Scaling Legal AI and Building Next-Generation Law Firms with Harvey Co-Founder and President Gabe Pereyra

Gabe Pereyra: And 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 can find this, go in the data room or do case law research, get feedback from a partner. And so I think that research direction is super, super interesting.

Gabe Pereyra: And to your point, 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 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. 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 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 lower level functions do change because of this technology.

Gabe Pereyra: Kind of the analogy I was giving is why is a senior distinguished 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 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 going to collapse for some reason that is super not intuitive. One of the examples that Gordon talked about early on was he was a part of when Michael Dell took Dell private and then restructured it and took it public again. 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, it's 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 in his head... all you get from these public mergers is like an SEC filing. 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 train these models to do kind of any of these reasoning tasks.