How Harvey is reinventing legal work with AI

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When you picture a good lawyer, the kind of lawyer who you'd want on your team, this guy might come to mind.

Guys like you always think other people get lucky. I don't get lucky. I make my own luck.

That's Harvey Specter, the charismatic, sharp-witted lawyer with a heart of gold from the TV show Suits. So when Gabe Pereyra and his partner were coming up with a name for their new AI-powered legal services company, Harvey seemed like a good fit. So you guys named the company after a character from the TV show Suits, which I don't watch, but is that true?

yeah, I think loosely based on that. And then I think we also joke that it sounds a lot like Harvard, and I think kind of that name association.

But for Gabe, the company name is about a lot more than its namesakes. It's about the impact.

I think what was most important for us is, I think even then we thought that this technology would be personified. Like with our product now, a lot of people say, Hey, did you ask Harvey? And I think that is like, as these models get more and more powerful and more human-like, I think you're seeing more of this with companies kind of naming themselves that way.

In just three years since their launch, Harvey is now valued at $5 billion and boasts a client roster of some of the biggest law firms out there. And for good reason, they're totally disrupting the legal landscape with an AI platform that supercharges the work lawyers do. On this episode, we're talking to Gabe about how Harvey supports lawyers using AI, building a scalable AI company, and the shifting legal playbook. I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

Hi Gabe, welcome to Pioneers of AI. I'm so excited for this conversation.

Thanks so much for having me.

So before Harvey, you had a career as a researcher at DeepMind and Meta, and then you kind of had the opportunity to co-found Harvey. What was that aha moment?

Yeah, so when I was working at Meta on their large language model team, this was around GPT two, GPT three, you kind of saw these models getting better. And in the past ten years before that, I had always been thinking about what is the right application of AI to the world and thinking about startup ideas. And at the time, my roommate and now co-founder Winston was working at a large law firm, and he showed me his legal tech, his legal workflows.

I showed him these models, and as we started playing with it, the more we dug into it, it just seemed like this was one of the perfect applications of of large language models.

You know, what's really interesting about this is sometimes founders start a company having a personal connection to the problem the company's solving. In your case, you have a background in technology, and then you saw this like massive business opportunity in a field that you weren't really an expert in. I mean, Winston is a is is the kind of the domain expert. What was that like?

Yeah, I I think I had always been very industry agnostic. When I was doing AI research, I think when I was doing research, you always had to be curious about how do humans solve problems in a bunch of different domains. I had done startups in a bunch of different domains. And so for me, when I was thinking about what type of company to start, it was much more important of, is this the right industry? Will this have a large societal impact?

Then am I an expert in this specific domain? But I think to your point, having someone like Winston, who is an expert on the legal side and really understood kind of how law firms work in the legal industry, I don't think you can do something like Harvey without, like, a legal founder and an AI founder. And I think that combination has been super important.

I agree when I invest in early stage AI startups, but I typically look for that combo, right? You want the AI expertise, but you also want a fair amount of domain expertise if it's a vertical AI application. So I do think that's a winning strategy. We're gonna dig into what Harvey does in a second. But before that, I I do think there's still a lot of skepticism about using AI in the legal field. And in particular, as I was prepping for this interview, I kept thinking back to the story from 2023 where there was this lawyer and he cited a bunch of court cases, but of course they were all basically AI hallucinating.

What what's your view on that?

Yeah, I th- I think this is still a big challenge. I think a big part of why we partnered with the largest law firms is they are actually some of the best companies in the world at avoiding not hallucinations, but mistakes. Like, if you think of what these large law firms do a great job of is they have these super talented associates that don't have partner level expertise, but they build these hierarchies of review where associates will do research.

They'll draft parts of contract, they'll get reviewed by a more senior associate, another more senior associate, a junior partner, a senior partner. And so when we sold to these law firms, Harvey wasn't being used as, hey, go write this memo and file it directly. It was being used as help this associate do their work better, and then that still gets reviewed. And so I think that was actually a really counterintuitive thing where we saw this very fast adoption because it fit nicely in their workflows, while avoiding, I think, a lot of the like, maybe from the outside problems that you would say, oh, this doesn't cite cases perfectly.

And then now, as we've gotten bigger, we've started partnering with Lexis, Wolters Kluwer, kind of data providers, working with these law firms to kind of improve these systems to avoid those challenges. But yeah, I think the the industry itself is very good at that, and so working with them has been super important.

