Gabe Pereira (3:41)

Thanks so much for having me. I would think of the problem that we're solving with Harvey is how do we help large law firms and their clients, which are large enterprises and increasingly all law firms and all companies go through exactly the transition that you talked about. So when we started the company four years ago, we built, I think what most companies built of some form of copilot for a professional cursor at cognition, built this for programming, we built this for legal. And, and I think what you're starting to see, the shift as these models get better and better is you need to start thinking not just about the productivity of individuals, but the productivity of entire organizations and what is the infrastructure that they need to be able for the entire firm to operate effectively. And so that's a lot of what we're building at Harvey.

Prof. G (Host) (4:33)

When did you come up with this and how did you realize that this was going to be a huge opportunity?

Gabe Pereira (4:39)

Yeah, we started the company summer of 2022. I was at Meta on their large language model team. GPT3 had just come out. I had been doing AI research for about 10 years before that. And so even in 2014, I think I was at Google Brain then DeepMind. A lot of people in that community had the belief that we would figure out a way to build these systems. I don't think the path was super clear. I think we're kind of still being surprised by the way it's going to. But there was just like I had this strong belief of we will be able to build superintelligence, AGI, things like this. And I think when you do a lot of that research, you're constantly thinking about what problems are the models good at, what problems are the models not good at. And in 2022, my roommate was Winston, who's now the CEO of Harvey. He was a lawyer at O'. Melveny. And I'd been brainstorming startup ideas as I saw these language models get better. And one day he kind of showed me the work he was doing and the workflows. And that was kind of the light bulb moment where at the time GPT3 couldn't do that work, but it was very clear they would keep getting better. And that felt like one of the big industries that would just be a very clear application of this technology.

Prof. G (Host) (5:52)

It does seem as though legal work is basically ground 0 for AI, or at least that seems to be the way people view it at this point. I mean, what we've seen is that AI appears to be able to do all of the grunt work of the white collar jobs. That is kind of like the starting point of what AI can do. And it does seem as though law is exactly that. Just for those who maybe don't understand how law firms work and what kinds of work they're actually doing, could you tell us a little bit about the legal industry? What are the workflows of a lawyer? What kinds of things could AI potentially automate in that business?

Gabe Pereira (6:35)

I think when most people think of law, they think of consumer law. And so I need to review a lease or, you know, I need kind of look at one document. And the models are obviously great at that. And I think to a large extent, the base models can do that. And then there's kind of corporate legal work and particularly big law, which is the massive law firms. And you can think of the work they're doing is the highly specialized legal work where you need these incredibly talented, talented, very specialized partners. And I think kind of the two best examples of this are you're doing a massive merger, right, or an acquisition. You want to go buy a company for $10 billion, $50 billion. Like you need the highest tier partner to advise you how to structure that transaction, or you're doing about the company litigation, right? Like a big antitrust or something like this. And the way that I would think of the workflows of all these firms is these projects take thousands, they can take tens of thousands of hours, teams of associates. And the big challenge is, for example, when you're going to buy a company, you need to go understand all of the contracts in that company, all of the things that are going to happen when those contracts change because of the merger or the acquisition, all of the legislation around it. And then there is also all of the negotiation dynamics. Right? It's a semi adversarial or could be an adversarial process. Same on litigation. And so to your earlier point, I think when language models came out, legal was kind of this great application where typically your workflows as an associate is you're getting emails from senior associates or partners and they're just giving you tons of tasks. Go research this. How do I write risk factors for this document? And what these associates are incredible at is they can just absorb these tasks. They know how to go use all these tools and solve all these problems. And that's kind of what you're seeing these agents starting to get better and better at. The part that I think is so difficult about these legal workflows and similar to programming is the boundaries between the tasks are super blurry. And so it's not easy to go to a law firm or go to a programmer and say, hey, the coding models or the legal models can do this and humans can do this. Like the boundaries are very blurred and the work is so complex that a lot of the challenge we're working with law firms is how do you rethink your workflows and what humans and agents should be doing. You're working on these large projects.

Prof. G (Host) (8:58)

When you talk about those boundaries, it's an interesting point. The boundaries are blurry. Are you saying the boundary between what AI is best at versus what humans are best at that it's hard to draw a distinction between those two things.

