No Priors Ep. 142 | With Harvey Co-Founder and President Gabe Pereyra

1. 0:00 – 0:09

   Gabe Pereyra Introduction

   1. EGElad Gil0:00

      (instrumental music) Gabe, thanks for doing this.

   2. GPGabe Pereyra0:07

      Of course.

   3. SGSarah Guo0:08

      Yeah, thanks for coming.

2. 0:09 – 2:04

   Introduction to Harvey

   1. SGSarah Guo0:09

   2. EGElad Gil0:09

      Maybe we can just start with, like, for anyone who hasn't heard of Harvey, what is the company? Can you talk about the scale and who you serve today?

   3. GPGabe Pereyra0:16

      At Harvey, we're building AI for law firms and large in-house teams. We're almost at 1,000 customers, 500 employees. Started about just over three and a half years ago, and so been kinda scaling quickly since then. And kinda you guys were some of our OG seed investors. So, yeah, good to be here.

   4. EGElad Gil0:36

      Maybe from a most basic perspective on the product, why is it not just, you know, Copilot or ChatGPT or Claude?

   5. GPGabe Pereyra0:43

      Yeah. I think that's how the product started. So when we first raised from OpenAI, we got access to GPT-4, and I think GPT-3 to GPT-4 was such a big model jump that the intuition at the time was just give the model to lawyers and have them play with it, and I think that industry was so text heavy that you got so much value from just interacting with the models. And then I think as soon as you gave it to lawyers, you also ran into all of the sharp edges of the models, of they hallucinate, they're not connected to a bunch of our context. And so I would say the past, the kind of first two years of the company were, how do we build essentially the IDE for lawyers around these models that connect it to all of the context you need to be productive as an individual lawyer? But I would say in the past year and going forward, the big problem we're solving is not how do you make individual lawyers more productive, it's how do you make a team of lawyers working on a client matter more productive? And more importantly, how do you make an entire law firm working on thousands of these client matters more productive and more profitable? And so I think when you get to that scale, a lot of the problems you're solving are not just model intelligence problems. They are these orchestration, governance, and kind of all of the enterprise product problems that you run into at, at scale.

   6. SGSarah Guo2:03

      Y-

3. 2:04 – 3:22

   Expanding Harvey’s Reach

   1. SGSarah Guo2:04

      you've also been broadening from just law firms into enterprises, into big companies using you in concert with both their in-house legal teams and external, uh, counsel. Can you talk more about that and how that's been evolving as well?

   2. GPGabe Pereyra2:14

      Yeah, so we started selling to the largest law firms, and something that ha- started happening about a year and a half ago was, these law firms started showing Harvey to their clients, and their clients both wanted to collaborate more effectively with their law firms and they also wanted to use this directly in their in-house departments. So we recently announced, we signed Walmart, we're working with AT&T, a bunch of these Fortune 500 large private equity firms, Global 2000, kind of the largest consumers of legal services. And what we're starting to build is a platform for the in-house teams to do the work that they do internally, so things like contracting and this long tail of all of the legal operations you need to do that you typically don't send out to law firms, but also the collaborative tissue of, "I'm working on a large transactional litigation, I need outside expertise, I wanna securely share this data with my law firm." And there's a lot of technical problems there around security, data privacy that we wanna solve so these law firms and their clients can collaborate effectively.

   3. EGElad Gil3:21

      I think for,

4. 3:22 – 6:20

   Understanding Legal Workflows

   1. EGElad Gil3:22

      uh, you know, we have, um, a largely technical audience, but also most people don't know exactly what legal workflow looks like.

   2. GPGabe Pereyra3:30

      Yeah.

   3. EGElad Gil3:30

      Um, I think before we really started working together, I imagined it as just like, "Well, what do you mean? I, like, email my lawyer and he thinks about it and, like-"

   4. GPGabe Pereyra3:37

      Yeah.

   5. EGElad Gil3:37

      "... reads a document and then sends something back," right?

   6. GPGabe Pereyra3:40

      Yeah.

   7. EGElad Gil3:40

      And, um, there's, like, redlining involved somewhere, maybe there's negotiation.

   8. GPGabe Pereyra3:43

      Yeah.

   9. EGElad Gil3:44

      Can you paint a picture of just what workflow means to, to you guys?

   10. GPGabe Pereyra3:48

       Yeah. Yeah, and I, I think a lot of people when they think about legal, they think of consumer legal. And so I have a lease and I need to get, you know, input on that. That's completely different than what these massive law firms are doing. And so I think a really good example of what these firms are doing that, I mean, you guys will be familiar with, and I think a lot of people in the startup space will be familiar with, is what law firms do for venture capital firms or private equity firms.

   11. EGElad Gil4:15

       Mm-hmm.

   12. GPGabe Pereyra4:15

       And so VCs, PE firms, they do two main things. You raise money and you invest it. And so the main legal-

   13. EGElad Gil4:23

       And we do podcasts now.

   14. GPGabe Pereyra4:23

       And podcasts-

   15. EGElad Gil4:24

       And podcasts. Yeah.

   16. GPGabe Pereyra4:24

       ... which is actually important, but there's less legal work there.

   17. EGElad Gil4:27

       Yeah.

   18. GPGabe Pereyra4:27

       Um, and, and the important things you need to do there are fund formation. So how do I structure the entity that is gonna hold all that money? And it sounds easy, but if you're a large private equity firm, you have a sovereign wealth fund that comes in and they say, "We need to structure it in this way because of tax implications."

