How Harvey AI is Changing the Legal Industry with Winston Weinberg

**Sarah** (0:05)
Hi, listeners, and welcome back to No Priors. Today, I'm here with Winston Weinberg, the co-founder and CEO of Harvey, which is building domain-specific AI for law, professional services, and the Fortune 500 They've now raised more than $500 million from investors such as OpenAI, Sequoia, Kleiner Perkins, GV, Elad Gil, and me. We're going to talk about how to do end-to-end workflows, how to serve conservative users, imposter syndrome, and keeping pace with the blitz of the AI ecosystem, and also what lawyers will do five years from now. Winston, thanks for doing this.

**Winston Weinberg** (0:42)
Yeah, of course.

**Sarah** (0:44)
It has been like a wild two and a half years for you and Gabe and Harvey. When you started the company in August of 2022, or at least when I met you guys for The Seed.

**Winston Weinberg** (0:53)
Yeah, we started a little bit earlier, but about then.

**Sarah** (0:56)
What was the moment of inspiration?

**Winston Weinberg** (0:58)
Yeah, so Gabe and I actually had met a couple years before, and I definitely didn't know anything about the startup world and didn't have a plan of doing a startup. And what had happened was he showed me GPT-3, which at the time was public. And I was, first of all, just incredibly surprised that no one was talking about GPT-3 and no one was using it in any way, shape or form. And he showed me that, and I showed him kind of my legal workflows. And we started the kind of a-ha moment was, we went on r slash legaladvice, which is basically a subreddit where people ask a bunch of legal questions and almost every single answer is, so who do I sue? Almost every single time. And we took about 100 landlord tenant questions and we came up with kind of some chain of thought prompts. And this is before anyone was talking about chain of thought or anything like that. And we applied it to those landlord tenant questions and we gave it to three landlord tenant attorneys. And we just said nothing about AI. We just said here is a question that a potential client asked and here is an answer. Would you send this answer without any edits to that client? Would you be fine with that? You know, is that ethical? Is it a good enough answer to send? And 86 out of 100 was yes. And actually, we cold emailed the General Council of OpenAI and we sent him these results. And his response basically was, oh, I had no idea the models were this good at legal. And we met with the C-suite of OpenAI a couple weeks after.

**Sarah** (2:31)
And the view was just, is going to be good enough. We should build a company around it.

**Winston Weinberg** (2:36)
Yeah.

**Sarah** (2:36)
Or what domains?

**Winston Weinberg** (2:37)
I mean, I think what happened or the reason we were so confident about this was the models and even with GPT-3, you could get it to do a lot of tasks. You just had to really brute force it, right? Like you had to brute force the amount of context, telling it which steps to take, et cetera. And the idea was over time, this is just going to get better, right? They're going to be either the models themselves are going to get better, or we're going to be, we're going to get better over time at figuring out how to provide them the correct context, how to improve them, how to evaluate the results, et cetera. And even just by playing with it for a decent amount of time, you could get that sense.

**Sarah** (3:16)
You are obviously not focused just on property law now. How do you think about the mission or scope of Harvey today?

**Winston Weinberg** (3:22)
Yeah. So mostly we're developing it for legal overall, but I would say that what we're building is the AI platform for legal and professional services, right? And if that sounds vague, or it sounds like there aren't incredibly defined use cases for the small areas that we're building, that's on purpose. Like the reality is, if you are using these tools and you don't think that you can take basically AI and apply to X industry and transform the entire industry, I don't think you're thinking ambitiously enough, right? And I think it's really hard because these models can't just one shot all of these really complex legal tasks or in these other domains like tacks and other professional services. And so what you have to do is you have to build a platform that is kind of constantly expanding and constantly collapsing. And so what I mean by that is you need to build specific features and maybe agentic workflows, et cetera, that can do parts of a task. And then you need to combine them all together so the UI is simple and you don't have this like tentacle monster of a platform.