Gabe Pereira (9:11)

I think it's both that but also the distinction of even what do you delegate to humans? When you're doing a merger, you kind of have a pyramid, right? You have a senior partner, some junior partners, senior associates, junior associates. There isn't like a concrete rule of when I'm doing a merger, this task goes to this associate. And there isn't even a concrete rule of what defines a task, right? Because it's all text based. And so it's a partner just saying, I need you to figure out xyz. And that could be something super simple like go look up this one case and tell me who is the other party in it. Or it could be something super complex that is like, go write the first draft of the merger agreement, right? But even that mapping and this is kind of what you see with ChatGPT where when we started the company, people would be like, what does your product do? And it's kind of the same as asking people what do you do with ChatGPT it's like everything, but it's really hard to define why you do something one way. And this is exactly what makes the transformation so difficult. Because to your point, given this kind of such an open natural language shape, how do you start defining the boundaries of this is what models are good at? Because it depends how you prompt it, it depends which model you're using, it depends on the agent harness. And so there's just this massive challenge of how do you organize all this work in this new way, given the models can do some stuff, but they make mistakes in ways that aren't intuitive. And so it is just this huge change management problem and up leveling problem for not just legal. All these industries. You're seeing the same thing in programming right now.

Prof. G (Host) (10:47)

Yeah, it does seem as though the great thing, what ChatGPT enabled us to do is ask questions that are actually blurrier and that cannot be answered in binary. That cannot be answered. I mean, it used to be that you had to spend a long time trying to phrase your question for Google Search very, very specifically. And then what was kind of remarkable and liberating about large language models is that you could be a little bit more blurry and rough and it would be willing to to go to those more ambiguous places and try to come up with more creative answers to more complicated questions. So on the one hand I kind of think, well, that's exactly the strength of AI, so maybe this is exactly the place where AI should thrive. But then at the same time you're also pointing out like there are places where it still gets confused. I mean, large management work is still actually very complicated and it's a lot more complicated in a large organization versus when you're just operating as a single individual trying to figure out questions on your own time. I just want to point out for people who might be listening, because I mean, there are AI startups for everything now. And I'm sure there are probably hundreds of legal AI companies that are trying to eat your lunch at the moment, trying to compete. I would just note for people, I mean, from my understanding of the AI industry, Harvey is the number one AI company in law right now. You guys hit $190 million in ARR in January. That's the most recent number we have. If you want to update it, go ahead. That was nearly double what it was five months earlier. So you guys are growing incredibly quickly. You are partnering with basically all of the biggest corporate law firms. You guys are kind of spearheading this transition. I guess the question then becomes, when you went to these law firms and you Said, we can do what you guys do with computers. What did they say? Were they excited about that? Were they scared by that? I mean, how did these big corporate law firms react when you went up to them and made the pitch?

Gabe Pereira (13:03)

So, I mean, the pitch is definitely not, we can do what you can do with computers. But I think what helped early on was we found kind of certain partners or innovation leaders that AI isn't new to law firms. They had been using things like TAR and other kind of AI technologies to do parts of legal work. I think this was just such a large step change. But early on, for example, our first client was A and O and David Wakeling there. When we showed him kind of, we got early access to GPT4 and we built a product around that and showed that to him. He just had the same light bulb moment we had where he's like, oh, this is going to change how we do work. And I think a lot of our pitch to law firms has been, there will be parts of the work you do that these models will do the same way. Now when you do discovery, you use TAR and use contract attorneys and you don't use associates. So that's going to happen. But there is also going to be a lot of work that these law firms do that these models aren't going to do. Right. Like, I don't see a world in the next 10 years where you're doing a large merger or a large litigation and it's fully automated. Right. Both for technology reasons, but also for regulatory insurance, all these reasons. And so a lot of the problem we want to work with law firms to help solve is what is the future of their business model going to look like? Because there are parts of this technology where you are selling expertise on an hourly basis. Like, there are parts of this that it will be complicated to figure out. There are new ways to collaborate with your clients. And so there's just going to be all these questions that this technology is going to raise for law firms, for all professional service providers, for most companies. And so I think a lot of the pitch is just, we want to be your partner and help you think through this entire transformation, not just the technology.

Prof. G (Host) (14:53)

What you're essentially saying is, you know, what we can do is powerful, but not that powerful to the point where we're going to automate everything and basically eliminate all of these jobs. And you mentioned that there are technological constraints, regulatory constraints, and also insurance constraints such that you couldn't just automate a big, complicated legal contract with your product. And with AI at the same time, though, someone like Dario Amadei, who is leading the frontier of large language models over at Anthropic, is also saying that we could see roughly half of white collar jobs wiped out over the next several years. And so there is this tension where it's like, on the one hand, a lot of people in AI would say might make the case that actually, no, you can do that. And I think a lot of people maybe listen to what you're saying now and they might be thinking you're trying to tell us that it's not going to be that bad so that maybe we could. People would be less afraid of your technology, less afraid of your product, maybe root for your product a little bit more. So I guess to press on that, what exactly are those constraints? Like, why couldn't you do this all with AI?