So let's talk about Harvey. What exactly do you do? And and maybe personify Harvey AI, like, what what kind of lawyer is Harvey?

So I would say Harvey started out as more of a transactional lawyer. So law firms are typically split into transactional and litigation departments. And when we started, a lot of the work you do in transactional is I have a lot of contracts, I need to analyze them, I need to go look at previous transactions, SEC filings. And so what we wanted to build was an AI associate, right? Kind of like a co-pilot that if you're a busy associate, you could delegate these tasks.

And what we've been building in the past couple years is how do you build a single interface, kind of like an IDE that doesn't really exist for lawyers, where they can do all their work and collaborate with this associate. That was kind of where it started. Now, what we're starting to increasingly do is how do we help teams of lawyers complete entire matters? So if you're working on an entire transaction, an entire litigation, how do we help that team be more effective?

And then how do you help the law firm be more effective? So law firms are managing tens of thousands of these matters at a time. How do you organize them? How do you query them effectively? And then I think the most interesting is how do you work with your clients? And so as we sell Harvey to in-house teams, the immediate thing they request is, hey, we know our law firm is also using Harvey. Can we share data?

Can we share workflows? Can we collaborate on the legal work we normally do together? And increasingly, can we do this with other professional service providers as well?

That is so fascinating. So, I mean, the legal field is huge, right? There's corporate law, there's immigration law, there's family law. Are there specific domains of law that you're focused on, or is it general?

I would say right now our primary focus is kind of the largest law firms in the world, uh, and the largest corporations. And so what we don't do is kind of consumer work because I think there's regulatory issues. And like the type of legal work that I think most people are familiar with, which review my lease or I need this, you know, document drafted for my personal use. We don't focus on that right now. Much more of the work we're focused on is, I'm have a large antitrust litigation, or I want to acquire this company.

Anything that you're doing if you're kind of a massive company. And that's where we kind of see the complexity where I think right now the off-the-shelf models are quite good at upload a lease and, you know, tell me, hey, is this like a standard lease? But what they're not good at is, you know, I'm Adobe and I want to try to acquire Figma. Can you draft me, like, the merger agreement? And can you do all the due diligence?

And so that's where we see a lot of the complexity, where you need these like vertical models and products to be built.

So maybe it would be cool to bring this to life. Like, let's imagine, you know, I'm I'm a lawyer at one of these big firms, and yes, we're about to acquire a super awesome AI startup. What services can Harvey offer, right? Like, are you doing doc review? Are you doing drafting? Are you doing research? Is it all of it?

Yeah, and so I think it's all of it. And I think it's useful to frame how do these lawyers work before things like Harvey? And it's basically just email and then a set of like legal tech tools. So if you're doing research, you're using something like Lexis or Westlaw. If you're doing an acquisition, you have a data room, which is kind of just a drive with a bunch of documents. And then maybe you have some tools that help you analyze those.

But for the most part, as an associate, you're getting an email from a partner that says, Hey, I think we did a similar transaction. Can you go in our document management system, find that? Can you check these terms, see if they're similar? And so I think what's so hard about legal is the work is so text-based that it doesn't fall into these very nice buckets, right? Like research bleeds into drafting, bleeds into all these things. And so a lot of how we've thought about building the product is you want to build it to match kind of the existing product surfaces that they're familiar with.

And then how do you merge that with kind of the email experience, which is why I think chat and that type of experience is so successful for lawyers, cause they're incredibly good at working with language and prompting and all these things. And so we've seen that part work really well.

I imagine a lot of these law firms, like they're the equivalent of their IP or intellectual property, is actually in the history of all the cases that they've done before, right? Like if they've done a hundred acquisitions before, it's all the data rooms and all the documents from that. How do you incorporate that into a custom AI?

I think the most valuable data for the most part is still in the partners' heads and actually in the law firm's emails. And so it's like a lot of the final work product that these law firms file is public, right? So if you do a public merger, if you do a litigation, those final documents get filed publicly. And so those, to some extent, are in the foundation models. The thing that isn't that I think now is the very valuable data is like the reasoning traces, like why did you draft that motion that way?

Why did you draft that transaction that way? And then I think the very big challenge is obviously the naive solution would be go take all their emails and all of their documents and just fine-tune some model with them with with all this data. And obviously the reason you can't do that is like a lot of this data is client data, right? If I'm working on an internal investigation for Walmart, a lot of that data is derived from the emails of Walmart, which I can't train on.

