

Marlene Gebauer (45:56)
It seems that there is a lot in flux, and this is sort of the perfect time for somebody who’s got expertise to be able to advise that way. So crystal ball question, 2027. Now our question says it’s going to be a lawyer freakout time. I’m going to be just like firm freakout time because not only are you having a different relationship between, you know, legal ops, between partners, between more experienced and less experienced attorneys. It’s also between legal ops and attorneys. It’s also between allied staff and attorneys. So what’s the single biggest change you see in terms of firm structure and the relationships and how work gets done?

Keith Maziarek (46:48)
Single biggest change, man, there are a lot of equally big changes.

Marlene Gebauer (46:50)
I know.

Greg Lambert (46:51)
You can name two or three.

Marlene Gebauer (46:53)
And you can answer the question the way you want to answer. We trust you. We trust you.

Keith Maziarek (46:55)
Are you sure you want to give me that level of latitude? I mean, I don’t know how much time we’re at now, but I think we’re coming to the end, right?

Greg Lambert (46:59)
Let’s see where it goes.

Keith Maziarek (47:01)
We can always cut out whatever you don’t want to use, right?

I think that as tools and processes that are being transformed a bit and automated more start to be progressed on the maturity curve more, and there’s less experimental nature to them and a little bit more of, here’s a new way to do things, I would expect you will start seeing an equally prompt level of urgency from law firms to go, okay, now these things are materializing, right?

I feel like a lot of the use case experimentation so far has kind of been like seeing the fields to go, okay, what is practical or sustainable? What is meaningful? What’s going to impact us? What can we leverage to our advantage? I think you’re going to start seeing more clarity on those things. Then I would expect you would see changes being made where it can be credibly done without upsetting the stability or the continuity of what law firms do now, right?

So you might see, I would hope, I would expect you would see a lot more focus on what is our revenue and profitability model now? Because we do have to rethink what are the means of production and what is the market value of what we do, right? And I think the maturity of that discussion of market value is still relatively immature, but that’s going to have to ramp up to go, okay, well, if it costs us this much, factoring in whatever cost accounting factors you want to for all the infrastructure that you’ve had to put in place to do the work that much faster, what premium do you place on leveraging that for this type of work, right?

So I think instead of, we’ve been in experimental mode with those things and how you monetize the work, I think the velocity of those conversations and models is going to start to increase. That will, by association, start transforming who are the winners or the losers. I think that triggers a lot of things from an industry structure standpoint. There are some firms that have not really done much to start adopting or even experimenting in how should we start changing the way we do things across different pieces of our business.

I feel like there may be a reckoning time coming or at least a rude awakening where it’s like, man, we really should have gotten on this sooner because now we’re struggling and we’re now an acquisition target as opposed to being a leader. You know what I mean? So I think as maturity in those transformations starts to come about, that will be the trigger event that’s going to start these business principle and business concept focuses to permeate more into how lawyers do their work and then how management needs to be considering, okay, what is the product we’re putting out in the market and how do we want to optimize that for the way some of these changes are happening?

Like I said, there’s been good experimentation. It’s good that people are taking it seriously. But you can’t, Toby always used this example, right? It’s hard to change the wheel on a car when it’s driving. It’s the same thing. If you start from scratch, and there are some AI-native firms out there that, you know, Michael Pearson I met at Legal Tech Fund last year when I spoke with him on a panel, and I didn’t know a whole lot about them until I met him and I started researching it more. You’re seeing a lot of traction with those things. So those are other approaches to doing it, but that’s starting with a different model as opposed to changing one while you’re already in process.

I just feel like there are so many interconnected dominoes, right? Once you trip one, these other ones start to go. But I feel like that will be the origin story of where it comes from, okay, the maturity of what the processes are, as well as tool reliability and recognized unreliability. Like, you know what it can do and you know what it can’t do. So know where you have to have the humans in the loop and establish the judgment or processes that are going to help facilitate using it effectively and responsibly. That’s what’s going to start tripping all these other circuits to change some of the other components.

Marlene Gebauer (50:52)
We live in interesting times.

Greg Lambert (50:52)
Definitely.

Keith Maziarek (50:53)
Definitely. Definitely interesting.

Greg Lambert (50:55)
Change is hard, but change is also constant. It’s an odd combination. Well, Keith Maziarek of Lucratic Method, we want to thank you very much for coming on the show finally and then letting us go completely off script while we were talking to you. This has been great.

Keith Maziarek (51:16)
Well, you can always count on me for that, and I thank you for finally inviting me. I hope it wasn’t my off-script tendencies that scared you away before, but see, I can be responsible. Awesome, awesome. Thank you so much, guys. Seriously, great conversation, great seeing you guys. I really do appreciate it.

Greg Lambert (51:23)
Well, we promise it won’t be eight years before we get you back on.

Marlene Gebauer (51:27)
We promise. We promise. Yeah, thanks, Keith.

And thanks to all of you listeners for taking the time to listen to the podcast. Don’t forget to like and subscribe, please.

Greg Lambert (51:41)
Yes. So Keith, what’s the best place for listeners to reach out and learn more about you and the Lucratic Method?

Keith Maziarek (51:49)
Yeah, so I’m on LinkedIn. I’m pretty active on there posting things and everything else. So you can find me there. Surprisingly, not too many Keith Maziareks.

Greg Lambert (51:55)
How many Keith Maziareks are there on LinkedIn?

Keith Maziarek (52:15)
I found other Maziareks, though. It’s not a very common name. I have found other ones, which has been interesting, that are not people I know I’m related to. But yeah, so you can search me in there. It’s really easy. I’m grateful for anybody that wants to follow me or connect with me. I’m always very open to connections.

And then, yeah, if you want to look up Lucratic Method, it’s lucraticmethod.com. There’s a website on there outlining some of the things that I’ve done and areas of focus that I’ve had exposure to that I can hopefully leverage there. And then bodhisolutions.io is the software company that we didn’t touch on too much, but another exciting thing that I’ve been working on. It’s another adventure I’m going through now too. So bodhisolutions.io. You can check that out too. Thank you, guys.

Marlene Gebauer (52:44)
And as always, music here is from Jerry David DeCicca. Thank you, Jerry. And bye, everybody.

Nuara explains why he resists the phrase “AI-first” in legal practice. For him, legal work begins with talent, judgment, and expertise. AI enters as a force multiplier, not the driver. At Flatiron, the firm’s model was already built around flat fees, lean staffing, process discipline, and structured data before generative AI entered the picture. AI now adds more horsepower to a system already designed to reduce waste, repeat touches, and unclear workflows.

Much of the discussion focuses on M&A due diligence, where Flatiron rethinks the deal life cycle from intake through closing. Instead of throwing documents into a massive repository and hoping AI sorts it out, Nuara describes breaking work into smaller pieces: diligence questions, responses, documents, clauses, topics, closing checklists, and reports. That structure lets lawyers use AI for deduplication, extraction, clause comparison, first-pass drafting, and issue spotting while keeping human judgment between higher-risk steps.

Nuara also warns against getting seduced by polished AI output. He describes generative AI as persuasive, fluent, and sometimes dangerously average. The bigger risk, in his view, is less hallucination and more “model monoculture,” where legal drafting drifts toward sameness because models train from overlapping bodies of public material. In complex private transactions, average language is often the wrong answer. Lawyers still need to understand leverage, client priorities, risk allocation, and where to push beyond market terms.

The episode closes with a look at pricing, training, and the future structure of law firms. Nuara argues that AI will pressure the billable hour, change junior lawyer training, and force firms to rethink the traditional pyramid. He also raises a practical concern from the early Westlaw and Lexis days: the cost of the tool matters. Flatiron tracks AI usage down to the clause level, treating tokens as part of matter economics. For legal professionals watching AI reshape transactions, this conversation offers a grounded reminder: better tools matter, but better process and better judgment still decide the outcome.

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’m here with our friend Nikki Shaver from Legal Technology Hub. And Nikki, you have a new premium content layer out with the litigation case management. you mind giving us some insight on that?

Nikki Shaver (00:16)
Yeah,

absolutely. Hi Greg, hi everyone. So one of the interesting things over the past year is that while when Generative AI first launched into legal, we saw a massive uptake of solutions in contracts and transaction management, as well as the broad AI legal assistant solutions. It took a little while for litigation to follow suit, but for those who have been listening for a while, you’ll know that.

sort of midway through last year, we saw a massive rise, all of a sudden kind of an explosion in litigation solutions. And that’s been really exciting. A lot of things coming out that really go beyond what was available pre-gen AI, know, things that can manage facts and provide you with insights into where there might be inconsistencies and testimony, all kinds of tools that frankly, I wish I’d had available to myself when I was a litigator. So we, as many of you know, we publish

what we call premium categories on Legal Tech Hub. These are collections of really in-depth content that allow buyers to review solutions in a particular category, evaluate them, provide the tools with which to evaluate solutions in that category. And good news is that in May, we are launching our premium category for litigation case management solutions, including a lot of these newer generative AI driven solutions, as well as

some of the incumbents that now have Gen.ai features. So log in to legaltechnologyhub.com and if you’re a premium subscriber, you’ll be able to access all of that good content around new litigation solutions. And if you are not yet a premium subscriber, you can reach out to us and you know what? You can easily become one and access that content.

Greg Lambert (02:01)
Well thanks, Nikki. It’s ⁓ good. The litigators always felt a little left out on the AI tools, so this is good news.

Nikki Shaver (02:06)
Yeah,

yeah exactly now they get to catch up.

Marlene Gebauer (02:18)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer

Greg Lambert (02:25)
And I’m Greg Lambert and today Marlene, we are joined by Lennie Nuara, who’s co-founder of Flatiron Law Group and a nationally recognized authority on technology, internet law, cybersecurity, privacy, M&A and complex commercial litigation. I’m sure there’s a longer list than that.

Marlene Gebauer (02:43)
There is a longer list. He does it all.

Lennie brings a rare mix of lawyer, operator, technologist, and problem solver experience from rebuilding a law firm’s technology infrastructure after 9-11 to helping launch regulated trading platforms and now building an AI first model for legal practice. So Lennie, welcome to the Geek in Review.

Lennie Nuara (03:05)
Thank you. Thank you both very much for having me on.

Greg Lambert (03:08)
Well, I remember when we had Conrad Everhard on the co-founder, he would constantly refer to Lennie did this, Lennie did that. So it’s great to see the infamous Lennie on the show here. So, but when Conrad joined us before, we talked a lot about Flatiron’s flat-fee M&A model and how it was challenging the traditional Big Law pyramid.

With Flatiron.ai, feels like the model seems to have evolved from just this alternative fee structure plus technology into something closer to what you had referred to us before we started recording as a talent-first AI-operated system for legal work. So Lennie, I want to ask you, when you say talent-first AI-operated, what does that actually mean for

for the practice.

Lennie Nuara (04:05)
Yeah,

so yeah, because a lot of people say you know AI native and I don’t I don’t like the emphasis on the AI first. It’s the practice of law is all about talent. That’s the number one thing that anyone hires. You have a problem you want to find the best that you can afford to handle that issue. You don’t hire AI and I’m not trying to malign anybody with regard to the AI first label. That’s not the point, but the point is is its talent and its talent that has to drive the AI. So as a firm we started.

over nine years ago now, and we were essentially leveraging technology significantly before the advent of AI. But still it was talent driven and then I started building systems. have a degree in computer science, and I’ve been playing with these tools for a long long time since since the creation of that machine in the behind me. And the idea is is to take a tool like that one or AI today. It’s a long way ⁓ forward and.

leverage that tool in the practice every single way you can. And I’ve been doing that in my practice for the 42-some-odd years I’ve been doing it is where I can use tech to enhance the practice. I use it. It started with word processing later on using databases to find embezzlers and get the money back and building flat fee litigation support.

Nationwide we did flat fee litigation in asbestos didn’t really matter where, but I tried to leverage the use of the technology, but it’s always driven by talent. What we do as counsel has to be not just the in the loop. I just don’t like that phrase. We’re not just in the loop. We should be driving it so the AI in the practice. I’ll call her a talent first AI powered is another phrase that I use is.

using AI to support the lawyers, which you guys know how that works, okay? But it’s in the nature of the architecture where the AI comes up. So, transactions, which we do the most of, Complex transactions, M M&A is our first area, but we do other complex transactions, software development, commercial agreements. And we always say, okay, well, what needs to happen in this deal? There’s a repetitive aspect, and we’ll talk about that later as we dig into this.

But there’s repetitive aspect of things. Well, you know, we don’t have to type anymore the same way we used to. We can we can generate things. We can essentially break things into pieces and use AI to analyze that. There’s a variety of things that are available, and the AI is an absolutely fantastic tool. It’s also an incredibly dangerous tool in the wrong hands like like a Lamborghini and a 16 year old hands. It’s a great car. It’s not the car’s fault.

that it wrapped around a tree. It’s the kid who drove it into the tree

Marlene Gebauer (06:53)
you

Greg Lambert (06:55)
I know some 57

year olds that probably shouldn’t be driving.

Marlene Gebauer (06:57)
hahahaha

Lennie Nuara (06:58)
So

it’s a tool, it’s a great tool. And if you use it, so within our firm, we’re taking that tool, which we started with paralegals nine years ago. I was using paralegals that were not really paralegals. I called them that, but they were honestly stay at home moms that did things remotely for me and built out my database on every transaction we did. I would extract data.

from every agreement or every document, every invoice, whatever, with people typing and people reviewing. And they cost 35 to $50 an hour versus what it would have been within a firm for a client. And we just rolled that into our flat fee. And that was an example of architecture and structure and workflow that didn’t even have AI. And then as AI came into being, and by the way, it was that manually, then it was.

Done with databases, we use a lot of different tools that are commercially available. And now AI just feeds that whole process with, we call it higher horsepower, the Lamborghini level, which is great.

Greg Lambert (07:59)
And just for the people that are just listening to this, the machine behind Lennie is an old Osborne, I think they call it a portable computer, right? ⁓

Marlene Gebauer (08:08)
Yes.

Lennie Nuara (08:08)
Yes, yes, it was 25. It is.

It still is 25 pounds and I had the 37 inch sleeves to prove the fact that I carried it through law school. OK, I honestly do. My sleeves are 37 and it’s a. It’s just an example of a tool that was used in my practice early on and actually in my school and and AI is another tool and it’s a wonderful wonderful addition to our our portfolio.

Marlene Gebauer (08:11)
Portable.

Greg Lambert (08:13)
Yeah.

Hahaha

Marlene Gebauer (08:17)
Hahaha.

So Lennie, let’s talk a little bit about workflows. When you talk to people at firms, it seems that they’re trying to bolt AI onto an existing process. So it would help me do my diligence memo. It helped me do a contract summary. It’s ’s helping me with drafting But Flatiron, as you’ve noted, is starting from a different place. You’re redesigning M&A workflow.

around the assumption that AI is part of the matter from intake through closing. So what parts of the deal life cycle have had to be rethought? And where did you discover like the old workflow simply didn’t make any sense anymore?

Lennie Nuara (09:20)
All right, so that’s a great question. So let me break it apart a little bit. ⁓

Marlene Gebauer (09:24)
It’s a hard question

because I think people are having a hard time wrapping their heads around that.

Lennie Nuara (09:30)
So let’s let’s let’s do some level setting. First thing we didn’t rethink like deal team strategy or negotiation. Balls or. What the read is on a counterparty or you know what the calibration of risk is.

Greg Lambert (09:43)
You don’t have their

⁓ agent talk to your agent and then just come up with it.

Lennie Nuara (09:48)
Yeah, no, I do not. In fact,

I wouldn’t even want to try to create another agent of what is Conrad. That would be, you know, like. Yes, so you wouldn’t want another one. I always said if there was an if I had an agent, I’d still be working in my agent would be on the beach. OK, so it’s like it doesn’t always work, but so first it’s AI powered practice.

Marlene Gebauer (09:54)
If anybody knows Conrad, that’s really funny.

Greg Lambert (10:05)
Yep.

Lennie Nuara (10:11)
We’re not taking any of those away, and this is true in litigation. It’s in pure transactional work, regulatory work, whatever. There’s a core that doesn’t change, but in terms of the practice and the flow that what we’ve done is we did re-architect I like Paul re engineered the practice of the way we do deals and so from the very, very beginning ⁓ in our workflow we break things into smaller pieces. So for example, due diligence.

Most of the products that you see out there focus on analyzing the diligence, what’s in. We start even before that. We get the diligence on behalf of the clients by side, sell side. We put all the questions up on the platform. People respond to the questions on the platform and they’re tracked on the platform from the very beginning of the deal, the start. And so immediately we have all this data with regard to what’s done, what’s not done, who’s done it, what documents relate to what questions.

And then and then we take those documents and we burst them and we extract data from every single document. No different than what I did with paralegals nine years ago. We’re doing that now with AI and HI, but it’s it’s human driven. The lawyers pick what they’re worried about. What the issues are, how to extract, and they always view everything that happens in pieces. One of the things that we don’t do and we’ll talk about it probably a couple times as we don’t boil the ocean. We don’t throw all the documents into a database.

and say, OK, search this, search that. We break things down from the very beginning. There are areas, categories, subcategories, topics, the questions, the documents that relate to that stream. And then we break those down into the individual clauses or elements that are part of that request and those documents and so on. So it’s small, small, really small, tiny microscopic all the way down so that

You’re not at the word level, but you may be at the sentence or the phrase level, and now you have data points on all that. And now you can start to build essentially answers to things like requests. Reps and warranties. Closing checklists and so on. So yes, we redrafted the or recreated what we do when we do it and how we do it from beginning to end. So it starts with the diligence.

not the diligence documents, the actual creation of the response to the diligence through to looking at that diligence to generate reports, helping the client essentially produce a report that might respond to which contracts can be assigned or can’t be assigned or need consent to other touch points that may be, these are very valuable customers and so on. All that used to be done by humans and it still will be, but in a very, we’ll call it the sliding scale.