Gabe Pereira (16:09)

I think the biggest is just the change management. So to be very clear, I think that technology is good enough now that it's like maybe the timelines Dario's talking about, like, I roughly agree with those, but I think it's going to be close. It's going to be somewhere between what he's saying and like self driving cars, where it's like self driving cars are better than most humans at driving, yet they're 0% of the cars on the road and they've been better than humans at driving for five years. Right. It's like it's clearly harder to roll out something like self driving cars than this digital technology. But from the law firms we work with and the large enterprises we work with, I guess I'm still skeptical that in a year or two, if you're working with a large regulated bank, it's just like I don't see a world where you just deploy these code models across the entire bank and say the regulatory agencies just won't let you do this. And so I think there's going to be challenges like that. I think it's not clear to me how it plays out in terms of like a lot of these arguments you could have made with computers and with the Internet. And it's like there was a huge class of jobs like librarians and all these things. And it's like, oh, now you don't need this. And I think with like a lot of the legal work that gets done, there is stuff that is just not digital. Right. And so like, from a capability perspective, I think I'm actually like, I did, I did AI research, I'm pretty aligned with like Dario and like the way these things are going And I think to me, the biggest thing that I see in the legal industry is people don't think this is going to be as serious or it's going to happen as fast as it is happening. Because we're seeing this happening in engineering right now, right? Like these models are getting so good at programming that they are better than most human engineers, right? And to me that's like there's no clear research blocker that that's not going to continue. And it's going to keep speeding up because I think that research path is pretty clear. And then a lot of what we're doing is how do we take those models that are really good at programming and translate them into legal? And so I do think we will build systems that have the capability of automating large parts, most parts of transactions. My point is just there's parts of that that are not technical problems, right? Like if you think of a negotiation, there is a part of a negotiation that has nothing. It doesn't matter how smart you are or how technical you are, right? It's like it is a human to human thing. And it's like when you meet the GCs of a lot of these large companies, it's like, when is the GC of a large private equity firm that is raising a $20 billion fund going to be comfortable with AI automating that entire fund, right? It's like the downside's so big it's just not worth it, right? It's like, okay, maybe you can do that 30% cheaper, but the cost of a mistake is like that fund is structured incorrectly and now I just lost $2 billion. It's like, I'll just have one of these law firms do it and I'll have the partner review it, but hopefully they use AI for parts of it. And so I think there's just enough structural things where I think a lot of people think about legal as, can you review this contract? If you can do that, then we've reached automation. But like, what these large law firms are doing and what these complicated regulatory industries are dealing with is so much more complicated than just purely like a capabilities intelligence problem. And so I think there will be things like that, that this is definitely going to happen on some timeline. I just don't think it's like in the next two years.

Prof. G (Host) (19:49)

We'll be right back.

Gabe Pereira (20:00)

Adobe Acrobat, your team's home base. Collaborate within a shared PDF space. You've got your docs, your plans, your specs, and then invite the crew to build what's next, talk up the teamwork using a DD render. They think that this design could be a contender. And when somebody wonders what's the next steps, AI helps you finish the rest. Bolts are tight now your plan's refined. Run a smoother business when you're all aligned. Do that with Acrobat learn more@adobe.com do that with Acrobat.

Sponsor Host 3 (20:31)

It's all about you. And when you fly with Virgin Atlantic in their upper class cabin, they take the VIP treatment to the next level. With a private wing to check in and your own security channel at London Heathrow, you can glide from your car to their clubhouse, a destination in its own right, in 10 minutes or less. On board, you can treat yourself to your own private suite to stretch out in. With lots of storage space, a lie flat bed and delicious dining from beginning to end. Just be sure to leave room for dessert. Their mile high tea with all the little cakes and sandwiches is a Showstopper. Go to VirginAtlantic.com to learn more.

Sponsor Host 2 (21:12)

When you need to build up your team to handle the growing chaos at work, use Indeed Sponsor jobs. It gives your job post the boost it needs to be seen, seen, and helps reach people with the right skills, certifications and more. Spend less time searching and more time actually interviewing candidates who check all your boxes.

Sponsor Host 3 (21:29)

Listeners of this show will get a

Sponsor Host 2 (21:30)

$75 sponsored job credit at indeed.com podcast that's indeed.com podcast. Terms and conditions apply. Need a hiring hero? This is a job for Indeed. Sponsored Job.

Prof. G (Host) (21:48)

We're back with Gabe Pereira. I think the autonomous vehicle comparison, the analogy is a good one because it's true. It's like self driving cars are technically better than humans at driving right now, or at least certain technologies will go with Waymo, for example. But at the same time, whenever there is an accident, it seems to be far more abrasive and scary and more concerning than when we see an accident which happens at higher rates among the human population. And it sounds like maybe what you're describing is the same system where, I mean you mentioned like a gc, one of the main roles of that job, one of the main responsibilities is negotiation. Would you really let an AI do that for you? I would argue maybe you would. It might be possible that actually the AI is better at negotiating. But it sounds like what you're saying is that if the AI were to make a mistake and if it were to eviscerate however many millions of dollars for the firm, that would be more difficult and more concerning and more of a problem than If a human being were to make a mistake and therefore we might be slower to roll this out than a lot of people think. Am I getting that right?