   19. EGElad Gil4:43

       Mm-hmm.

   20. GPGabe Pereyra4:44

       Then you have a pension fund that has these other requirements. And so it ends up being this incredibly complex process of how do you draft the limited partnership agreement, which can be 100 pages. Every investor that's investing, you can have 100, and they all have side letters that modify that. And you need to understand, if I modify it this way, it's gonna have these implications. And a lot of it is the project management that also goes around coordinating all of these products if you're raising, you know, a billion-dollar fund. And then once you've created that fund, there's all the investments you do out of that fund. And so, for example, when we did, you know, any of our series, you need to get a data room, we share a bunch of data, you look at that, you need to understand the contracts we have to make sure that the revenue we say we have is actually structured in the way we've claimed, and, you know, are there litigation? All these things. And so it's this massively complex process of understanding and... I th- I think one analogy you can think of, like, understanding a code base, but the code base is all of these contracts and all this legal work. And I think the reason...... legal is so difficult is the workflows aren't structured.

   21. SGSarah Guo5:50

       Mm-hmm.

   22. GPGabe Pereyra5:50

       So the same way with programming, it's really hard, until these models, to build tools for programmers. You basically just had an ID, and then programmers did stuff in all the different languages, but you didn't have like, "Oh, here's a tool for Python. Here's a tool for C++." Legal is kind of the same way, and so I think a lot of why you're seeing traction in programming and legal is, I think, there's a lot of analogies of, of these workflows, that they're so tech-heavy. And the workflows, until you had these models, you couldn't structure them in the way I think you can now.

5. 6:20 – 9:06

   Agentic AI Applications in Law

   1. GPGabe Pereyra6:20

   2. SGSarah Guo6:20

      So, one of the directions that people are going on the coding side is to build things that are being called, like, agentic. And it's very early on in terms of what agentic means, but-

   3. GPGabe Pereyra6:27

      Yeah.

   4. SGSarah Guo6:27

      ... basically being able to deconstruct a logic tree in terms of what are the set of actions that you need to take in a certain situation.

   5. GPGabe Pereyra6:33

      Yeah.

   6. SGSarah Guo6:34

      And then having the, um, AI agent go back and sort of check each one of those items, do it, go onto the next item, double-check it against the prior one.

   7. GPGabe Pereyra6:41

      Yeah.

   8. SGSarah Guo6:42

      Do you do that from a legal perspective, or is that something that's a little bit more in the future relative to where code is today?

   9. GPGabe Pereyra6:46

      Yeah. We're, we're starting to do this now, and actually, like, when I was at DeepMind, a lot of the RL research I did was that, and so when we first got access to GPT-4, we had the very strong intuition of, "Okay, you're gonna be able to, you know, string a bunch of these model calls or eventually do things like reasoning models where the full agent is differentiable." And even the first day we got access to GPT-4, Winston went in his room for 14 hours and just redid a bunch of his associate tasks, and when I looked at the work he was doing, it was essentially like this hackey agentic where he said, "Okay, I would need to go look up this case law, summarize it, take that summary, use it to draft."

   10. SGSarah Guo7:27

       Mm-hmm.

   11. GPGabe Pereyra7:28

       And so seeing him do that gave us the intuition very early on of, "That's the direction this is going," and you can kind of think of associates as agents. They get this task from a partner that's, "Hey, I have this high-level case strategy. I wanna see if I can find a bunch of case law that supports it. Can you go research that, look it up, cite it-"

   12. SGSarah Guo7:47

       Mm-hmm.

   13. GPGabe Pereyra7:47

       "... write me a memo?" And so a lot of the systems we're starting to build look a lot like that, and I, I think one interesting direction that the coding labs, the research labs are going is building these RL environments where you deploy these agents, and they can interact with a code base and see if they can pass unit tests. And in legal that RL environment is a client matter, so you have all of the context of a fund formation, an acquisition, a litigation, and the models are starting to learn, "Let me go in the document management system and see if I-"

   14. SGSarah Guo8:19

       Mm-hmm.

   15. GPGabe Pereyra8:19

       "... can find this, go in the data room, or do case law research, get feedback from a partner."

   16. SGSarah Guo8:24

       Mm-hmm.

   17. GPGabe Pereyra8:24

       And so I think that research direction is, is super, super interesting. It's-

   18. SGSarah Guo8:28

       It's really interesting you make the associate analogy, 'cause I remember, um, when I led your guys' series B, which I think was maybe two years ago now, it's a while ago, I called a lot of your big customers and talked to the head of the law firm or talked to the head of, you know, um, some of these institutions. And one of the things that I thought was really striking is, number one, they were adopting legal software, which before was really hard to sell onto them, and because of what you were doing being so striking and important, they were adopting you really fast. The second is, um, they weren't, uh, threatened by it, and I w- I thought, "Oh, they'd be threatened because it may augment or eventually replace certain aspects of law," et cetera, or, or, you know, help sort of change that dramatically. And one of the insights they

6. 9:06 – 13:36

   The Future Evolution of Law Firms

   1. SGSarah Guo9:06

      kept bringing up that I thought was really interesting is, they said, "As we think ahead, as this sort of AI tooling and agentic workflow spread through Harvey and companies like you, um, how do you think about the future of a law firm?" Because instead of hiring a hundred associates, of which you assume 10 will be partners eventually, maybe you only need 50, maybe you only need-

   2. GPGabe Pereyra9:24

      Yeah.