But the more volume there is, you may use more AI to extract all that stuff. It’s a small case. You may not use a lot of AI, just a little bit, because there’s not a lot of documents. But on average for us, it’s 500 to 1,000 diligence questions per deal. And that used to be handled by a lot of human talent. We don’t need as much of that anymore. That’s not a bad thing. It’s just a different thing.

No different than the mechanization of any manufacturing line. They used to build cars one at a time. They used to build houses one at a time. Now they’re being automated with regard to construction of homes, constructions of buildings. You bring in equipment, you can do it faster and better. Some of the early class might be displaced, but ultimately they’ll be pushed off to do other things and other things well. That’s just an example.

Greg Lambert (13:38)
It sounds like

one of the things that you’re doing that I know Marlene and I talk a lot about that firms still struggle with is you’ve got to start with a solid set of data to work with. if if you start with a messy processes and data, you’re going to amplify that messy process and that messy data rather than clean it up.

Lennie Nuara (14:02)
Yes.

Greg Lambert (14:04)
that’s you know you’re doing M&A you’re doing due diligence and you’re breaking down into pieces and you can apply that to pretty much any practice and that’s something that I think people need to understand you know kind of wrap their heads around how to break that into those pieces and clean that up.

Lennie Nuara (14:20)
Yeah, it flows, absolutely, it flows specifically from the talent, right? So I did work, a friend of mine, he’s in the financial services sector, not legal at all. And he saw what I was doing years ago with regard to databases and extracting data from documents, I called it turning documents into data. And he hired me on the side in a not legal capacity to help build a database for them where we were extracting the data.

and all the reports, the analytics that they were generating within the firm. And it gave them a view of something that they never had before. And it just took some time. I needed their help. said, well, what’s important to you? What are you looking for? ⁓ we’re trying to find this report we did in that. they’re all buried in 30 years of writing reports in the financial services sector. And it’s the same thing that we can do as counsel. There is data in all that we do, data in terms of the steps.

but data in terms of the things that we’re moving around, the words, the paragraphs, the documents, that data can be captured and used in a variety of ways to improve the practice of law and the accuracy of what we’re doing on a go-forward basis down that transaction scheme or through litigation or other.

So, so one last piece of that. So it required taking a step back and the step back happened in the following manner, right? Conrad was doing deals for his whole career. I was always riding on the outside of deals. We were at different firms many years ago, but we were friends and we joined forces when we formed Flatiron. He’s like, hey, Lennie, can you help me run these deals? And I’m like, okay, I got my practice, but I’ll help you do the M&A stuff. You know, I said, yeah, we’re going to, we’re going to flat fees. I’m like, okay. And then I look.

Greg Lambert (15:37)
Cheers.

Lennie Nuara (16:03)
at the way he did it. And it was incredibly inefficient because they would touch the documents once and then touch the documents again and touch the document. So you know, during pre before the LOI or during the pre-LOI phase and the diligence, then during reps and warranties, we look at the documents again, then we look at them again for closing checklists and then post closing integration. I’m like, this is nice. You’re going to go look for that same documents four times. So I started reengineering the process out of it. You know, basically the desire to be more efficient and then allowing us to really hone that.

those flat fee quotes. And that just required, you know, a closer analysis of what’s important and what’s not. And as I said earlier, that can happen in any domain, right? It can be transactional work, it could be litigation, could be regulatory. It’s just that if it’s driven by the right driver, somebody that has the intellect of what happens, and they take a step back and say, hey, is there another way to do this that’s more efficient? Then it should be applied. And the tools…

I’ve gotten significantly better where you can do that. Now I did it initially with paralegals and databases. Now I’m doing it with AI and databases, but still databases because everything we do as lawyers lawyers don’t want to hear that. But everything we do as lawyers is data driven. It really is. It’s we’re not. We’re not significantly different than than the street. I used to work on Wall Street, but we’re not significantly different.

Greg Lambert (17:21)
Well, I mean, so far in all of this, I mean, we’ve touched a little bit on the periphery about what you’re doing with the AI. But really, a lot of your foundational work here was, again, cleaning up the data, getting your processes right, understanding how many times you need to touch a document, and reducing that overall.

So as you develop this M&A tool around that style of model, now we want to understand how do you bring in the AI part of it? Are you looking at…

kind of compacting the different steps or how are you throwing the AI at the process that you’ve already seemed to have made very efficient.

Lennie Nuara (18:10)
Okay, so in the first instance, there are different ways to use the AI. So I wouldn’t want to say it’s all AI, it’s AI in this style here, in that style there, and another style someplace else. So for example, you can use AI to de-duplicate, for example, all the due diligence questions. In the 500 to 1000 questions, I guarantee you there’s like at least 10 and sometimes a 30 % overlap.

which is nuts. And I’ve had clients literally just collapse under the weight of that. And so just deduping things. And actually I have a scale and the scale runs from exact match to similar to, ⁓ you know, not exact, but still on the same topic and so on. And I can present that. And then I push it back into the due diligence layout that we have so that the buy side sees

by the way, this is the same question you’ve asked now four times, but it refers back to this question. And so our answer is going to be different. But then we, know, everybody can see that and they see the numbers. So that’s one way. If you’re drafting documents, it’s another use of AI. Much higher risk profile than finding duplicates, right? If you miss a duplicate, OK, someone says, damn, I got to answer the same question twice. OK, OK, or if you point to something as a duplicate and it’s not.

Greg Lambert (19:08)
you

Lennie Nuara (19:33)
somebody comes in and says, hey, no, they’re different questions. So please answer them both. Nobody so far has said, please answer them both even though they’re the same. Usually we just point them to the other answer. But if you’re drafting or if you’re contract lifecycle management, you’re doing review, or you’re trying to go against a model like a playbook or something like that, the risk is significantly different. And you have to know that you’re using the tool differently for different things.

So in the first instance, we apply AI wherever we can. It’s the very simplest thing of organizing the information during the due diligence process of collecting and analyzing. But then as you go further downstream, you have a higher and higher risk value that you would place on a potential mistake or the misuse of the AI. And so I look at that as still very valuable, but how do I use that?

We use that tool, AI, from let’s say comparing clauses or using the tool to help draft a version of a document, and we evaluate that the lawyer that’s working on it can look. So for example, closing checklist has 30 different documents, 50 different documents that have to be generated from the assignment consent letters to FERPTA letters, these letters that have to go out to confirm certain regulatory compliance.

whole litany of things. Some of those letters are standard fare. They really are. And if you can extract the to and the from and the section of the agreement and so on and so forth, which then can be eyeballed by a partner or an associate, that they’re correct and they can go out, that’s great use of the tool. In another realm, if you’re actually drafting the master agreement, that’s a lot more difficult ask of the AI.

And maybe you will, but you’re going to give it a lot of feedstock, a lot of documents that will essentially frame up what you’re looking for. And then you’re going to have to have a really serious analysis of the quality of it. I found that, this is two years ago, let’s say, you could look at a document that’s generated by AI. I swear, it looks fabulous. It reads incredibly well. And as counsel,

We get sucked into that. It’s like, oh my God, this is done. We’re done. You take a step back and say, wait a minute, it didn’t address this. It didn’t address this. It did it backwards. It’s like, oh my God. Oh yeah, it said something really well. It’s the art. It’s the articulate con man. And I used the phrase last year at Legal Innovators. Everyone’s hot in the back room getting high on gen AI. It’s like, oh, this is awesome.

Greg Lambert (21:55)
Didn’t actually say anything. But it said it well. But it said something well. I’m not sure what that something is.

Marlene Gebauer (21:58)
It said something, but it didn’t say everything you needed to know. Yeah, yeah, yeah.

Greg Lambert (22:14)
Ha ha

ha!

Lennie Nuara (22:15)
Holy cow. Wow. It’s like, wait a minute, wait a minute, you know, like, you know, I’m not experimenting with drugs. I really know what I’m doing. No, no, you’re not. so AI can, can have that tendency. So as you move down the spectrum of, its, of its use, if you recognize how it’s going to be used, and then you take a moment. One of the things that we do is that we break things into pieces. I’ve mentioned it before, we parse and break the smaller the pieces that you give an AI.

Greg Lambert (22:24)
Ha ha ha ha.

Lennie Nuara (22:44)
The higher likelihood of success that you will have with it, the bigger task that you give it, the worse it will be. And the reality is, is that’s the same with humans. If I give an associate 10 things to do, some of them are going to come back wrong. Okay. If I say, just do this, just do that and so on. And I’m, I’m been known to be a micromanager as you can probably already tell. And I get a lot of grief for that. But the reality is, that when you micromanage,

It takes more of my time, but ultimately the product and the training to the student is or the mentee or the or the associate is incredibly more valuable. Yes, they can. Wander in the in the forest on their own for hours or weeks at a time and and produce materials that then I would edit and so on. But I find if you do things in pieces, it works out much better with humans and it works out much better with AI. I thought it lost more from a long time and I’ve.

used to run hiring at some of the firms I was at, so on. So it’s a big thing to me to help bring up the youngers and bring them through. And it just takes, and you got to deal with AI the same way. And so they’re parallel.

Greg Lambert (23:50)
How do you, Lennie, as

a self-proclaimed micromanager, how do you know when to stop? It’s almost like doing research. When do you know when, okay, think that, at least for right now, this is where we need to stop, because otherwise we’re getting diminishing returns on.

Lennie Nuara (24:11)
I

can’t give you a quantum. That’s a quality issue that the H.I., the human intelligence factor, is critical on. I will tell you that a first, second, third, or fourth year will say, OK, it’s done. Then a fourth, fifth, sixth, or seventh year will say, no, no, it needs to be fixed. And the partner says, you’re both wrong. It’s still not done. And that comes from wisdom. And it’s the same thing. I would treat A.I. the same way you treat young associates.

but also break it into smaller pieces. So that way you can essentially trust but verify, right? You can trust them to do something, but if you do it in small pieces, you’ll find the mistake in that one place. So maybe it’s finding the mistake in the hallucinated citation or in the logic that someone missed. I have a whole other big speech and article that I’m working on about, it’s not so much hallucinations that are the problem. It’s a race to the mean with regard to the use of AI, which

Maybe maybe you’ll ask me a question later. I’ll let that pop out later.

Greg Lambert (25:14)
Ha

Marlene Gebauer (25:16)
I like how you were describing this. Like if it’s, if it’s more complex, you know, you have more chance of, of problems using AI. but I, I, two things like, think it’s, it’s sometimes challenging to, for, for attorneys or to explain to attorneys, like certain types of things. Like are more complex than other types of things and that you’re going to have more of a chance of.

of not getting the results that you want doing one thing versus doing another thing. And so I’m curious how you make that determination or you explain that to people that are kind of working with you and working with the tool. And also what about sort of a genetic workflows? And I mean, is that tackling some of this because you are able to take a something that is more complex and kind of break it into steps.

Lennie Nuara (26:08)
Yeah, so I’ll do the second half first. So breaking things in the steps is what I was talking about, right? And you can do that with agentic components. The key, my perspective, is to stop and have HI, human intelligence, in between the steps. Many people are building agentic workflows. Great, OK, but there’s no verification opportunity between the agents. That’s no different than saying you’re not going to look at each of the steps from the first associate.

the senior most associated junior partner. That’s just a recipe for disaster. You break things into pieces and then you can verify that. It’s hard to judge upfront all the steps, but. If you can reward the senior talent, the partner with the ultimate. Goal of more efficiency later, they’re going to have to invest more time.

early to break down their process into smaller pieces. They know all the steps. They know where the issues or the problems will erupt. And if they take the time to look at their process with a critical eye and break it into pieces, that’s an efficiency hit on them. They’re not efficient, but hopefully it will be a multiplier for later when they invest the time now.

⁓ It’s no different than investing time in an associate. You invest the time now and build the process in small increments along the way. Then you can build out the technology again with verified steps in between to build it out and create reliability over time. But it is a time sync and that’s something that you I’ve spent an inordinate amount of time the past couple of years. So you know building deal driver and dealing with Megan Ma at Stanford Deal Mentor.

which is a negotiation simulation that’s agentic based and so on. And the amount of time that we’ve been devoted to those, and I’m not bragging, it’s just, if you want it to be right, you must spend the time. And I have the flexibility because I don’t have the labor stack that existed when I was a partner at Greenberg Troward or Thatcher Profit or any of the other firms I worked at. I can do that. And if I was at a firm, they’d either say, okay, go for this, we’re gonna switch your role, your numbers are gonna be different and so on and so forth.

hopefully we’ll generate efficiency from live. But we just we did that investment into our firm and into Dealmentor. It’s a hard thing to swallow for many firms. don’t begrudge them at all. It’s hard if you’re at a big firm and you’re grinding through and you’re making your numbers and you’re doing well. Why switch? You got to be kidding me. I’m not going to switch that. I’m not going to change my comp. I don’t want to. I’ll help a little bit. I mean, I remember I was laughing at it.

conference I went to and the firm, which remains nameless, was bragging about the fact that, you know, they give whatever 50 hours a year or 100 hours a year to the associates to think of operationally how to make things better when I can do that in a month. I could spend an extra 200 hours in a month. Sounds insane, but I will. And I’ll do that because it’s giving us tremendous operational efficiency later. But I can do that.

Firms need to look at that and they can do that with their ops group, but at some point the real talent needs to spend that time. And that’s hard to get, it’s understandable.

Marlene Gebauer (29:30)
So you were mentioning this sort of judgment calls and some of these AI enhanced ⁓ workflows. So the HI, why it matters, what’s the next step layer that sits above what the AI is actually doing. As you’re designing the M&A tool and a broader type of AI-powered workflow,

I know you say that people know like, know, seasoned practitioners know what the steps are, but also you’re saying that sometimes it’s hard to figure out the steps. And I have, I have experienced that too, because it’s like, it’s in your head, but when you have to sort of document each step, that is a little more challenging. And then at that point, you know, when do you decide, you know, AI can take the first pass and then when does human judgment, you know, have to set step in, like, you know, where are you going to draw the line?

between the AI assisted execution and where the business judgment comes in.

Lennie Nuara (30:27)
I think it’s again, it’s a great question and keep going back to it’s in parts, right? So and by the way, a trick can be if you spend time with a lawyer, they want to know, I’ll throw myself back into the old days. They want to just dictate out the flow of a deal from beginning to end. See it written down once, just dictate it or or tell it to someone and that they can take notes. OK, do it a second time.

Let them fill in more, do it a third time. Okay, now put that into AI and say, write out this process and show me the steps in the process. You can put that into the AI or then it’ll give a 10 page or a 20 page list of the tasks that have to be accomplished. And you keep feeding that. That’s not a risk event. Someone now can look at that flow and say, but they forgot this. They forgot that. They forgot that. Lawyers are really good at finding a problem with your stuff.

not telling you in advance what the problem will be, but reacting to something on paper. So that’s just a little trick that I use to force myself saying, I know what I want to do. Bang, bang, bang, bang, bang, bang. I’ll get five things and then I’ll put it in the AI. And then I get 30 back and I’m like, but you missed this, this and this, you idiot. You’re useless, Mr. AI. And then I put more in and all of sudden the AI, because they’re sycophantic, will come back and say, ⁓ good catch. Let me add those ideas now.

which is a good, it’s a wonderful experience, but they suck up to us so much.

Greg Lambert (31:51)
done. You’re right. You’re right. There are 10 Rs in strawberry. Well,

Marlene Gebauer (31:56)
ha.

Greg Lambert (31:56)
it

sounds a lot like what we’ve had Wendy Jepson from Let’s Think On where she talks about

taking advantage of having the partner walk through, talk through that process multiple times without realizing that they’re talking through the process multiple times. it’s an art, think.

Lennie Nuara (32:18)
It is, it’s an art. It’s, it’s not significantly different than your art. Okay. When interviewing me, okay. You guys spent some time in advance things you wanted to cover. You wrote it down, you created your outline and now you can ask me the questions or preparing for a deposition. They know what they have to get to. They got to nail things. Okay. I remember the early deposition I took. I didn’t even go at all into the concept of damages. All I do is focus on liability and the partner said, well, okay. And so what were their damages? I’m like, Oh.

I didn’t get into that. You know, I a second year associate taking one of my early depths and big lesson to learn. Okay. can cover it all, right? But you got to think in advance. So you’ll do that, break things down, but then applying the judgment of, of what you use for, for what comes down to, well, what’s the risk factor for that? As I said earlier, right? The risk factor for deduping something is significantly different than the risk factor for directing the indemnification provision with regard to an M&A deal.

⁓ or with regard to, let’s say, the risk profile, and that will have an impact on the reps and warranties and the disclosure statements and so on and so forth. The drafting of those things are critical. And yeah, you might make something to a first pass. It’s very easy for some partner to react to the first pass, great, or even an associate can react to the first pass and so on. How do you pick where those issues are? It will vary significantly. I can’t give you a magic wand and say, works, and I know the AI doesn’t.

I know it’s a good first pass on many, things, even a second or third pass. can, you know, I joke with my wife, you know, I can say, Claude is like the greatest associate I’ve ever had. Never complains, never, it’s never, never late, always on time, delivers things in minutes, not days and so on and so forth. But it’s, it’s imperfect. And I accept, I accept that. That’s okay. you know, and it, it will not make the judgment call. It will not. it will make an offer to me, but I just treat them as an associate. But

The deeper risk when it comes to drafting, drafting isn’t hallucinations. I don’t care about hallucinations. That will ultimately clean up over time. It’s conformity. It’s this constant, all the models are converging at the lowest level of commonality. So you’ll get what everyone else did. That’s not what you need in your deal. I said in the very beginning.

