How Harvey AI is Changing the Legal Industry with Winston Weinberg

This week on No Priors, Sarah sits down with Harvey cofounder and CEO Winston Weinberg. Harvey is one of the leading application layer AI companies, building domain-specific AI for law firms, professional service providers, and the Fortune 500. They are already working with companies like Bridgewater, KKR, PWC, and O’Melveny with over $500M in funding from OpenAI, Sequoia, Kleiner, GV and Elad and Sarah. In this episode, Sarah and Winston cover AI product strategy, the future of professional services, company values, keeping up with research, and the law industry of the future. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @WinstonWeinberg Show Notes: 0:00 Introduction 2:39 Harvey’s founding story 3:46 Capability improvement 6:39 Building teams around AI capabilities 9:17 End to end task completion 12:37 Beginning with large industry leaders 17:21 Working with users skeptical of automation 20:40 Being a lawyer today and in the future 26:02 Adapting product for other domains 26:58 Hiring philosophy at Harvey 30:39 Lessons and mistakes as a founder 32:53 Personal drive 40:21 Advice to other founders 44:35 Prediction for next ChatGPT moment

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:05Sarah Guo?

  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, Bloodgill, and me. We're going to talk about how to do end-to-end workflows, how to serve conservative users, imposter syndrome, keeping pace with the blitz of the AI ecosystem, and also what lawyers will do five years from now. Winston, thanks for doing this.

• 0:42Speaker 01

  Yeah, of course.

• 0:44Sarah Guo?

  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.

• 0:53Speaker 01

  Yeah, we started a little bit earlier, but about then, yeah.

• 0:56Sarah Guo?

  What was the moment of inspiration?

• 0:58Speaker 01

  Yeah, so Gabe and I actually had met a couple of 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, you know, 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 aha moment was we went on r/legaladvice, which is basically, you know, 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.

• 1:41Speaker 01

  And we took about 100 landlord-tenant questions, and we came up with kind of some chain-of-thought prompts. And this is before, you know, 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 Counsel of OpenAI. And we sent him these results and his response basically was, oh, I had no idea the

• 2:25Speaker 01

  models were this good at legal. And we we met with the the C suite of OpenAI a couple of weeks after.

• 2:31Sarah Guo?

  And and the view was just, it's going to be good enough. We should build a company around it.

• 2:36Speaker 01

  Yeah.

• 2:36Sarah Guo?

  For what domains?

• 2:37Speaker 01

  I mean, I 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 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.

• 3:16Sarah Guo?

  You are obviously not focused just on property law now.

• 3:19Speaker 01

  Yeah.

• 3:19Sarah Guo?

  Like, how do you think about the mission or scope of Harvey today?

• 3:22Speaker 01

  Yeah. So mostly, we're 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, you know, 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, you know, these models can't just one-shot all of these really complex legal tasks, or or in these other domains like tax and provide other professional services.

• 4:07Speaker 01

  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 that the UI is simple and you don't have this, like, tentacle monster of a platform.

• 4:28Sarah Guo?

  When did you realize that that was the, like, I think it's a really elegant framing of sort of product strategy for the company?

• 4:34Speaker 01

  Yeah.

• 4:35Sarah Guo?

  First implied in it is just like this, you know, and this has been true for you guys from the very beginning. I I did think you were a little bit crazy when you described the level of sophistication of tasks end to end that Harvey would be able to.

• 4:46Speaker 01

  I still feel crazy.

• 4:48Sarah Guo?

  But you appear to be more, you know, like you don't look as crazy when you're right.

• 4:52Speaker 01

  Fair.

• 4:52Sarah Guo?

  Right. But I remember like a year and a half, two years ago,

• 4:57Sarah Guo?

  like it was very distinct to me how much you guys believed in capability improvement.

• 5:02Speaker 01

  Yeah.

• 5:02Sarah Guo?

  Like, where does that come from? And then also, how do you think about it internally in the company?

• 5:06Speaker 01

  Yeah. So where does it come from using the tools? I mean, I think I have been blown away by the amount of kind of C-suite, et cetera, that haven't actually like spent that much time using AI. And the the big moment for me was just the jump between GPT-3 and GPT-4. Like, when we got access to GPT-4, I went into my room, I think for just 24 hours straight, and tried to do every single thing that I couldn't do with GPT-3, or I had to very much brute force and like wasn't getting any efficiency gains from, and tried to do it with GPT-4. It didn't do all of them, but the improvement was crazy, right? And no one was talking about it. And I I found that something that also was very weird is

• 5:49Speaker 01

  I would show GPT-3 and and GPT-4 to a bunch of my friends, and they would try to get it to do one thing. It didn't do the thing perfectly, and then they just stop, right? And so it seems like there was this large gap between I log into ChatGPT, or I just use GPT-4 from an API or whatever it is, and I try something a couple of times versus I'm going to sit there and just hammer on this until it works, right? And I think if you do that enough, you get the intuition for where things are going and how much better they can get. How do we do that from a product standpoint? You can kind of think about, if you go back to that expand and collapse as like two general themes, you need to build productivity tools, and what I mean by this is things that are useful for the highest amount of seats, right?

• 6:35Speaker 01

  And then you also need to build things that are streamlined, vertical workflow from start to finish, right? And what you can do is you can take those streamlined, vertical workflows, and you can chain them together to do more increasingly powerful things, right? And so that's kind of how we think about it internally at the company is, you know, there's a bunch of bells and whistles and things to add in terms of like sharing, collaboration, things like that. But you also just can basically build certain specific features that will do something from start to finish, like I upload all my documents and a target company's documents and it'll tell me in 72 countries where I need to file antitrust and what do I need to file and what other information do I need? And you can take pieces from that and add it to another part of a project.

• 7:20Sarah Guo?