Gabe Pereira (23:15)

Yeah, I think it's like the accountability is one piece. I think the other thing is. So to me, the argument against why this goes faster than self driving cars is the obvious. Beyond regulatory for self driving cars, it's just you need to build all these cars and it's hard to get all of them on the road. And so this will go faster because it's digital and you don't have that restriction. But I think to me the biggest challenge is actually just how correlated all of the risk of adopting this technology all at once is like, if you think of what's so nice about self driving cars is even for people not working in AI, it's quite intuitive what a self driving car is, right? Like you're just like, this drives, it didn't crash. I can kind of evaluate it. Accidents per mile makes a ton of sense. But now if you think of a bank, for example, adopting this technology, these agents, not just in legal, across the entire bank at the scale you would need to do the disruption you're talking about, you're taking on all of this correlated risk on this technology that makes mistakes in ways that isn't intuitive. And it's like company destroying if you take this risk incorrectly. Right. And the same reason that self driving cars freak people out, it's because they cause accidents in ways that you don't anticipate because you're like, as a human driver, I wouldn't make that mistake even if statistically it's safer. And I think there's just going to be so many variations of that where all of the second order effects and things like this. And if you think of legal, this is like the underpinning of our entire society. And it's just like, does anyone understand the entire legal system and all the implications well enough that if you just automated this whole thing in the next two years, we'd be like, that's going to go well. It's like it's just too complicated. And so to your point, will NDAs be fully automated in a year or two? Definitely will. Merger agreements for multi billion dollar mergers, like I would be skeptical of that. And I think that's also the hard part of legal is there's just such a massive spectrum. Right. And that's a lot of what we're helping law firms and enterprises figure out where it's like you want to roll this out slowly, you want to start using it on the low risk use cases so you can build this mental model. You want to find pieces of the higher risk that you can take out, but it's not this super intuitive. Just like, here's a bunch of legal agents, all your problems are solved. It's the same way if you hired a thousand lawyers and you'd never run a law firm, you're like, I don't know how to manage them. And I think that's the big challenge everyone's facing.

Prof. G (Host) (25:54)

It does seem a huge problem for AI that AI does make mistakes, and it still seems to make mistakes relatively frequently. And on top of that, your point about an NDA versus a multi billion dollar merger agreement. Humans make mistakes too. But at least you can tell a human, hey, you cannot fuck this up. You better get this right, because there's billions of dollars on the line, and if you don't get it right, you're fired. Or maybe you're going to get into some legal trouble. I mean, you can really explain to a human the stakes of the problem and have some more assurance that they're not going to make the mistake. It seems like you can't really do that with AI at the moment.

Gabe Pereira (26:32)

That's one of the biggest values that I think people underappreciate of law firm partners. Right. It's like at the end of the day, when you're doing one of these litigations or one of these mergers, there is like a single human being that has spent the past 20 years of their life doing all these similar transactions and being held accountable to that. And they're willing to bet their entire career that they're going to do that correctly. And it's like that is a level of trust that I think we will get there with these systems. Right? It's like the stock market runs on like an automated financial system that like, we now all trust. And so there's ways to get there. I just think there are a lot of problems we need to figure out to get there.

Prof. G (Host) (27:11)

Just in terms of where you're at for the company right now, you, you started the company a few years ago, you're now up to an $11 billion valuation, nearly $200 million in annualized revenue. What are you seeing in terms of adoption on the front lines of these legal firms? I mean, how are they actually integrating it? And you mentioned earlier that for people within the legal industry, they don't think it's going to massively disrupt the industry in the same way that people from the outside might. So talk A little bit about the adoption so far.

Gabe Pereira (27:47)

To clarify the last point, I think most law firms and in house legal teams now think this is going to change the industry a lot. I think my point is just in the past couple months, the jump we've seen coding models, I think most of the world is still underestimating how much this is going to change everything. I think everyone has somewhat gotten comfortable with what GPT4, GPT5, this caliber of models means. I don't think most people have baked in what this next capability jump, which I think is equivalent to like GPT3 to GPT4. And so in terms of adoption, most law firms we work with now or all law firms are like, we need to adopt this. And I would say where most law firms are at is every one of my lawyers needs to be using this technology individually. There are a lot of law firms that are thinking about here is how I need to change entire practice areas, like the workflows of a client matter that I'm doing with an entire team. And so starting to think about, okay, here's the parts of a merger that I'm just going to delegate to AI. And then I would say there is a decent number of firms that are then looking at the different ways they collaborate with their clients and price that work. And so I would say the industry is definitely moving in the right direction. People are starting to think about this and then their clients are also starting to look at this. So I think one thing that will be interesting is most large enterprises have large internal legal teams and then also work with their law firms. And there's obviously a blurred line between which work stays internal and stays external. And there will be this healthy tension of enterprises thinking about this is the work that I should do myself as these models get better versus this is the very specialized work that I want these outside law firms to do.