   3. SGSarah Guo9:24

      ... 20. And so are you even hiring enough people to know who'd be a great partner?

   4. GPGabe Pereyra9:28

      Yeah.

   5. SGSarah Guo9:28

      Because you're, you're gonna shrink the set of people that are needed to do certain tasks over time, right? Right now, that isn't true. It's augmentation, it's expanding business, but that could happen in the long run. How do you think about the future of law or what law firms will look like, or, you know, the evolution of all that?

   6. GPGabe Pereyra9:41

      Yeah, this, this is a great question, and I think it's changed a lot in the past couple years. I think something we are starting to talk with law firms a lot is, how do we think of training the future generation of partners? Where, to your point, these law firms have these leverage ratios where you have a lot of associates but much less partners, and there is value to that because not everyone is gonna become a partner. And part of going through that process is how you find, "Oh, this is the person that I would trust to do this very complex acquisition," because they've gone through that experience. And so I think the part I'm optimistic about is, if I think about over 10 years ago when I learned to program, it was super painful, right?

   7. SGSarah Guo10:22

      Mm-hmm.

   8. GPGabe Pereyra10:22

      Like, you had to go on Stack Overflow. It was hard to learn multiple languages because you're like, "Okay, I'm just gonna, like, learn Python. I'm gonna learn TensorFlow or something," and it was just, like, hard to even learn that. It was hard to ask questions. When I was at Google, you don't wanna ask a bunch of questions 'cause people would be like, "Oh, you don't know that." And I think-

   9. SGSarah Guo10:37

      You get stuck all the time.

   10. GPGabe Pereyra10:38

       Yeah, exactly. And now with the models, it's like programming is so fun to learn because you can just be like, "Here's how to write this in Python. Translate it. Why is it written this way?" And you can learn this so much more quickly.

   11. SGSarah Guo10:50

       Mm-hmm.

   12. GPGabe Pereyra10:50

       And we see lawyers doing that with Harvey, where they'll say, "Generate this merger agreement. Why did we structure it that way?" And so we're already starting to see some of that, but I think the really big opportunity for law firms is, how do they take all of the internal partner feedback and data that they've created and use that to start training? I think that's one big piece. I think another conversation we're having is, to your point, how do you just generally start restructuring firms? I think this is one where we have some intuitions, but a lot of it is going to depend on the firm, the region-

   13. SGSarah Guo11:25

       Mm-hmm.

   14. GPGabe Pereyra11:25

       ... the size, their specialty, the types of clients they serve. I think one of the things that's very challenging with law firms is, they are really a collection of all these practice areas, and so the firms that specialize in litigation look different than the firms that specialize in large transactions versus, like, mid-sized transactions, and usually the big firms do a collection of these. And so a lot of what we're spending time is practice area by practice area. Can we go and sit with... Here's the fund formation group and their private equity clients-

   15. SGSarah Guo11:53

       Mm-hmm.

   16. GPGabe Pereyra11:53

       ... and start thinking about what that would look like in terms of the workflows, the staffing, the pricing?

   17. SGSarah Guo11:59

       Mm-hmm.

   18. GPGabe Pereyra12:00

       Uh, and I think it is a really interesting problem, where...... a lot of the value in the product and the platform is not just the product itself, but how do we help enable these firms to transform?

   19. SGSarah Guo12:12

       Mm.

   20. GPGabe Pereyra12:13

       And so when you think about it from that perspective, like our goal is how do we make these law firms more profitable? And it's not just a product problem, it's thinking about their holistic business and where do we fit in in kind of that bigger picture.

   21. SGSarah Guo12:26

       Mm-hmm. Yeah, it's really interesting 'cause when you look at the set of functions that a partner fills, and I- I'm thinking in particular of consulting firms and less about law firms, simply because I'm a little bit more, uh, familiar with consultancies. Uh, some of it's the pattern recognition, the high-level thinking, the strategy, and then part of it is like the sales-

   22. GPGabe Pereyra12:42

       Yeah.

   23. SGSarah Guo12:42

       ... and really being able to make that client connection. And, and so it's interesting to, to your point, think about more broadly how can AI augment all parts of their business versus-

   24. GPGabe Pereyra12:49

       Yeah.

   25. SGSarah Guo12:49

       ... just the, the legal workflows.

   26. GPGabe Pereyra12:50

       Yeah, and, uh, to your point, it, I don't think that part changes where it's like when we think of the, like we're now larger consumers of legal services and when we think of the best partners we've worked with, I don't think the models are doing-

   27. SGSarah Guo13:03

       Yeah.

   28. GPGabe Pereyra13:03

       ... what they do anytime soon. And I think what's interesting is I think the role of law firm partners actually doesn't change that much in the same way I don't think the role of very senior engineers changes with this because you're largely delegating work.

   29. SGSarah Guo13:16

       Mm-hmm.

   30. GPGabe Pereyra13:16

       And what you're getting paid to do is here's the high-level strategy, here's the right abstractions. Go write the code or write, do the legal research to help me do it and I will interface with the client. And so I think that, my guess is that doesn't change too much, but some of the like lower level functions do change because of this technology.
7. 13:36 – 19:46

   RL in Law

   1. EGElad Gil13:36

      I thought was interesting, like in another conversation, um, that we were having was that there's an analogy that you can make between like a, a great senior partner, like a Gordon Moody type-

   2. GPGabe Pereyra13:45

      Yeah.