  How do you organize that effort at Harvey? Because it's, you know, like, I think you have a very special instinct and commitment to going on the AI vendor, I suppose, to like figure out what to have.

• 7:32Speaker 01

  It's gonna be a long vendor.

• 7:33Sarah Guo?

  Yeah, long, you know, multi-year vendor, but you know, one day at a time, to develop intuition for, you know, what the models are capable of doing with this, like, expansion, workflows, manipulation, assembly process. I imagine that it's really hard to find all of that in a single person that understands the research, the engineering, the domain, the data available, like, interacting with users. So, like, you know, how do you do that across teams?

• 7:57Speaker 01

  Yeah, so I think we're starting to get better at it. And the thing that we're starting to land on is basically identify these systems as what we're calling AI patterns. So, these are the 30, 50 things that we need to build that will be integrated into all of these different pieces of the product, right? So, I'll give you an example of this. If you build an AI system that is really good at case law research, that can go into a motion for summary judgment, that can go into a billion different types of litigation use cases, right? It doesn't do that use case from start to finish, but you can add that on top. And so what we've been doing is we basically have teams that will work on building these are the patterns, and then teams that are kind of implementing that across the entire platform.

• 8:42Speaker 01

  And that has been working really well. It is definitely a work in progress. I think the other interesting piece of this is we have a lot of lawyers on our staff, right? And we have a lot of, we're gonna do this also in the other verticals that we're going into. And the domain experts are very helpful for basically like two main reasons. One is as design partners. So they're very good at saying, actually, you know, this is what we need to teach the models how to do. This is the step-by-step thinking we need to do. This is the output that the user wants, etc. And then the second piece, which is really hard, is eval, right? And so most benchmarks are completely useless for us, right? And so we'll get a model, you know, someone will give us early access to a model and they'll say it's way better on all of these benchmarks.

• 9:23Speaker 01

  And we'll respond, it actually isn't like it's not as useful for us as a different checkpoint or something like that. And the reality is you have to hire very good lawyers who can actually evaluate these systems and the same within tax and these other areas. And they can't be too junior because if they were too junior and they were able to eval it, they would be senior, right? And so I think the intertwining of the domain experts into the actual like product sequence and development and then evaluation at the end state is very hard. And it's something we're constantly working on.

• 9:58Sarah Guo?

  What is the end to end task you're most excited about that you think Harvey will be able to do this year?

• 10:02Speaker 01

  Yeah, filing an S4. I think it's something that is... The reason I like that process is it is a combination of external data, internal data and a million steps, right? And I think of workflows as basically a bunch of agentic systems that need to combine together, right? And if you think about knowledge work, professional services, legal, honestly, any swath of that. What you are doing is manipulating things based off of your internal context, external context, whether that's external data, whether that's data from whoever your customer is, your client is, etc. And then process of this is how, you know, this is how you do an LBO. This is how you do side letter compliance.

• 10:45Speaker 01

  And then there's another step of that, which is this is how you do side letter compliance for this particular private equity firm, right? This is market for this particular clause. And the more complicated the workflow is, the more you have to combine all of those different elements together.

• 11:00Sarah Guo?

  When you think about like having these very senior lawyers internally at Harvey doing eval, I imagine another piece of what is important there is like the experience that you had with your friends looking at these models, which is like you don't get that many shots on goal.

• 11:16Speaker 01

  Yeah.

• 11:16Sarah Guo?

  Right? It works or it doesn't.

• 11:18Speaker 01

  Yep.

• 11:19Sarah Guo?

  The legal profession, especially like more senior, more prestigious lawyers, you know, billable hour rate is very high.

• 11:29Speaker 01

  Yeah.

• 11:29Sarah Guo?

  And expectation of quality from, let's say, like an associate or a model is very high. Like, how do you, how did you go about building that trust because the, like, you didn't start at the capabilities you have today?

• 11:41Speaker 01

  Yeah, I'll start from one kind of like high level distinction between two pieces. So if you go back to the productivity versus like specific specialized output, right? On the productivity side, the minimum viable quality of that output can be lower because you're selling seats and at the end of the day, there are multiple people reviewing it, right? And so what you want to do in that state is have, just show your work, right? Like, that is the most important thing. You want to mimic exactly how a senior associate reviews the work of a junior associate. So you say, this is why I did this. This is the information that I pulled. And here's an inline citation to the literal sentence that I pulled it from. Is this correct or not, right?

• 12:19Sarah Guo?

  Make it cheap to check and partially correct is still useful.

• 12:22Speaker 01

  Yes, exactly. And because, you know, a lot of how these law firms work, and again, the big four as well, is the junior produces some sort of output, the mid-level associate reviews that, and then the senior level associate reviews that, and then the partner reviews that, and then it goes to the client, right? And so you do get a structured pyramid of like a hierarchical review, right? Now, let's go to the specialized system. The specialized system, at the end of the day, you're actually trying to just from start to finish, produce that output, right? So the minimum viable quality on that is much higher. But it's also easier to get higher because you are trying to build systems that just produce the same output and do the same task from start to finish, kind of like a specialist, right?

• 13:02Sarah Guo?

  It's just much more scoped.

• 13:03Speaker 01

  It's much more scoped and evaluation is much easier, right? Like, how do you do evaluation on Copilot or Enterprise GPT, et cetera? That is very difficult. It is much easier to do evaluation if you can do evaluation at each step and make sure that recursively that step was done correctly.

• 13:18Sarah Guo?

  Okay, so there are different expectations for different pieces of the Harvey product.

• 13:22Speaker 01

  Yeah.

• 13:23Sarah Guo?

  There's still like conservative customers, right?

• 13:26Speaker 01

  Massively.

• 13:26Sarah Guo?

  So, you know, one of the things that was surprising to me in like 2023, early 2024, was the set of people that you decided to work with first.

