Harvey AI如何改变法律行业：Winston Weinberg

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 five hundred. They've now raised more than five hundred million dollars 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.

Yeah, of course.

It has been a wild two and a half years for you, Gabe, and Harvey.

When you started the company in August of twenty twenty-two, or at least when I met you guys for the seed—

We started a little bit earlier, but about then, yeah.

What was the moment of inspiration?

1. The GPT 3 Legal Aha

Gabe and I had actually met a couple of years before, and I definitely didn't know anything about the startup world and didn't have a plan to do a startup. What happened was that he showed me GPT-3, which at the time was public, and I was incredibly surprised that no one was talking about GPT-3 and no one was using it in any way, shape, or form.

He showed me that, and I showed him my legal workflows. The aha moment was when we went on r/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.

We took about 100 landlord-tenant questions and came up with some chain-of-thought prompts. This was before anyone was talking about chain-of-thought or anything like that. We applied it to those landlord-tenant questions, and we gave it to 3 landlord-tenant attorneys. We 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? Is that ethical? Is it a good enough answer to send?”

Eighty-six out of 100 said yes. We cold-emailed the general counsel of OpenAI and sent him these results, and his response basically was, “Oh, I had no idea the models were this good at legal.”

Nice.

We met with the C-suite of OpenAI a couple of weeks after that.

The view was just that it was going to be good enough and that we should build a company around it. For what domains?

I think the reason we were so confident about this was that the models—even with GPT-3—could do a lot of tasks. You just had to really brute-force it. You had to brute-force the amount of context, tell it which steps to take, and so on.

The idea was that over time, this was just going to get better. Either the models themselves were going to get better, or we were going to get better at figuring out how to provide them with the correct context, how to improve them, how to evaluate the results, and so on. Even just by playing with it for a decent amount of time, you could get that sense.

You are obviously not focused just on property law now. How do you think about the mission or scope of Harvey today?

2. The Expand And Collapse Platform

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. If that sounds vague, or if it sounds like there aren't incredibly defined use cases for the small areas that we're building, that's on purpose.

The reality is, if you are using these tools and you don't think that you can take AI, apply it to an industry, and transform the entire industry, I don't think you're thinking ambitiously enough. It's really hard because these models can't just one-shot all of these really complex legal tasks, or tasks in other domains like tax and other professional services.

What you have to do is build a platform that is constantly expanding and constantly collapsing. By that, I mean you need to build specific features and perhaps agentic workflows that can do parts of a task. Then you need to combine them all together so the UI is simple and you don't have this tentacle monster of a platform.

When did you realize that was the product strategy for the company? I think it's a really elegant framing. First implied in it is that this has been true for you guys from the very beginning. I did think you were a little bit crazy when you described the level of sophistication of the end-to-end tasks that Harvey would be able to handle.

I still feel crazy.

But you appear to look less crazy when you're right.

Fair.

I remember a year and a half or 2 years ago, it was very distinct to me how much you guys believed in capability improvement. Where does that come from? How do you think about it internally in the company?

Where does it come from? From using the tools. I've been blown away by the number of people in the C-suite who haven't actually spent that much time using AI.

The big moment for me was the jump between GPT-3 and GPT-4. When we got access to GPT-4, I went into my room for, I think, 24 hours straight and tried to do every single thing that I couldn't do with GPT-3, or that I had to very much brute-force and 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. No one was talking about it.

Something else that was very weird was that I would show GPT-3 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'd just stop.

There was this large gap between logging into ChatGPT, or using GPT-4 from an API, and trying something a couple of times, versus sitting there and hammering on it until it works. If you do that enough, you get the intuition for where things are going and how much better they can get.

From a product standpoint, you can think about “expand and collapse” as 2 general themes. You need to build productivity tools, meaning things that are useful for the highest number of seats. You also need to build streamlined vertical workflows from start to finish.

You can take those streamlined vertical workflows and chain them together to do increasingly powerful things. That's how we think about it internally at the company. There are a bunch of bells and whistles and things to add in terms of sharing, collaboration, and so on, but you can also build specific features that do something from start to finish.