   3. EGElad Gil13:45

      ... and like a distinguished engineer working on systems at Google, right? Um, I think for a more technical audience or just general business audience that doesn't really know what Gordon Moody does-

   4. GPGabe Pereyra13:55

      Yeah.

   5. EGElad Gil13:55

      ... they might assume what Elad said, which is like, "Isn't like 50% of that like his network or his reputation?"

   6. GPGabe Pereyra14:01

      Yeah.

   7. EGElad Gil14:01

      Um, but you know, what you were pointing out is like there's expertise in the ability to predict like a sequence of arguments that is going to like get you to the answer you want or manage risk.

   8. GPGabe Pereyra14:11

      Exactly.

   9. EGElad Gil14:12

      Um, how does that, how does that translate to an RL environment or a task for you?

   10. GPGabe Pereyra14:16

       Yeah, this is a good question. So for, maybe for background context for the audience, Gordon Moody was a partner at Wachtell, which is one of the top transactional firms in the world that joined us early on and is now an advisor. And kind of the analogy I was giving is why is a senior distinguished, uh, distributed systems engineer at Google so valuable? And a lot of it is the experience they have architecting these systems that none of this is public. This will, won't go into the models for a long time. And so if you're building search at Google, these people can just point out, "Hey, if you build this system this way, at this scale, it's gonna collapse for some reason that is super not intuitive."

   11. EGElad Gil14:57

       Mm-hmm.

   12. GPGabe Pereyra14:58

       And one of the examples that Gordon talked about early on was he was a part of, uh, when Michael Dell took Dell private and then restructured it and took it public again.

   13. EGElad Gil15:10

       Mm-hmm.

   14. GPGabe Pereyra15:10

       And this was like a multi-year, super complex financial and legal restructuring of an incredibly large business. And what he, when you talk with him, is incredibly good at is the same feeling as when you talk with a very senior engineer where he can just, he has the whole picture of this legal entity-

   15. EGElad Gil15:30

       Mm-hmm.

   16. GPGabe Pereyra15:30

       ... in his head. At the time, they had to do the largest debt offering of all time. They had to create, they had to invent a new financial instrument. And so it's just understanding if I need to raise this much money to do this part of the transaction, this is how I would structure it. And so a lot of the value he brings is not just the relationship, but it's just that technical understanding of how you architect these things the same way that, how you architect like very large software projects. And I think when that translates to an RL environment, part of what is missing from the public models is the process of looking at one of these entities and figuring out given all of the context of I wanna do this merger, this is the right way to structure it.

   17. EGElad Gil16:15

       Mm-hmm.

   18. GPGabe Pereyra16:15

       Just that process. And a lot of-

   19. EGElad Gil16:17

       That's a reasoning trace, right?

   20. GPGabe Pereyra16:18

       Exa- exactly, yeah.

   21. EGElad Gil16:19

       Like for an expert, just like it would be in code.

   22. GPGabe Pereyra16:21

       Yeah.

   23. EGElad Gil16:21

       Yeah.

   24. GPGabe Pereyra16:21

       And if you looked at that dataset-

   25. EGElad Gil16:23

       Mm-hmm.

   26. GPGabe Pereyra16:23

       ... for one of those transactions, it would be client comes to Gordon, says, "I wanna do this large merger acquisition." And then there would be meetings and emails talking about, okay, this is the background of the two companies. This is roughly how we would structure them. These are all the things we need to look into. And a lot of the data would be Gordon giving these tasks to associates to say, "Okay, look into these risk factors of similar transactions we've done." They would do research and say, "Okay, maybe we could structure it this way." And then he would point out this really subtle thing that, "Hey, actually in this case, if you structured it this way, this thing's gonna happen."

   27. EGElad Gil16:57

       Mm-hmm.

   28. GPGabe Pereyra16:58

       But none of that shows up. Like all you get from these public mergers is like an SEC filing.

   29. EGElad Gil17:04

       Right.

   30. GPGabe Pereyra17:04

       And so you do see the final result, but most of the value or what you need I think to eventually improve these models is the decision-making process, the same way you need these reasoning traces to, to train these models to do kind of any of these reasoning tasks.
8. 19:46 – 23:46

   Deploying Harvey and Customization

   1. GPGabe Pereyra19:46

   2. EGElad Gil19:46

      One thing that you guys are doing on sort of the, um, other end from pushing the bounds of, uh, what Harvey products and, um, the models can do is, like, just get them deployed. And, uh, you recently started this forward-deployed engineering force.

   3. GPGabe Pereyra20:02

      Yeah.

   4. EGElad Gil20:02

      Um, this is confusing to, to me, because I'm like, well, you're not necessarily, like, an application-building company, which is how people have traditionally thought of FDE. Like, why are you doing this?

   5. GPGabe Pereyra20:12

      I would say the model that we're operating under is not a full Palantir, let's go into the code base and kind of build custom software.

   6. EGElad Gil20:21

      Mm-hmm.

   7. GPGabe Pereyra20:22

      Uh, I, I would say this is closer to, like, Sierra's agent engineering, um, program. But what we're starting to run into a lot is... I think early on, we did a really good job of building a horizontal platform. We did not that much customization for customers in the sense of building specific things for specific customers. I think the thing that was nice about legal was we could build things like workflow builder and things into the product that would let customers customize the product. And then for very large customers like PwC, we did some customization. But now we're getting to the point where when we're starting to talk with law firms about, "Hey, we wanna take a bunch of this data and help you build a model or build agents," there is some amount of, "We need to go in this environment, into your environment and figure out how to connect all the data." We're starting to connect to a lot of their, like, business systems, so their billing systems, governance systems.