• 13:35Speaker 01

  Yeah.

• 13:35Sarah Guo?

  And that were willing to work with you, right? A and O, P W C, like high-quality brands that were generally larger. Like, there's been a, I think for maybe a decade in technology investing, a expectation that if you start with the mid-market, you start with your friends and startups, like, it's just easier, the requirements are lower, they're willing to take risk. Why do the opposite here?

• 13:59Speaker 01

  Yeah, so I think that if you are, I guess going back to what I said in the beginning of the ambition should be, take X industry, apply AI to it.

• 14:09Speaker 01

  If you are doing that, you need to partner with that industry. And I think you also need to make sure that your brand and what you're building actually makes sense to what they care about. I'll give you an example of this, or just kind of like a reference point. A and O Sherman is almost 100 years old, and they became famous, basically advising on the abdication of, I think it was King Edward VII from the throne.

• 14:31Sarah Guo?

  Okay, good trivia.

• 14:32Speaker 01

  My point is, these are incredibly old, like have very prestigious and a long line of history, right? And so in order to work with them, you actually have to kind of partner with them and say, these are the different workflows that we're going to start building with you, like use them as design partners. And you also need to make sure that you are kind of like building a brand around that. So not only is it just actually working with those higher-tier firms, but you also want to work with the data providers that are really important, right? So Lexis is involved in this round. And we're really partnering with them to also bring a lot of the kind of goodwill and trust and things that they have brought to the industry and really good products and combine that with our AI as well.

• 15:16Speaker 01

  And so if you are able to do that, if you are able to show that you are working with the industry and get the trust from the folks that are basically seen as the highest, you know, degree in those industries, it is much easier to work with the rest of the industry. And we thought that that was very important. You know, we, we thought for a while that maybe we would do, you know, a self-serve model and we would kind of try to do PLG and things like that. But we found that no matter what, you eventually have to get the trust of the industry. And the best way to do that is to actually go after the hardest people first.

• 15:51Sarah Guo?

  Yeah, I, I think there is perhaps an opportunity in AI. We'll, we'll see if this is true. You're already like continually collapsing and trying to simplify the experience of using Harvey, right? And this has been like impossible in software to date. One of the reasons people would start like, you know, SMB or mid-market is they'd be like, well, like, once it becomes an enterprise product, we will never have the simplicity required to serve other customers.

• 16:19Speaker 01

  The training, just the training cost of like implementing that, that technology is going to be impossible. No one will buy it.

• 16:24Sarah Guo?

  Yeah, yeah, exactly. But I, I think like because you like, you know, a bunch of different factors, like I'd like to see if you agree, but maybe building UX ends up being cheaper in the future than in the past. And like you're also building a muscle as a company that other companies are not encouraged to have in terms of like the constant simplification.

• 16:46Speaker 01

  So, I mean, the thing that the models are incredible at is orchestration, right? And so if you are thinking about like how to constantly collapse the UI, I'll give you an example, you can build a bunch of different workflows that deal with SPAs, share purchase agreements, right? It extracts the reps and warranties from the share purchase agreement. It takes them and turns them into a some, a complex summary. It does the closing conditions, all of these different things, right? So you can build those separately because, you know, maybe your system can't handle that plus every other horizontal use case at once, which it can't. So you build those separately and then you just make it so that whenever a user uploads an SPA, Harvey says, hey, would you like to do one of seven things to it? Right? And so you can collapse that back down into a UI that is actually very simple.

• 17:30Speaker 01

  And if you actually think about how the UI for professional services is just email. Like that's it. Right? And so what you can do is, again, you can build all these different features in the system, but then you have to have a really clean and elegant way to combine it all. So the user can actually find those things. Right? You don't want a system that has 10,000 workflows and you have to somehow filter through them and figure out what you need.

• 17:52Sarah Guo?

  Kind of what we have in business software.

• 17:54Speaker 01

  Yeah, that's exactly what we have. Yeah. And it's awful and it takes forever to learn how to use it.

• 17:59Sarah Guo?

  I know how to use email.

• 18:00Speaker 01

  Yeah. It's still hard, but.

• 18:03Sarah Guo?

  I think it takes a like a particular attitude to play with GPT-3 and then as a, you know, young lawyer be like, we should do this versus like be threatened by it. You guys now serve, you know, more than 250 clients, more than 50 million ARR. People are obviously adopting it. I look at the like weekly user charts. What is the reaction you get from users and how do you talk to them about the impact of automation for them?

• 18:29Speaker 01

  It changed massively over time. I think that there was a lot of fear of automation when there was a lot of press, but they haven't used it, basically. And I think like, I'm more bullish on model capabilities and just capabilities in general on the application side and everything.

• 18:49Sarah Guo?

  So you still think you're crazy, but you're going to go in that direction?

• 18:51Speaker 01

  I still think I'm crazy. But I do think that one thing that not just our customers are realizing, but I think a lot of people that haven't worked in legal or professional services are realizing is these industries are very messy in the sense that all of this is not kind of a simple, oh, I know how to draft an SPA, I'm a lawyer, like I'm done. Or I'm going to grab all of the legal data on earth and just train on top of it and the model works somehow. That's not how any of these industries work. Right? And so I think that from the lawyer's side, once you have used the product, you start saying, oh, wow, this is really good and I can see that this is going to get better. Especially if you're someone who used Harvey a year ago and you use it now. Right?

• 19:34Speaker 01

  But it does seem like no matter what, we're going to need a bunch of lawyer collaboration involved in the process, right? And so they're very happy about that. And the junior folks are incredibly happy about this because what ends up happening in the legal industry and other professional services is, you know, you go to a really good school, you study a bunch of things that have nothing to do with, by the way, like practicing law.

• 19:54Sarah Guo?

  Okay.