For example, I upload all of a target company's documents, and it will tell me, in 72 countries, where I need to file for antitrust, what I need to file, and what other information I need. You can take pieces from that and add them to another part of a project.

How do you organize that effort at Harvey? I think you have a very special instinct and commitment to going on the AI bender, I suppose, to develop intuition for what the models are capable of doing with this expansion, workflows, manipulation, and assembly process.

It's going to be a long bender.

A long, multiyear bender, but one day at a time. I imagine it's really hard to find all of that in a single person who understands the research, the engineering, the domain, the data available, and how to interact with users. How do you do that across teams?

We're starting to get better at it. The thing we're starting to land on is identifying these systems as what we're calling AI patterns. These are the 30 or 50 things that we need to build that will be integrated into all of these different pieces of the product.

For example, if you build an AI system that's really good at case law research, that can go into a motion for summary judgment. It can go into a billion different types of litigation use cases. It doesn't do that use case from start to finish, but you can add that on top.

We've been building teams that work on these patterns, and other teams that implement them across the entire platform. That has been working really well. It's definitely a work in progress.

The other interesting piece is that we have a lot of lawyers on our staff. We're going to do this in the other verticals we're going into as well.

The domain experts are very helpful for 2 main reasons. One is as design partners, so they’re very good at saying, “Actually, 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,” et cetera. And then the second piece, which is really hard, is evaluation.

Most benchmarks are completely useless for us. We’ll get a model—someone will give us early access to a model—and they’ll say, “It’s way better on all of these benchmarks.” And we’ll respond, “What? It actually isn’t.” It’s not as useful for us as a different checkpoint or something like that.

The reality is you have to hire very good lawyers who can actually evaluate these systems. The same is true within tax and these other areas, and they can’t be too junior. Because if they were too junior and they were able to evaluate it, they would be senior. I think the intertwining of the domain experts into the actual product sequence and development, and then into evaluation at the end state, is very hard. It’s something we’re constantly working on.

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

Filing a Form S-4 is something that is exciting to me. The reason I like that process is that it’s a combination of external data, internal data, and a million steps. I think of workflows as basically a bunch of agentic systems that need to combine together.

If you think about knowledge work, professional services, legal—honestly, any swath of that—what you are doing is manipulating things based on your internal context and external context, whether that’s external data or data from whoever your customer or client is, et cetera. And then there’s process: This is how you do an LBO. This is how you do side-letter compliance. And then there’s another step, which is: This is how you do side-letter compliance for this particular private equity firm. This is market for this particular clause.

The more complicated the workflow is, the more you have to combine all of those different elements together.

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

Yeah.

Right? It works or it doesn’t.

Yep.

The legal profession, especially more senior, more prestigious lawyers—the billable-hour rate is very high. And the expectation of quality from, let’s say, an associate or a model is very high. How did you go about building that trust? Because you didn’t start at the capabilities you have today.

3. Making AI Easy To Check

Yeah. I’ll start from one high-level distinction between 2 pieces. If you go back to productivity versus specific specialized output, 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.

What you want to do in that state is just show your work. 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?”

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

Yes.

Yeah.

Exactly. A lot of how these law firms work—and the Big Four as well—is that the junior produces some sort of output, the mid-level associate reviews that, then the senior-level associate reviews that, then the partner reviews that, and then it goes to the client.

You do get a structured pyramid of hierarchical review. Now let’s go to the specialized system. The specialized system, at the end of the day, you’re actually trying to produce that output from start to finish. The minimum viable quality on that is much higher, but it’s also easier to get higher because you’re trying to build systems that just produce the same output and do the same task from start to finish, like a specialist.

It’s just much more scoped.

It’s much more scoped, and evaluation is much easier. 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.

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

Yeah.

There are still conservative customers, right?

Massively.

One of the things that was surprising to me in 2023 and early 2024 was the set of people that you decided to work with first, and that were willing to work with you. A&O Sherman, PwC—high-quality brands that were generally larger. There’s been an expectation in technology investing for maybe a decade that if you start with the mid-market, you start with your friends and startups.

Yeah.

It’s just easier. The requirements are lower, and they’re willing to take risk. Why do the opposite here?

4. Winning Industry Trust

I think that, going back to what I said in the beginning, the ambition should be: Take an industry, apply AI to it. If you’re 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 make sense for what they care about.

