Chapter 1: What is the main topic discussed in this episode?
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Welcome to the Prof G Market's Founder Series. I'm Ed Elson.
Chapter 2: How does Harvey aim to disrupt the legal industry?
Since the start of 2026, investors have been asking one big question. How much of the economy will AI disrupt? We've already seen the fallout in software, where waves of saspocalypse sell-offs wiped out hundreds of billions in market value. But that may just be the beginning.
Now, attention is shifting to the legal industry, a trillion-dollar market built on manual, time-intensive work like document review, due diligence, and compliance, exactly the kind of workflows that AI was built to transform. Well, my next guest saw that as an opportunity. In 2022, he built an AI company designed to streamline legal work at scale.
Now the question is, can it actually disrupt one of the world's most archaic industries? Well, with over a billion dollars raised, an $11 billion valuation and adoption in over 60 countries, it's well on its way to doing just that. This is my conversation with Gabe Pereira, co-founder and president of Harvey. Gabe, welcome to the show. Thank you so much for joining me. So much to get into here.
I guess maybe lay out for listeners what actually is Harvey and how did this company get started?
Thanks so much for having me.
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Chapter 3: What challenges does AI face in the legal sector?
I would think of the problem that we're solving with Harvey is how do we help large law firms and their clients, which are large enterprises and increasingly all law firms and all companies, go through exactly the transition that you talked about.
So when we started the company four years ago, we built, I think, what most companies built of some form of co-pilot for a professional cursor and cognition, built this for programming, we built this for legal.
And I think what you're starting to see the shift as these models get better and better is you need to start thinking not just about the productivity of individuals, but the productivity of entire organizations and what is the infrastructure that they need to be able for the entire firm to operate effectively. And so that's a lot of what we're building at. at Harvey.
When did you come up with this and how did you realize that this was going to be a huge opportunity?
Yeah, we started the company summer of 2022. I was at Meta on their large language model team. GP3 had just come out. I had been doing AI research for about 10 years before that. And so even in 2014, I think I was at Google Brain, then DeepMind. A lot of people
in that community had the belief that we would figure out a way to build these systems i don't think the path was super clear i think we're kind of still being surprised by the way it's going um but there was just like i had this strong belief of we will be able to build super intelligence agi things like this and i think when you do a lot of that research you're constantly thinking about what problems are the models good at what problems are the models not good at and
In 2022, my roommate was Winston, who's now the CEO of Harvey.
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Chapter 4: What specific legal tasks can AI realistically automate?
He was a lawyer at O'Melveny. And I'd been brainstorming startup ideas as I saw these language models get better. And one day, he kind of showed me the work he was doing and the workflows. And that was kind of the light bulb moment where at the time, GP3 couldn't do that work, but it was very clear they would keep getting better.
And that felt like one of the big industries that would just be a very clear application of this technology.
It does seem as though legal work is basically ground zero for AI, or at least that seems to be the way people view it at this point. I mean, what we've seen is that AI appears to be able to do all of the grunt work of the white-collar jobs. That is kind of like the starting point of what AI can do. And it does seem as though law is exactly that.
Just for those who maybe don't understand how law firms work and what kinds of work they're actually doing, could you tell us a little bit about the legal industry? What are the workflows of a lawyer? What kinds of things could AI potentially automate in that business?
I think when most people think of law, they think of consumer law. And so I need to review a lease or, you know, I need to kind of look at one document and the models are obviously great at that. And I think to a large extent, the base models can do that.
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Chapter 5: How do law firms perceive the integration of AI technology?
And then there's kind of corporate legal work and particularly big law, which is the massive law firms. And you can think of the work they're doing is the highly specialized legal work where you need these incredibly talented, very specialized partners And I think kind of the two best examples of this are you're doing a massive merger, right? Or an acquisition.
You want to go buy a company for $10 billion, $50 billion. Like you need the highest tier partner to advise you how to structure that transaction. Or you're doing about the company litigation, right? Like a big antitrust or something like this. The way that I would think of the workflows of all these firms is these projects take thousands.
They can take tens of thousands of hours, teams of associates. The big challenge is, for example, when you're going to buy a company, you need to go understand all of the contracts in that company, all of the things that are going to happen when those contracts change because of the merger or the acquisition, all of the legislation around it.
And then there is also all of the negotiation dynamics, right? It's a semi-adversarial or it could be an adversarial process, same on litigation. And so to your earlier point of, I think when
Chapter 6: What are the barriers to AI adoption in legal practices?