• 19:55Speaker 01

  And you come out and you're in a new kind of vertical and you're trying to figure out how to do private equity or an LBO or something like that. And it's, you spend a very long time kind of doing repetitive tasks, right? So whether that's in, you know, reviewing documents in discovery or it's reviewing documents in a data room, et cetera. And you end up not being able to do the strategic level things until like 10 years into your career. You know, if you're lucky, five. And so I think that-

• 20:19Sarah Guo?

  You did complain about that grind.

• 20:21Speaker 01

  Yeah.

• 20:22Sarah Guo?

  You're like, I should not be doing this.

• 20:23Speaker 01

  There was a joke that I, we were presenting to the firm I used to work at, that was O'Melvyn and Myers, and this is, I think, maybe a year ago. And one of the partners said, you know, Winston, there are way easier ways to get a doc review than trying to automate doc review, which I don't know if that's true. But my point is there are all these lower end tasks that you end up having to do just because the industry is really complex and legal is getting more and more complex, right? And these tools allow you to do that stuff faster. And so what I think will end up happening is the timeline will compress. So you will start being able to actually do the high level strategic work and interact with clients, which is what people really want to do-

• 21:01Sarah Guo?

  Mm-hmm.

• 21:02Speaker 01

  -earlier on your career. And so they're happy about that. And so I don't think there is as much displacement fear. It is not job displacement, it's task displacement. And I think that's a super important distinction because getting rid of those tasks does not mean the legal industry falls apart. It will evolve.

• 21:19Sarah Guo?

  My younger sister, Camilla, is an engineer by background and works at an AI and robotics company. She still wants to go to law school. She's thinking about it. What advice would you have for somebody who wants to be like a star lawyer five years from now?

• 21:34Speaker 01

  Yeah, I would actually have given the same.

• 21:36Sarah Guo?

  Ten years from now.

• 21:36Speaker 01

  I would have given the same advice before doing this startup.

• 21:39Sarah Guo?

  Oh, okay.

• 21:41Speaker 01

  I would say that now spend even more time on it. But the most important thing in legal and professional services is how well you can deal with client requests. Like how much you can navigate and figure out what the client, what is actually best for the client. And so I would try to get as much hands-on experience, even if that's at something that isn't as prestigious. It isn't the, you know, bet the company litigation because you're probably not going to get hands-on experience. Or the massive hundred billion dollar merger, et cetera. Like get as much hand on experience because that is the skill that is most important, is figuring that out. And now I would just say that is the main skill that is going to matter over time. But even three, you know, three, four years ago, I think I would have given the same advice.

• 22:24Sarah Guo?

  What do you think happens to the structure of law firms that have been built on the backs of, you know, junior associates doing these repetitive tasks?

• 22:31Speaker 01

  Yeah, um, so I think there's a bunch of ways this can evolve. I think the most likely one is I don't think the billable hour is going to just completely disappear.

• 22:39Sarah Guo?

  Okay.

• 22:40Speaker 01

  I think what is going to happen is a lot of these tasks that are going to get, you know, AI can automate a lot of that task with a lawyer in the loop. Those tasks will end up being kind of a fixed fee model. And I think the high level advisory work on top will still be billable hour and will be actually maybe more expensive. Like there's an argument that the specialist at a law firm who has seen all of these different mergers in the pharmaceutical industry, their rate should hours should not be actually three X the junior associate in the data room, maybe 10 X. I don't know. My point is there is a specialization in professional services that is incredibly valuable and is going to be more valuable over time. And so I think it'll be a mix of those two. And we're also already seeing this.

• 23:25Speaker 01

  We're seeing law firms willing to do things like. You know, take a bunch of their domain expertise and turn it into software. And we're starting to do that. We're starting to work with firms and basically take, you know, the special things that they do and the way that they practice law, and turn it into their specialized system in Harvey. And then they go and sell that to their clients, right? And so that's a completely new business model.

• 23:47Sarah Guo?

  Uh, how does that happen? Who drives that at one of your partners?

• 23:50Speaker 01

  Yeah.

• 23:50Sarah Guo?

  What's the incentive?

• 23:52Speaker 01

  Um, I mean, it is massively based off of a law firm. Um, I think that you will find a couple of partners that think that this is the future, and they will rally the entire firm behind them. And that's what we have seen. And it's really started snowballing in the past kind of three to six months. And the way that it works really is it's a combination of, you know, they provide the domain expertise and we provide the translation of the domain expertise and then the tech, right? Um, the incentive for them is there is so much legal work that law firms actually do at a loss. And they do it at a loss.

• 24:26Sarah Guo?

  It doesn't feel like that.

• 24:27Speaker 01

  I know, I know, but there actually is. Um, there's a lot of legal work that they do at a loss in order to get the big deal. So, you know, a lot of law firms will do work for private equity, and they will do that work at a massive discount, or even, again, even at a loss, in order to get the LBO or the M and A, right? And so I think these systems are a way for law firms to compete in these spaces. And it also another way for law firms that maybe aren't as large and don't have as much of those resources. And they can compete in these others because they have software margins for this work. And then they can get the really intense strategic deal afterwards.

• 25:00Sarah Guo?

  Mm hmm. Yeah, that feels analogous to like the client service work.

• 25:05Speaker 01

  Yeah.

• 25:05Sarah Guo?

  Um, in an investment bank.

• 25:07Speaker 01

  Yep.

• 25:07Sarah Guo?

  To work on, you know, large transactions that really matter.

• 25:10Speaker 01

  Yeah, exactly. I mean, you see this already, and it grows over time.

• 25:14Sarah Guo?

  The legal industry is one of reasoning. How much has the growth of models and interest in developing models that, um, scale, test time inference impacted you guys?