I’ll give you an example of this, or just a reference point. A&O Sherman is almost 100 years old, and they became famous advising on the abdication of, I think it was, King Edward VII from the throne.

Okay. Good trivia.

My point is, these are incredibly old, very prestigious, and have a long line of history.

Yeah.

In order to work with them, you actually have to partner with them and say, “These are the different workflows that we’re going to start building with you,” and use them as design partners. You also need to make sure that you’re building a brand around that.

Not only is it actually working with those higher-tier firms, but you also want to work with the data providers that are really important. Lexis is involved in this round, and we’re really partnering with them to also bring a lot of the goodwill, trust, and really good products that they have brought to the industry, and combine that with our AI as well.

If you’re able to show that you’re working with the industry and get the trust of the folks who are seen as the highest authorities in those industries, it is much easier to work with the rest of the industry, and we thought that was very important.

We thought for a while that maybe we would do a self-serve model, and we would 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.

Yeah. I think there’s perhaps an opportunity in AI—we’ll see if this is true. You’re already continually collapsing and trying to simplify the experience of using Harvey, right? And this has been impossible in software to date.

One of the reasons people would start with an SMB or the mid-market is they’d say, “Once it becomes an enterprise product, we will never have the simplicity required to serve other customers.”

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

Yeah.

Yeah.

Yeah, exactly. But I think, because of a bunch of different factors, I’d like to see if you agree, but maybe building UX ends up being cheaper in the future than in the past.

Yes.

And you’re also building a muscle as a company that other companies are not encouraged to have in terms of the constant simplification.

That’s right.

Yeah.

The thing that the models are incredible at is orchestration. If you’re thinking about how to constantly collapse the UI, I’ll give you an example.

You can build a bunch of different workflows that deal with SPAs, or share purchase agreements. It extracts the reps and warranties from the share purchase agreement. It takes them and turns them into a complex summary. It does the closing conditions, all of these different things.

You can build those separately because 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 7 things to it?” Right? And so you can collapse that back down into a UI that is actually very simple. If you actually think about how the UI for professional services is just email, that’s it.

Mm.

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, right?

That’s kind of what we have in business software.

Yeah. That’s exactly what we have. Yeah, and it’s awful, and it takes forever to learn how to use it.

I know how to use email.

Yeah. It’s still hard.

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

Yeah.

—for them?

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 I am more bullish on model capabilities, and just capabilities in general on the application side, and everything.

So you still think you’re crazy, but you’re going to go in that direction.

Yes, I still think I’m crazy. But I do think that one thing that our customers are realizing—and I think a lot of people who haven’t worked in legal or professional services are realizing—is that these industries are very messy, in the sense that all of this is not a simple, “Oh, I know how to draft an SPA; I’m a lawyer; 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 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?

Mm-hmm.

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 go to a really good school, you study a bunch of things that have nothing to do with practicing law.

Okay.

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 you spend a very long time doing repetitive tasks, right? So whether that’s reviewing documents in discovery or reviewing documents in a data room, et cetera, you end up not being able to do the strategic-level things until 10 years into your career.

Mm-hmm.

You know, if you’re lucky, 5.

You did complain about that grind at work. You were like, “I should not be doing this.”

Yeah.

There was a joke that, um, I, we, I was presenting to the firm I used to work at, that was O'Melveny & 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 out of doc review than trying to automate doc review,” which I, I don't know if that's true. Um, but my point is there are all these lower-end tasks that you end up having to do just because the industry's 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 w- people really wanna do-

Mm-hmm.

... earlier on in 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 is 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'll evolve.

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 a star lawyer 5 years from now?

Yeah.

10 years from now?

I would have given the same advice before doing this startup, actually. Now, I would say 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—how much you can navigate and figure out what is actually best for the client.

So I would try to get as much hands-on experience as possible, even if that’s at something that isn’t as prestigious. It isn’t the bet-the-company litigation, because you’re probably not going to get hands-on experience, or the massive $100 billion merger, et cetera. Get as much hands-on experience as possible, because that is the most important skill: figuring that out. And now I would just say that is the main skill that’s going to matter over time, but even 3 or 4 years ago, I think I would have given the same advice.