And so a lot of the like research and technical problem we're solving is how do you go to a law firm and take all of their client matters, all of their internal know-how and help them build some model that respects, you know, confidentiality, privilege, the ethical walls. Cause a lot of these law firms are working for competitors and that data can't like touch. And so I think there's a bunch of legal specific and then also just general privacy specific of like, how do you train these models with very sensitive data?

In a minute, we go behind the scenes and dig into the AI models powering Harvey. The key customization. Stay with us. The companies winning right now aren't just building great AI. They're building the infrastructure to actually run a business on top of it, which is where Stripe comes in. Stripe is built for whatever comes next, with planetary scale financial infrastructure to help you power any business model. As a matter of fact, 86% of the Forbes AI fifty run on Stripe, along with industry leaders like Salesforce and Pepsi.

Last year, Stripe users processed $1.9 1 trillion combined. See how Stripe can power what's next for your business at stripe dot com. Harvey isn't building foundation models from scratch, right? Like you're using existing foundation models, but it it sounds like you're building an additional layer on top that is customized based on every law firm's private data. Is that correct?

Yeah, I I would think of like, we don't pre-train models, and so we will use, you know, pre-trained models from the foundation or cloud providers. We do post-train and build kind of all the RAG and agent infrastructure to make our general purpose product better at legal. But where we see the really exciting part is how do we help every law firm build their own custom model and custom workflows with all their lo- uh, with all their data.

Cause I think the biggest question we get from law firms now is, you know, everyone is buying Harvey, how do we differentiate? And I think the same way, if you look back, you know, everyone bought Lexis, everyone bought Westlaw. You don't differentiate by having a case laws subscription, you differentiate by having the best litigation partners and the best litigation associates. And I think by analogy, the way these law firms will differentiate is how well can they build custom solutions, custom models, innovate on kind of how they deliver services and even price and their business model and things like that.

Are there any applications that you have a hard line against, either because the technology isn't there yet or there are just too many risks?

I think probably where we draw the line is is we don't give legal advice, for example. So, like, we think of this as a tool for lawyers. And actually, when we were first starting Harvey, uh, something we did was we went on r slash legal advice, which is like a Reddit subcommunity where people ask legal questions. And we just downloaded a bunch of those and had 3.5 and four answer these questions. And then we went and just gave those answers to lawyers and said, Would you give this to one of your clients without editing?

And it was something like eighty, 85% said yes for a lot of these answers. And so I think the opportunity there was obviously, like, quite big of, like, democratizing access to legal services. And so I think that's something we're interested in. But in terms of, like, core business right now, it's very much like, how do you augment lawyers? But I think there are some things we're starting to think about, and we're working with some court systems largely outside of the US.

Of how can we provide this to pro se litigants or people who can't afford lawyers to give them advice on here's how you navigate our court system? Or in these small claims cases, like, can we help with this? But I think that that area is is challenging and you need to be very careful.

It's fascinating. That's a kind of direct-to-consumer model, right? Where if I if I actually, I'll give an example. My daughter is twenty-two and she just signed an agreement with her first job ever. And she uploaded the contract to ChatGPT, and she was like, Are there any red flags here? And I was like, Well, let's also get proper legal advice here. And so we retained kind of, you know, just legal counsel. And it was interesting, she actually iterated on the contract with ChatGPT.

And I mean, I think on the one hand, this is really cool because it does democratize access to legal counsel, especially for people who can't afford it, right? Legal counsel is, for the most part, pretty expensive. But it's also a little scary, right? Cause you're delegating these important decisions to an AI.

No, and I and I think this is why, rightfully so, the legal industry is regulated and you need a bar to give legal advice. And I think we're starting to think about, and I think the industry needs to think about. It's like there will be some use cases where you wanna do this, there'll be some cases where you don't. Um, but to your point, the challenge is like everyone has ChatGPT, and so the boundaries get very blurry.

But for corporate, I think this ends up being kind of fully separate, which is nice.

You know, my thesis around AI is is what I call human-centric AI, which is basically AI that augments and amplifies human abilities and not replace it. It sounds to me like Harvey's not designed to replace a lawyer, but augment what a lawyer does. Was that kind of intentional?