• 25:25Speaker 01

  Massively in a good way. Um, the best way to think about this is if you are building a system where you are trying to basically break down every single problem into a sub-problem because the models can't quite do it, that just unlocks different pieces of it. So let me give you an example of this. We go back to that antitrust example, right? Where the first step of it is we're trying to take all of the, you know, target financials, your acquirers' financials, and just say, in all these different countries, yeah, this is what you need to file. Well, the next step to that would be, can you help it actually do all of the filings? Right? And we were having trouble figuring out how to do that. Now with reasoning models, you can start unlocking those steps, right?

• 26:07Speaker 01

  And so the best way to think about this is we are constantly building all of the steps that we can and being on the cutting edge. And then when a model improves, that just pushes our ability to go out the next cutting edge more. And then the other thing, too, is just cost going down is incredible for us, right? We try, you know, we're not optimizing for costs at all times right now. We're optimizing for quality. And if the prices go down, that makes it so that we can increase our quality across every single user base much faster.

• 26:38Sarah Guo?

  How do you think about the domains that are not legal? Right? You have announced like research and work and tax and audit now. What makes this the right time to do that?

• 26:48Speaker 01

  Yeah, so the way that we think about it is there's so many areas where legal is kind of the tip of the spear.

• 26:55Sarah Guo?

  Yep.

• 26:55Speaker 01

  And then you can actually just kind of paralyze the same things that you're building and tweak them, and it works really well in other industries. So an example of this is, you know, you do tax diligence, and then you do commercial diligence, and then you do financial diligence, right? Over all of these documents. And tax and legal diligence, there are a lot of similarities in terms of what is the high level problem that you are trying to solve. Right? It is basically apply all of these rules to these documents. Right? And so we have found that there are a bunch of ways to take what we have learned and the things that we've built in legal, and then just improve on that and apply it to tax or deals or these other areas.

• 27:36Sarah Guo?

  So I'm thinking about like how unique it feels in terms of just the product effort. At Harvey, I think it's like very first of its kind and feel very lucky to be involved. When you think about like the team that goes and works on this, like. Does your like new engineering hire understand like tax diligence after a while? How do you think about like what makes somebody like the right next person to add to Harvey?

• 28:01Speaker 01

  We're working on it.

• 28:02Sarah Guo?

  You are hiring?

• 28:03Speaker 01

  Yes, yeah, tons. We're working on the context sharing. I think that is something that's really important. One thing that was really nice that I actually think we did get right is the respect is incredibly high. And so what I mean by that is a lot of engineers don't have experience in legal and professional services. And we had a couple of talks that were basically talking about the structure of these really complex like take private deals. And you could just see the engineers basically saying, oh, wow. This is the work that all of these folks are doing is really impressive, right? And so that I think we've done well. I think on the context sharing, it is a difficult problem where a lot of the folks building the product, they haven't actually used it in the sense of in their past career.

• 28:48Sarah Guo?

  Sure.

• 28:48Speaker 01

  You know, they haven't done tax diligence or whatever it is.

• 28:52Sarah Guo?

  Do you guys do like a weekly suits viewing or something? Is that how that works?

• 28:56Speaker 01

  And then you're done. Yeah, if you watch all of Suits, we actually just make everyone watch every season of Suits and then they're good. No, it's a very difficult problem. I would say that the way that we look for hiring folks is basically agency, right? Like this is one of the biggest things is there are so many things that we are doing that is new. And I have found that over time, hiring folks that might not have a bunch of experience in this particular thing doesn't matter. Like it is so important that you hire people that are really smart, hungry, care about what you're doing, and are willing to, you know, be very decisive in what they decide to ship.

• 29:40Speaker 01

  And if it doesn't quite work, iterate on that and go again. That is better than necessarily someone who has tons of experience. And I am very much doubling down on that as a company. And I, I think this is probably always the case, but I think it's more the case now because everything changes every six months. And you have to be able to adapt to that change. And if you can't, you're not going to make it regardless of how much experience you have.

• 30:07Sarah Guo?

  Yeah, I think that's true even in the experience of like the founders that we are working with.

• 30:12Speaker 01

  Yeah.

• 30:12Sarah Guo?

  Right? In terms of like, well, like your ability to predict or follow somebody else's pattern for more than two months at a time is it's very bad.

• 30:21Speaker 01

  Yes.

• 30:21Sarah Guo?

  Right? I mean, some things are monotonic, like, you know, capability improvement.

• 30:25Speaker 01

  Totally.

• 30:25Sarah Guo?

  But still figuring out like, I'm gonna pick this point in the future and like, put engineering resources on solving a particular task because I think it'll be possible. I think it's just very, very different from building deterministic SaaS workflows.

• 30:39Speaker 01

  Yeah.

• 30:40Sarah Guo?

  From a few years ago.

• 30:40Speaker 01

  I think you have to pay more attention. So I think like, that's one of the biggest things is I have found that the people that have been very successful in this space so far are like, very obsessed. And it's not just obsessed with your problem, but you have to pay attention to what's happening with all the model providers as well. Like, you have to pay attention to all of these different facets, or I think you're gonna miss out on something.

• 31:03Sarah Guo?

  What you have, you know, now been a founder for two and a half years.

• 31:06Speaker 01

  Yeah.

• 31:07Sarah Guo?

  Uh, I'm sure that's a learning experience. You feel like you got a focus on agency, right? In, you know, hiring and the business, like, what did you get wrong?

• 31:17Speaker 01

  Yeah, I mean, I don't think we have like 12 hours to do this. Um, a lot, a lot of things. I think if I had to say there's one thing that I got wrong over everything else, it is figuring out when to scale yourself. Um, I have a certain kind of like tendency for how I work, and there are some things I want to keep. So one of the lessons that I definitely want to keep is I do think you should do every single role for a certain amount of time before you hire for it. Almost all of my mishires were because I did not understand what that role was. And maybe it's a lack of my experience, I don't know, but there's a hands-on piece. Having said that, you can't scale a company by wanting to be hands-on in everything at all times, right? Um, and I think that I didn't spend

• 32:02Speaker 01

  enough time transitioning from, I mean, the beginning of last year, we were 40 people. Transitioning from everyone knew what was going on, everyone had all the context because they were working with me directly, right?