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

5. The Law Firm Business Model

I think there are a bunch of ways this can evolve. I think the most likely one is that the billable hour is not going to just completely disappear.

I think what is going to happen is that a lot of these tasks that AI can automate with a lawyer in the loop will end up being a fixed-fee model, and I think the high-level advisory work on top will still be billable-hour work and may actually be more expensive. There’s an argument that the specialist at a law firm who has seen all of these different mergers in the pharmaceutical industry—their hourly rates should not actually be 3X those of the junior associate in the data room, maybe 10X. I don’t know. My point is that 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. We’re seeing law firms willing to do things like take a bunch of their domain expertise and turn it into software, and we are starting to do that. We are starting to work with firms and basically take 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.

How does that happen? Who drives that at one of your partner firms?

Yeah.

And what’s the incentive?

It is massively based on the law firm. I think that you will find a couple of partners who think that this is the future, and they will rally the entire firm behind them. That’s what we have seen, and it’s really started snowballing in the past 3 to 6 months.

And the way that it works really is a combination of them providing the domain expertise and us providing the translation of the domain expertise and then the tech, right? The incentive for them is that there is so much legal work that law firms actually do at a loss, and they do it at a loss so that—

It doesn’t feel like that.

I know, I know. But there actually is. There’s a lot of legal work that they do at a loss in order to get the big deal.

Mm-hmm.

So a lot of law firms will do work for private equity, and they will do that work at a massive discount or even at a loss in order to get the LBO—

Mm-hmm.

—or the M&A, right? And so I think these systems are a way for law firms to compete in these spaces. It’s also another way for law firms that maybe aren’t as large and don’t have as many of those resources to compete in these areas, because they have software margins for this work, and then they can get the really intense strategic deal afterward.

That feels analogous to the client service work—

Yeah.

—in an investment bank—

Yep.

—in order to work on large transactions that really matter.

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

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

Massively, in a good way.

6. Reasoning Models Raise The Ceiling

The best way to think about this is if you are building a system where you are trying to break down every single problem into a subproblem because the models can’t quite do it, that just unlocks different pieces of it. So let me give you an example of this. If we go back to that antitrust example, the first step is that we’re trying to take all of the target financials and your acquirer’s financials and say, in all these different countries, this is what you need to file. The next step would be: can you help it actually do all of the filings?

We were having trouble figuring out how to do that. Now, with reasoning models, you can start unlocking those steps. The best way to think about this is that we are constantly building out all of the steps that we can and being on the cutting edge. Then, when a model improves, that just pushes our ability to go out to the next cutting edge even more.

The other thing, too, is that costs going down is incredible for us. We’re not optimizing for cost at all times right now; we’re optimizing for quality. If the prices go down, that means we can increase our quality across every single user base much faster.

How do you think about the domains that are not legal?

Yeah.

You have announced research and work in tax and audit now. What makes this the right time to do that?

Yeah. The way that we think about it is there are so many areas where legal is the tip of the spear.

Yeah.

You can actually just parallelize the same things that you’re building and tweak them, and they work really well in other industries. An example of this is that you do tax diligence, then commercial diligence, and then financial diligence over all of these documents. Tax and legal diligence have a lot of similarities in terms of the high-level problem that you are trying to solve. It is basically applying all of these rules to these documents.

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, improve on that, and apply it to tax, deals, or these other areas.

I’m thinking about how unique it feels in terms of just the product effort. At Harvey, I think it’s very first of its kind, and I feel very lucky to be involved. When you think about the team that goes and works on this, does your new engineering hire understand—

Yeah.

—tax diligence after a while? How do you think about what makes somebody the right next person to add to Harvey?

We’re working on it.

You are hiring?

Yes.

Okay.

Yeah, tons. We’re working on the context sharing.

Yeah.

I think that is something that’s really important. One thing that was really nice, and that I actually think we did get right, is that the respect is incredibly high.

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 about the structure of these really complex take-private deals. You could just see the engineers saying, “Oh, wow. The work that all of these folks are doing is really impressive.”

Mm.