   8. EGElad Gil21:21

      Mm-hmm.

   9. GPGabe Pereyra21:21

      And then especially when we start working with the Walmarts, the very large banks, the Fortune 500, they're much less standardized than these law firms. And so there is just this massive amount of work where we go to a large bank, and they say, "We don't have any document management system for our legal department. Can you just build us one?"

   10. EGElad Gil21:38

       Mm-hmm.

   11. GPGabe Pereyra21:39

       And there is a massive amount of demand of, "We just want smart technical people to sit here and help us think about our business and our operations and how we should start mapping that into gen AI systems." And for us, it's a really good way to figure out the roadmap, where, for example, Blue Owl is, like, one of the fastest-growing private equity firms that, uh, we recently started working with. And they... We meet with them all the time, and they're just like, "There's all these things that we feel like we could map into gen AI. We don't quite know what it's gonna look like, but let's just sit together and figure it out." And so I would say that's a lot of the genesis of the, of the program, of how do we just get more people that can work with all these customers and start kind of paving the way of some of these new roadmaps in different verticals.

   12. SGSarah Guo22:28

       Yeah, I think what you're describing too is a very standard enterprise playbook.

   13. GPGabe Pereyra22:33

       Yeah, yeah.

   14. SGSarah Guo22:33

       And I think in Silicon Valley, people almost forgot because of the SaaS era that if you're Oracle, if you're Dell, if you're IBM, if you're any of these larger organizations, this is how you sell software.

   15. GPGabe Pereyra22:44

       Exactly.

   16. SGSarah Guo22:44

       Right? You have a platform. You have a bunch of customization around it. People have bespoke data sets. They may not always have the ability internally or enough people to implement certain connectors or systems.

   17. GPGabe Pereyra22:53

       Right.

   18. SGSarah Guo22:53

       And this is, like, the standard way to do it. And then as you do it over and over again, you start repeatedly turning that into part of the platform. And so-

   19. GPGabe Pereyra22:58

       Right. And, and I, I think a lot of these started with doing something that resembled FE, and then you get big enough that you get this, like, implementation ecosystem. There's all these third parties that will come in and implement-

   20. SGSarah Guo23:10

       They'll be, like, the certified, like, vendor or whatever.

   21. GPGabe Pereyra23:12

       Exactly.

   22. SGSarah Guo23:13

       Yeah.

   23. GPGabe Pereyra23:13

       And, and I think the interesting thing we're actually starting to see is law firms are starting to do this for their in-house clients.

   24. SGSarah Guo23:19

       Mm-hmm.

   25. GPGabe Pereyra23:19

       So they're starting to go and take Harvey and go to their clients and say, "Hey, buy Harvey, and we'll help you build all the workflows and implement it, because we have the scale and the expertise to build this." Where typically these in-house teams-

   26. SGSarah Guo23:32

       That's really cool.

   27. GPGabe Pereyra23:32

       ... the smaller ones don't have, like, the budget or the in-house to build this. And so yeah, I think there is kind of a lot of-

   28. SGSarah Guo23:37

       And that seems like that could be a good revenue driver for the law firms that you work with in terms of-

   29. GPGabe Pereyra23:41

       Exactly.

   30. SGSarah Guo23:41

       ... a new line of business that they can offer.
9. 23:46 – 25:28

   Adoption and Customer Success

   1. EGElad Gil23:46

      really struck by... Uh, it wasn't... I don't know if it was day zero. You can correct me. But it was within the first year, where the very first version of Harvey was really an individual lawyer productivity tool, right? Uh, I'm an associate or a more senior person at law firm. I wanna get a piece of work done. Can you just make it less painful? Uh, but the transition quickly to, like, how do we transform the business, make the business more profitable, like, organized teams-

   2. GPGabe Pereyra24:09

      Yeah.

   3. EGElad Gil24:09

      ... be in the ecosystem, I think happened, happened pretty quickly. And, like, anything that is a business transformation just requires, like, you know, a lot of engagement.

   4. GPGabe Pereyra24:20

      Yeah.

   5. EGElad Gil24:21

      Um, and so I, I... W- given how much you guys have invested in customer success and how that's, like, driven adoption, I feel like a big piece of it is just how quickly AI has happened.

   6. GPGabe Pereyra24:29

      Yeah.

   7. EGElad Gil24:30

      Right? I, I would not necessarily have predicted that all the customers you're working with would be like, "Yes, in year one and two of this company selling, we're adopting."

   8. GPGabe Pereyra24:39

      Yeah.

   9. EGElad Gil24:39

      Um, but...... you know, part of it is you guys are helping them, right?

   10. GPGabe Pereyra24:42

       Yeah. No, no. And I, I think this was, like when I look back, it's still surprising how quickly some of these law firms adopted this. Like, our first customer we actually met through... You introduced us to an ex-partner who was doing business school here, and he introduced us to David Wakeling at A&O. That was in our first year, and they went from a small pilot to firm-wide in investing in this. And I think... I mean, I think you're seeing this in a couple of verticals with, like, Cursor OpenEvidence, where this technology is so transformative for these industries that just are so tech-heavy and knowledge-based. They just haven't had tools like this that I think early on we did find these customers that were like, "Oh, this is worth really betting on." But yeah, I think the pace has still been pretty surprising.