• 32:15Sarah Guo?

  You touched every single customer all the time.

• 32:16Speaker 01

  Yeah, basically, right? And like all pieces of the product, et cetera. And I took a long time to, I mean, sorry, I haven't solved it at all. I had, took a long time to even think about solving it of you just need to learn how to scale yourself and you can't be in every single thing. Having said that, I do think that you want to be in as many things as you can because you are, it's an environment where you have to be very decisive and make very quick decisions. And the easiest way to make quick decisions is you're paying attention to everything, right? You're kind of monitoring everything at all times. And then your gut is normally actually pretty right because it's not your gut. It's you have taken all of these different stimuli and you're just absorbing them at all times, right?

• 32:59Speaker 01

  And so I guess balancing how important that piece is with actually learning how to share context with the team, how to make sure, you know, upleveling everyone, how to transition yourself as a founder has been hard for me. And I think it was continuously hard. And I'm sure if you ask anybody, they say that it's, you know, it's going okay, but it still needs work.

• 33:19Sarah Guo?

  So when you agreed to do this podcast, you were like, okay, okay, sure, Sarah, but we're going to talk about the company and not me.

• 33:26Speaker 01

  Yeah.

• 33:27Sarah Guo?

  And I lied. So I'm going to ask you just a couple of things. One is like you, you work continuously. You obviously really like think that there is something important to be built here. As far as I can tell, you don't do a lot else, like maybe workout.

• 33:42Speaker 01

  Yeah, that's like basically it.

• 33:44Sarah Guo?

  Yeah. Um, where does, where does the like a drive come from?

• 33:48Speaker 01

  I mean, as simple as possible, like this is the most fun thing ever. I don't think, you know, I started this company when I was 27. There was nothing in the 27 years leading up to there that was even close to as much fun as this is. I, I think that. It is just so much, you get so much energy from things moving so quickly. And you being able to actually have the agency to come up with an idea and then see it built. That is a crazy experience. And I think that you can do that faster than you used to be able to, and it is addicting. Like it is incredibly addicting. And so I actually think that it's, it's less like where do you get the drive or where do you get the inspiration?

• 34:32Speaker 01

  I think that most people that I've met that have been successful in the space, they just have it. Like it's just naturally, they love the moment or the like very compressed timeline of you can have a very large impact.

• 34:46Sarah Guo?

  It's the most fun you've ever had.

• 34:50Sarah Guo?

  Some people like they just have that, like the, the desire to like know what's happening, have the leverage, see that like really fast impact cycle. Is that the expectation you have of everybody at Harvey culturally?

• 35:03Speaker 01

  Yeah, good, good question. I think the expectation that I have for everyone at Harvey, we, we have a line that we have kind of like talked about a lot and it actually comes from Kobe Bryant. And it's job not finished.

• 35:16Sarah Guo?

  Okay.

• 35:17Speaker 01

  Basically, he was in an interview and I, I forget which game it was, but you know, they're up like, I think it was like three hour, two hour, something like that. And a reporter asked and he's like, you know, how, how do you feel right now? Like, how are you feeling? He's like, you know, I feel good. It's, it's not done. And he's like, well, shouldn't you be excited? You're up like three O and he's like, well, the job's not finished. Like we're, we're not done. Like we haven't won, right? The, the thing's not over. And I think that what I'm trying to convey when I say that, or and hopefully what Kobe was trying to convey when, when he said that, was there are certain moments when you need to give it your all. And if you give it your all in this massively compressed timeline, it will serve you for a very long time afterwards.

• 36:00Speaker 01

  And so that's what I, I think I expect of everyone that we hire at Harvey is, look. You need to recognize and trust us. So it's maybe a combination of either recognize or trust that we are on that timeline, that we are on an incredibly compressed timeline for honestly, just all of humans, right? I'm serious.

• 36:21Sarah Guo?

  Yeah, it's a very special moment.

• 36:22Speaker 01

  It's a very special moment. And there are going to be insane expectations. And the company isn't always going to be there. I'm not always going to be there at all times to tap you on the shoulder and be like, hey, remember those expectations, remember this is true. And so you have to be able to do that to yourself. And you have to have that mentality, right? And so I think what that looks like is some combination of recognition and a willingness to keep up the intensity and keep raising the bar. And that is an expectation of anyone who joins.

• 36:54Sarah Guo?

  Yeah, that resonates very strongly for me because I think in a, like a very parallel journey.

• 36:59Speaker 01

  Yeah.

• 36:59Sarah Guo?

  Like this opportunity is going to happen once. And so for me and my team, and even, you know, Mike, who joined us a little while ago, he's like, how could I not do it right now?

• 37:08Speaker 01

  Yeah.

• 37:09Sarah Guo?

  Right? Like, you know, land of many different options, but I can't sit this one out. And then if we're going to play, we're going to fully play.

• 37:15Speaker 01

  Yeah, and that is maybe a really good way to look at it. I can't sit this one out. That is like an amazing signal for somebody.

• 37:23Sarah Guo?

  One other thing that you said to me that I think is like universal, but also a little specific to this moment in time is like, you know, you're the founder of this company that's scaling really fast and delivering value to users. You're not a research scientist.

• 37:36Speaker 01

  Yeah.

• 37:37Sarah Guo?

  And it's a very technical field. And so there's some imposter syndrome around that. How do you square it for yourself?

• 37:42Speaker 01

  Yeah, it's another work in progress. I mean, I'm lucky.