Greg Lambert (34:33)
It will own the

mediocre.

Lennie Nuara (34:37)
Yes, and

that’s a much bigger risk. OK, now you’re you’re essentially abdicating your responsibility as counsel to constantly give your client what everybody else gave. You have leverage and both sides have leverage. The buyer wants to buy and the seller wants to sell. But there are points that are different for each one of them. One of them might say, I don’t care about the indemnity. I know my cap table is clean. I don’t care. I’ll indemnify up and down, left and right.

And yeah, we had a cybersecurity breach, but I know what the breach was and I’ll indemnify it for that. just had that and it’s happened to me, you know, numerous times with clients and say, that’s fine. And then, and all of a the buyer’s like, the full value. Yeah, sure. The full value of the transaction. I’ll indemnify. Okay. So you, that is a judgment call, right? But you can do that on a item by item basis later on in the transaction as the, cause the risk gets higher and higher and higher all the way through.

Marlene Gebauer (35:23)
you

Lennie Nuara (35:34)
Yeah, an early problem, you know, in use of AI, it happened. But when it comes to drafting agreements, okay, ⁓ and producing output, you can’t expect the machine, AI, to leverage your client’s position. You might say to it, hey, we are going to take a hard stance on X or Y or Z, but you have to drive that. Again, talent first, okay, and then AI second.

Essentially, you’re never going to get the tall blade of grass out of an AI. You’re going to get the nice, smooth, Augusta level golf course. There’s not a single blade of grass too high or too low. They’re all the same. Marvelous. That’s pretty, but that doesn’t help your client. You’ll give up on certain points. You’ll have to get others. Some clients will walk away. I have people that have walked away from deals. And that intelligence, that wisdom really changes

the use of your AI, if you recognize it, an associate won’t, maybe some, but most won’t. And partners that are time-pressured might not see it instantly. When they take a step back, take a breath, and then they read the output, they’ll be like, ⁓ man, this is slop. This is not helpful. And the problem is that my role is often, okay, you’re right.

Greg Lambert (36:46)
You

Lennie Nuara (36:54)
It’s slop right now. Let’s go back and ask for more pointed answers on X, or Z, or pointed drafting on X, and Z. And the slop then becomes better. The point is that the device, AI, writes really well. But it just doesn’t know what to write. So if you say, it to me, it will give you the standard. If you recognize the standard is not what you want, then you have to drive it to give you the in.

Nice pros, nice pros, but you have to tell it what you want to drive for.

Greg Lambert (37:27)
Are you giving it like playbooks to help it get a little better at the mediocrity?

Lennie Nuara (37:34)
Well,

I’m not a big fan of playbooks because again, it’s that’s another version of mediocrity at some level. And two, we’re not a corporate, you know, we’re not an enterprise. OK, if I was representing the same client all the time, always I might I might revert to that, but that’s just not what our practice is. The deals are somewhat unique all the time, but I will give it a stack of things that we’ve done that push the envelope a certain direction. Let’s say, you know.

The calculation of the matter, but the there’s certain calculations that have to happen and you want them to break a certain way. So the working capital calculation is really what it is. It has to break a certain way.

Greg Lambert (38:12)
Are

you finding the AI is getting better or worse when you give it things to do?

Lennie Nuara (38:19)
It’s right now it’s about

the same no matter how many times and sometimes I play one against the other. Our platform lets you use four if you want to put another AI on there. We can just put in API and off we go. And you can play against one another, but from what I’m reading from the various sources, they’re all being trained on the same corpus. Much of it is the same purpose and particularly with regard to private transactions opposed to public deals.

Very troublesome, there’s not a lot of data on the private transaction. So your experience base is really great. So that’s where we’ll, we do the old fashioned way. We pull our old deals and we put them in and it’ll create a first pass, but it is still literally just still a first pass. That may change over time in larger firms than mine. You know, the mega firms, the big law, they have a corpus that they can point to that are significantly larger than what we have.

And they might be able to create, you know, through RAG, right? Retrieval of a generation. They can push that data in for drafting purposes. That would be great.

Greg Lambert (39:18)
So with flat iron, mean, do you have, since almost day one, you’ve looked at putting pressure on both the bilbel hour and the staffing pyramid style that you see.

in Big Law and it’s kind of interesting because right now everyone in Big Law is talking about how they’re anticipating a change in the model but at the same time they’re now recruiting 1Ls before they even take their first semester exams. it’s almost like they’re doubling down on the existing model while knowing that there’s a change on the horizon.

So how do you see the, I guess with the M&A practice specifically, is there like a new apprenticeship model? How are you seeing the industry bringing along not just new talent but existing talent as the models seem to, I think they’re gonna change, they’re gonna have to, I think.

Lennie Nuara (40:17)
I think they will. In the first instance, we are extremely lean as a firm, right? We don’t have associates. We will bring them in. There’s lots of talent that the big firms have trained for us that are sitting that don’t want to be in the big firms for a variety of reasons. Usually we’re not, we don’t bring in very young associates, but lately I’ve been building at least internally a model out where we will start with

Basically, the idea is, you know, what is it? A barbell. You’ll have a ton of senior talent here. You have a ton of very young lawyers here and in the middle you may have a thinner path. The young the young side at one sense, let’s say one to zero. In other words, they’re not in law school at all, but with one to three years. So you have talent that you can utilize that are smart people, but didn’t want us, you know, drop 100 or $200,000 for law school.

But they’re very smart, OK? And you can call them paralegals or whatever. And I think we can build a model with if I’ve done it on paper and I’ve had these people working for me, ⁓ the paralegals that I’ve used and it worked out quite well before AI. Where you spend the time letting them essentially trudge through the mud doing an amount of work that is. Something that will give them an understanding of.

the legal issues or the things that they need to spot. And they spend time not on AI to do those things. So they have to walk through the mud and get dirty and sweat it out. And some of them will progress into the practice and so on for later years and so on. The middle years, three to 10 or whatever it is now, the bigger firms, I think unless they adapt quickly,

are more challenged because a lot of what their work was doing was overseeing the various, you know, the years below them. So I think if we spend more time at the front end training, we have our deal mentor, ⁓ school that we did with Megan and other methodologies where we train the very earliest ones, but you train them, I’m sorry, the old fashioned way, where they’re not dependent upon the AI so that when, they are using it, they

can learn from and take some of their hard earned knowledge and wisdom and then apply it and use it to oversee the AI that they’re using. Now you can apply what I just said to fourth, fifth, sixth, seventh and eighth year lawyers now. The difficulty is to break them out of the mold that they’re in now, which is the traditional pyramid. And that’s a risk issue that firms have to face that they don’t want to.

Look, firms don’t like flat fees because there’s risk, but we take it all the time. I don’t care. And so, you know, I don’t make as much money. I make this versus this, but I still made this. OK, so it’s just the height of how much and how we value our time and so on. So the flat fees at big firms are harder to do because no one wants to stick their neck out and take risk that the thousand hours that they build across seven people really was replaced by X.

And now what are they going to do with those bodies? And what are they going to do if you know? In a variety of ways, how do they handle? Hey, you don’t have the thousand hours, so what you but you finish the deal so you don’t have the hours the hours weren’t built. Am I going to get compensated? That should drive them towards a different fee structure. But right now it’s very, very difficult to. It’s very difficult to, know, to do a U turn in Queen Elizabeth 2. OK, you just you can’t just.

turn around, know, the steamship takes a while to turn. And I think that’s a problem. But again, it’s people hired talent.

Greg Lambert (43:47)
I got an unrelated, well,

I got a semi-related question to this. And I think it’s one of the things that I’ve been talking with other firms. When it comes to AI, I think the last couple of years, there hasn’t been too much worry about the amount of money that we’re spending on the AI tools. But I think you are probably seeing it as well that the token

costs or in the amount especially as you may be throwing more agentic processes in there. I mean there’s some software companies where people are spending as much as their salaries and just token costs. So are you somewhat afraid that you’re just shifting price, know, cost of an associate over to the cost of the tokens?

Is that something you’re thinking about?

Lennie Nuara (44:43)
Well, yes, I definitely think about it. If you were to. Go on deal driver, there’s actually a tracking of the cost per clause. Her model that you’re spending so you can call up that you looked at document X and you can see that you’ve ran 14 different agentic flows and what the cost of each run is for the clause in that one document. And then there’s an aggregate of what your spend is across.

the full document and then across the entire screen of all the documents. And the reason being is that, again, I come from a long time ago where legal research was incredibly expensive. And on a weekly basis, maybe when I first started practicing at 84, actually no, 86, because the first firm I was at didn’t have Westlaw or Lexis, but I got to a firm in 86 and they did. And for a couple of years, there’d be a partner running down the hall screaming.

That four page memo cost me $9,000 because of you. What did you do? Because they didn’t know how to do Boolean searching or so on and so forth. And I’ve never forgotten that. And the cost of a tool is part of the economics of the transaction and how you quote. So we track it literally to the clause and you can use any model you want. You can use three different models on the same clause to do compare.

Marlene Gebauer (45:43)
Ha ha ha ha ha ha.

Greg Lambert (45:46)
Ha ha ha.

you

Lennie Nuara (46:07)
And you can track all that. the reason is exactly what you just said, because I see the change. Now, my experience has been at least the past two years, the token cost is going down, but the usage of how many agentic events that you have is going up. So I wanted to track that and it’s, been fine so far. It’s not out of line. And I do believe overall, it will be significantly more efficient than the people.

doing that work, whatever that work is that we assign. Significantly so, like by an enormous factor. It’s like, you know, a hundred to one, but it’s still, you have to recognize that that’s a cost and many vendors do not expose that. They just give you a bill ⁓ and it’s, know, yeah, you used our platform and your AI upcharge is, and you have no way of knowing, was it Marlene? Was it Greg? Or was it Lennie or someone else that did that?

So.

Greg Lambert (47:01)
It was Marlene.

Marlene Gebauer (47:02)
I’m listening to him I’m getting flashbacks of the many conversations I’ve had with people about like, yeah, you spent this much money to do this. It’s like, because you didn’t know what you were doing.

Greg Lambert (47:08)
your Westlaw bill was $50,000. ⁓

Lennie Nuara (47:13)
Right. And it’s,

and it matters. It’s the idea is, is to give people all the tools necessary to be more efficient, not just, you know, this blanket, I’ll get, throw everything here, get an output there and know where we’re going. It’s going to change. going to, it’s changing every quarter, let alone sometimes every other week. But I mean, honestly, it’s, it’s ramping and it’s great. I love tech, but let’s recognize what it is. It’s a tool and let’s manage that tool and our talent and then produce better results.

Marlene Gebauer (47:41)
So Lennie, you mentioned monoculture monoculture a little bit earlier. Can you expand upon that a little bit for us?

Lennie Nuara (47:49)
Yeah, and I think I touched on it a little bit. It’s the model model culture is basically saying that, you know, all the models are pulling a significant amount of their content from the same sources. Example, they’re all looking at the Edgar database from the SEC for contract clauses. Yes, they can have others, most of them are not. And over time, if you’re drafting based upon those, it’s a drive to mediocrity. And if you don’t recognize that issue, it’ll

bite you in the ass at the end. That’s the problem is that, you think this is a good example. This is a good document. As I said, it’s written well, but that’s market. Okay, but you don’t want to be market. You want to use your leverage in your deal. It’s a way of thinking that we get paid for. Get me the best deal you can get based upon my circumstances. Don’t get me what’s standard. Now,

You know, in the VC world, for example, there’s the NVCA, the National Venture Account, that’s your capital association that has standard agreements. But if you look to see what’s actually done on those deals, they’re all tweaked. They start with that, but then they all tweak them. And they’re not all filed, by the way. Some are, but they’re not all filed. ⁓ And so this concept of, you know, all the models giving the same answers, yes, on our platform, you can actually run

different models and different versions of the producer. So various versions of Anthropic, various versions of OpenAI, various versions of Gemini. I usually pick the best one and just spend the money, but you can pick and choose what you want to do and see what results you get. But you want to see some cross and you want to see differences. And if people aren’t cognizant of that, I think it’s a greater risk to our wisdom. Our biggest issue in our practice is our wisdom is our value.

Our talent is our value. If it’s just cookie cutter stuff, well then fine. You don’t need, you know, super elite lawyers. It’s truly cookie cutter and you’re not, and you don’t want to be working on that work anyway. I think all of us are at firms that are doing more difficult work than many other firms. It doesn’t mean that the other firms are unimportant. They aren’t, but they don’t need to be spending at that level. There’s lots of other issues. But if now all those big firms are reliant on, you know,

LMS models that are all pulling from the same base of agreements and you’re expecting them to give you the spin that you need, don’t. Expect them to give you what is flat, know, mediocrity. And mediocrity might be okay for certain clauses, but not for most. It might be fine for, you know, getting an assignment letter, you know, out to the landlord to get their consent on something. Okay, there’s no risk there. Does it ask for consent? Does the person sign it? Yeah, you might want to…

in a couple of the clauses. don’t want to miss that issue. This monoculture thing is, and people call it model monoculture. I’m not sure I really love the word, I adopted it because it’s out there. There’s research that’s been done that says that people are missing this completely. There’s research in Stanford and other places. think it’s called, there’s two labs that I’ll get a lot of information from, Dr. Nick and Maz’s lab.

which is a Lift Lab at Stanford and then Reg, I think it’s Reg Lab, I forget. But Stanford’s got some great stuff. Some of the other schools have it as well. You gotta be looking to see where the models are. because that’s basically the associate pool that you’re pulling from. You hire associates because there’s a spark or something in them that you really wanted. You don’t just say, me a first year, give me a second year. That’s not what you hire. You hire somebody for.

that spark that you see that they’re really going to be good. They wrote something creative. They write really well. Okay, well, the model can do that, but they wrote something that I didn’t expect. There’s, I used to look for the fire in the belly of the associates that I was interviewing. I wanted to see that fire. If I didn’t see it, I’m like, great kid, great statistics, you know, great grades. They’re not going to do it. They’re not going to cut it.

Marlene Gebauer (51:51)
So Lennie, you actually got a little ahead of my next question, but I’m sure that I know you’re huge reader, you’re a huge ingester of information, you’re a lawyer technologist. So you mentioned a couple things that you go to for staying ahead of the business of law, but what are maybe some other go-to resources that you use?

Lennie Nuara (51:50)
You

Greg Lambert (51:56)
Hahaha

Lennie Nuara (52:15)
one of my favorite things to read is the information, which is a newsletter of, from Silicon Valley. ⁓ they track all technology companies, all the latest, but not just the startups, which they do. they, they’re not legal tech. They’re just tech. spend most of my time reading about tech. Cause I buy tools, right. And I use tools. don’t care if they’re legal tech tools. They’re just tools. I like databases. know, most lawyers don’t care to read about databases. I did. was, I was an early adopter of.

than after that, Airtable. I thought Airtable was marvelous. It’s a relational database. You say that to most people and they’ll just fall asleep. They don’t care. They don’t want to know. But I track, so I track the traditional technology sector very, very closely. Between that and LinkedIn and my feed on LinkedIn, those are two go-to sources. But I also track like Bloomberg and Wall Street Journal, their coverage on the tech industry and trading. Why? was trading.

essentially funds the tech industry, which then funds the innovation. So I look for that, I don’t want to say virtuous look, but that relationship really matters. Obviously I mentioned, know, Megan, who’s a great friend and our partner on Deal Mentor, but also the work that she’s doing out of the Lyft Labs is fabulous to see what’s coming and essentially frontier work. It’s about frontier, you know, models.

Marlene Gebauer (53:17)
relationship.

Lennie Nuara (53:38)
But her work is truly on the frontier, which is just so much fun. spent, I see her a couple of times, six times a year, I’m out there working with her on deal mentor and other stuff. Other things, tech industry publications like PC Magazine all the way through to Info Week and the cyber security journals that are out there. And then the more mundane for the legal profession, I looked at the sanctions, ethics litigation.

where someone hallucinated the letters, you know, on law.com and others that, know, what happened to the unnamed top five law firm recently with their filing, ⁓ with hallucinations in it. ⁓ and the other ones that were down in Alabama and so on, those are informative. find it laughable though, that they’re complaining so much about hallucinations. Do you know that early on for email that sending an email was a violation of the ethics opinions? Which I was just like laughing. I was like,

Greg Lambert (54:32)
Thank

Lennie Nuara (54:36)
When I saw that, I’m like, my God, you people are so, look, I come from a world where there were these, remember these things, okay? This is what fed the machine behind me. Of course I did, because it’s right behind me. But you know, it’s like email, my God, Lennie, you’re sending an email, that’s a violation of our ethics, I’m like, no, it’s not, I don’t care, I’ll take that fight, nobody ever sued me. So I’ll take it, but you know, so, but you gotta track that stuff.

Greg Lambert (54:41)
Good. 5.25-inch foppy disc. into your Osborne.

Marlene Gebauer (54:44)
like that you have them handy because you need it for that.

Greg Lambert (54:58)
All right,

we peaked into the past with your 5.25 inch floppy disk in your Osborne portable computer. So now let’s peek into the future with your crystal ball. So what do you think over the next short period of time that the legal industry needs to be prepared for? What’s your take?

Lennie Nuara (55:09)
Hahaha

goodness.

Well, I already mentioned the model model culture that that is critical, and it depends on where you are in using the stack, right? So if you’re using it, the pedestrian stuff, you’re not going to be worried about if you’re using it for CLM, you know, contract, life cycle management, contract review, drafting. You have to be worried about that. You have to be looking much more carefully at that. So and if you’re not, you’re toast. I mean, like you’re really in trouble. You’re missing a big issue bigger in my mind than hallucinations.