10. 25:28 – 27:25

    Why Harvey Isn’t Building a Law Firm

    1. GPGabe Pereyra25:28

    2. EGElad Gil25:28

       I asked the internet through X, uh, what questions we should ask you. Um, and a popular one was like, "Why aren't you guys building a law firm? Are you gonna build a law firm and compete with all your customers?"

    3. GPGabe Pereyra25:39

       Yeah. No, we get this question. And I mean, I think when we first started Harvey and we were doing research, we actually talked to 30 people from Atrium. And I think also interestingly, Sam and, uh, Jason, who was the GC of OpenAI at the time-

    4. EGElad Gil25:56

       Mm-hmm.

    5. GPGabe Pereyra25:56

       ... and was the GC at Y Combinator when they did the Atrium investment, what struck us was the people who worked there said it was a really good idea, and they were super excited about the prospects, and then there were some challenges around the legal and the execution. But when we dug more into it, the big challenge that they ran into was you're essentially just building two different companies, right?

    6. EGElad Gil26:18

       Hmm.

    7. GPGabe Pereyra26:18

       You're building a law firm, and you're building a tech company.

    8. EGElad Gil26:21

       Mm-hmm.

    9. GPGabe Pereyra26:21

       And it's already really hard to, like, build product engineering, do AI, scale sales. And I think the big issue you run into if you try to do both of these is, I think you can only do one thing well, and doing a law firm well is very different than building a software company well. I think that's one point. The bigger point is, for us, it feels like the best outcome is if we can figure out how do we make every law firm, how do we help every law firm become an AI-first law firm, not how do we build one ourselves? And I think the real problem we're trying to solve is, can we make every law firm more profitable? And a part of that is how they work with their clients, and can you make their clients get better, faster, cheaper legal services? And I think solving that equation at scale is a much bigger opportunity than if you build a single law firm, because you get conflicted out. You can't scale this. And so, I think this is probably... Like, this is something we don't do, but we've gotten this question. But yeah, I think it's kind of not the focus for the company.

    10. EGElad Gil27:24

        Analogous

11. 27:25 – 29:26

    Challenges and Opportunities in Legal Tech

    1. EGElad Gil27:25

       to other, uh, markets in software, um, law feels like an area where I've been very surprised personally about how, how large the scope of the problem is eventually if you're really ambitious about what you can do.

    2. GPGabe Pereyra27:38

       Yeah.

    3. EGElad Gil27:38

       Um, I didn't realize, like you, you were telling me, you know, if you do a really large M&A, let's say of, like, two global companies-

    4. GPGabe Pereyra27:45

       Yeah.

    5. EGElad Gil27:45

       ... Microsoft, Activision or something, you're like-

    6. GPGabe Pereyra27:47

       Yeah.

    7. EGElad Gil27:47

       ... "There's a hundred outside council firms here."

    8. GPGabe Pereyra27:50

       Yeah.

    9. EGElad Gil27:50

       You know why? Because in New Zealand, where both companies have customers, you have, like, a tax implication, and the dude who understands that-

    10. GPGabe Pereyra27:56

        Yeah.

    11. EGElad Gil27:56

        ... lives in New Zealand, right?

    12. GPGabe Pereyra27:58

        Yeah. Oh, it's cr- Yeah, it's crazy.

    13. EGElad Gil27:59

        And, and so I, I think, like, you know, like other markets, like, the SMB version of this looks really different from the, like, high-end enterprise version of this.

    14. GPGabe Pereyra28:07

        Yeah.

    15. EGElad Gil28:07

        Um, and so I do think it's, like, hard... It just seems hard to imagine, like, coalescing all of that expertise in a law firm and a software company at the same time-

    16. GPGabe Pereyra28:16

        Right.

    17. EGElad Gil28:16

        ... versus I'm like, well, Harvey now has, like, what, 40 customers in New Zealand or 50. Yeah.

    18. GPGabe Pereyra28:20

        Exactly, yeah. And, and I mean, if you think about those transactions, it's also not just law firms.

    19. EGElad Gil28:25

        Mm-hmm.

    20. GPGabe Pereyra28:25

        So, there's investment banks, and you maybe have PwC or a tax advisor, and there could be an HR consultancy that helps you think about how you're merging headcount.

    21. EGElad Gil28:35

        Mm-hmm.

    22. GPGabe Pereyra28:35

        And so for us, the bigger opportunity seems, how do we build the platform that lets professional service providers and their clients collaborate?

    23. EGElad Gil28:44

        Mm-hmm.

    24. GPGabe Pereyra28:44

        And I think a lot of the problems you need to solve there are... Like, the biggest is the secure collaboration across many of these entities, the secure data sharing. How do you build and deploy AI systems across these very complex projects? And I think, to your point, the scope of this, like, legal is a trillion, professional services is something like three to five trillion. There's just this massive amount of room to grow. And we think our expertise is going to be in building the product, the technical systems, the AI systems that enable that, and we want to give that infrastructure to all of the different law firms rather than compete with them, because I just don't think you can.

    25. SGSarah Guo29:25

        I think one of

12. 29:26 – 37:24

    Building a Company During the Rise of Gen AI

    1. SGSarah Guo29:26

       the things that's really striking, um, about this sort of wave or era of AI is that there's deeply technical people building giant companies in really different industries.

    2. GPGabe Pereyra29:36

       Yeah.