• 37:45Sarah Guo?

  I am also not a research scientist.

• 37:47Speaker 01

  Yeah, there we go.

• 37:48Sarah Guo?

  So it's an important question.

• 37:50Speaker 01

  My co-founder is. And I think that the thing, the most effective way to do this is just figure out a couple of people that you really trust and spend as much time with them as possible. I think maybe this is also another piece of, I think that it is very overvalued. How much value you get from asking people that you really respect very specific questions, I think is overvalued. I think spending time with people that are incredible at whatever they do is undervalued. And I think it's because the latter has like a nebulous effect. Like you're like, I don't really know what I'm getting out of this, right? And versus the former is, I have a question, they gave me an answer. But the reality is, they don't have, they have like a basis point of the context that you have, right? If that.

• 38:35Speaker 01

  And so what has been really helpful for me is just spending time with a lot of people that I really respect in this industry. And you start to just learn the intuition that they have for things. You start to absorb it and all of a sudden it starts becoming your intuition, right? I think that's the only way that I've gotten to this point. And I am going to keep doing as much as possible.

• 38:59Sarah Guo?

  Yeah. I get comfortable with it mostly just from my, I mean, actually what you described makes sense to me.

• 39:04Speaker 01

  Yeah.

• 39:04Sarah Guo?

  Like I'm glad Andres hang out in the office.

• 39:06Speaker 01

  Yeah, exactly, right?

• 39:07Sarah Guo?

  But, but also like I think like the outcomes make me feel better.

• 39:11Speaker 01

  Correct.

• 39:12Sarah Guo?

  Where I'm like, well, companies look like they're working.

• 39:14Speaker 01

  Yeah.

• 39:15Sarah Guo?

  Right? And users seem to be doing something in the product or, you know, consuming more infrastructure, whatever it is. And I think, as you said, if you get the repeated pattern back that your intuition is right, then I'm a pragmatist.

• 39:27Speaker 01

  Yeah. And I do think, so one thing we had was in the beginning of the, of last year, we had a lot of kind of like usage problems in the sense that a lot of customers were actually getting blocked from using our product. Or they got to our product and they didn't know how to use it, right? And so we had massive power users and then we had some other problems. And actually about six, seven months into the year, it had done a complete 180 and it was working really well. And we took a bunch of bets on product things that not, product features that not only were kind of bets we made, but also bets on the models getting better. Things like follow-up questions, things like you enter a user profile and it tells you

• 40:12Speaker 01

  kind of like, here are all the different things that you should do based off of your practice area and things like that. And it worked, right? And so speaking of kind of the intuition. You start to absorb that intuition, you start making bets on your own, and then you see it work and all of a sudden you get a little bit more confident. Hopefully not too confident, but a little bit more.

• 40:30Sarah Guo?

  So you're saying you and the people at Harvey feel the AGI just enough?

• 40:34Speaker 01

  Just enough, not too much.

• 40:35Sarah Guo?

  Not too much?

• 40:36Speaker 01

  Yeah.

• 40:37Sarah Guo?

  Yes, not clear what too much is, actually.

• 40:38Speaker 01

  Yeah, I don't think there is a limit at this point.

• 40:40Sarah Guo?

  So like the, you guys have been on like the right side of a raging debate in the investing and tech community for a long time, which is just like, is there value in the application layer? Like, it's pretty clear there is now. What advice would you have for the many like founders, tech business people that are gonna listen to this about like a good application to build?

• 41:04Speaker 01

  Yeah, so one thing that I look at is, you can look at this by industry and you can look at it by task, which is how expensive is the token? And I've come up with like different ways to call this, but basically if you look at something like that share purchase agreement or a merger agreement, or just legal in general, the cost or the price of producing each part of a word in a 50-page document can be incredibly expensive, right? And so I think those areas, like that is one of the tests that I would do for these things. The thing I would say, I would make sure you don't discourage yourself from is, this industry has never been, you know, touched by tech or this is not something that I know that tons about, etc.

• 41:50Speaker 01

  You can really figure out a lot about what people do by just talking to them and asking a bunch of questions, right? And it might not, you know, you might have to talk to a lot of people and that might not be exactly what you thought it was gonna be as a founder. You know, you might not just be sitting in, I don't know, like a construction firm for, I don't know, 20 hours a day, but it's insanely useful, right? And so whatever the area is that you're interested in, I would look at, does this have long-term value? In the sense of, you know, what is the price per token, things like that? And then actually spend time with it and you'll get a much better sense for it.

• 42:26Sarah Guo?

  Yeah, I have a lot of friends who are talented product and engineering folks and and business folks who want to start a company. And I think people generally start by like ideating in their underwear in their house.

• 42:40Speaker 01

  Yeah.

• 42:40Sarah Guo?

  And like almost like almost universally, some ideas happen that way, right? But almost universally, like it helps to see the problem in real life.

• 42:49Speaker 01

  It's massive.

• 42:50Sarah Guo?

  And I'm like, just go hang out with one of your friends or at any industry that you like have some fundamental interest in.

• 42:57Speaker 01

  Yeah.

• 42:58Sarah Guo?

  And just like, watch what people do. Because if you have a really general hammer that is getting better all the time, like now is the time to do that and you're going to be much more inspired by ideas of what is possible by like being faced with the problem.

• 43:12Speaker 01

  I think that's right, and I might say something that might upset some people, but spend some time out of Silicon Valley, too. I think it's important. Like, I think that, you know, there, and I don't necessarily mean, by the way, if you're building a tech company, like, I think it needs to be in San Francisco.

• 43:26Sarah Guo?

  Yes.

• 43:26Speaker 01

  To be clear.

• 43:27Sarah Guo?

  Harvey's in SF, in New York.

• 43:30Speaker 01

  And London.