And so we’ll see, it will probably present itself when you see more of, it’s not slop, but it or more mediocrity in the output that you’re getting from the machine. that’s item one. Simulation training, that’s not just a plug for Deal Mentor, although it’s a great product, but we need more simulation training to create a more engaging environment for younger lawyers to learn. And AI can do that. We created dialogue, you know, it’s,

It’s a simulator, but it’s not based upon any prewritten dialogue at all. Okay. The dialogue comes from a language model and it’s innovative beyond words. And we should see more of that. Hopefully just everybody will buy a deal mentor, but I’m not here to sell that. The point is, is that you need that kind of inspiration to get people to really learn better because otherwise we’re going to be skipping a chunk of years and leaving them behind. And we don’t want that.

Private equity is gonna drive the spend at law firms, because they see this innovation. They’ll push harder than the enterprises will, although enterprises will push, but private equity will push even harder. They’re much more expense focused, and they’re gonna say, are we spending X, Y, or Z on counsel for whatever it is? Any kind of operational expense of lawyers, when they know they’re using it to write, you know.

essentially memos on multi hundred million dollar acquisitions. They can do that. Why aren’t our council using that? And so that will drive a spend cycle that’s going to compress the big firm ability to bill. And then maybe they’ll switch to flat fees or otherwise. And then, you know, I don’t want to say the pyramid breaks, but the pyramid

You may use those bricks in not a pyramid style. You may have silo, silo, or I said before barbells or whatever. I think there has to be a change there. It’s going to change. AI is going to absorb a lot of junior level work. The only other thing that might happen that will ameliorate that is just the nature of the practice of law. When I started practicing 40 some odd years ago, 84, there was nowhere near the amount of regulatory practices there is today.

the regulatory practices has exploded as a percentage of what a law firm does and the complexity on every transaction around the regulatory sector. No one really anticipated that, but we kind of grew into it. The existence of AI will help with regard to that. But the point is, that everybody thought, well, know, the practice of law will get more and more efficient and so on and so forth. It didn’t really, it created new sectors of things to look at.

⁓ And so smart firms will figure out that maybe we have to shift people around and train them differently and so on. I don’t think they’re going to break, but what people do and how they do it is definitely going to change. And the smart firms will figure out how to repurpose people in different ways, change the dynamics and so on. I think if they continue to do what they’ve always done, which is put talent first,

smart minds will figure out how to deploy that talent with the right tech. I’m not trying to undermine or speak poorly of tech because obviously I love tech, right? Okay, but that’s where it will go. Ultimately is that if they’re smart, they’ll adopt it and use it properly and successfully. And if they don’t, the dinosaurs will die. mean, although the dinosaurs did rule for, you

a couple of hundred million years and we’ve only been here for a few. So anyway, so.

Greg Lambert (59:26)
They had a good run. ⁓ All right.

Well, Lennie Noir from Flatiron Legal, want to thank you for coming in and nerding out with us today and showing us some of the old tech and then peeking into the future with us.

Lennie Nuara (59:45)
Thank you so, much. I really appreciate it, Greg and Marlene. It was really a fun time. Let me blather on about things. I appreciate it and look forward to seeing you guys more on this great podcast.

Marlene Gebauer (59:45)
Thanks, Lennie.

Yeah, well, thank you, Lennie. And thanks to all of you, our listeners, for taking the time to listen to the Geek in Review podcast. if you enjoy our dive into this data, please share it with a colleague.

Greg Lambert (1:00:07)
And Lennie, if people want to learn more about you or Flatiron, where’s the best place for them to go?

Lennie Nuara (1:00:13)
Our

website is now flatironlaw.ai and my email address and contact details are there. So it’s flatironlaw.ai.

Greg Lambert (1:00:22)
catch.

Marlene Gebauer (1:00:23)
And as always, the music you hear is from Jerry David DeCicca Thank you, Jerry. And bye everybody.

She won’t open Westlaw. She won’t open Lexis. She won’t open her browser at all.

She types her question into a work-approved AI chat window. Twenty-four minutes later she has a memo, citations included, sent off to the partner. Entered 0.4 hours on her time entry system. And she is done.

The KeyCite red flag that Thomson Reuters spent generations building? Never saw it. The Shepard’s signal? Didn’t see that either. The annotated treatise hierarchy that some editor in Eagan, Minnesota agonized over? Came through as a flat blob of text in a JSON response that the model summarized into a single sentence.

Don’t get me wrong. Westlaw and Lexis were in the research process. She just didn’t noticed they were there. And that, friends, is the near-future I want to talk about.

I’ve been privately calling this “Shadow UX.” Think of it as the user-experience cousin of Shadow IT. We all know the effects of Shadow IT, right? That was when the marketing team started using Dropbox without telling the IT department, and three years later IT realized the entire company’s roadmap was sitting on someone’s personal account. Shadow UX is the same thing at the interface layer. An unauthorized layer sitting between the user and the vendor’s product, and the vendor doesn’t control it, doesn’t design it, and increasingly doesn’t even know it exists.

For legal information vendors, the Shadow UX layer is mostly an LLM with a few tool calls bolted on. There are other versions out there too: browser extensions that re-skin search results, paralegals building Notion dashboards off APIs, scraping wrappers feeding firm intranets. The AI agent is the one eating everyone’s lunch though.

Here’s why I’ve been thinking about Shadow UX so much lately.

For thirty years vendors competed on the browser/dashboard. The fancy charts. The little visual icons. The hover states. Pixel-perfect interfaces designed for a human eye scanning a screen. In 2026, the user is increasingly something else. It’s a model reading a JSON schema at inference time. If you’ve optimized your product for an audience that’s becoming the minority of your traffic, you’re going to find out the hard way.

OK so why now? Three things had to happen at the same time, and they all did within about eighteen months.

First, the models actually got good. The 2024 models couldn’t handle jurisdictional nuance. The 2026 models draft memos that pass partner review. Not every time, sure, though often enough that associates are using them anyway.

Next, the billable hour math became impossible to ignore. We bill in six minute increments. Any tool that turns a ninety minute task into nine minutes is going to get used, with or without IT’s blessing. (Sound familiar? Hello again, Shadow IT.)

And Finally, the Model Context Protocol showed up. MCP is the part of this story that doesn’t get enough attention. Imagine if every database, every research platform, every internal wiki spoke a common language to AI agents. That’s MCP. Companies like NetDocuments and Midpage adopted it. Specialized vendors are rolling out MCP servers for everything from patent search to legislative tracking. Once the protocol got standardized, the vendor’s UI stopped being a moat and started being a speed bump.

Now here’s the part that should worry legal information providers. The editorial work that built these companies, the headnotes, the Key Number system, KeyCite, Shepard’s, all of that gets flattened.

In a portal, a KeyCite red flag is loud. It’s red. It’s literally a flag. You see it before you see anything else on the page. In the Shadow UX layer, it’s a token in a JSON field. If the model’s summarization logic doesn’t promote it, the user never sees it. The signal is technically still there. It’s just invisible.

The headnote tree is worse. Editors spent generations nesting these things to show legal relationships. Models hate hierarchies. They flatten them into bullet lists, or worse, into prose. The categorical context disappears.

And then there’s the provenance problem, which is the one that actually worries me. When an agent synthesizes ten cases into one paragraph, the user gets a confident narrative. They don’t see that eight came from KeyCite-validated sources and two came from a sketchy public database the model decided to trust. The vendor’s brand was always the proxy for “this is reliable.” When the brand is invisible, the proxy is gone.

I’ll put it bluntly. If you’re a research vendor, your brand value is currently being laundered through someone else’s chat interface, and you’re not getting credit for it.

The pricing model is the other shoe about to drop.

Seat-based pricing is the deal we’ve all lived with since the 90s. You pay per lawyer. The lawyer logs in. Everybody understands. Now… an AI agent doesn’t log in. It doesn’t have a seat. It can do the work of fifteen associates in an afternoon though. So vendors are watching seat counts flatten while their compute costs spike. The infrastructure bill goes up while the revenue line goes sideways. That’s not a sustainable shape.

The industry is wobbling toward usage-based and outcome-based pricing. Pay per query. Pay per resolved research task. Pay per drafted clause. Salesforce and Zendesk are already doing this in their own categories. The math makes sense for vendors. The problem is that law firms hate metered bills. CIOs cite cost forecasting as the number one headache with consumption pricing. Nobody wants their Westlaw bill to look like an AWS invoice.

Here’s where the real fight is going to happen, and I haven’t seen anybody talk about it openly yet.

Put yourself in the chair of a Westlaw or Lexis sales VP. You’re watching seat utilization drop. Associates are logging in less. Partners barely log in at all. The minutes-per-seat metric you’ve been using internally to justify renewals is collapsing. Meanwhile your compute costs are spiking because the firm’s MCP-connected agents are hammering your APIs and MCPs at three in the morning to draft research memos.

What do you do?

I’ll tell you what you do. You add an AI agent access fee on top of the seat license. Premium tier. “Enterprise agentic access.” Whatever the marketing team lands on. And you keep raising the per-seat price every renewal cycle. Because if each seat is getting cheaper for the firm to actually use, your only path to flat or growing revenue is to charge more for each one. Double dip. Seats plus agents. Stack them.

Now flip the chair. You’re a firm CIO or a law firm library director. Your usage data shows seat logins dropping. Your associates are no longer going directly to Westlaw or Lexis. The vendor calls to renew, the price per seat is up 10%, and now there’s a separate line item for “agent access” that wasn’t on last year’s quote. You ask why you’re paying more for less. The vendor explains, with a straight face, that the value sits in the data, the agent extracts more value per query, and the bill reflects that. You disagree.

That’s the battle.

Firms have leverage they haven’t quite figured out how to use yet. If a general-purpose model with a good MCP integration can produce a defensible memo using free public data plus a couple of specialized vendor pipes, the firm doesn’t need a full Westlaw or Lexis subscription anymore. They need a few targeted pipes. Maybe federal cases, maybe a particular state’s regulatory feed, maybe a specialized treatise. The bundled subscription that’s been the vendors’ moat for thirty years is exactly the thing the agentic ecosystem can unbundle.

The firm-side response is going to come in three flavors. First, the headcount-only firms: “we’ll keep paying for seats at last year’s rate, take it or leave it, and we’re not paying a separate agent fee on top.” Second, the audit-and-cut firms: “show us actual usage data, justify the renewal price against actual logins, or we cut the seat count to match.” Third, the route-around firms: “we’ll keep a small premium subscription for the editorial signals we can’t get anywhere else, and we’ll point our agents at public data plus a few targeted MCP feeds for everything else.” Each of those is a different kind of headache for the vendor, and each one has a different ceiling on what the vendor can actually charge.

The vendors who win this fight will be the ones who can credibly argue their MCP server delivers something the agent can’t get anywhere else. KeyCite citator data, validated public-records overlays, proprietary treatises, expert witness analytics, the stuff that took fifty years of editorial labor to assemble. That’s the moat that survives. The vendors who try to hold the line on seat prices while gating their best data behind a separate agent fee will find their customers routing around them, because at that point the firm just buys the agent-access tier and treats the seats as a courtesy login for partners who still like the old interface.

My prediction: the first major firm to publicly announce they’re cutting Westlaw or Lexis seat count by 40% while keeping their MCP-tier subscription will set off an industry panic. Somebody is going to do this. Watch for it.

Now let’s talk about the verification tax, because this is where the AI evangelists get quiet.

The better the models get, the harder they are to audit. Sounds backwards, although it’s true. When errors are common, you spot them. When errors are rare, you stop looking, and that’s exactly when one slips through and ends up in a brief. There’s actual statistics on this. Researchers proved that the cost of estimating calibration error grows as models improve. For a ten-step agent loop, the verification cost can be a thousand times the cost of a single-step model. Lovely.

Then there’s what MIT called the Confidence Paradox. Models use more confident language when they’re hallucinating than when they’re stating facts. Thirty-four percent more, according to their 2025 work. So the smoothest, most reassuring chunk of your AI memo? Statistically, that’s the part most likely to be wrong.

Friends, this is a malpractice waiting room.

The worst version of the Verification Tax happens when associates trust the agent because it sounded confident, partners trust the associate because the memo looks clean, and the bar trusts the firm because nothing got flagged. Mariana Trench of false confidence. Somebody is going to get sanctioned, and the case is going to read like a horror story.

So what should the vendors do? I get asked this a lot lately, and my answer probably annoys them.

Throw out the human-first (or at least human-only) design playbook. The audience is the model now.

That sounds heretical to a UX designer, I know. Every editorial signal needs to be a structured field in the response payload. KeyCite/Shepard status should be a typed enum with a confidence score and a direct citation to the underlying authority. The model can then promote that signal in the summary, because it’s data instead of decoration.

Ship real MCP servers. Press releases about “AI partnerships” don’t count. Actual production grade tool surfaces with rate limits, auth, and schemas the model can read at inference. If you’re not in the agent’s toolbox, you’re not in the workflow.

Build provenance into every response. Hash-pinned citations. Quote spans with character offsets. Per-claim source attribution. Make hallucination expensive for the model to generate and easy for the human to detect. This turns the Verification Tax from a tax on the user into a feature for the vendor.

And reprice. Just reprice. Seat pricing is over. The metering infrastructure you’ll have to build is annoying, although it’s the only way the math closes.

The Legalweek 2026 lineup told us where the incumbents have landed. Thomson Reuters rebuilt CoCounsel on the Claude Agent SDK and is trying to “own the shadow” by being the agent itself. LexisNexis is leaning the other way, embedding Cowork into Protégé and treating Lexis as the “primary connection point” for content. Two strategies, same underlying bet, which is that lawyers will choose a curated agentic environment over a general-purpose model with specialized pipes.

I’m not sure they will. Lawyers use what works. If the general purpose model with a good MCP integration produces a better memo in less time, the walled garden becomes a walled relic.

If you don’t create solid MCP integration, your users will create workarounds that will get them what they need. I haven’t even scratched the surface of things like Codex Computer Use or Perplexity Computer in this article. But, trust me, tools like that will make it very easy for creative lawyers to just have the AI interact with the legal information. It’s just too much to try to cover here, but at least I’ll mention it for those vendors who think they control all the access points to their product.

For the lawyers reading this, here’s my unsolicited advice. Audit the grounding. When the AI summarizes a case, ask explicitly whether it checked subsequent treatment, and demand it surface the KeyCite or Shepard’s signal verbatim. Verify the pipes. Know which sources your agent is actually calling, because a “research result” from a web-search plugin and a research result from an MCP-connected professional database are very different animals. And keep the judgment. The application of law to fact and the strategic counsel you give a client cannot be delegated. That’s the part of the workflow that has to stay outside the shadow.

By 2030 the dashboard is dead. In fact, the browser may be dead. The most important UX hire at a major legal information vendor won’t be drawing pixels. She’ll be writing tool descriptions and tuning system prompts so an agent representing a lawyer she’ll never meet can find, validate, and cite the right authority on the first call. The portal becomes the back office. The pipe becomes the product.

Some vendors will accept this and build the best possible pipes. Those vendors will keep the editorial moat and figure out how to charge for it in a usage-based world. The ones who refuse will end up as the “Intel Inside” of legal research. Real. Important. Invisible. Priced like a commodity.

So here’s where I land. Shadow UX isn’t coming. It’s already in your firm, right now, and it’s growing. The interface your customers actually use is one you didn’t build, and the experience you spent decades polishing is being rendered, badly, through somebody else’s chat window.

You can’t fight it. The remaining choice is whether you’d rather be a great pipe or an irrelevant portal.

What are you going to do about it?

Nikki Shaver (00:00)
Hi Greg and Marlene, I’m coming to you live from the English countryside. Really want to let all of your viewers and listeners hear about something that I think will be really interesting for them. One of the best ways of course to stay on top of technology in this crowded market is to see demos of new products and new product features. All of us think about new products, but of course the products that are out there also because it’s easier to build with AI.

are moving faster, evolving faster than ever before and dropping new features all the time. So it’s also important to stay on top of that. But of course, it takes time to individually reach out to vendors. And also you may not necessarily know what’s new and worth looking or perhaps you don’t want to commit to a relationship with a vendor right now by reaching out and opening up that dialogue, but you’d still like a sneak peek at the solution.

Why not let us at Legaltech Hub take care of that for you? We are organizing what we call demo dozens. These are sessions where we get 12 vendors to come in and show us an update on their product or the first demo of their product at their early stage. You get to register for free, come along, you get the schedule ahead of time so you can pop in for as many or as few as you’d like to see. get a recording afterwards. Great way to stay on top of the market.

go to legaltechnologyhub.com, look for the events dropdown on the top menu, go to LTH events and you’ll see the link to register for the next demo dozen which is coming up on May 19th. And stay tuned, we’ll be doing these on a regular basis.

Marlene Gebauer (01:42)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (01:49)
And I’m Greg Lambert. And Marlene, we’ve seen over the past few months where the AI market has felt like it’s just gone through all of these shifts. And even when companies like foundational model companies like Anthropic, know.

create a legal plug-in and it causes a huge disruption in the market. And yet we’re still seeing specialist vertical startups that are raising massive rounds to prove that these foundational models can’t necessarily solve it all.

Marlene Gebauer (02:18)
Yeah, that’s right. You know, we’re currently in a high stakes debate over whether the future of law belongs to the one model for everything giants or the specialized scalpel designed for specific practice areas. And our guest today is right at the epicenter of this debate.

Greg Lambert (02:36)
We’re thrilled to welcome Andrew Thompson, the CTO of Orbital. Andrew is a lifelong geek. We love that. Who’s an engineer and has over two decades of experience in a career that spans across fintech to prop tech. And now he’s diving into legal AI.