Language models came out. Legal was kind of this great application where typically your workflows as an associate is you're getting emails from senior associates or partners, and they're just giving you tons of tasks. Go research this. How do I write risk factors for this document? And what these associates are incredible at is they can just absorb these tasks.
They know how to go use all these tools and solve all these problems. And that's kind of what you're seeing these agents starting to get better and better at. The part that I think is so difficult about these legal workflows and similar to programming is the boundaries between the tasks are super blurry.
And so it's not easy to go to a law firm or go to a programmer and say, hey, the coding models or the legal models can do this and humans can do this. Like the boundaries are very blurred and the work is so complex that a lot of the challenge we're working with law firms is how do you rethink your workflows and what humans and agents should be doing when you're working on these large projects.
When you talk about those boundaries, it's an interesting point that the boundaries are blurry. Are you saying the boundary between what AI is best at versus what humans are best at, that it's hard to draw a distinction between those two things?
I think it's both that, but also the distinction of even what do you delegate to humans. When you're doing a merger, you kind of have a pyramid, right? You have like a senior partner, some junior partners, senior associates, junior associates.
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Chapter 7: How does trust play a role in the adoption of AI by law firms?
there isn't like a concrete rule of when i'm doing a merger this task goes to this associate and there isn't even a concrete rule of what defines a task right because it's all text based and so it's a partner just saying i need you to figure out xyz and that could be something super simple like go look up this one case and tell me who is the other party in it or it could be something super complex that is like go write the first draft of the merger agreement
right? But even that mapping, and this is kind of what you see with ChachiBT, where when we started the company, people would be like, what does your product do? And it's kind of the same as asking people, what do you do with ChachiBT? It's like everything, but it's really hard to define why you do something one way. And this is exactly what makes the transformation so difficult.
Because to your point, given this kind of such an open natural language shape, how do you start defining the boundaries of this is what models are good at? Because it depends how you prompt it. It depends which model you're using. It depends on the agent harness.
And so there's just this massive challenge of how do you organize all this work in this new way, given the models can do some stuff, but they make mistakes in ways that aren't intuitive. And so it is just this huge change management problem and like up-leveling problem. for not just legal, like all these industries. Like you're seeing the same thing in programming right now.
Chapter 8: What advice does Gabe Pereira offer to aspiring entrepreneurs?
Yeah, it does seem as though the great thing, what ChatGPT enabled us to do is ask questions that are actually blurrier and that cannot be answered in binary, that cannot be answered. I mean, it used to be that you had to spend a long time trying to phrase your question for Google search very, very specifically. And then what was kind of remarkable and liberating about
large language models is that you could be a little bit more blurry and rough, and it would be willing to go to those more ambiguous places and try to come up with more creative answers to more complicated questions. So on the one hand, I kind of think, well, that's exactly the strength of AI. So maybe this is exactly the place where AI should thrive.
But then at the same time, you're also pointing out, like, there are places where it still gets confused. I mean, large management work is still actually very complicated, and it's a lot more complicated in a large organization versus when you're just operating as a single individual trying to figure out questions on your own time.
I just want to point out for people who might be listening, because, I mean, there are AI startups for everything now. And I'm sure there are probably hundreds of legal AI companies that are trying to eat your lunch at the moment, trying to compete. I would just note for people, I mean, from my understanding of the AI industry, Harvey is the number one AI company in law right now.
You guys hit $190 million in ARR in January. That's the most recent number we have, if you want to update it. go ahead, that was nearly double what it was five months earlier. So you guys are growing incredibly quickly. You are partnering with basically all of the biggest corporate law firms. You guys are kind of spearheading this transition.
I guess the question then becomes, when you went to these law firms and you said, we can do what you guys do with computers, What did they say? Were they excited about that? Were they scared by that? I mean, how did these big corporate law firms react when you went up to them and made the pitch?
So, I mean, the pitch is definitely not we can do what you can do with computers. But I think what helped early on was we found kind of certain partners or innovation leaders that... AI isn't new to law firms. They had been using things like TAR and other kind of AI technologies to do parts of legal work. I think this was just such a large step change.
But early on, for example, our first client was A&O and David Wakeling there when we showed him kind of, we got early access to GPT-4 and we built a product around that and showed that to him. He just had this light, the same light bulb moment we had where he's like, oh, this is going to change how we do work.
And I think a lot of our pitch to law firms has been there will be parts of the work you do that these models will do the same way. Now, when you do discovery, you use tar and use contract attorneys and you don't use associates. So that's going to happen. But. there is also going to be a lot of work that these law firms do that these models aren't going to do, right?
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