I think we’ve done well there. On the context-sharing side, it is a difficult problem. A lot of the folks building the product haven’t actually used it in the sense of their past careers. They haven’t done tax diligence or whatever it is.

Sure.

And so that’s—

Do you guys do a weekly Suits viewing or something? Is that how this works?

Yeah, and then you’re done. 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. This is one of the biggest things: there are so many things that we are doing that are new.

I have found that, over time, hiring folks who might not have a bunch of experience in this particular thing doesn’t matter. It is so important that you hire people who are really smart, hungry, care about what you’re doing, and are willing to be very decisive in what they decide to ship. If it doesn’t quite work, they need to 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. I think this is probably always the case, but I think it’s more the case now because everything changes every 6 months, and you have to be able to adapt to that change. If you can’t, you’re not going to make it, regardless of how much experience you have.

Yeah, I think that’s true even in the experience of the founders that we are working with—

Yeah.

—in terms of your ability to predict or follow somebody else’s pattern for more than 2 months at a time. It’s very bad.

Yes.

Some things are monotonic, like capability improvement.

Totally. Yeah.

But still, figuring out, “I’m going to pick this point in the future and put engineering resources on solving a particular task because I think it’ll be possible,” is just very, very different from building deterministic SaaS workflows from a few years ago.

I think you have to pay more attention. One of the biggest things is that I have found the people who have been very successful in this space so far are very obsessed. It’s not just being obsessed with your problem; you also have to pay attention to what’s happening with all the model providers. You have to pay attention to all of these different facets, or I think you’re going to miss out on something.

You have now been a founder for 2.5 years. I’m sure that’s a learning experience. Do you feel like you’ve got to focus on agency in hiring and in the business? What did you get wrong?

7. The Founder Operating System

Yeah. I don’t think we have 12 hours to do this. A lot of things. If I had to say there’s one thing that I got wrong over everything else, it is figuring out when to scale yourself.

I have a certain tendency for how I work, and there are some things I want to keep. One of the lessons that I definitely want to keep is that I do think you should do every single role for a certain amount of time before you hire for it. Almost all of my mis-hires were because I did not understand what that role was. Maybe it’s a lack of my experience—I don’t know—but there is a hands-on piece.

Having said that, you can’t scale a company by wanting to be hands-on in everything at all times. I think that I didn’t spend enough time transitioning from the beginning of last year, when we were 40 people and everyone knew what was going on because they were working with me directly.

You touched every single customer all the time.

Yeah, basically.

Yeah.

And all pieces of the product, et cetera. I took a long time to—I haven’t solved it at all. I took a long time to even think about solving it. 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’re in an environment where you have to be very decisive and make very quick decisions. The easiest way to make quick decisions is to pay attention to everything. You’re monitoring everything at all times.

Your gut is normally actually pretty right because it’s not your gut. You have taken all of these different stimuli and you’re just absorbing them at all times. Balancing how important that piece is with actually learning how to share context with the team, how to make sure you’re up-leveling everyone, and how to transition yourself as a founder has been hard for me. I think it is continuously hard.

I’m sure if you ask anybody, they’ll say that it’s going okay, but it still needs work.

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.”

Yeah.

And I lied.

Okay.

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

Yeah, that’s basically it.

Yeah. Where does the drive come from?

As simple as possible, this is the most fun thing ever. I don’t think there was anything in the 27 years leading up to when I started this company that was even close to as much fun as this is.

You get so much energy from things moving so quickly and 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 you can do that faster than you used to be able to, and it is addicting. It is incredibly addicting.

I actually think that it's less about where you get the drive or where you get the inspiration. Most people that I've met who have been successful in this space just have it. They naturally love the moment, or the very compressed timeline, in which you can have a very large impact.

It's the most fun you've ever had. Some people just have that desire to know what's happening, have the leverage, and see that really fast impact cycle. Is that the expectation you have of everybody at Harvey culturally?

Good question. I think the expectation that I have for everyone at Harvey—we have a line that we've talked about a lot, and it actually comes from Kobe Bryant. It's, “The job's—

This is good.

—not finished.”

Okay.