Marlene Gebauer (02:54)
Today he leads the technical vision for Orbital, ⁓ a real estate legal AI platform that has processed over 200,000 transactions and recently closed a massive 60 million series B round to fuel its expansion into the US market. Andrew, welcome to the Geek in Review.

Andrew Thompson (03:13)
Thank you very much for having me, Marlene and Greg.

Greg Lambert (03:15)
All right, Andrew, before we jump into our questions and talk about your work there at Orbital, would you mind giving our listeners a bit of an elevator talk on what Orbital does and who your main clients are and how they use your product?

Andrew Thompson (03:30)
Yeah, absolutely. So, Orbital is an AI built specifically for real estate. That’s specialism is what sort of sets us apart from sort of generalist legal tech and foundation model plugins. We go really deep on that domain. Our team combines sort of real estate lawyers and technical experts who sort of have first-hand experience with real estate legal work and property transactions. And we play in this space sort of with real estate that is worth about 140 billion.

And sort of some of our clients include Clifford Chance, Seyfarth Shaw, Grosvenor, Vinson & Elkins. and many more. We did about 200,000 property transactions last year in the US and the UK.

Greg Lambert (04:10)
I’ve talked to a number of real estate lawyers who saw your demo on Legaltech Hub.

They were really impressed ⁓ by what they saw there. So Andrew, let’s talk about you for a second. Your path to becoming CTO as a major legal tech player hasn’t been exactly a straight path, which is not unusual in this industry. So how did the operational discipline and the focus on the unsexy parts of efficiency in the service industry help shape your

Andrew Thompson (04:18)
Thank you.

Greg Lambert (04:45)
approach to building this agentic system for real estate lawyers here at Orbital.

Andrew Thompson (04:51)
Okay, great question Greg. So let me start with sort of the first part and then I’ll go into the second part of sort of the agentic bit. So yeah, my career is a little bit sort of nonlinear. I’ve been a software engineer, but always sort of really early on in my career was always well aware that if you point software engineers at the wrong problem, that doesn’t turn out well. So I’ve always sort of been an engineer at heart.

Greg Lambert (05:13)
They create really

cool stuff that has no practical effect. Yes, I know I’ve been there.

Andrew Thompson (05:16)
Look, yeah, yeah, that happens a lot. So

Marlene Gebauer (05:17)
Hahaha.

Andrew Thompson (05:19)
I was always being like, I’m going to be technical, but like, how do we make sure that we actually build a product customers want? And that’s been sort of one of the key threads throughout my career. I’ve been involved, interestingly, in Canada in a prop tech business and then here in London. And prior to coming to Orbital, I was at a business called Appear Here. And it was essentially an Airbnb for sort of Main Street properties.

So we would go to landlords in Paris, New York, London, Amsterdam, Milan, Chicago, find sort of spaces that we can put on a platform and sell or sort of rent out on the short term to brands like Chanel, Dior, Nike, Adidas. And so sort of, had engineers, I had product people, but I also had real estate brokers on my team, which is very different from a CTO. And the reason we were doing that is we would have brands that come to us and…

sort of tell us what exact spaces they want in the best cities in the world, what shop frontage, how much square foot, what they were potentially willing to pay, and I would send brokers out into Main Street to go and find those spaces. Once we found them, then we needed to transact them really quickly. And as you can imagine, that sort of obviously became a bit of a painful process. Those spaces…

Greg Lambert (06:24)
Yeah, I see

the problem in that process, the quickly part. ⁓

Andrew Thompson (06:28)
Exactly. Yeah.

And so, you know, the quicker we could, you know, get the leasing done on that and get the space on our platform once we found it, it was obviously a competitive market for some of the best spaces. And so I was dealing with real estate lawyers day in and day out there as well in order to sort of provide value to our business. So sort of coming into Orbital, I understood the sort of unique challenge that that had and sort of maybe what was required. This is prior to the whole new current AI revolution. You know, we were building kind of

machine learning or deep learning when I first started with the business over five and a half years ago. And so sort of that’s my sort of non-linear path that I’ve always been at this intersection between both the technology but also sort of trying to get to the heart of really the customer and often the physical environment to sort of play a sort of key part. So sort of the second part of your question, how does that actually fit into building these agentic systems? So, you know, most people have this experience, ChatGPT comes out.

You have this sort of like, wow, this is amazing moment. How do I actually use this fundamental new technology to sort of build product at our company? And so maybe I’ll, we have mantras inside our organization, the sort of principles that you sort of focus on and to sort of, again, whenever something new comes out, I’ve seen going from on-premise software to the cloud, from desktop to mobile, there’s always these intuitions that are very right going from one paradigm shift to the next.

But there’s always some intuitions that are very, very wrong. Like if you just imagine mobile, I know, let’s take a giant website and shrink it down to the tiny bit of your screen. That’ll work for customers. And clearly that didn’t. You needed to reimagine sort of the UX. And so in this sort of agentic paradigm, you sort of have to operate differently. This, I tell you, remember when, you know, GPT 3.5 blew everybody’s socks off, and if you squinted, you could see it doing a little bit of legal reasoning, but you if you…

If you sort of pushed it any further than one or two prompts, you realized it would sort of fall over. But it was that sort of insight. One of the mantras we have is sort of betting on the model. So this idea of like, regardless of where it is today, sort of if you looked in your crystal ball, where can you imagine that these models are gonna go cost-wise, intelligence-wise, capability-wise, speed-wise, and then try to build a product for that? And so ultimately, sort of trying to figure out

You know, every company wants to build a product that customers love and you want to get out there and be first to market. But when a brand new technology comes out, like some of these AI models that are, you know, at least in the early days, very rough around the edges, you kind of need to figure out what is the shape of the product that customers actually want, knowing that as it got better over the next six, 12, 18 months, like, you know, we’ve all seen that the sort of product will build into that. I think there’s, you know, it’s obviously hasn’t been a massive long time already since sort of ChatGPT and AI has been out, but there’s already been lots of

startups that have sort of built something very impressive and then disappeared into the ether. And I think the game you need to play is like betting on the model, build something that’s valuable today, but also in six months and 12 months. And that is surprisingly different from building SaaS software. And I think that’s, you know, to your preamble right at the beginning, I think that’s what a lot of the market is reacting to. That’s a lot of what software businesses are trying to grapple with.

Marlene Gebauer (09:34)
So I want to set the stage for the next next question, Andrew. ⁓ In early 2026, we saw this massive market panic after Anthropic launched a legal plugin and investors were fearing that, you know, middlemen and legal tech were going to be dismantled overnight. You’ve been quite vocal that this one model for everything narrative is really missing something fundamental about the legal market. So why do you believe that

practice area AI is ultimately going to win out over generalist tools and what does a specialized platform like Orbital provide that a general purpose model like Claude or chat GPT simply can’t?

Andrew Thompson (10:15)
Yeah, it’s a great question, but it’s also pretty much like the question I think most people are sort of grappling with it at the moment. Yeah. You obviously sort of highlighted legal, but I see this everywhere. This idea that, you know, AI labs are building better and better general intelligence. What was bleeding edge two years ago, 18 months ago, even a year ago or six months ago, and now kind of table stakes, everybody can do it. And it’s sort of…

Marlene Gebauer (10:21)
Ha ha.

Greg Lambert (10:22)
Tell us what the secret sauce is.

Marlene Gebauer (10:24)
What is it? We want to know.

Andrew Thompson (10:43)
Ultimately, is everything going to be consumed or is there a way to build software where you’re constantly sort of a level above what the AI labs are of churning out with their models? And so sort of with that in mind, you’ve obviously articulated there, I think about it sort of this barbell approach. It’s not sort of an I can either or. I think that’s ultimately the hollow sort of not being opinionated in the middle and trying to be, leverage a little bit of the generic, but only go so far deep into sort of the solving of

problem deeply for your customers, you kind of want both on sort of either side. And so maybe let’s sort of, instead of just purely talking about book, let’s take a step back and just think about sort of not thinking about AI. But if you have like such clever people, and again, I’m gonna anthropomorphize slightly here, so sort of take away the pinch of salt. But there’s a lot of smart people, let’s just take for example, a bunch of PhDs, they’re not necessarily great at real estate law right out of the gate.

There’s sort of a whole bunch of training and tools that they need and it’s sort of on the job experience that sort of intelligence as a precursor is obviously really good. The more intelligent lawyers often are sort of really good at what they do, but there’s a whole sort of category of stuff that comes after that. sort of using that as a bit of an analogy from this idea of sort of the generalized intelligence from the sort of vertical ones. You can sort of start with something general and build something sort of light on top. And yeah, you’re a little bit better than the general, but you’re almost…

at threat is as each time they get better and better, you very quickly get eclipsed. And we’ve obviously sort of, you know, very much seen this ⁓ recently over the last few months. And so I think the way that AI sort of changes this is that, you know, from this analogy with humans, AI already has all of this sort of legal and real estate knowledge built in. And so that’s why it’s a little different than humans, right? Humans…

do take years to train and sort of years, sometimes decades on the job experience to gain all that experience. A lot of that, not everything, a lot of that is sort of built inside the model. And so it’s sort of how do you leverage that model? How do you prompt it? How do you instruct it to think like in our case, a real estate lawyer, and then what specific tools do you need to give them in order to be sort of vastly better than the sort of general tools? And I think that’s ultimately the, when I talked about sort of the question.

That’s ultimately, I think, the rough playbook that a lot of companies like us and others are sort of thinking about from a sort software engineering perspective. But then I think if you, you know, just let’s take a step back from AI and software engineering and products. If you just look at what our customers, other people’s customers on the ground, everybody’s enamored with AI. They get a lot of value out of it. And oftentimes you can get 80 % there. I’m just sort of cherry picking here, but 80 % of the solution.

but that last 20 % is really hard. It requires not just prompting the model better, it requires sort of proprietary knowledge or some of the tools that you’re giving it. They need to be hyper specialized for sort of the real estate practice area. And so sort of that’s ultimately for us, we will continue to sort of leverage the AR models and we’re expecting them to get, you know.

a little bit better, twice as better, doubly as better, 10x better over the coming years. But so long as the tools and the data on top of those models, will leverage to solve our customers’ problems in a truly deep way that these generalized systems aren’t able to do.

Marlene Gebauer (14:11)
I have a sort of out of left field question. So I’ll just throw it out there and see what happens. Do you think this kind of debate in terms of point solutions versus the general solutions, is that going to have any impact in terms of what wins out, unlike the energy consumption that we are all thinking about in terms of using GenAI models?

Andrew Thompson (14:14)
Go for it.

So you’re saying it’s like, will the energy consumption be different if you’re using a general model or a…

Marlene Gebauer (14:44)
Yeah, will it have a positive impact?

Andrew Thompson (14:46)
I don’t think I’m educated enough to know categorically the answer on that actually. Like was just thinking ultimately if we sort of from first principles what really matters here? The kind of unit of measurement or tokens. The more tokens you use, the more GPUs are crunching them, the more energy you’re using. And so ultimately I think there’s from an application developer’s perspective, the less tokens you use, the less power you use.

I think you could probably argue that there might be a slightly, you’d be more efficient if you really understood the domain and with real estate there are sometimes a huge amount of documents. We’re talking a portfolio of a thousand properties and each property has 10 documents and each side of those documents are 50 to 100 pages. You ultimately do need to read every single word or quote unquote token on those pages and so you are still sort of burning some of the power there.

I think ultimately where the efficiency comes from more is sort of a layer below the application stack. Obviously some application developers can be wasteful, but most of the time we are not wasteful because we have to sort of spend the money on tokens. And so I think, you know, we’ve already seen cost reductions in tokens. And I think partly that comes from Nvidia’s GPUs getting better and more energy efficient, as well as the sort of AI labs producing sort of

better algorithms that are vastly more efficient. I think that’s ultimately where the gains are mostly gonna come from. That would be my sort of just thinking on the spot response to that.

Greg Lambert (16:11)
I like that question though. Maybe one of the PhDs that’s listening to this can write a paper on it, Marlene. Andrew, I want to ⁓ talk about real estate itself. We talk about real estate being the largest asset in the world, class in the world.

Marlene Gebauer (16:12)
And we, go ahead.

Andrew Thompson (16:14)
Hmm.

Greg Lambert (16:33)
And yet the work, and I know Marlene and I have both seen this from our real estate lawyers, is still very manual, it’s fragmented, there’s all kinds of nuance in it. people…

may actually say that it really hasn’t changed that much in the last couple hundred years. So you focused Orbital on the spatial visualization and connecting deeds to the physical reality of the land. And I’ve actually seen demos of this and it’s really, really interesting how you do the overlays on it and you’re working with the land.

⁓ itself on those maps. So you might be talking about, can you explain the technical challenges of teaching the AI to kind of read the property boundaries and the historical maps and how the spatial intelligence actually speeds up the property deal?

Andrew Thompson (17:21)
Mmm.

Yeah, absolutely. So, maybe I’ll sort of touch on a few of the challenges. There’s quite a lot in real estate. You lot of people are used to document heavy, lot of text, and then reasoning over that, and now there’s this sort of visual and spatial layer. So I think, first and foremost, there’s a reasoning challenge on sort of turning what is typically sort of textual legal description of the property into a collection of essentially points and lines and curves. And you then need to sort of

create the property boundary from that. So you can imagine, I’m just going to cherry pick something that’s often sort of quite slightly humorous, there’ll be written in the technical description, go to the tree on the corner, walk 20 paces, ⁓ turn 30 degrees, and you’re sort of mapping this all out. That is written sometimes in a beautifully photocopied document, sometimes not so much so. So you first have to extract that, you then have to sort of interpret that and make sure that sort of…

the lines and points are correct and then actually try to sort of etch that in a more sort of classical way without using sort of AI. You’ve sort of done the extraction and now you’re sort of plotting it on a map. I think there’s visual challenges off the back of this. A lot of people, you know, we all know the word LLMs, large language models, people have talked about VLMs, visual language models. They are kind of quite a few steps behind.

where we are intelligence-wise with large language models. You know, can all take a picture of our fridge and tell her, do I want for dinner tonight? But if you take a picture of a very complex real estate survey or plan, you know, it just sort of falls over. And so you need sort of a combination of these VLMs analyzing, but then you also need to sort of drop down into more sort of classical machine learning or computer vision techniques that you have. And this is all orchestrated by AI. So much like a human can come in and, you know,

look at the legal description on the, sort let’s just say the title commitment, and then they can go and sort of plot that manually on a map. You can now get AI to do that all automatically, where it’s sort of piecing all of those pieces together that typically would take hours for a human to do manually.

Greg Lambert (19:35)
Yeah, I am so glad you mentioned VLM’s because I’ve been aching to talk to somebody about this since I played around with some a couple of weeks ago. And just to kind of talk about the difference between the large language models and the visual language models, I actually did a test a couple of weeks ago where I threw a scan document at both and Claude actually gave me a really good explanation. I think I used the

Andrew Thompson (19:56)
Mm-hmm.

Greg Lambert (20:02)
when VLM modeled to do the visual.

And I asked it, know, kind of what was the difference and the response, and I wanted to kind of verify this with you, Andrew, that I got back from Claude is that the large language models can look at a document, can look at an image, and can kind of give you the explanation of what’s there. It can kind of tell you, you know, this is your refrigerator, I see some milk, I see some, you know, some apples or whatever. ⁓

The

visual language model can actually explain everything that’s on the image. Here’s the text, here’s the handwritten text, here’s the image of the apple and the milk. But it can’t necessarily give you the explanation of how it all gets put together. So how do you use the combination of the two?

Andrew Thompson (20:55)
Hmm.

Marlene Gebauer (20:59)
I’m also curious the difference between how they’re trained. You know, is one trained on sort of text, but it understands visuals, the other trained on visual that you can like, how does, what is the difference?

Andrew Thompson (21:11)
Yeah, let’s start with that question. I obviously don’t, we don’t train sort of our own vision VLM. But I believe, you know, all of these systems, they need training data. When you look at the LLMs, they’re fed on the internet, they’re fed on books. There’s, you know, multi-billion pound companies producing label data with doctors and lawyers and software engineers, and you’re ingesting code bases. And so that’s its training data. And then you need to sort of layer sort of reinforcement data to sort of steer it in the right direction.

I believe the approach is very similar for VLMs. And so you need sort of images and then you need sort of textual explanations on top of that. That’s either historic data that’s just always been there and you can sort of the AI labs can sort of scoop that up and include that in training data much like they do the internet. Or I don’t know this for sure, but I imagine they’re trying to get their hands on lots of images and then getting sort of, quote unquote, experts

to look over them and produce a bit of a write-up. And then you can kind of understand the sort of what’s happening in the image, what does it have? You can understand some of the more sort of domain specific elements of that image. Like again, just back to this trivial example of a fridge, you might have a chef with a picture of a fridge, then go, okay, well you can make spaghetti bolognese tonight, or maybe you can make a salad based on those ingredients, but not much else. And so I think…

All of that is getting sort of sucked in from a sort of training perspective to make these better and better. The more data you have, the better the VLMs get, and the better quality of that data, the better the VLMs get. I think the reason they are behind is that we just don’t have a lot of that data in the world, relative to all the text. You think about newspapers, you think about even this recording and it’s getting turned into a transcript, or you think about, you know, code.

that is open source sitting in GitHub, all of that is rich. Let’s take our domain, very detailed, real estate specific, legal images that are buried in 100 page PDFs that are private information. The Model Labs don’t have access to that and so they just can’t reason well over those images yet. To your question, Greg, just remind me what that was again.

Greg Lambert (23:16)
when you’re combining the use of it, how are you kind of bridging the VLM information and the LL information to get, instead of getting the one plus one equals two, you’re getting the sums greater than the individual parts.

Andrew Thompson (23:31)
Yeah.

