Will This $11B AI Startup Disrupt Big Law?

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“Should the U.S. start bringing troops home from Europe?”

I worry a lot that this is where we were headed, Trump's sort of signal it a lot during

his first term, and in my conversations with European friends, I've been telling them,

be ready for this because I do not think we get through four years without the United States reducing its European footprint. I'm Jake Sullivan, and I'm John Finer, and we're the host of the long game. A weekly national security podcast. This week, we debate whether the U.S. should draw down its true presence in Europe,

and we break down the latest developments in the Iran War. The episodes out now, search for and follow the long game wherever you get your podcasts. Welcome to the Proof to you, Markets found a series. I'm Ed Elson. Since the start of 2026, investors have been asking one big question. How much of the economy will AI disrupt?

We've already seen the fallout in software, where waves of SaaS pop-lip setups wiped out hundreds of billions in Markets Valley, but that may just be the beginning. Now, attention is shifting to the legal industry, a trillion dollar market built on manual time intensive work, like document review, due diligence and compliance, exactly the kind of workflows that AI was built to transform.

Well, my next guest saw that as an opportunity. In 2022, he built an AI company designed to streamline legal work at scale.

“Now the question is, can it actually disrupt one of the world's most archaic industries?”

Well, with over a billion dollars raised and 11 billion dollars valuation and adoption in over 60 countries,

it's well on its way to do ingest that. This is my conversation with Gabe Pereira, co-founder and president of Harvey. Gabe, welcome to the show. Thank you so much for joining me. So much to get into here.

I guess maybe lay out for listeners what actually is Harvey and how did this company get started? 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 in 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 co-pilot for a professional cursor and cognition built this for programming. We built this for legal, and I think what you're starting to see this 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. When did you come up with this and how did you realize that this was going to be a huge opportunity? Yeah, we started the company summer of 2022.

I was, I was at Meta on their large language model team, GP3 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 that deep mine 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 still being surprised by the way it's going, but there was just like I had this strong belief of we will be able to build super intelligence, AGI, things like this. And I think when you do a lot of that research or 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 brain-storming startup ideas as I saw these

And one day, he kind of showed me the work he was doing in the workflows, and that was kind of the light bulb moment, where at the time GP3 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.

It does seem as though legal work is basically ground zero 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 grant 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? All the workflows of a lawyer, what kinds of things could AI potentially automate in that business? I think when most people think of law, they think of consumer law. And so I need to review a lease, or 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, 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.

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 work flows 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, it could be an adversarial process, same on litigation.

“And so to your earlier point of, I think when language models came out, legal was kind”

of this great application where typically your work flows 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 or 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 when you're working on these large projects? When you talk about those boundaries, it's interesting point, the boundaries of blurr.

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?

“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 like 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 part in it or it could be something super complex that is like go right the first draft of the merger agreement, right? But even that mapping and this is kind of what you see with Trash BT, where when we started the company people would be like, what is your product do and it's kind of the same as asking people, what do you do with Trash BT? 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 and ways that

problems for a dodges legal like all these industries like you're seeing the same thing in programming right now. Yeah it does seem as though the great thing, what Trash BT enabled us to do is ask questions that are actually blurrier and that cannot be answered in binary that cannot be answered that I mean it used to be that you had to spend a long time trying to phrase your 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 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 for my understanding

of the AI industry Harvey is the number one AI company in law right now you guys hit 190 million

“dollars 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 so I mean the pitch is definitely not we can do it 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 ANO and David Wakling there when we showed him kind of

we got early access to GPD foreign we built a product around that and showed that to him he just had this like 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 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 right 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 pitches just we want to be your”

partner and help you think through this entire transformation not just the technology.

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 Darrya Amadeh who is leading the frontier of large language models over it anthropic is also saying that we could see roughly half of white

college jobs wiped out over the next several years and so there is this tension where it's like

you can do that and I think a lot of people may be listening to what you're saying now and that

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

“all of those constraints like why couldn't you do this all with AI? 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 timeline story I was 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 or 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 the digital technology but from the law firms we work with and the large enterprises we work with I guess I'm still skeptical that like in a year or two if you're working with like a large regulated bank right it's just like I don't see a world where you just deploy these code models across the entire bank and say

“like 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 like brands 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 it I did a 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 going to programming and like translate them into legal and so I do think we will build systems that have the capability of like 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 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 so big it's just not worth it right it's like oh 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 gonna happen on some timeline I just don't think it's like in the next two years we'll be right back

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“we're back with gay parayera I think that the autonomous vehicle comparison 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 way more 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 maybe you would maybe it's maybe 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 evister it 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 slow at a roll this out than a lot of people think am I getting that right yeah I think it's like the accountability is one piece I think the other thing is like so to me the argument against why this goes faster than self-driving cars is like

“the obvious you know beyond regulatory for self-driving cars it's just you need to build all these”

cars and it's like 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 yeah 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 make 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 this second order effects and

things like this and if you think of legal it's this is like the underpinning of our entire society

but 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 it's your point will NDAs be fully automated in a year too 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 like 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 the super intuitive just like here's a bunch of legal agents all your problems

are solved like it's the same way if you hired a thousand lawyers and you'd never run a law from

you're like I don't know how to manage them and I think that's the big challenge everyone's facing it does seem a huge problem for AI that AI does make mistakes and it still seems to make mistakes like 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 yeah 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 seems that you can't really do that with AI at the moment that's