• 43:30Sarah Guo?

  And London.

• 43:31Speaker 01

  But I do think that there are so many industries and so many different areas of work that a lot of people that have worked in tech just don't know anything about. And I would recommend that you spend time with those folks. And a lot of the times, you know, just your kind of small network of friends, they might not actually be a great representation of that industry, right? It might be a, you know, who knows, what version of it, but it might not be the best representation. And so make sure you're willing to explore things outside of your comfort zone. Like, that I push so strongly.

• 44:04Sarah Guo?

  Is there anything that has like really surprised you in the company in the last couple of months?

• 44:12Speaker 01

  Yeah. There are a considerable amount of people at the company that do not necessarily have management experience. And I put them in a management role and the improvement has been insane. And I think it has not just surprised me, it has surprised a bunch of our leadership team. It has surprised our investors. It has kind of surprised everyone. I'm really proud of that, like incredibly proud of that. And I think, you know, maybe part of it is my background as well of I don't have experience in this. And so I'm willing to take bets on people that do not have maybe like the perfect resume or the perfect experience. And we've seen it work out really well.

• 44:54Speaker 01

  And I guess maybe it's been, it's surprising because everyone said that's not going to happen. And so that's maybe why it's surprising.

• 45:02Sarah Guo?

  Like if you could pick like top two, three signals of like why this person should be a leader for you?

• 45:07Speaker 01

  Yeah. They, I mean, care is one of the most important things. Like there is a sense of ownership, whether that ownership shows itself in obsession, whether that ownership shows itself in doesn't make excuses and like says, I got this wrong. These are the things that I got wrong. I'm going to fix them. One of the best ways to tell this is there are a few folks that will come up to me and basically just not ask for positive feedback and just say, what did I get? Like, what do I need to improve? That is a massive signal because I think that something that happens as you get more senior in your career is you don't want to look like you made a mistake. Like a lot of what you're doing is kind of like covering and protecting your position. And it's really hard to learn that way and get better.

• 45:51Speaker 01

  And the reality is like you learn from success, you also learn from mistakes, I think a lot. Or you learn from actually like taking mindfulness with your mistakes. And so that's maybe one of the best signals ever is how do they take ownership? Is it an upset, whether that's a combination of obsessiveness plus like self-reflection? Plus, I just want to get better.

• 46:10Sarah Guo?

  Okay. Last question for you. If you look outside of your own ecosystem of legal and professional services, or maybe just, you know, one concentric circle, professional services is fair game. Biggest change you think happens this year?

• 46:25Speaker 01

  Um, I think we will end up seeing work completion done in very sophisticated manners, like in medicine and in coding, like a lot in coding, um, and a lot also in, you know, other areas too, that will kind of bring us back to the chat GPT moment, but it will be specialized. And so there will be a doctor that sees something or a researcher or something, and it will be like it was the first time they saw chat GPT, but they'll show it to everyone else and it'll just be like, I don't get what's going on here. Right? My point is, I think these systems are going to get to the point where they're doing things that are incredibly impressive, even though we're used to them now. And it will be kind of a new version of impressive, but it will be impressive to very verticalize

• 47:09Speaker 01

  and specialize people.

• 47:11Sarah Guo?

  I agree with that. If I think about the like implication for us or for founders, like one of the things that has just felt really impenetrable about industries that Silicon Valley folks don't spend a lot of time in.

• 47:24Speaker 01

  Yeah.

• 47:25Sarah Guo?

  Is they're like, oh, well, you know, the, the customer won't buy, or there's a bunch of regulation, for example, healthcare.

• 47:32Speaker 01

  Yep.

• 47:32Sarah Guo?

  Um, or, uh, uh, or like, we can't, it, it's not obvious how to cut the task in a way where the minimum viable quality is like.

• 47:41Speaker 01

  Is there or is enough of a value add?

• 47:44Sarah Guo?

  Yeah, where like the, like the, the ROI is there. And I think like, I think we're going to get an accelerating pace of people saying like, I can figure out how to shape the capability to like a task completion.

• 47:55Speaker 01

  Yep.

• 47:55Sarah Guo?

  That people want. And actually like these industries are far more open to that change than, than we thought. Right? At least that's been, that's been my experience where I'm like, oh, like as, maybe as we were saying earlier, like the ROI just weren't, wasn't there.

• 48:07Speaker 01

  Yeah.

• 48:07Sarah Guo?

  Lawyers or doctors or whatever else, they, um, they want it. Like you just have to figure out the shape.

• 48:13Speaker 01

  That I think is the biggest piece is I think people are confused and they don't think that they want it. And the reality is a lot of these folks do, like they don't want, when you talk about AI and it's like a first. Blast or whatever. It's like Skynet and all these things.

• 48:27Sarah Guo?

  Yeah.

• 48:27Speaker 01

  Yeah, no one wants it.

• 48:28Sarah Guo?

  Skynet replacing your job. Of course people don't want that.

• 48:30Speaker 01

  Yeah, of course people don't want that. But when folks can see and they actually use these tools, they want this. And it's because, and especially the folks that care tons about their profession.

• 48:39Sarah Guo?

  Yeah.

• 48:39Speaker 01

  Because a lot of these conservative, you know, professions haven't changed in a really long time. And so there's, you know, the spirit of them is still there, but there's all this kind of junk on top of that spirit, right? And, and this is true in medicine. This is true in law. This is true in all of these areas. And I think that you will find a bunch of, just like you said, people who are really willing to fight for you as a company because they do want that change. There just hasn't been enough of a capability for them to really actually put their, you know, their neck on the line to push it.

• 49:12Sarah Guo?

  Awesome. This has been great. Thanks, Winston.

• 49:14Speaker 01

  Yeah, thank you.

• 49:16Sarah Guo?

  Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.