Great idea. I think there’s been a wholesale, you know, again, let’s just take the textual side. Prior to ChatGPT, most people doing, you know, of LegalTech 1.0 were using classical machine learning, and pretty much there are some players who are not doing this quite yet, but most people have just wholesale moved over to using LLMs. I think we’re still in a world where in order to kind of maximize accuracy, trust, minimize hallucinations,

we have to be doing both of those at the same time. I don’t think, I don’t know of anybody, I’m sure there are, but from our sort of experience and our domain, you can’t just plug these sort of real estate legal images into a VLM at the moment. You can get some information, but you still need to use sort of classical machine learning techniques. These documents still need to be OCR’d. And so it’s sort of, it’s a combination of the two to almost sense check each other, much like you go to a doctor and then get a second recommendation and sort of.

VLMs have their strength over classical machine learning and classical machine learning has its strength over VLMs and sort of the two together are great. Long term, you can imagine the same thing that happened with sort of LLMs will happen with VLMs, but we’re not quite there today.

Greg Lambert (24:40)
Thanks.

Marlene Gebauer (24:40)
dynamic duo.

Andrew Thompson (24:42)
Yeah, back to that thing.

It’s like everything’s different now. Three, six, nine, twelve months from now, where a VLM is going to be. Part of our job is trying to figure out and squint and go, I think it’s going to be better or it’s stagnating. And I guess that’s part of the challenge, but also the fun of this game at the moment of sort of building software in this industry is you’re not entirely sure all the time where, like how fast the world is going to move in what trajectory.

Greg Lambert (25:06)
Yeah, well even so, Andrew, in that conversation, you were still talking about the need for OCR. You were still talking about the need for machine learning. So I mean, it’s almost like we haven’t really gotten away to where one tool runs at all. You still need to know this whole kind of toolbox of different tools and which one to use at the right time.

Marlene Gebauer (25:28)
Well, talking about moving quickly, um, at, the 2025 AI engineer world’s fair. know, now I’m not now I know that there’s an engineer’s world’s fair. Um, you introduced a concept called the prompt tax, which is the hidden cost of, staying on the bleeding edge while you have the foundational models are constantly upgrading and, know, and breaking existing workflows. So.

Andrew Thompson (25:40)
Yeah.

Marlene Gebauer (25:57)
Your mantra of betting on the model, know, building for where the tech is going, you know, not where it is today. How do you use that? How do you practically manage like, let’s say like a prompt library that might become obsolete every six months, you know, without turning your product into, you know, a, I think the quote was like a fragile mesh mesh mess. I can’t talk.

Greg Lambert (26:15)
Six weeks.

Andrew Thompson (26:17)
Yeah.

Great point. that AI engineering world’s fair is an absolute gold mine for folks in our industry. It’s sort of people at the bleeding edge all staring at similar problems in different directions going, how do we grapple with what’s happening? And you learn so much from sort of people right at the bleeding edge. So I’d recommend that for some of your viewers if they’re not familiar. But to your question, let me separate two things. I think there’s like teaching an AI system

how to be a real estate lawyer. And then there’s all these other things like people have called it prompt engineering or prompt tax, or sort of like overfitting your AI to a given sort of model that happens to have come out maybe six months ago or three months ago. And so let’s sort of talk about the first one, teaching an AI to think like a real estate lawyer. What we’ve discovered over time, if you get that wrong, you specify it so specifically that each new model, you have to change a huge amount.

versus if you find the right abstraction level, and again I’m going to sort anthropomorphize a second back to what we said earlier, you’ve got a really intelligent human, they go to university, and they sort of learn some of the theory and the fundamentals. It’s sort of that level of abstraction when we talk about kind of how we’re writing prompts to sort of teach an AI system how to be a real estate lawyer. That’s our kind of proprietary knowledge that we have real estate lawyers on our team in the US, in the UK.

sort of adding incremental value or sort of sweeping amounts of value on how to do various workflows. So things like, you we talked earlier about legal descriptions. How exactly do, you know, where do you find those in the real estate documents? What happens when you have two legal descriptions that don’t match related to the same property? How do you extract those? How do you plot them? What do you do when the boundary, you know, doesn’t match up? And so all of that information isn’t lost. And I think what…

is also helpful as these models have gotten better and better, used to be if you, know, that talk came up last year and sort of the world moves pretty fast, I was, a little bit of a worry that as our prompt library got bigger and bigger, it would just sort of more and more work to sort of prune that every time a new model came out. But as models are coming out, they’re getting better to handle all of these things. And so because we have what a real estate lawyer is at a sort of at a sufficient abstraction level, that

stays around for all of time and it sort of continues to compound and be valuable for our product. The thing that I think has very quickly become irrelevant or no longer needed or just sort of the models don’t even care about anymore is what people call prompt engineering or prompt tax. There were all these papers coming out that, you if you told the model that it was super important to your career and it couldn’t get this wrong, hallucinations rate would go down by a few percentage.

We tried all of that, we saw the results, and was like, that’s great. A lot of that stuff is just melting away into nothing, and you no longer have to do that. I think a lot of that knowledge that was found out is now baked into the AI model. We keep talking about it getting better and better. And so that was never, that was a short term fix for a problem that has now disappeared. And so sort of back then when I created that presentation, I was a little worried. What happens when our prompt library is double the size, 10 times as big? I’m actually no longer worried.

partly for those reasons, but also partly we can take our prompt library, feed it to these models, and just iteratively go on and say, this is what we’re finding from one model to the next. Help us update all of these prompt libraries. And a lot of this work, even internally for us, is getting automated.

Greg Lambert (29:42)
You can tell me I can stop lying to my AI tool to tell it I’m going to give it a $150 tip if it gets it right. Good.

Marlene Gebauer (29:46)
you

Andrew Thompson (29:50)
I would need to see the data, but probably

Marlene Gebauer (29:53)
Hahaha

Andrew Thompson (29:53)
so. Or at least the days are quite numbered on needing to do that anymore.

Greg Lambert (29:58)
Yeah, I’m always worried that its memory will remember that I’ve offered to give it money and all of a sudden it has access to my account. Yeah. So Orbital recently opened up some US offices. I know you’ve got New York and I know you’re looking at…

Marlene Gebauer (30:03)
Follows up, yeah.

Andrew Thompson (30:04)
Exactly, it’ll extort you later.

Marlene Gebauer (30:08)
All of sudden a guy comes to visit you, you know.

Greg Lambert (30:18)
Chicago and Austin as possible following the $60 million Series B round. And you’ve noted that the U.S. real estate legal services market is like $140 billion opportunity that still has, and again, we’ve seen it, it’s very dramatically under-automated in the processes that it does. So as you move into the U.S. market, what are the biggest differences you’re seeing

and how say the AmLaw 100 firms approach AI compared to what you were seeing in the Magic Circle firms back in the UK.

Andrew Thompson (30:57)
Yeah, absolutely. think that’s a great question. Just to reframe that sort of slightly, we’ve definitely fully moved in. We’re in the US now. We’ve got the majority of sort of the top 20 real estate practices using Orbital. I’ve mentioned some of the names before, like Seyfarth Shaw, BCLP, Vinson & Elkins, Goodwin, Polsinelli. It was always sort of a, you I’ve been in a lot of businesses and you sort of take your product to a different country, especially in a regulated industry like real estate legal.

And you’re like, how much of my product needs to be completely rebuilt from the ground up versus how much of it is applicable? And we found that sort of, you know, not everywhere, but most of the differences haven’t sort of fundamentally stopped or sort of changed our core value proposition to real estate lawyers. They want things to be, you know, speed of response to clients. They want the quality to be as high as it could possibly be. They want a second pair of eyes on everything. And they want to ultimately sort of reduce the risk.

that they have. so that sort of those fundamentals have been the same, the products are built around those. And so whether it’s sort of, you know, we started in the UK, we’ve now gone to the US, that’s our biggest market, it’s sort of, you know, those differences aren’t sort of, they’re slight. But if I had to sort of tease out a few differences, one of them is InfoSec. You know, ISO 27001, you know, much more prevalent here in the UK versus SOC 2.

and data residency. Obviously, if you have a service that’s sort of built in the UK and it stores data there and there was GDPR, now in the US, there’s sort of different requirements. So obviously, infosec requirements need to be different. The billable hour, lot more prevalence in the US than in the UK. I think the UK has pushed really hard on this. There’s a lot of fixed fee work, especially in real estate. And so we sort of did see a little bit of a difference there.

And then US law firms have initially been a little bit more sort of conservative. Obviously the ABA ruling that came out on AI a while back, law firms had to react to that in the US. so UK law firms were sort of a little bit quicker off the blocks to sort of adopt that and sort of ⁓ use the product.

But I think a lot of that has been ironed out now. A lot of large US law firms have committees to handle that. So I think that’s becoming ⁓ less of a difference between the two regions. And then I think the big one is, I mentioned it earlier, title and survey review. That’s a very US-centric piece. You get a title commitment policy. There’s often sometimes hundreds of exceptions and linked documents that you need to download and review. And then part of this sort of

geospatial of visualizing the property is sort of heavily sort of tied into how real estate lawyers work with title companies. And so we sort of built out that offering more and more and it’s become sort of a leading product in the US for what it does related to that. So I’d say that’s probably one of the biggest differences.

Marlene Gebauer (33:46)
So one of the things we talk about on the podcast a lot is how AI is going to be affecting sort of up and coming people in the workforce. we enjoyed reading about your experience teaching a class of 30, 11 year olds about AI at your son’s school. And you mentioned that they have some really surprisingly deep questions like whether someone is actually coding you.

So what did that experience teach you about the future of work and creativity? And how should the geeks in the industry currently leading be prepared for the next generation of the AI native workforce?

Andrew Thompson (34:23)
Yeah, love this question. That was two years ago when my son was 11. I literally just celebrated his birthday a little week early this weekend and he’s now 13. And like two years in this AI world just feels like a lifetime ago. I went back and sort of quickly looked at that presentation was like, wow, okay, things have changed. So sort of, I’ve had to update a few of my priors, but I’ll sort of pull out maybe three things from this.

Greg Lambert (34:28)
God.

Andrew Thompson (34:49)
As per usual, children often mimic their parents, especially at that sort of young age. And I think their parents’ attitude to sort AI is often sort of channeled through them. And you can kind of sort of picked up, you know, when I was in that class and when I even have now where I’ve done sort of subsequent presentations or sort of chatted to my son’s sort of friends. And I think maybe the piece to just think about your parents, like we all sometimes fear the unknown.

There’s this big thing that’s happening. It’s really exciting. There’s lots of opportunity, but there’s also lots of change afoot. And you can see, you know, almost when I’m chatting to my son’s friends, you can see which ones almost their parents are a little bit more on the more paranoid side versus which ones see it more as an opportunity. And this sort of dovetails into the second piece that adults have a huge amount of baggage when it comes to like work had to be done this way.

You know, I’ve got a lot of my career and reputation built on doing something in a bit more of a manual way. Children don’t have any of that, right? They don’t have that baggage. And so it’s really sort of, they get to come into age in a world where all of that is just, doesn’t matter. Kind of like the internet. Imagine, you know, I grew up prior to the internet and then after, and I look at my son, he knows nothing different from that. And I think it almost puts them at a level at a starting point ahead. Imagine if you’re running a race.

My son’s like 100 meters ahead of me immediately coming out of the gate. He can code up things now that I wasn’t even doing in university. And he was doing that when he was 11. And so the starting point, it’s just so great to see where they are at. And I think my son, it enables, had his, for his birthday, had one of his mates over to play around. And he’s coded up a game. They play football in the attic. There’s like flicking these little football players and they’re knocking balls. And he’s created a little app.

that sort of mimics commentary and it goes off to 11 labs with some text and comes back and it’s like, the rain is thundering down. And it’s just like, I walk up there and the boys, I have to like go and tell them to go to bed, you know, it’s 10 PM at night. And they are having the time of their life. And again, that’s play and there’s work. But it’s really interesting to see, you know, I get the luxury to have a foot in what’s happening with software engineering and that whole industry is sort of changing. I get to see what’s happening in real estate legal and that whole industry is changing.

And then I get to see what my son is doing and he’s just loving it. He does not seem held back at all. He’s just, you know, there’s nothing that he feels he can’t do. He’s constantly thinking about entrepreneurialism and then sort of one last piece to end with. He is constantly running out of tokens. know, we no longer talk about this, like there was the AI bubble just, you know, a few sort of months back.

Greg Lambert (37:19)
I was gonna ask. It sounds expensive.

Marlene Gebauer (37:19)
Hahaha.

Andrew Thompson (37:27)
But when I look at my son, I pay, what is it, $20 a month for his plan. I feel like the $200 plan is probably a little bit too much for a 12-year-old. But he’s constantly running out. I thought, geez, imagine he’s probably in the top 1 % of kids who are doing it, because obviously I’m in AI. What happens if 100 % of kids are using this? We just don’t have enough tokens to go around. And I guess to me, that’s really inspiring to see sort of…

kids sort of using it and you can tell that by the time he goes to university and he gets into the job he’ll just be so AI native he’ll understand the world in a way that most of us sort of are still kind of grappling with whereas he doesn’t have any of that.

Greg Lambert (38:07)
Andrew, you talked a lot about, and I’ll frame it ⁓ as the Wayne Gretzky quote of skating to where the puck’s going to be rather than where it is. When you talk about…

Andrew Thompson (38:15)
Yes.

Greg Lambert (38:20)
As you’re building, you’re looking at where the foundational models and the other advancements are going to be in six months rather than where they are right now. And that really takes a lot of knowing the industry, knowing what’s coming in the industry. So I wanted to ask you, what is it that you do personally to kind of keep up with things? Is there one or two must read or must listen to resources?

that help you kind of predict where things are going to be in six months.

Andrew Thompson (38:52)
Yeah, absolutely. It’s like, I feel like I’m just have a ⁓ fire hose connected to my brain and I have to just sort of do filtering of like what’s important, what’s not. And that’s obviously important to my job, but it does feel like…

Greg Lambert (39:04)
But just

get the AI to summarize everything and then inject it straight into your brain. There we go.

Marlene Gebauer (39:09)
All

Andrew Thompson (39:10)
You know what, there’s actually some truth to that, to be fair. I think my latest thing that I love, getting up on Saturday morning, grabbing a coffee, Harry Stebbings of 20VC here in the UK, but he’s sort of very global focused and obviously a huge amount of the development is sort of happening in AI. He has a chap, he obviously interviews lots of sort of one-off people, but there’s a recurring podcast he has between him, Rory O’Driscoll, who’s a VC, and Jason Lemkin, who’s sort of ex-founder and VC.

And I just love having them, three of them, they’re all coming from very different vantage points and that sort of open, healthy debate. One person says, this is brilliant, or this is the end of the world, or this is great. And they’re constantly sort of chiming in, but it’s not, it’s less political. They actually have kind of a thing of like, we don’t talk politics, let’s just talk sort of tech and what this means for the average developer, the average investor. That I find is both entertaining, but also really good weekly insight into sort of what’s happening. And they discuss a lot of…

You know, even the topic that’s sort of the theme of this chat. I think the other piece is X is a fantastic place where a lot of people who are right at the forefront of AI are kind of putting out opinionated takes, people are commenting, and there’s a lot of back and forth. And you sort of get the first bit of the fire hose seems to be on X. And I think, least for sort of this audience, Aaron Levie the CEO of Box.

He takes some incredible insights and distills them down into sort of easy to read paragraphs that really tell you what’s happening in the world and how software engineering is changing, what CIOs are looking at. And again, for this audience, if you want to see where the bleeding edge is at, look at software engineering. Not just because software engineers like to adopt things, but the training data. There’s more code in the world that has sort of been ingested in that you can see how far ahead it is doing that. And then the last person that really fits into this,

Boris Cherny who created Claude Code, he’s recently got onto X. And if there was ever somebody to really just sort of get inside his head to understand what you’ve built, what’s happening, where is the world going next, he’s a fantastic resource who sort of posts on X on a sort of daily weekly basis. And I sort of read everything he’s got to kind of get a little bit of a sniff test as to sort of where the world is going.

Greg Lambert (41:22)
Thanks.

Marlene, you’re muted.

Marlene Gebauer (41:24)
Sorry about that. we have come to the time in the podcast where we’re to do the crystal ball question, Andrew. So, yeah. So looking ahead to three to five years when every real estate lawyer has, has a bunch of, of proactive AI agents at their disposal. what’s the single single biggest shift you see coming for the traditional billable hour and the way property professionals actually derive value from their labor.