Yeah, I I I think like when you look at the work that these large law firms do, they are doing some of the most complex knowledge work in the world. Where if you think of something like Microsoft acquiring Activision, just thinking about how you structure that transaction. I don't think the models are anywhere close to doing it. What I do think happens is there is parts pieces of the work that the models do automate, right? And there's even full end-to-end tasks that these models will be able to do, right?

Like already internally, we have systems that automatically negotiate our NDAs. But I think to me, a lot of it is how do we move humans to doing the things they can uniquely do. And when we think of like, what are the most valuable lawyers? It's these partners that are these like strategic counselors to these companies. Where you have this super complex litigation, you have this super complex transaction. And they can tell you beyond what is in the documents, this is what you need to do if this is what you want.

Um, and so how do we enable that?

Yeah, I I think of my days when we were selling my company, Affektiva, and we ended up selling it to a Swedish publicly traded company. And our counsel was Jay Hatchigan at Gunderson. And honestly, you're absolutely right. Like, yes, there were all a ton of mechanics that had to get done, but he was so strategic. And it's it's a result of, like, twenty plus years of experience doing M and A. And right,

And it's also the the personal relationships, right? Like most of these legal problems don't have objective answers, right? Like the outcome you want in that transaction is really different than the outcome maybe someone else would want. And I think that is still what these like very good lawyers are are uniquely good at. And we wanna free up their time to do that instead of like, hey, let me read all these redlines, for example.

you also talked about how, like, some of your customers are the law firms and they're using Harvey, but sometimes it's a big, you know, organization like Bridgewater, and they're using Harvey too. And then the two Harveys can talk to each other. How does that work? What's an example of that?

Using Bridgewater, and I think just generally the like private equity and financial services firms. One very big use case is fund formation. So I'm an investment firm, and I mean, you probably did this as well. It's like, I want to raise a bunch of capital, I need to make a fund and structure it properly. And I think what you see is there is a lot of work in fund formation that is not profitable for law firms.

It just gets written off.

Oh, interesting, cause now I have a little bit more empathy for our law firm, cause I'm like, this is like, so expensive.

Yeah, oh, I mean, I think this is where the like dynamics get really interesting, where it's like, I think a lot of people think about the billable hours.

Cause obviously in in the legal world, the business model is the billable hour. But how does integrating AI change that?

So I think we're still in the early stages of these law firms thinking about how they change their their business models. I think with PwC, we've talked with them a lot, and they're kind of, I think, ahead of the game in terms of thinking about, okay, how we need to provide services in this new way. I think some interesting things we've seen, probably the most interesting thing is there's some litigation firms that are just contingency-based or fixed fee.

So it's purely I'm defending you, and if I win, I take some percentage of that win. And there the incentives are very clearly aligned.

What's the business model for you in that case look like?

I think we're starting to think about, can we take a percentage of the client matter, for example? And so if you use Harvey on this litigation, Harvey, you know, takes some percentage of that, and we automate some percentage of that work. I think where we see this going long term is the reason you need billable hours is this work is so complex that it's, I think, until now, impossible to price. It's impossible to estimate how long this is gonna take, all the things that are go gonna come up.

But I think now these models are getting so good where you can actually do much more accurate pricing of legal work, and so you can move to more like value-based, fixed fee-based pricing. I think there's kind of like a win-win-win for, like, Harvey, clients, and law firms.

You know, I'm I'm an investor in a number of companies where they basically it's almost like an AI staffing agency. So instead of hiring, I don't know, a human healthcare administrator, you're hiring the AI equivalent of it as almost like a fraction of a full-time human head. Would that be a business model where you're charging for Harvey as, uh, you know, the equivalent of an associate or I I don't know, a paralegal or something?

Yeah, I mean, I I I would say this is kind of what, like, our enterprise SaaS model right now is like, we charge seat-based pricing. And then I think a lot of the question is like, how much of that value are you able to charge for or capture? And I I think it's like that analogy is very useful, like a lot of times in building the product and these things, it's like very useful to analogize it to human labor.

And then I think there'll be very unintuitive things about this technology that don't map to that. And you need to think about, like, where the intersections are.

Can you talk about what the Harvey team looks like? I mean, uh, is it mostly like machine learning engineers and software developers, or is it also like a lot of lawyers? Like, often in these like vertical AI applications, a lot can get lost in translation, right? Cause I if I'm a machine learning engineer and I know nothing about law, like, how do you actually understand the pain points of your customers? So how do you deal with that?