Basically, he was in an interview, and I forget which game it was, but they were up—I think it was 3–0 or 2–0 or something like that—and a reporter asked him, “How do you feel right now? How are you feeling?” And he's like, “I feel good. It's not done.” And the reporter said, “Shouldn't you be excited? You're up 3–0.” And he's like, “The job's not finished. We're not done. We haven't won. The thing's not over.”

I think what I'm trying to convey when I say that—and hopefully what Kobe was trying to convey when he said that—is that there are certain moments when you need to give it your all. If you give it your all in this massively compressed timeline, it will serve you for a very long time afterward.

That's what I expect of everyone that we hire at Harvey. “Look, you need to recognize and trust us.” So it's maybe a combination of either recognizing or trusting—

Mm-hmm.

—that we are on that timeline, that we are on an incredibly compressed timeline for, honestly, all of humanity. I'm serious.

Yeah, it's a very special moment.

It's a very special moment, and there are going to be insane expectations. The company isn't always going to be there. I'm not going to be there at all times to tap you on the shoulder and say, “Hey, remember those expectations. Remember this is true.” You have to be able to do that to yourself, and you have to have that mentality.

I think what that looks like is some combination of recognition and a willingness to keep up the intensity and keep raising the bar. That is an expectation of anyone who joins.

Yeah. That resonates very strongly for me because I think in a very parallel journey, this opportunity's going to happen once. For me and my team, and even Mike, who joined us a little while ago, it's like, “How can I not do it right now, for one?”

Yeah.

There are many different options, but I can't sit this one out. If we're going to play, we're going to fully play.

Yeah.

Yeah.

That is maybe a really good way to look at it: “I can't sit this one out.” That is an amazing signal for somebody.

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

Yeah.

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

It's another work in progress.

I am also not a research scientist.

Yeah, there we go.

So I'm—

Yeah.

It's an important question, actually.

My co-founder is. I think the most effective way to do this is just to 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 it: I think the value you get from asking people that you really respect very specific questions is overvalued.

Hmm.

I think spending time with people who are incredible at whatever they do is undervalued, and I think it's because the latter has a nebulous effect. You're like, “I don't really know what I'm getting out of this,” right? Whereas the former is, “I have a question. They gave me an answer.”

But the reality is, they have a basis point of the context that you have, if that. What has been really helpful for me is spending time with a lot of people that I really respect in this industry. You start to learn the intuition that they have for things. You start to absorb it, and all of a sudden it starts becoming your intuition.

I think that's the only way that I've gotten to this point, and I am going to keep doing it as much as possible.

Actually, what you describe makes sense to me.

Yeah.

I'm glad Andre's hanging out in the office, telling me stuff.

Yeah, exactly.

But also, I think the outcomes make me feel better. I'm like, “Well, companies look like they're working.”

Correct.

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

Yeah. One thing we had in the beginning of last year was a lot of 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.

We had massive power users, and then we had some other problems. About 6–7 months into the year, it had done a complete 180 and it was working really well.

We took a bunch of bets on product features that were not only bets we made, but also bets on the models getting better. Things like follow-up questions, things like entering a user profile and having it tell you, “Here are all the different things that you should do based off of your practice area,” and things like that. It worked.

Speaking of intuition, you start to absorb that intuition, you start making bets on your own, and then you see it work. All of a sudden, you get a little bit more confident. Hopefully not too confident, but a little bit more.

Mm-hmm. So you're saying you and the people at Harvey feel the AGI just enough.

Just enough. Not too much.

Not too much.

Yeah.

Yeah, it's not clear what too much is, actually.

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

You guys have been on the right side of a raging debate in the investing and tech community for a long time, which is: Is there value in the application layer?

Yeah.

It's pretty clear there is now. What advice would you have for the many founders, tech business people, and others who are going to listen to this about a good application to build?

Yeah. One thing that I look at is this: You can look at it by industry, and you can look at it by task. How expensive is the token?

I've come up with different ways to call this, but basically, if you look at something like a 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. I think those areas—that is one of the tests that I would use for these things.

The thing I would make sure you don't discourage yourself from is, “This industry has never been touched by tech,” or, “This is not something that I know tons about,” et cetera. You can really figure out a lot about what people do by just talking to them and asking a bunch of questions.