Andrew Thompson (41:51)
Hmm. Another one of these questions. This is the question a lot of people, I was at Legal Week, a month or so ago, and this was one of the big questions around the billable hour and things. But maybe to start off with this time frame, I think three to five years is, like, it’s coming sooner. This idea of of humans being able to…

⁓ orchestrate a huge amount of AI agents to do far more work than was possible. I’m already seeing it with engineers on my team, back to this point around, look at what software engineers are doing, and probably that’s coming for lots of other industries. And so, kind of the way I think about this, raw cognitive ability, so sort of intelligence, I know AI is sort of more intelligent than us in certain things, less in other things, but sort of, if you had to sort of, you know, average raw cognitive ability,

sort of knowledge of the market, and this is sort of particularly interesting in sort of legal, depending on where the data comes from. And then just the speed at which you can perform work, whether it’s writing code or whether it’s reviewing a 200 page lease, all of those things are getting democratized at an incredible clip. METR M-E-T-R, is a research group that shows how this is sort of the exponential increase in terms of how much work

can the equivalent AI system do that a human took N hours? And it was, when it started out, it was like minutes and then it was half an hour. And it’s now sort of, I believe, you know, in the kind of more than half a day to more than a day worth of work and Opus, you know, 4.7 has just come out. And so if we look at it a world where all those things get democratized, I think, you know, lawyers have always played in a world where they’re competing with other clever, knowledgeable,

and sort of foster humans or teams to give their clients sort of the best possible service. And I think now we’ve just layered in this new thing that sort of takes some of the things humans can do and it is much better than them, but it’s also sort of not as good in other things. So I don’t have like one silver bullet thing to sort of answer here, but the things I have heard, at least I can, talking to our customers, being at sort of legal week, human empathy seems to come up more and more. is clearly not gonna be, it can mimic empathy.

but it doesn’t actually have true empathy. Being accountable, again, really important in a sort of regulated domain when there’s insurance involved. As of yet, most AI systems can’t be regulated, they’re not insured, even if they are. Let’s take planes, for example. I was originally a pilot and wanted to be. Boeing 747s can fly themself, if you program them. But I think I don’t wanna put myself, even though I know logically, I don’t think I wanna put myself and my family on one of those planes.

without a human pilot in the seat. Maybe that will change in the future, but I think having humans being accountable is important. And then this sort of third one, this idea of sort of instructing or orchestrating AI. And I’ve sort of highlighted this of, know, previously if I’m a manager, I orchestrate humans and sort of my value and what I’m adding is how effectively do I orchestrate humans. Now I have kind of an extra tool in my toolbox. I’m orchestrating humans, but I’m also orchestrating tokens.

And there’s this real interesting interplay between the two. And so I can imagine, again, I don’t know what’s gonna happen with the billable hour. There was so much debate at Legal Week. Why did it exist to begin with? Clients wanted it, that’s why we brought it in. Now clients are asking for take it away. And it’s been incredibly persistent through year after year after year of people calling that the billable hour is going down. There is an inherent conflict with AI because it is making the work more efficient. But I think, you know,

humans will be valuable to, you in what way. I think we’re all figuring that out and I’ve given sort of a couple of ideas as to sort of maybe where that is. But I think this is an ongoing debate that we’re all having and I think we’ll figure it out. It’s just a sort of work in progress as we go.

Greg Lambert (45:38)
All right, well, Andrew Thompson, CTO there at Orbital. Thank you very much for coming in and I really appreciate you taking the side trip to talk VLMs, LLMs with me. I appreciate that.

Andrew Thompson (45:49)
It’s a pleasure.

Marlene Gebauer (45:51)
Yeah, thank you, Andrew. And thanks to you, our listeners, for listening 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 (46:02)
And Andrew, for our listeners who want to follow your engineering mantras or learn more about Orbital, where’s the best place for them to ⁓ connect with you?

Andrew Thompson (46:12)
Yeah, great. Head over to orbital.tech. That’s our website and the tech blog is linked from there. We’ve got lots of content on the mantras and everything else and some of the sort of interesting things we’ve been working on over the years and some of the really interesting things that are probably going to drop fairly soon.

Marlene Gebauer (46:28)
And as always, the music you hear is from Jerry David DeSica. Thank you, Jerry, and goodbye, everybody.

The sessions reflected it: legal operations, client intelligence, AI, change management, video. The conversation in every room was heightened and exciting. I was even on the ACAC (shout out the best Co-Chairs, President, Staff and committee!).

And yet, something still felt missing.

Not more tools. Not more tactics.

What’s missing is a shift in operating model.

Legal Marketing is Advancing—But Still Disconnected

Legal marketing teams are doing more than ever—supporting sophisticated BD efforts, producing targeted campaigns, scaling thought leadership, and experimenting with AI.

But much of it is still fragmented:

• Campaigns disconnected from long-term positioning
• Messaging that shifts by practice or partner
• Client intelligence that isn’t operationalized
• AI used tactically, not systemically

We’re moving faster—but not always more coherently.

The Missing Layer: Product Marketing Discipline

What’s coming next—likely accelerating into 2027—is a shift toward a product marketing ethos for legal marketing.

Not because law firms become product companies—but because the problems product marketing solves are now legal marketing’s problems.

At its core, product marketing brings structure to go-to-market:

• Market intelligence and validation
• Clear value propositions
• Consistent messaging and positioning
• Enablement of front-line teams aka lawyers

Legal marketing already touches all of these—but rarely as a cohesive, repeatable system.

This isn’t about more content. It’s about clarity, consistency, and scalability in how firms go to market.

Why This Matters Now

The traditional model—relationships, reputation, responsiveness—is under pressure.

Buyers now expect:

• Clear articulation of value  – especially in the #AIEra
• Industry-specific insight
• Differentiation beyond credentials
• Faster, more tailored engagement

At the same time, firms are expanding into repeatable offerings—managed services, alternative delivery models, and more structured solutions.

That combination demands something new:

A disciplined, scalable approach to how firms define and deliver value to the market.

Most AI adoption in legal marketing today is still tool-based—drafting, summarizing, automating tasks.

Helpful, but incremental.

The real shift is toward agentic AI workflows—systems that can:

• Continuously monitor client industries and trigger insights
• Adapt messaging dynamically
• Assemble pitches grounded in validated value propositions
• Enable lawyers with real-time, tailored talking points
• Learn from outcomes and improve over time

But these systems only work with structure and reliably clean data.

Without clear positioning, messaging, and audience definition, AI just scales inconsistency.

With it, AI becomes a strategic execution layer.

The Convergence That Changes the Model

This is why the next evolution of legal marketing isn’t just AI adoption—it’s the convergence of:

Product marketing discipline + agentic AI execution

Together, they shift legal marketing from:

• Campaigns → Systems
• Content → Intelligence
• Support → Enablement
• Reactive → Proactive

Marketing doesn’t just support growth—it helps systematically create it.

What This Looks Like in Practice

In the near future, leading firms will operate with:

• Continuous client and market intelligence feeding BD efforts  – I have been trying to get the industry here for years. Today’s tech makes my last 15 years of effort a wash.
• Messaging that is consistent but dynamically applied
• Pitches and proposals built from validated value frameworks
• Lawyers equipped with tailored insights before every interaction
• Thought leadership driven by real client pain, not just editorial calendars

The building blocks already exist.

What’s missing is the integration.

This Isn’t About Productizing Law

There will be pushback.

“We’re not a product company.”
“Our work is bespoke.”
“Our partners won’t adopt this.”

But this isn’t about productizing legal work. That’s already happening thanks to AI and process automation.

It’s about productizing how firms go to market—how they define value, communicate it, and deliver it consistently, especially as the needs of buyers are shifting under pricing pressure and AI engagement.

A move to agentic PMM doesn’t remove nuance. It scales it.

Better go to market doesn’t replace relationships. It strengthens them.

The Competitive Reality

The conversations at LMA made one thing clear: legal marketing is ready for its next phase.

But that phase won’t be defined by who uses the most AI tools.

It will be defined by who builds the most effective go-to-market systems.

As commercial models in the legal industry continue to evolve—toward more structured offerings, pricing innovation, and increased competition from alternative providers—firms will need more than strong relationships and good marketing.

They’ll need repeatable, intelligent, and scalable ways to compete.

That’s why the shift to an agentic, product-led legal marketing model matters.

Because in the next phase of legal marketing, this isn’t about being more relevant.

It’s about being staying competitive to effectively win more client work in a transformational market.

A central theme in the conversation is Mazares’ belief that AI works best when paired with people and disciplined process. He argues that the future does not belong to technology alone, but to organizations that know how to combine tools, talent, and operational rigor. That philosophy sits behind Purpose Legal’s acquisition of Hire Counsel and its broader push to reunite technology and staffing under one roof. In Mazares’ view, clients do not simply want software. They want experienced professionals who know how to apply AI in defensible, repeatable ways that improve outcomes without sacrificing judgment.

The discussion also highlights Purpose Legal’s new offerings, including Purpose Xi and Case Optics, which aim to deliver early case insights in days rather than weeks. What makes Mazares’ framing stand out is his insistence that speed alone is not the point. Faster results matter only when paired with expert validation, tested workflows, and credible guardrails. He describes a legal market where clients once assumed AI would let them bring everything in-house, but now increasingly value outside experts who bring both technological fluency and hard-earned experience. That shift, he suggests, is raising the level of service providers from operational support teams to strategic partners embedded more deeply in legal work.

Greg and Marlene also press Mazares on data security, client trust, and the cultural pressures that come with rapid growth. Here again, his answers return to discipline and execution. He points to major investments in cloud security, around-the-clock protection teams, and tighter controls over on-site review environments. He also argues that many of the greatest risks still come from human behavior, which makes vetting, supervision, and protocol design as important as any technical control. On culture, Mazares emphasizes recognition, communication, and adaptability as the backbone of a company that wants to grow without losing its identity. For him, scaling a business is not only about revenue. It is about building a place where people feel seen, trusted, and prepared for change.

The episode closes on a thoughtful look at the next few years for litigation, junior associates, and the billable hour. Mazares predicts that junior lawyers will not disappear, but their role will shift toward becoming guides in the use of AI, both inside firms and in conversations with clients. As routine work becomes more compressed, he expects associates to provide higher-value service in fewer hours, with stronger technical fluency and a more consultative posture. It is a fitting end to an episode grounded in realism rather than hype. Mazares does not present AI as magic, and he does not dismiss its significance either. Instead, he offers a view of the future shaped by adaptability, experience, and the belief that in legal services, the winning formula still comes down to people, process, and sound judgment.

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)
Hey everyone, I’m Marlene from The Geek in Review and I have Sam Moore here from Legal Tech Hub. And Sam, I think you’re going to tell us about AI enablement advisory projects. All right, let’s hear it.

Sam Moore (00:11)
Mm-hmm. Yep, that’s correct.

Thank you, Marlene. So one of the requests that we’ve been seeing quite commonly recently on the advisory side of Legal Tech Hub is what we’re calling AI enablement. This means we’re assessing a law firm or law department’s AI positioning, their readiness, existing use, if any. And we’re seeing this come up in a couple of interesting different ways. Firstly, when a firm is ready to make a commitment and investment into an AI tool or platform,

and they want to make sure they have the best possible experience and see early ROI if they can. They can engage us to run sprints with various teams in the firm to find those early use cases, help refine some workflows, and come up with a roadmap of where to start and where they should be trying to get to. And sometimes those sprints reveal really interesting differences between management perception and attorneys’ actual readiness, which can be really valuable. And the second way we’re seeing this come up

is firms who were early adopters of AI. So maybe they bought into a tool a year ago, 18 months ago, and they’re coming up on renewal. And they’re looking really carefully at the cost and the benefits. And they’ll engage us to run a similar kind of sprint format with a different discovery focus, and we’ll help them surface where that existing tool is and isn’t generating value for the business, help the firm understand why that might be,

and then provide them with the kind of objective understanding needed to really inform that go, no-go decision on renewal. And those subscriptions can be quite big numbers. So being confident in the renewal decision is really important. And we’re finding that those AI enablement projects often lead into further work around AI procurement, which I think is a really encouraging sign that the industry is moving beyond the FOMO and the push to buy something now and

figure out how to use it later. And instead, I think we’re now taking a more nuanced and measured approach that goes beyond the hype, and that could only be a good thing.

Marlene Gebauer (02:14)
It sounds like a really important advisory service that you’re offering because it’s really important to kind of have a third party give an impartial review and an opinion on this. So, sounds great.

Sam Moore (02:29)
Fantastic. Thank you.

Marlene Gebauer (02:37)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gebauer.

Greg Lambert (02:44)
I’m Greg Lambert. Marlene, you know, we’ve brought in a bunch of the new kids on the block recently with all the legal tech, but the new kids are not old enough to know the New Kids. So, today we’re going to bring on a veteran who has successfully navigated the industry in the era all the way back when we were

Marlene Gebauer (02:53)
I hope the new kids actually know that reference.

That’s all right.

Greg Lambert (03:08)
pulling up all those paper banker boxes all the way to the current generative AI phase.

Marlene Gebauer (03:11)
Mm-hmm.

We’re thrilled to welcome Greg Mazares, Sr., the CEO of Purpose Legal.

Greg is a Harvard MBA and has over 45 years of executive experience building and scaling companies in consumer products, financial services, and litigation support.

Greg Lambert (03:32)
And Greg’s an executive who likes to describe himself as a student of the game who has applied classic strategic frameworks to the evolution of e-discovery. So Greg, welcome to The Geek in Review.

Greg Mazares (03:44)
Thank you. It’s great to be with you both.

Marlene Gebauer (03:47)
So Greg, you have a unique daily routine. We’ve heard that you start every morning with an hour of research. So, reading everything from legal tech news to McKinsey white papers while keeping Michael Porter’s Five Forces top of mind. So in an industry that often feels like it’s reinventing itself every six months,

Greg Lambert (04:00)
Wow.

Marlene Gebauer (04:07)
how does grounded historical study and classic economic theory help you cut through AI hype and identify real strategic opportunities?

Greg Mazares (04:16)
I would say that the most important thing to me is not to be afraid or concerned about change. And I read looking for ways to adapt, looking for ways other people are adapting,

and trying to see where trends are going as opposed to looking for reasons to get out of doing the things that have helped us be successful through the years. I’ve been thinking about AI quite a bit. I remember back when I started my career at a Carnation company, a subsidiary of Nestlé, we had six members of the team who were working on what I would call entry-level typewriters. And then suddenly one of the senior

executives decided to bring in a very large IBM Selectric, you know, the newest piece of great technology. And they cut six people down to one person to support the entire marketing department at that point in time. But the other thing I learned is that the other five people, rather than eliminating them, they were redeployed in other roles. And so I look at that and then we jump to what has transpired in

where we started, you talked about banker boxes and paper and people. It was amazing. We’d run into each other crossing the street, you know, which boxes were going to collide with other boxes, so forth, so on. We moved from that into technology, TIFF images, all of the above. And once again, we had to adapt and we had to change. And so when I’m looking at, in the case of AI, I see AI as an amazing opportunity,

not a problem. And all of the people who have predicted doom and gloom for our industry, I just don’t agree with that. I think there are strategic ways to embrace AI and embrace the AI age and help our clients in ways we’ve never been able to do so before. So I read everything that I can, but I’m somewhat focused. I also

look at what the competition’s doing. I look at other industries and how they’re applying AI. It’s not just about the legal industry. It’s about general management more broadly. And I think there are great opportunities right now. I’m as excited today as I was when I started in the legal field in 1988. And I think it’s just a new age for us all.

Greg Lambert (06:50)
Well, speaking of Carnation, it reminded me my uncle was a Carnation delivery driver for like 30 years in Arizona. So I’ve got some family ties there as well. Well, Greg, it sounds like you’re like us, you’re a lifelong learner. You’re constantly trying to look at new information, but not chase the trend. But one of the things that you did in early 2026

is Purpose Legal acquired Hire Counsel, which brought a network of, I think it was over 70,000 vetted attorneys into the fold. And you described this as an inflection point where technology providers and staffing firms have moved too far apart. So why is it that you feel like the market needed to bring back in a people layer and the technology layer and put those back

together under one roof? And what does the modern legal services provider look like now that these two layers are unified?

Greg Mazares (07:55)
I think the most important thing is the realization that AI by itself, it’s not all about the technology. The age of AI is about technology, people, and process. And probably process working in conjunction with great people

is going to turn out to be the most important aspect of AI. That’s because we have those that have totally embraced it, and we have those that are on the other end of the spectrum that are fearful of it. And then we have some that are dabbling in it. They’re not sure which way to go just yet. But I think the combination of great people working with and implementing impeccable processes using AI, that’s going to be the winning formula for everybody going forward.

Greg Lambert (08:44)
How do you spin it so, because a lot of us talk about thinking in a workflow where AI first, where you think of what is it that the tools, the automation, can do for you to get you to the starting point, a little further along from where your starting point is. How is it that, how do you explain this to the people on how to

integrate the AI tools or automation tools or the process into their workflow in a way that makes sense for them?

Greg Mazares (09:17)
First and foremost, I think we need to look at are there tasks that are relatively simple and easy and almost machine-like that AI can replace for people? Can we free people’s time to work on higher level tasks and higher level work product, et cetera? So one of the ways that AI can be incorporated is in things that the clients see.

Another way is the things that clients don’t see that we have to do each and every day. If we can free up some of the tasks, many of the tasks that we do when we’re working on our own,

in order to create time that we can spend in helping our clients, helping our colleagues, et cetera, at a higher level, then I think the use of AI is really important in any business, any organization at any time. So it’s not AI or people, it’s AI plus people,

utilizing great process that I think is going to carry the day.

Marlene Gebauer (10:30)
So I’m going to add one more pillar to your people, process, tech. I’m going to throw in knowledge. And I want to preface the question by noting that today Harvey announced that they basically have an agentic workflow now incorporated into their product. It’s like from start to finish, you tell it what you want to do. You tell it what the result is. And it will do it now.

Greg Lambert (10:48)
Everybody’s got an agentic workflow now.

Marlene Gebauer (10:59)
I haven’t seen this yet, but you’ve introduced a concept called engineered intelligence, which powers your new Purpose XI. Is that how you… Purpose…

Greg Mazares (11:08)
Purpose XI,

and then one of the modules of that that we have introduced up front is Case Optics.

Marlene Gebauer (11:18)
Okay, so if I’m understanding this correctly, this codifies a human intelligence plus AI philosophy. So it rejects the idea of AI as a standalone substitute for human judgment. So with the launch of Purpose XI and Case Optics, you’re promising early case insights in days rather than weeks, which is great. How do you practically ensure that speed doesn’t compromise defensibility,

and why is this expert validation an essential guardrail for today’s litigation?