Yeah, we we think of building the team a lot like kind of Winston and I's relationship, where you need kind of the technical expert and then you also need the domain expert. And so both on our sales team, we have kind of a traditional like GTM org, but we also have a bunch of lawyers from top law firms that were associates, senior associates that help sell the product, map kind of workflows of these law firms into the product.

And then similarly on the product team, we have kind of a tradit- I wouldn't say traditional, but we have kind of a EPD org with now AI. And I think these are kind of that alone is a new way of building product and figuring that out. But we also additionally have

Wait, double click on that, like, what what do you mean?

Well, I think before you built these products that were kind of centered around the models, um, maybe the way you would develop product was a bit different, right? You would like write this product spec and then you would have engineers build this. And then now I think you have this additional dimension where it's like you have the traditional product spec, but you also have the model spec. And so you need to make sure the product works in the traditional sense, but you also need to make sure that the model works.

And then oftentimes, as the model gets better, maybe the like product doesn't make sense in this way anymore because the model has changed how it acts. And so I think thinking about how you build an org for that, if you are just a general purpose AI company, that I think is already a new challenge. And then you add in legal or some domain expertise where now most people on your team don't know what the model is doing.

I think that adds like an additional level of complexity. And we solve that by we have a we've hired a bunch of really great lawyers that spend a bunch of time on the product. But I think also very importantly, spend a ton of time using the models.

To build a truly successful AI company, you need to be prioritizing responsible AI, which means mitigating the risks. In a minute, Gabe talks about how Harvey decreases the risk of hallucinations and protects client data. So I spent basically twenty-five years of my life building artificial emotional intelligence and empathy and emotional intelligence into machines. Does Harvey have empathy? Do you think it needs to have emotional intelligence? And if so, how are you building it?

I think right now it does to the extent that I I think these models have that built in just because a lot of that training data on the internet, I think just emotions and that intelligence is such a big part of the text that we generate. I would say it's less of a focus right now, but even as we train these models to do things like negotiation and things like that, I think there will be some kind of interesting angles there.

And then I think, like, to me, where it gets very important is we are starting to talk to some court systems, some litigation firms. How do you do AI arbitration? And so when you start thinking about these models need to make uh judgments and things like that and these like ethical decisions, that then I think this is like, incredibly important.

This is a great segue to a conversation around safety and how do you build this AI responsibly? First thing that comes to mind is the, you know, AI hallucinating. So how how do you handle that?

Yeah, so this is like a big part of what the like legal product and evaluation team does. And so we have a bunch of benchmarks, we do a ton of human evaluations, so we have both our internal team, we use a ton of contractors, we work with our clients to basically make sure that you minimize these hallucinations. And I think, you know, even as good as these models get, you never drive that to zero, but you can greatly reduce it.

And then I think there's things on the product side you can do. So something that's super important for lawyers is any answer that's generated, you're citing to the case or the document, and so you're grounding this, you're making it easy to check within the product. And we found kind of both those things have gotten us to the point where these law firms, I think at this point, are pretty comfortable using this technology.

Okay, so hallucination is is one concern, and it sounds like you're you're really addressing that. Another one is attorneys have to comply with very strict confidentiality. How do you deal with that? If you are using some of these foundation models, does the data go to the, you know, like OpenAI's cloud or Anthropic's cloud?

Yeah, no, I think this is like a very big part of the problem we are solving for these law firms. So early on, when we were building Harvey, that was one of the biggest concerns of we don't wanna send this data to a foundation model provider. We don't want this data mixed with other consumer data and things like that. And so a lot of what we did is how do we build the infrastructure where, even if we're using a cloud provider, we have our own instances, we have dedicated capacity.

This is isolated, um, potentially for individual customers. Can you isolate all their data? And then especially as we work with governments, banks, uh, kind of these highly regulated institutions, I think that will be a very large part of the problem. I do think we can get into the world where for a lot of these solutions, you can have these multi-tenant solutions, but they are architected in a way that you can maintain privilege, security, privacy. Like now, most of the systems that, you know, even these large corporations use are built that way, but you can give these, like, very, you know, strong security guarantees.

And so we've invested a lot in security kind of from very early on.

Let's talk about how, like, the whole legal landscape is shifting given everything that's happening in AI. And one question I have, you know, a lot of the work that Harvey's doing is basically kind of the work that was traditionally taken care of by junior lawyers. And now you've got, you know, you've got the senior partners partnering with Harvey. How does that change the career ladder for somebody who's, you know, like Winston, when Winston started out, how does Harvey change that career trajectory?