You might have to talk to a lot of people, and that might not be exactly what you thought it was going to be as a founder. You might not just be sitting in a construction firm for 20 hours a day, but it's insanely useful.

Whatever the area is that you're interested in, I would look at whether it has long-term value, in the sense of what the price per token is and things like that. Then actually spend time with it, and you'll get a much better sense for it.

Yeah. I have a lot of friends who are talented product and engineering folks, as well as business folks, who want to start a company.

And I think people generally start by ideating in their underwear in their house.

Yeah.

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

It's massive.

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

Yeah.

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

I think that's right, and I might say something that might upset some people: spend some time outside of Silicon Valley, too. I think it's important. I think that there—And I don't necessarily mean—By the way, if you're building a tech company, I think it needs to be in San Francisco—

Yes.

—to be clear.

Harvey's in SF, in New York—

And London, yeah.

—and London.

But I do think that there are so many industries and so many different areas of work that a lot of people who have worked in tech just don't know anything about.

Mm-hmm.

And I would recommend that you spend time with those folks. A lot of the time, your small network of friends might not actually be a great representation of that industry, right? It might be a version of it, but it might not be the best representation. So make sure you're willing to explore things outside of your comfort zone. That's something I push so strongly.

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

Yeah. There are a considerable number of people at the company who do not necessarily have management experience, and I put them in management roles, and the improvement has been insane. 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—incredibly proud of that. Maybe part of it is my background as well: I don't have experience in this, and so I'm willing to take bets on people who do not have maybe the perfect résumé or the perfect experience. We've seen it work out really well. I guess maybe it's surprising because everyone said that's not going to happen, and so that's maybe why it's surprising.

If you could pick the top 2 or 3 signals of why this person should be a leader for you—

Yeah. I mean, care is one of the most important things. There is a sense of ownership, whether that ownership shows itself in obsession, or whether that ownership shows itself in not making excuses and saying, “I got this wrong.”

Mm-hmm.

“These are the things that I got wrong. I'm going to fix them.” One of the best ways to tell is that there are a few folks who will come up to me and basically not ask for positive feedback and just say, “What do I need to improve?” That is a massive signal, because I think something that happens as you get more senior in your career is that you don't want to look like you made a mistake. A lot of what you're doing is covering and protecting your position, and it's really hard to learn that way and get better.

The reality is, you learn from success. You also learn from mistakes, I think, a lot, or from actually being mindful with your mistakes. Maybe one of the best signals ever is how they take ownership. Is it a combination of obsessiveness, self-reflection, and “I just want to get better”?

Okay. Last question for you. If you look outside of your own ecosystem of legal and professional services, or maybe just one concentric circle out—professional services is fair game—what's the biggest change you think happens this year?

I think we will end up seeing work getting completed in very sophisticated ways—in medicine and coding, especially coding—and in a lot of other areas, too. That will kind of bring us back to the ChatGPT moment, but it will be specialized.

There will be a doctor or a researcher who sees something, and it will be like it was the first time they saw ChatGPT, 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'll be kind of a new version of impressive. But it will be impressive to very verticalized and specialized people.

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

Yeah.

—is they're like, “Oh, well, the customer won't buy,” or there's a bunch of regulation. For example, healthcare.

Yep.

Or we can't—it's not obvious how to cut the task in a way where the minimum viable quality is—

Is there—

—something that can be met?

—or is it enough of a value add?

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

Yep.

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

Yeah.

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

That, I think, is the biggest piece: I think people are confused, and they don't think that they want it. When you talk about AI, and it's like a first blast or whatever, it's like Skynet and all these things—

Yeah.

—and yeah, no one wants that.

Skynet replacing your job.

So it's only folks that—

Of course people don't want that.

—yeah, of course people don't want that.

Yeah, yeah.

But when folks can see and actually use these tools, they want this.

Mm-hmm.

And especially the folks who care a ton about their profession.

Yeah.

Because a lot of these conservative professions haven't changed in a really long time. The spirit of them is still there, but there's all this kind of junk on top of that spirit, right? And this is true in medicine. This is true in law. This is true in all of these areas.

I think we'll 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 actually put their neck on the line to push it.

Awesome. This has been great. Thanks, Winston.

Yeah. Thank you.