Greg Mazares (11:49)
That’s a great point. I think if it were technology by itself and we’re talking about speed, that’s one thing. If we’re talking about speed using amazing technology, and there are so many choices when we’re talking about AI, I think we’d all agree there’s not one solution. There’s a myriad that are out there and there are many that are very, very good. But when you have people that have had experience and understand how to use the technology

to incorporate excellent judgment into what we’re doing, then that is another layer of value that we’re adding to clients. So clients used to hire us and other companies

because of the people and their belief in the people. Now we have people that can be supercharged with the help of new technologies. And these experts are able to develop workflows that are tested, that are true, that are defensible in nature, that reduce risk. And the combination of the two is so powerful. And we now have many clients that say to us, we thought

that when AI came out, we weren’t going to need you anymore after a certain point in time. You would teach us and then we could just go off and we could do it all by ourselves in-house. Now they’re saying, we think it’s going to be a long time, if not forever, that we’re going to need to work with people like you because you add a layer of experience, judgment, and you give us the guardrails and the

defensibility that we need to be even more efficient and effective in the work that we do. So we’re viewing it now as a partnership in which we work with our clients. Our people, who are very experienced, have worked on multiple projects, have made mistakes, have learned from the mistakes, have avoided mistakes, because people will always, using judgment, they will

refine what they do over time. The fact of the matter is the clients have more confidence in having experts who can basically elevate their game using this very powerful technology. They are more confident working as a team. So in effect, it’s elevated the kind of work that we’re doing. We’ve created new services and we have people that are doing new and different and better things that are helping

our clients in such a way, it’s giving them even more confidence in the technology than if they were off on their own trying to utilize it. And don’t get me wrong, there are brilliant clients everywhere you look, but they like to have another expert add a layer of confidence. When I talk about defensibility and we talk about guardrails, we’re talking about

clients who want to use the technology, but want to also know that they’re not going to be criticized. They’re not going to make a fatal mistake and that they can use it over and over and over again in newer and better ways as they gain experience. So it is something that’s going to take some time. I would call it an evolution rather than a revolution. And everybody’s learning as we go along.

In some cases, it’s being adopted very quickly. In other cases, there’s a level of hesitancy to go all in. And for this reason, I would suggest that our industry has changed, but it’s not changing all at one time. It’s something that’s going to happen over many years. And it will be different for some versus others. So we’re excited. We look at it as a great opportunity to have our many

talented, experienced, proven people be able to help our clients advance their practices in ways that they haven’t experienced before.

Marlene Gebauer (16:03)
Yeah, so all of us are smarter than one of us, right?

Greg Lambert (16:03)
Greg, I…

And just to follow up on that, Greg, I think, you know, and I’ve seen it with our vendor partners, that it used to be, you’d be like set this up, get us ready to go, train us, and then turn us loose. And then we might have monthly or quarterly follow-ups on it. But now I’m seeing almost weekly, daily, almost interactions with our

Greg Mazares (16:08)
I think.

Greg Lambert (16:34)
vendor partners on this. Are you seeing that there’s a much tighter relationship between you and your customer clients, and how are your people kind of adjusting to that? Because it’s got to be a little bit of a different workflow over the past couple of years now.

Greg Mazares (16:52)
It is, and it’s a different feeling and a different role. Right now, a lot of our people who are the experts in AI, such as our CIO, who heads up this whole area for us, his name is

Jeff Johnson, he’s based out of Dallas and he does an amazing job. Jeff is actually viewed as an extension of the law firm team. And they almost view it as they don’t want to go to war without having this type of expertise working with them. So we love this, it’s elevated everyone’s game. We’re probably doing less of the more elementary work

that we might have done in the past, and we’re doing some higher level consulting work right now. We have an advisory group that works with clients depending upon their specific needs in a lot of different areas. So what I’m really talking about is this is part of the opportunity. Instead of doing lots and lots of elementary work, we’re now doing quite a bit, if not the same quantity, of higher level work. And so we, like everybody

else, have to adapt to providing services and making money in different ways. And it’s exciting, scary, exciting, all of the above. But boy, it gets you motivated to get up each day and find ways to do a great job on behalf of our clients and on behalf of our people. If we take care of our people and we take care of our clients, we will continue to build a winning organization.

Greg Lambert (18:27)
Well, not to throw a wet towel on the conversation here, but we got to talk about data security and all the fun stuff that comes along with it. So we’re in this era now of a post-zero trust environment. So,

what are you doing and how is Purpose Legal re-engineering the discovery process to ensure that the data sovereignty is there while still allowing for this massive scale required for things like the HSR second request, things like that, that you and your customers run into with security issues?

Greg Mazares (19:11)
Well, one of the things that’s changed versus years past, and what you’re bringing up is such an important point and a great question,

is that so much of the data, of course, is now stored in the cloud as opposed to on-premise. Big, big change. We’re working with organizations such as Relativity, Everlaw, 4iG, I can go on and on and on, who have with us set up security mechanisms that are actually better than they have been before.

We have invested heavily in putting together a large technology group that works around the clock. We have people both in the US and outside who are employees, have been part of our team, to help us protect the data. We also want to make sure that

we basically haven’t seen, we’ve seen attempts as everybody will see, at security breaches. We have been able to work hard by setting up mechanisms in advance that will prevent penetration, for example. So we spend a lot of money on data security because clients need to go to sleep at night knowing 100 percent. I mean, it’s not a 50 percent chance.

It’s 100 percent. They need to know that we’re doing everything we can to protect data. But again, I make the point that so much of the data these days is stored in the cloud. Much of the data is also stored on client systems in certain cases where clients have invested in their own instances for data storage. So data is absolutely moving away from on-premise to cloud environments that are highly protected,

both by the provider as well as by a large team of security experts in our company. It’s the nut without which you have to have security so that your clients go to sleep at night knowing that there’s not going to be a problem with data.

Greg Lambert (21:16)
Greg, I’m going to hit you with a hard follow-up here. What are some security issues that you think people may be over concerned about? And then what’s something that you think they’re under concerned about right now?

Greg Mazares (21:29)
Well, I think they’re over concerned about

a data breach of the corpus of data that we may be reviewing on their behalf in some instances. I think that what they need to be concerned about, I’ll give an example. I’m aware of a situation where document reviewers were using 3D glasses in order to try to capture data, look at data, so forth. They’re using technologies.

This wasn’t within our company, but it’s something I’ve heard within the industry. We are now making sure that any on-site reviews are first and foremost secure. So we do not allow reviewers to bring in anything but the clothing they’re wearing, so to speak, maybe traditional reading glasses, et cetera, contact lenses. But bottom line is there are

Marlene Gebauer (22:23)
I

Greg Mazares (22:23)
Thank

Marlene Gebauer (22:23)
hope you let them wear their glasses. It’s like if it were me, I’d never be able to read anything.

Greg Lambert (22:23)
I was actually thinking,

I would say the little red and blue, yeah,

the red and blue 3D glasses that you wear at a movie theater.

Marlene Gebauer (22:32)
You

Greg Mazares (22:33)
It’s unreal. There are things that people bring in with them and again, we just outlaw it. You just can’t have any of that. So we have to protect the clients because there are still a number of on-site reviews where we have lots of people in a room. Frankly, you have to protect against this

on the outside as well. So the bottom line is those are the things that we feel there’s greater risk because there are a lot of people that you don’t have direct control over as we did in the old days where we would have supervisors in a single room with anywhere from 10, 50, 100 people reviewing documents at one time. So this has to be impeccable. That’s an example that I would throw out there.

Marlene Gebauer (23:23)
Yeah, it’s interesting because everything I read, the main risk is human error as opposed to the technology failing or something like that. Would you agree with that?

Greg Mazares (23:35)
Well, I think you can always have technology failure. I think people, first and foremost, you need to make sure that you vet reviewers properly. You need to have multiple layers of vetting. We do have certain criteria where we won’t use certain people. We many times will reject

many more people on projects than we will accept. They’re just not the right people, don’t have the right level of experience or the track record. And this is why we bought a company with, I’ll call it a small army of experienced people that’s been in operation for multiple decades. We can be very selective of the right people for the right project to make sure that we give clients exactly what they need, both in terms of skills, experience, expertise,

and a track record of doing things the right way. So beyond that, I can tell you a lot of money goes into data security systems and the people that safeguard it day in, day out. Once again, clients keep coming back because they know their data is going to be in good hands and it’s going to be taken care of the way it should be. There’s no shortcuts.

We need to be unbelievably stringent in data security and in protocols and in processes. And if you do that and you keep doing great work, clients will keep coming back. And that’s not something they will worry about anymore, but they should worry about it. We all need to be worried about it because there’s a lot of smart, bad people out there too, as we know.

Marlene Gebauer (25:13)
Exactly. All right, I want to switch the conversation a little bit to sort of institutional growth and its impact on culture. So Purpose Legal has seen a 500 percent growth in recent years and is a multi-time Inc. 5000 honoree and backed by Blue Sage Capital. And in fact, Blue Sage has said that they partner with

management that they like, trust, and admire. So congratulations. But as you scale through aggressive M&A, I mean, that’s challenging from a financial perspective, but it’s also challenging from a culture perspective. That certainly has an impact on the organization from a cultural perspective and a human perspective. How do you maintain

a human-centered culture and still stay that company of choice, both for elite legal talent and sophisticated clients?

Greg Mazares (26:08)
No, it’s a great question, especially in the current economic environment, world environment, the impact of AI, which is worrying a lot of people about their future, rightly so.

In some cases, maybe not. Maybe it opens up opportunities too. I guess that’s the way you look at it. First of all, let me make the point. While we’re many times larger than we were, let’s say, three years ago, most of it has been organic growth. We haven’t purchased a lot of companies. And we were very selective and strategic, I think, in the acquisition of Hire Counsel.

Why did we do that? And I want to just start there. Why did some people say it’s counterintuitive? Because if AI is coming onto the scene, why are you acquiring a company that is heavy, heavy, heavy people-oriented? And the fact of the matter is,

we saw early on that while AI is amazingly powerful, you’ve got to have great people behind the wheel of AI. That’s number one. Number two, not all document review projects are going to utilize AI. And so you will have to have people. There’s going to be buckets. You’re going to have AI all in, AI partly in, and no AI. It’s going to be that way, I think,

for a few years, although it is speeding up where some level of AI is being incorporated in the process, which it should be. I think it should be across the board. But I’ve learned enough to know that not everybody jumps on the bandwagon at the same time. There are early adopters and there are those that want to wait and see. So in looking at M&A as an opportunity,

we thought this particular acquisition was going to help us cover all of the constituencies in this emerging AI environment. And so we can handle projects that involve very few people because the technology can do lots of the work. We can handle the midsize projects and then we can put hundreds of people onto a single project who are all experienced, vetted,

and people we can rely on. We want to be able to handle the different constituencies and different requirements that are out there that I think will be there for many, many years to come. Our growth is about the people that we have becoming more and more productive. The reason we like AI is because we want to make our people more capable of doing more and better

work faster and be able to be repetitive and do great things for our clients over and over again. So it’s going to take the combination of great technology plus great experienced people. And we’re still developing the experience levels with people because that’s where the training comes into play. And then the other thing is processes. I know that we all didn’t invent people, process, technology.

But in the case of AI, I can’t stress enough, process, process, process, driven by really good people and using one of several choices for great technology. That’s going to carry the day. It won’t be technology alone. It won’t be process alone. It’s the combination, the three-legged stool, that is going to be so critical to the long-term success of companies

in this industry. And as I mentioned earlier, we all have to look at ways on our side of the equation, we have to look at different ways to help our clients and to earn a fair profit. Because we’re not just hosting terabytes and terabytes of data anymore. As you know, companies like Relativity and Everlaw and others, they’re handling that, or firms are making commitments on their own. Law firms are smart. They’re making

good business decisions on their own to invest in technology to host their own data. So through a combination of consulting, a combination of the work that we do on core competencies like forensics and analytics, having very smart people to work on what I’ll call science projects, for lack of a better term, where people haven’t seen certain types of data or how to utilize

it or how to analyze it or how to store it, et cetera, we’re able to add value on some of the hard things about our industry that I think makes us unique. And so we’re building the business through repeat clients who start off giving us smaller projects to midsize projects to mega projects to managed services agreements. And that’s how we’re building the business. Will we look at other M&A opportunities?

Yes, we will look at everything, but we’re going to be extremely selective as we move forward. And so I really do think it’s going to be more organic growth and finding different ways to help our clients and to keep growing our people that’s going to help us build the business first and foremost.

Greg Lambert (31:35)
Greg, before we get to our crystal ball question, since you’re a lifelong learner, I’m interested to hear this. What are one or two kind of must-read, must-listen, must-watch things that you use that you think other people would

Marlene Gebauer (31:40)
I’m very interested to hear what this is going to be.

Greg Lambert (31:53)
be beneficial for them to watch or listen to?

Greg Mazares (31:57)
Well, first and foremost, to start out, this may sound really simple, I would do a whole bunch of setup and a whole bunch of Google Alerts on various topics. And I’m getting information throughout the day. Now I’m going to convert my Google Alerts into an AI deliverable because I can get an assessment each day of what’s happening in the industry, et cetera, et cetera. That’s kind of baseline types of things.

But believe it or not, you find things and you see things and you learn things pretty fast if you take the time to think about what categories of information would you like to capture. I read JD Supra every day. I think there are a lot of great articles. I read most of the legal trade publications. I read a lot of books such as,

right on my desk right now. I’m reading Measure What Matters by John Doerr. I’m reading Pattern Breakers. I’m looking at where things are headed two or three years out, if not beyond. I don’t think anybody can predict what’s going to happen 10 years from now.

Greg Lambert (33:11)
Hold that thought because we’re about to ask you that.

Marlene Gebauer (33:13)
Hahaha!

Greg Mazares (33:17)
But I think the most important thing is to try not to become complacent, thinking that after 10, 20, 30, 40 years in an industry that you know everything there is to know. I learn so much every day. I make mistakes every day. I also make some good decisions every day.

The bottom line is I think there are books we can read. I think having conversations with people in the industry that we trust is a great way to learn. Talking to competitors, I call them cooperative competitors.

Greg Lambert (33:59)
Yeah.

Greg Mazares (34:00)
Talk to competitors and see if there’s a common theme in what people are thinking and saying is smart. I think on the law firm side, I’m sure you do that all the time with people in other law firms or corporate clients, et cetera. There are so many smart people out there that we can learn from. And so I make it a point every day to read, to have discussions, to talk to my colleagues internally,

people in our company that can run circles around me on a whole bunch of topics. And then they come to me on things where they think I might be able to add some value. The last thing I’m going to focus on is always spending a lot of time each day communicating with people. Recognize their anniversaries, recognize their birthdays, know about what’s going on in their families. Let them know how much you appreciate

what they’re doing. Catch them doing things right is a term that we use all the time. And we’re actually setting up a committee that each month will recognize wonderful things that people have done, both in the business, in their community, helping each other, et cetera. So that’s a roundabout way of saying, I wish I could tell you it’s one thing. There’s a whole bunch of things we do in order to help

build a solid, solid company that will have a long runway, I hope, long after I’m no longer involved with it.

Marlene Gebauer (35:32)
So Greg, as Greg mentioned, we do have our crystal ball question. So this is where we ask you to predict the future. So looking ahead over the next three to five years as the engineered intelligence model matures, what do you think is the single biggest shift you see coming for the traditional role of a junior associate and the billable hour in litigation?

Greg Mazares (35:57)
It’s a great and obvious question because clients are not going to want to pay for hundreds and thousands of hours of work that could be done in tens of hours, let’s say. And so I think that junior associates are going to become, in my opinion, the ambassadors on how to use AI. They’re going to be able to use AI in ways that they haven’t before to teach their clients, to help their clients. They’re going to elevate the game. I think junior associates are going to become consultants at high levels for clients. They’re going to have to learn. The key thing is to make sure they

still learn enough. And I’m not a lawyer, although sometimes I feel I should be after almost four decades in the industry, but I’m not, so I’ll preface. But I think junior associates got so much training from reviewing documents and understanding what the documents contain and the different types of information and issues and so forth. I think they’re now going to have to not only get a grounding in the law, obviously,

but they’re going to have to now elevate to becoming experts in the use and application of new technology so that they can better help their clients. They may become the teachers to senior partners in some ways. They may come in with more knowledge into the firm than some of their elders have. And they may become incredibly helpful in that

regard. They will also learn. They can also teach and help their corporate clients in some ways. So I think what’s going to happen is they will bill hours, but they will probably bill hours for different levels of services at lower levels of hours, but at higher rates in some cases, in many cases. It’s going to have to work that way because there will be a trade-off. But I don’t see, there may not

be quite as many junior associates, but those that there are, it’s going to be like having two or three people because they’re going to be able to use amazing personal knowledge coupled with very powerful technology. And it’s almost like taking one person and turning that one person into two or three that will help carry the day. But I don’t think that all junior associates or the role of the junior associate is going to go.

It’s just going to change, and I come back to adaptability. So we all need to change and if we do, as we have over many decades, we can flourish.

Greg Lambert (38:47)
Greg Mazares, Sr., CEO of Purpose Legal. Thank you very much for joining us and sharing your daily routine and your knowledge with us. This has been a fun conversation. Thanks.

Marlene Gebauer (39:00)
Thank

Greg Mazares (39:01)
Thank you both.

Marlene Gebauer (39:03)
And thanks to all of you for listening 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 our Substack page.

Greg Lambert (39:13)
And Greg, for listeners who want to follow the Mazares method, as we’re going to call it, or learn more about your company or Purpose XI, what’s the best place for them to find out more?

Greg Mazares (39:27)
Certainly contact me on LinkedIn or also go to our website, www.purposelegal.io, and send a message there. I would certainly love to connect with anyone who would like to chat or communicate online.

Marlene Gebauer (39:42)
As always, the music here is from Jerry David DeCicca. Thank you, Jerry. And bye, everybody.