I think this is something we are working with a lot of law firms because I think there is the very valid concern that, and I think this is not just in legal, but as these models get better and they're able to do a lot of this work, like humans learn by doing the work, how are you training the next generation of partners, software engineers, kind of any profession? And so I think there's a couple ways that we think about this.

I think one, especially in legal, I would say a lot of the work that gets done by juniors, uh, you're probably not maximizing your learning. And so maybe the first time you, you know, review a contract, you learn about the structure, maybe the second, third time, but after a thousand times doing that for ten years, I think there's a point where it's like, now this has turned into kind of not enjoyable work. And so how do you remove that?

And then I would say, when I think about my experience learning computer science, math, AI without these models, I'm like, it was impossible, right? You would just buy all these textbooks, you would go on like blogs. It was like most people, I didn't have access to the best professors. Now with these models, it's like you can just ask them any question, and as they get better, it's like they're better at most math than I am, and I can just ask it any math question, give me a similar problem.

Uh, and so we're starting to work with firms to think about, you know, how do we take your firm's curriculum, things like that, and use these models to help train your associates. And so I'm optimistic that there are ways to do this. And then I think if you look at the structure of, I think some of these law firms, most people don't become partners, right? Like most people who go work at these big law firms really quickly find out they're like, this is not the career path that I want.

And so if you look at the top law firms, they have this like lower leverage ratio where probably you start seeing more law firms like that, where it's like the number of associates is closer to the number of partners. You don't have to burn out, you know, 90% of them. You just find the people who really wanna do this, mentor them, and then it's worth doing it for a smaller number and they become the next generation of partners.

And then I also think people can become partners sooner, right? Like, how do you give them more client interactions earlier on?

I also saw that Harvey's partnering with Notre Dame's law school. How did that partnership come about? And are you kind of exposing these young lawyers to tools like Harvey early on? Is that the idea?

Yeah, exactly. We're we're partnering with a bunch of a bunch of law schools, and I think the idea is exactly that of, I think one of the most important skills in any profession is going to be how you use these models. And I think one way to responsibly develop that is work with these law schools to put this into part of their curriculum.

So what advice do you have for kind of n new new folks in the in the legal space as they're kind of embarking on their journey?

I would say the biggest is probably just use the models a lot. The gap between, like, a casual gen AI user versus the most talented users of these models is night and day, and I think that gap is only going to increase. I think the thing I'm still blown away f uh by is there's like a couple partners that I talk regularly with that are some of the top transactional litigation partners in the world. And they will send me examples of the things they are doing with Harvey and just seeing what's possible.

If you, like, have a really deep domain understanding and also know how to how to leverage these models, I think that, to me, is going to be like one of the really valuable skill sets that that you can develop.

All right, final question, and this is a question I ask of all my guests. What do you think it means to be human in the age of AI?

I would say it kinda doesn't change it. Like, I think a lot of people talk about, oh, these models are intelligent, we're not unique. But when I think of the human experience, it's not being the smartest, it's not building the best company. It's kind of like connection, friendship, falling in love, suffering, pain, like all of these things. And I think just be the same way if someone else experienced this, it doesn't diminish your experience. I don't think some technology also being able to, like, experience this or imitate it, I think changes the human experience.

With that said, I think there'll be a lot of interesting things we need to, like, figure out and how we live our lives given this technology. Because I I think it will lead to, like, a very big change.

Well, Gabe, thank you so much for joining us on the show. This was fascinating.

Thanks so much for having me.

This was our first conversation about AI in the legal field, and it's an area that I'm keeping my eye on. What I find most fascinating about Harvey is they took an industry that's been stagnant and disrupted it with AI. It's evidence that there's a huge opportunity for vertical AI startups that go after stale industries with a focused solution and find success. My second takeaway is about focus. Usually, startups are advised to focus, focus, focus. But something different is happening with AI companies.

We typically talk about economies of scale, but now we're also seeing economies of scope. Essentially, with AI startups, once your AI starts servicing an industry, over time, the AI can learn to do more and more tasks. Harvey is a great example of that, expanding beyond legal work to do tax and audit. This growth strategy is something I'm definitely on the lookout for as I meet early-stage AI startups. We'd love to hear from you. What do you think about AI in the legal field?

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