    3. SGSarah Guo29:37

       And you come from a research background. You worked at one of the major labs in terms of foundation models and other areas, RL environments, reinforcement learning. What has been your biggest surprise in terms of transitioning into being a founder and running a company and building something from the ground up like that?

    4. GPGabe Pereyra29:52

       I think maybe, maybe not surprise, but biggest, like, mental model shift, is I think the 10 years before Harvey, I was doing a mix of mainly AI research and then trying to start companies, but always largely as an IC.

    5. SGSarah Guo30:06

       Mm-hmm.

    6. GPGabe Pereyra30:06

       And I think the shift from this started working and scaling, just how much-

    7. SGSarah Guo30:12

       Hmm.

    8. GPGabe Pereyra30:12

       ... I had to change my mental model of the type of company we're building, how you do this at scale, how you operate, I think that was the biggest surprise or, like, thing that I've had to, had to change. But it's been a crazy experience kind of going from, you know, Winston and I in an Airbnb to 500 people in, like, three and a half years. And I think also how you build these products at scale and kind of the complexity of this industry, like, that has been, like, a really hard but interesting experience.

    9. SGSarah Guo30:42

       It's been amazing to see what you all have accomplished. You know, it's such a short period of time and-

    10. GPGabe Pereyra30:46

        I, I was thinking back to, like-

    11. SGSarah Guo30:48

        Yeah.

    12. GPGabe Pereyra30:48

        ... when we pitched to both of you guys, like, three and a half years ago, and we were like, "Hey-"

    13. SGSarah Guo30:52

        (laughs)

    14. GPGabe Pereyra30:52

        "... AI pla- for legal?" And you guys were like, "Sounds good." (laughs)

    15. SGSarah Guo30:55

        Yeah. (laughs)

    16. GPGabe Pereyra30:55

        But we, like... I mean, we had some of these ideas, but I think they've really, like...

    17. SGSarah Guo31:00

        I think, a really important aspect of that, too, is, um, you all started this company, um, before GPT-4 came out and before a lot of the shifts in the models happened. And so, I remember you showing side by side GPT-3.5 versus 4, and what you were doing worked on 4.0 but not on 3.5.

    18. GPGabe Pereyra31:16

        Yeah.

    19. SGSarah Guo31:16

        And you were part of that very early wave that had conviction this was so important-

    20. GPGabe Pereyra31:20

        Yeah.

    21. SGSarah Guo31:20

        ... as a trend. Was that because of your experience in the labs? Was it something else? Like, what drove you... Because not many people were actually starting AI companies when you all got started.

    22. GPGabe Pereyra31:27

        Yeah.

    23. SGSarah Guo31:27

        And it was kind of just, to your point, AI plus legal, like, nobody was doing that.

    24. GPGabe Pereyra31:31

        Yeah. Yeah. It's something where now everyone's like, "Oh, this is such an obvious idea," but at the time-

    25. EGElad Gil31:36

        It's a text in display, yeah.

    26. SGSarah Guo31:36

        Yeah. Yeah.

    27. GPGabe Pereyra31:37

        Yeah, yeah. Text in, text out. But at the time, yeah, no one was thinking about this. I think it was a combination of a couple things. A lot of the best people at the time I had worked with had gone to OpenAI.

    28. SGSarah Guo31:49

        Mm-hmm.

    29. GPGabe Pereyra31:49

        And so I was working on large language models at Meta. You saw GPT-1, GPT-2, GPT-3 if you were working in AI for the past 10 years.

    30. SGSarah Guo31:58

        Mm-hmm.
13. 37:24 – 40:19

    Hiring at Harvey

    1. SGSarah Guo37:24

       What are... You mentioned that you folks have gone, uh, from basically the two founders to 500 people over the last three and a half years or so. You're obviously growing really quickly. The business is working. You know, you have tons of customer demand. Uh, what are you hiring for? What are you looking for in terms of the next set of employees or what types of roles are you hiring for right now?

    2. GPGabe Pereyra37:40

       Yeah. On the, on the technical side, so we mentioned FDEs. I think looking in general, like across roles of just strong engineers, and then I would say maybe specific call-outs. We just hired a site lead for New York, so starting to scale up that office. Um, more folks on kind of front end and scaling product in general, and then more AI folks as well, so.

    3. SGSarah Guo38:05

       Mm-hmm.

    4. GPGabe Pereyra38:05

       But yeah, anyone strong engineer, like, please apply.

    5. EGElad Gil38:08

       Okay, last question for you.

    6. GPGabe Pereyra38:09

       How many pull-ups can you do? (laughs) Just kidding.

    7. SGSarah Guo38:12

       Yeah.

    8. EGElad Gil38:12

       We did find out, yeah. Well, I, I don't know if that was the max, but these guys can both do 15 pull-ups, guys.

    9. SGSarah Guo38:18

       (laughs)

    10. GPGabe Pereyra38:18

        (laughs)

    11. EGElad Gil38:18

        With a wink in the middle.

    12. SGSarah Guo38:19

        I can do more.

    13. GPGabe Pereyra38:20

        Could be-

    14. EGElad Gil38:20

        Okay, okay, okay, guys, we get it.

    15. SGSarah Guo38:22

        (laughs)

    16. GPGabe Pereyra38:22

        Um... Uh-

    17. SGSarah Guo38:23

        You mean in one set?

    18. GPGabe Pereyra38:25

        (laughs)

    19. EGElad Gil38:25

        In one set, in one set of three.

    20. GPGabe Pereyra38:26

        Okay, we gotta do-

    21. EGElad Gil38:27

        Yes.