Prof. G (Host) (29:40)

I just want to clarify your position on this because it sounded a little bit different from what you said earlier. I thought earlier you were saying within the legal world and we were talking in the context of job destruction within the legal world. It seems that lawyers, I mean, so from the outside, when I look at what's happening, I'm like, oh, this AI thing is going to completely transform the legal industry and it's going to destroy a lot of jobs. Destroy is a harsh word, but it's going to remove the need for a lot of jobs. And it sounded like you were saying, well, actually within the legal industry, when we look at what's happened so far, people in Law are less worried about that than you might be. But I'm also hearing at the same time that you're pointing out the capabilities of these new models. We've just seen what happened with Anthropic's new model, Claude Mythos, which is just, I mean, eviscerating stocks across the board, and you're saying, yeah, people aren't seeing how much. How transformative this is really going to be. So I guess my question is, like, where do you really stand on this? Do you think that we are overestimating the impact or underestimating the impact specifically when it comes to law?

Gabe Pereira (30:48)

If we go to the extreme, do I think 50% of these jobs will go away in the next two years? I would say that's too extreme. And then I would say, where are law firms right now? I would say most of them are underestimating, to your point, of how good this technology is going to get. And then I think there is, from a purely capabilities perspective, I think these models are exactly what you're talking about of Mythos. These coding models are incredibly powerful. I think there is a lag of the effort to productionize that in a way that these law firms can use. And so I think part of the capability gap you're seeing right now is, like, the reason programming is happening so fast is there's basically no implementation cost. Right? Like, anytime a new model comes out, I can just go in terminal and I can be like, oh, Codex 5.4 extra high fast. Let me just use that and swap models, and I can use it on my entire code base. And it's, like, very easy to, like, absorb all the new capabilities. The lag we're seeing with law firms is you can't use desktop products. Right? Like, if I'm working at a law firm, I'm working on an internal investigation for Goldman Sachs. I'm not allowed to download that data onto my desktop and use a code model on it. Right. And so even though there is, to your point, this massive capability jump, I think there is still a lag to deploy this technology into a law firm and enterprise.

Prof. G (Host) (32:16)

Yes.

Gabe Pereira (32:16)

A lot of what we're building now is all of the, like, security and things that you need to be able to use all of these capabilities in, like, a controlled way, where just like a simple example, there's all these examples of use the code model. And you're like, hey, can you change this song on Spotify? And it's like, oh, I don't have an API. I just, like, went on your desktop and Wrote this Apple script to like get around these restrictions. Right. But if you're doing that for a sensitive merger that's not public and it's like, whoops, emailed this to the wrong person, I think there's all these things that you need to do to constrain it. And so I think that will slow this down a bit. But to your point, from a purely capabilities perspective, yeah, these models are like senior associates. They're just incredible. The shape of them is just still weird. The same way the coding models aren't quite there where it's like we can't have them at the scale we're at. They don't quite architect our product correctly. Right. If I'm just like, go build this new product at the scale we need and make all the right architectural decisions, they don't quite do that. But if you take a principal engineer and these models, the stuff you can do is insane. There is senior partners that I talk to that are working on very large mergers that are just like, I'm able to do most of this merger with me and the model and doing the work. But I think the other thing that is going to make this go slower is these models are very powerful when you know how to use them. And so what you see in programming is because the code models are just so perfectly aligned with the way you do engineering. Like most engineers are grokking how to get the value out of these systems very quickly. There are very few lawyers that are understanding these models the way like engineers because it's just not as intuitive. And so I think, like to answer your question, because I think it's complicated. Like in two years, do 50% of these jobs just all go away? I don't think that happens for the reason I said from a capabilities perspective, if we could perfectly diffuse this into the industry, can it do 50% of what people are doing today? My guess is probably yes. But I think that diffusion, for all the reasons I talked about, I think happens a bit slower than maybe like Dario's predicting in terms of just like this happens next year for regulatory security, all of these reasons. And then the last point is I do think law firms in general think this is going to happen slower than I'm saying.

Prof. G (Host) (34:48)

We'll be right back.

Sponsor Host 3 (34:56)

Starting a business can seem like a daunting task unless you have a partner like Shopify. They have the tools you need to start and grow your business. From designing a website to marketing to selling and beyond. Shopify can help with everything you need. There's a reason Millions of companies like Mattel, Heinz and Allbirds continue to trust and use them. With Shopify on your side, turn your big business idea into Sign up for your $1 per month trial@shopify.com SpecialOffer this episode is brought to you by State Farm. You know those friends who support your preference for podcasts over music on road trips. That's the energy State Farm brings to insurance. With over 19,000 local agents, they help you find the coverage that fits your needs so you can spend less time worrying about insurance and more time enjoying the ride. Download the State Farm app or go online@statefarm.com like a good neighbor, State Farm is there.