“one of the biggest values that I think people under appreciate 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 just in terms of where you're

for the company right now you you started the company a few years ago you're now up to 11 billion

dollar valuation nearly $200 million in annualized revenue what are you seeing in terms of adoption on on on on the front lines of these legal firms I mean how are they actually integrating it and you mentioned earlier that you know for people within the legal industry they don't think it's going to like massively disrupt the industry in the same way that people from

“the outside might so talk a little bit about the adoption so far to clarify the last one 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 with the in the past couple months that jump with seem coding models I think most of the world is still underestimating how much this is going to change everything like

I think everyone has somewhat gotten comfortable with what GPD4 GPD5 this like 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 GPD3 to GPD4 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 work

flows 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 like 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 like most large enterprises have large internal legal teams and then also work with their law firms and there's obviously like 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 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 law the legal world and we were talking in the context of job destruction within the legal world it seems that lawyers

completely transform the the legal industry and it's going to destroy a lot of jobs destroy 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 you're pointing out the capabilities of these new models we've just seen what happened

with with anthropics new model Claude Mythos which is just I mean a viscerating stalks 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 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

“if we go to the extreme do I think 50% of these jobs will go away in the next two years like 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 like how good this technology is going to get and then I think there is from a purely capabilities perspective I think these models are like exactly what you're talking

about of like 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 any time 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 in swap models and I can use it on my entire codebase 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 yes 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 and like a controlled way we're 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 when on your desktop and wrote this apple script to like get around these restrictions right but if you're doing that for like 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 I to your point like from a purely capabilities

perspective like yeah these models are like senior assist like they're just incredible the shape

of them is just still weird the same way like the coding models aren't quite there where it's like we can't have them or 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 like a principal engineer in these models the stuff you can do is insane like 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 what the way you do engineering like most engineers are rocking how to get the like 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 capability's 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 we'll be right back we're back with Gabe Pereira so I very much agree with the points you're making here and this is

SaaS apocalypse that we're seeing because you know we we've seen these incredible models come out

they're incredibly smart they're incredibly capable and sales force gets battered and crowd strike gets battered and cloud flare and service now and all of these all of these software enterprise CRM management companies just just getting battered right now because the idea is like okay I'm throw up it can do the it 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 you there 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 cyber security 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 coated 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 you haven't even got the approvals from your data partner 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 sales forces strength right now but in terms of

privacy cyber security 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 they're 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 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 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 and an

“organization that another company is willing to bet their company on you that's what put like if sales”

force goes down you're like sales org the 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 fun formation I need to wait 10 years until that fun pays out to know that you structure this correctly and it's like these systems are getting so complex that you can 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 like 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 is 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 cloud flare 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 going to have one model that solves everything it's just the world's too complicated I think for things to play out that way I think this is a pretty good segue into the next question

XAA I mean I guess it's like there's literally just a handful of companies that are actually building the foundation models and that is the case for basically every AI startup I mean I know founders who are building AI for finance and 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 two companies like you

to a founder like you what is stopping anthropic from killing you what's stopping open AI 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 open AI can build codex and they can just insert that into the engineering the software engineering space what's

“stopping them from doing that with legal I mean I 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 multi-billion 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 with 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 transformation where the way you structure your firm needs to change the way that you build 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 be 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 co-work, chatchBT 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 their 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 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 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 it sounds like one of the things that we're learning here is that the one of the biggest shortcomings of AI right now in terms of actually implementing it

because your company is literally three years old and so the idea that you're going to come to these big law firms they don't worry you can you can count on us I I worked at matter I worked at deep mine 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 I'm found like dealing with trying to get people to trust you like

“how do you actually do that as an entrepreneur? The thing definitely that helps now is I think we”

were telling law firms about this before any of this happen like we started the company before chat you BT a lot of the things that we have told law firms investors and are probably like 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 like 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 sweet these are people that our CTO has said that has managed the 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 like that is how we build trust

“over time but I think to my earlier point like you can only do it so quickly and so for us this”

is you know a 10 20 year company and we're just going to keep doing it 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 color 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 but this is law this isn't a place for technology this isn't 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 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.

“I think one thing when Sinonai 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 flag 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 transaction law firm like Wachtell 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 works 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 right 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 you know these Fortune 500 companies these top law firms and we've worked with them in a way where they speak highly of us and like 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. As someone who has built an extremely successful company 11 billion

dollar valuation for the entrepreneurs and the first time found is listening to this podcast what advice would you give them? 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 24/7 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 was an obvious and I think the 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 seem 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, TechTalk, Dordash these 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 interesting question. Gabe Pereira is the co-founder and”

president of Harvey Gabe appreciate your time thank you thanks so much for having me this episode

was produced by Alison Weiss and engineered by Benjamin Spencer or Research Associates are

“Dan Schlon and Kristen of Donahue and our senior producer is Claire Miller thank you for listening”

to the Proftry Market's Founder series we'll see you next month with another Founder story.