    22. GPGabe Pereyra38:27

        ... the 24-hour challenge.

    23. SGSarah Guo38:28

        Yeah. Oh, what's that?

    24. GPGabe Pereyra38:29

        Just how many can you do in a day?

    25. EGElad Gil38:31

        How many can you do in 24?

    26. SGSarah Guo38:31

        No way.

    27. EGElad Gil38:32

        Yeah, yeah.

    28. GPGabe Pereyra38:32

        It's a lot.

    29. EGElad Gil38:33

        Yeah. You can upload to your TikTok.

    30. SGSarah Guo38:35

        Oh, that's cool.
14. 40:19 – 44:17

    Future Predictions

    1. EGElad Gil40:19

       was, um, you know, there's a bunch of foresight in starting Harvey when you guys did. When you look forward, do you have a prediction that you think others don't necessarily agree with you right now? That is not mainstream?

    2. GPGabe Pereyra40:32

       So I have one comment I'll definitely make on the foresight is I think a lot of... Like, we've gotten comments of like, "Oh, overnight success," and, "Oh, you saw this coming?" And I would say, actually just spent the decade before Harvey trying to start a company like Harvey, so I think it was just... I was super early, and then eventually, it was like, "Oh, now's the right time," and then you were kind of in the right position. I... My guess is I think people now are catching up to how capability-pilled, as you called it, like Winston and I were. I think people... In Silicon Valley, I think people have a good sense of where these models are going, but I think generally people don't appreciate how much better they're gonna continue getting.

    3. EGElad Gil41:12

       It's hard to internalize.

    4. GPGabe Pereyra41:14

       It's really weird. Yeah, it's really weird, and I think-

    5. EGElad Gil41:16

       I build things and I'm like, "Oh my God, code gen works. It just really works now."

    6. GPGabe Pereyra41:20

       It's crazy. Yeah, and to me it's like, I think the interesting thing will be the transition from... Like, these models are really smart individually, but if you think about, like, a lot of what we've done in the past 20 years with SaaS, it's how do we use software to make these massive organizations?

    7. EGElad Gil41:36

       Mm-hmm.

    8. GPGabe Pereyra41:36

       And I think that will be the continued trend where a lot of what we're starting to think about is, like, law firms have, like, 10Xed in size compared to before computers and the internet, and I think that's gonna happen again, but in, like, maybe a different way than the past 20 years. But I think that, to me... Like, a lot of people still talk about copilots and individual productivity, and I think a lot of the, the things we're starting to think about is, like, organizational productivity-

    9. EGElad Gil42:03

       Mm-hmm.

    10. GPGabe Pereyra42:04

        ... and, like, how do you build these systems at scale where both for our internal engineering team... Like, I think a really interesting question for the, for the cursors, the Codexes is, like, making someone program 20% faster doesn't make you build a product 20% faster. And so starting to think about, like, what is the broader infrastructure you need so these companies can develop software and product faster? And then kind of same analogy to legal. I think that's kind of one of the things we're thinking about that I maybe don't hear people talk about as much.

    11. SGSarah Guo42:33

        Kind of collaborative AI in some sense. It's, it's sort of like the Figma transition of your, uh, individual contributor designer versus working collaboratively with a design team.

    12. GPGabe Pereyra42:42

        Exactly, yeah.

    13. SGSarah Guo42:42

        And what you're talking about is doing that for law, doing that for code, doing that for different verticals and having AI as a layer on top of that, so it's super interesting.

    14. GPGabe Pereyra42:49

        Yeah, and I, I, I think to that point, it's like how do... how are humans and AIs going to work super effectively? 'Cause even at these large companies, you have huge teams of different specialized people that have different functions, and I think when I hear a lot of people talk about these models, they kind of talk about it as like, "Oh, AI will just get smart and do all of this," and I don't think that's-

    15. SGSarah Guo43:08

        Mm-hmm.

    16. GPGabe Pereyra43:08

        ... the way this evolves. The same way it's not just like hire 100,000 people and now you've built Walmart. It's like-

    17. EGElad Gil43:14

        (laughs)

    18. GPGabe Pereyra43:14

        ... so much of it is like-

    19. EGElad Gil43:15

        A million.

    20. GPGabe Pereyra43:16

        How you... Um, three mil- Yeah, three million, actually, yeah. How you organize all of these, and I think that will be, like, one of the really interesting problems for these-

    21. SGSarah Guo43:23

        Yeah, that's really interesting. Yeah, I'm seeing that a lot in the context of both AI-driven rollups as well as this company BrainCo that-

    22. GPGabe Pereyra43:28

        Yeah.

    23. SGSarah Guo43:28

        ... helped get up and running, where a lot of the AI implementation issues are around-

    24. GPGabe Pereyra43:33

        Yeah.

    25. SGSarah Guo43:33

        ... people management, workflow, optimization. It's-

    26. GPGabe Pereyra43:36

        Yeah.

    27. SGSarah Guo43:36

        ... it's much less about can you build the AI and much more how do you actually change the organization to be able to adopt it properly, so.

    28. GPGabe Pereyra43:42

        Yeah, no, and we're starting to work with a lot of private equity firms that I think it's interesting, like, starting to see how they're thinking about that 'cause I think that will be, like, really interesting space.

    29. EGElad Gil43:51

        Awesome. Thanks, Gabe.

    30. SGSarah Guo43:52

        Thanks for having me on.

Episode duration: 44:17