Sponsor Host 2 (35:50)

Tomorrow morning is knocking. Stock your fridge now. How about a creamy mocha Frappuccino drink? Or a sweet vanilla smooth caramel maybe? Or a white chocolate mocha?

Sponsor Host 3 (36:00)

Whichever you choose, delicious coffee awaits.

Sponsor Host 2 (36:02)

Find Starbucks Frappuccino drinks wherever you buy your groceries.

Prof. G (Host) (36:10)

We're back with Gabe Pereira. So I very much agree with the points you're making here and this is something that I found kind of interesting and I think the market is getting it wrong on the SaaS apocalypse that we're seeing because we see these incredible models come out. They're incredibly smart, they're incredibly capable and Salesforce gets battered and Crowdstrike gets battered and Cloudflare and ServiceNow and all of these software enterprise CRM management companies just getting battered right now because the idea is like, okay, anthropic can create the products that they do, but the thing that they seem to be forgetting, and that seems to be a very, very big piece of the puzzle, is there are other things that are important when you're managing a business that aside from capabilities. One of them would be trust. One of them would be security, cybersecurity. One of them would be privacy. Another would be relationships. I mean, those are the kinds of things that seem to matter a lot across basically all forms of white collar work where yes, there might be some vibe coded alternative that can do the job of the associate pretty quickly, but if you can't trust that it's going to protect the data or that it's going to do something kind of crazy, or that you haven't even gotten the regulatory approvals or you haven't even got the approvals from your data partner or whatever it might be, then you simply cannot use that product. And it seems as though that, I mean, from a product perspective, maybe you would argue, yeah, that's, that's not Salesforce's strength right now. But in terms of privacy, cybersecurity, the relationship with the. With the customer and the client. That seems to be the real strength. And it sounds like what you're saying is like, yes, these models could do a lot of the work in the. In the world of legal, but you. There are all these guarantees and all these other parts of the business relationship that they can't do yet. And so they're just not. They can't use them at the moment, or at least they can't use them in the extent that a lot of people seem to think, I guess. Would you agree with that characterization?

Gabe Pereira (38:24)

Yeah, I think that's right. Where it's like, I think what's going to happen is the models will do much of the. Like if you're an associate at a law firm, like you are for the most part not interacting with the client. Right. You are doing all the work that the partner delegates. And I do think the models will increasingly do more of that and you will need to build these hybrid law firms that are here is a ton of agents and probably less associates that do all the things that the models can't do. And there is a huge amount of legal work that is that. And so I do think like there will be a lot of legal work that these models can do. And then I think the point you made that I think it does feel like people miss of like, what is the value of these enterprise SaaS companies is exactly what you said where it's like, there is a huge difference between vibe coding a product and building a product in an organization that another company is willing to bet their company on you.

Prof. G (Host) (39:18)

That's well put.

Gabe Pereira (39:19)

Like if Salesforce goes down, you're like sales Org that can't function. Right. And like this is the same in legal with the private equity example I gave. Like, if you want this to do a fund formation, I need to wait 10 years until that fund pays out to know that you structured this correctly. And it's like these systems are getting so complex that you can't evaluate them. Right. Like, if you think of how do I evaluate that a partner is a good partner, there's no test I can give that partner. It's just that person has done mergers for the past 20 years and for the most part those mergers have gone well. And it's the same with software, right? I think anyone who's built software, it's like you can test stuff, but at the end of the day it's like, did your system run in production for 10 years and not go down? And there's no test for that. And all of these things are ways that you build trust. Like, you can't shortcut the trust. That, to me, is going to be one of the biggest things that slows this deployment of this technology. Because especially if you say, I'm going to have one company or one model build all of this, you're just taking all this correlated risk on that single system where it's like, if one thing is wrong with that model and it wrote all of your code and all of your infrastructure and you don't understand it, like your company's over. And so I just don't think banks private, like, you just can't take this correlated risk. And that's the value of something like Salesforce, cloudflare. It's like you have now spread out this risk to a bunch of these different parties that are accountable for very important, but like separate, uncorrelated parts of your business. And it's like that, to me, is, I think when sometimes people talk about, oh, we're just gonna have one model that solves everything. It's just the world's too complicated, I think, for things to play out that way.

Prof. G (Host) (41:13)

I think this is a pretty good segue into the next question. Your company relies on the foundation models from companies like Anthropic and OpenAI and xai. I mean, I guess it's like there's literally just a handful of companies that are actually building foundation models, and that is the case for basically every AI startup. I mean, I know founders who are building AI for finance and who are building AI for all these other industries, and they all rely on the foundation labs. And the question is always, to those guys and to companies like you, to a founder like you, what is stopping Anthropic from killing you? What's stopping OpenAI from killing you? If you are literally dependent on their models and they appear to have an ability to build the models themselves and to implement them into various industries. I mean, OpenAI can build codecs and they can just insert that into the engineering, the software engineering space. What's stopping them from doing that?

Gabe Pereira (42:14)

With legal, I mean, I think all of these companies are going to do that. And I think, to me, the tension is, I think there's actually a lot of companies building these models, right? It's the foundation model companies you mentioned, plus all the cloud providers where they either have their own or you can get the foundation models through the cloud provider. And so I think you've kind of distributed that risk. And then to me, one way I would Think about it is like, why didn't Google build a data room product, right? Like, data rooms in the previous generation of software was like a multibillion dollar industry. It's basically just Google Drive, right? But no one uses Google Drive to do a transaction. There's like an entire industry of companies that build data rooms, right? And if you think of the biggest challenge for these companies is how often does Dario or Sam think about the legal industry in terms of building a product? Never, right? It's like they're thinking about how do I compete with hardware, how do I get the funding I need to build data centers, how am I going to compete with the cloud? It's just like no part of that company is thinking about that, right? It's like, and then when you look at Microsoft or these large organizations, it's like they have a small GTM team that sells some of their products to law firms. But the thing that I think people don't understand is like the problem you need to solve for law firms is not just can this model do legal work? Like this entire industry is about to go through a transformation where the way you structure your firm needs to change, the way that you bill clients needs to change, the way you train associates needs to change, right? Who's going to help these law firms and their clients go through that transition? Right? And that to me is like the problem we're solving where it's not just who can build the best models, it's what is the platform and all the change management. And if you think of companies like Salesforce, like that's the value they provide. Where when you need to build a sales organization like Salesforce just has so much of that learning from helping every company do this. And I think what's going to happen will be similar to what happened with cloud, right? The models are going to become a core part of like societal infrastructure. Like, that's very clear now. And these model companies are going to build massive businesses selling these to companies and then there will be parts of products they can build, right? It's like cowork chatgpt. It's like these are incredible products that are very horizontal. But I just think these industries and the world is so complex that is there very large businesses you can also build on top of these platforms. Like, I think for sure, right? Like this is what you've seen with every other platform shift, with the Internet, with computers, with mobile phones, with cloud. Like, the reason these things become such large companies and such powerful technologies is because they are platforms that enable companies like us to build massive businesses on top of them. Like, that's almost what Nessus is, necessitates them being able to capture so much revenue. And then I guess the last piece I would think of is like, right now, what's stopping anyone from starting a company, right? Like anyone can go hire a bunch of people and coordinate them, but it's actually turns out to be quite hard to do this. And I think that's, I think that's what you're going to see with agents, right? Like very soon every person is going to have the ability to hire infinite employees. And it's like this is going to hugely democratize people's ability to build companies. And there will be really valuable small companies that are super specialized and then there'll be people that figure out how to do this at scale. But I just think this opens the pie so much that it's like there's just no world where just one provider does all of this, right? Because if the argument is like, oh, they do this for legal, then presumably they do it for every other industry in the world. And it's like, I just don't think that's how this plays out.

Prof. G (Host) (46:11)

It sounds like one of the things that we're learning here is that one of the biggest shortcomings of AI right now in terms of actually implementing it into the framework of an enterprise is trust and security. And that puts you in an interesting position because your company is literally three years old. And so the idea that you're going to come to these big law firms and say, don't worry, you can count on us. I worked at Meta, I worked at DeepMind, I'm a young guy and this is my roommate and you can trust us with your data. That seems to be a pretty bold, a bold statement. How have you navigated as a founder and as a pretty early first time founder, like dealing with, trying to get people to trust you, like, how do you actually do that as an entrepreneur?

Gabe Pereira (46:59)

The thing definitely that helps now is I think we were telling law firms about this before any of this happened. Like we started the company before ChatGPT. A lot of the things that we have told law firms, investors, enterprise, a lot of these things have come true. And I think that is a big way you build trust over time, right? The things that you say are going to happen or that you say you're going to do, you do those. So I think that's one piece, I think the second is the team you're able to build. And so I think we've just built an incredible team. And it's like Winston and I are relatively young, but if you look at our C suite, these are people that our CTO has, siva has managed a thousand person engineering Org, our CLO has taken a company public. And so we've built a team. Not just the leadership, the entire team where I think you can trust them. But I think to your point, it's, we haven't fully won this trust. And so there is a ton of work of we need to build the best product, we need to build the best team, we need to keep scaling. We're partnering with kind of all the other providers and so we work with the existing legal technology companies and enterprise companies. And I think the more that you can just work with the entire industry, that is how we build trust over time. But I think to my earlier point, you can only do it so quickly. And so for us, this is a 10, 20 year company and we're just going to keep doing it.

Prof. G (Host) (48:25)

How much does branding play a role in all of that? Because something I think about with this is what you really want, especially for these very storied white collar institutions, is you want to present as institutional. But if you are a startup, you are by definition not institutional. And that seems to be the real problem for a lot of companies that are trying to break into these very institutional industries is that this isn't a place for startup, this is law. This isn't a place for technology, a place for young guys who are founders. At which point it seems like part of the job is to be like, no, no, we, we have an institutional credibility. And I would imagine that a lot of that falls on the, on the responsibility of the brand side. Is that true and how have you thought about branding?

Gabe Pereira (49:10)

I think one thing Winston and I thought a lot about branding is by all the things that you don't do. And so I think we got kind of a bunch of flack early on because we like didn't do a bunch of marketing, didn't talk about what we're doing. And I think one thing that always inspired us was when you look at the websites of these top law firms, like they never do any marketing, right? Like if you look at a top transactional law firm like Wachtel and you go on their website, all you see is the caliber of their team and the size of the transactions that they've done, right? And they let the work speak for themselves. And I think when we think about our brand, like that is very much the brand we want to build. Where I think one thing we take great pride in is all of the law firms and the enterprises that we associate with. And that to me is one of the biggest ways that we are able to build trust and show this. Where we have gained the trust of these Fortune 500 companies, these top law firms, and we've worked with them in a way where they speak highly of us. And to me that's the ultimate way to build trust. I think a lot of times when people think of brand, they think of kind of like marketing and design and I think those things matter. But when you think of an institution, it comes more from like the things that I'm talking about and less of like how you design the product.

Prof. G (Host) (50:26)

As someone who has built an extremely successful company, $11 billion valuation for the entrepreneurs and the first time founders listening to this podcast, what advice would you give them?

Gabe Pereira (50:38)

I think the biggest right now is just use the models. Like I like if I was right now to, you know, start a company again, I would just be using the coding models 247 because I think to me I would say the big opportunity coming is I think the company we started seems obvious now, but at the time wasn't obvious. And I think that companies that will be successful starting now are the ones that don't seem obvious now. And it's like going and doing legal or any of these verticals I think seems somewhat obvious now. But the thing that is not obvious now is I think when people, when we invented the Internet, no one anticipated Uber, TikTok, DoorDash, these companies. To me, the really interesting startup question is like what is the shape of those companies on top of generative AI? And so I would say like use the models and you know, figure out what those are. That that to me feels like the big, the big interesting question.

Prof. G (Host) (51:32)

Gabe Pereira is the co founder and president of Harvey. Gabe, appreciate your time. Thank you.

Gabe Pereira (51:38)

Thanks so much for having me.

Prof. G (Host) (51:43)

This episode was produced by Alison Weiss and engineered by Benjamin Spencer. Our research associates are Dan Shalon and Kristen o' Donoghue and our senior producer is Claire Miller. Thank you for listening to the Prof. G Markets Founder series. We'll see you next month with another founder's story.

Sponsor Host 3 (52:12)

Some follow the noise. Bloomberg follows the money. Because behind every headline is a bottom line. Whether it's the funds fueling AI or crypto's trillion dollar swings. There's a money side to every story. And when you see the money side, you understand what other miss get the money side of the story. Subscribe now@bloomberg.com

Gabe Pereira (52:38)

Ryan Reynolds here from Mint Mobile with a message for everyone paying big wireless way too much. Please, for the love of everything good in this world, stop with Mint. You can get premium wireless for just $15 a month. Of course, if you enjoy overpaying. No judgments. But that's weird. Okay, one judgment anyway. Give it a try@mintmobile.com Switch upfront payment

Sponsor Host 3 (53:00)

of $45 for 3 month plan equivalent to $15 per month required intro rate first 3 months only, then full price plan options available, taxes and fees extra. See full terms@mintmobile.com hey mama. Thanks for making all my favorite recipes.

Prof. G (Host) (53:12)

Hi Ma. Thanks for your unfiltered advice.

Gabe Pereira (53:15)

Hi Mom.

Sponsor Host 3 (53:16)

Thanks for always being by the phone.

Prof. G (Host) (53:19)

Hey Mom. Happy Mother's Day.

Sponsor Host 3 (53:21)

When you ship UPS Air at the UPS Store, your items arrive on time or your money back guaranteed at no extra cost. Exclusively at the UPS store US retail locations. Visit theupsstore.com airshipping for full details. Terms and conditions apply. Send your Mother's Day gifts at the UPS Store and we'll get your gratitude there on time.