In short
The episode is a behind-the-scenes tour and business deep dive of Harvey AI, a legal AI company. The host discusses Harvey’s growth and product metrics: ~$100M ARR last August to ~$300M ARR now; ~960 employees across 12 offices; ~2,000 customers; January token usage around $1T, projected $12–$13T this month; DAU/MAU rising from ~36% to ~51–52%; and a switch to “cloud agents” that doubled usage quarter over quarter. Guests include Harvey leadership on an office tour (Winston is mentioned as the guide) and Pat Grady is referenced as a source for a question.
Key claims
growth is “100% product”; post-training is expensive and enabled by synthetic legal datasets; corporates are 42% of customers and financial services is the fastest-growing vertical; benchmarks are often inadequate because they don’t test end-to-end legal tasks.
Notable examples
latency “gavel” jokes in an Airbnb-based office; Azure data residency driving office expansion; and the “billable hour” ROI problem as the next frontier.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Chapters
Tap a time to open that second in VOIntroduction to Harvey AI's Growth
0:00 to 0:46
Discover how Harvey AI is revolutionizing document accuracy and its impressive growth metrics.
“The purpose of Harvey, Harvey AI, Harvey AI, if you're reviewing a million documents, is how do you check that its work is accurate?”
A Tour of the Harvey Office
0:46 to 1:53
Join a tour of the Harvey office and learn about its layout and company culture.
“So I actually, this is probably the best thing.”
Evolution of Harvey's Workspace
1:53 to 3:17
Explore how Harvey's office design reflects its brand and evolution over time.
“We're going to see the floor that's been renovated.”
Global Expansion Strategy
3:17 to 5:45
Learn how Harvey selects cities for new offices based on customer demand and legal requirements.
“Okay, so we're gonna cover a lot of different things today.”
Culture and Community at Harvey
5:45 to 8:17
Understand how Harvey fosters a collaborative culture and why employees enjoy coming to the office.
“you would have been able to tell who's a lawyer and who isn't because.”
Harvey's Rapid Growth Metrics
8:17 to 11:29
Dive into Harvey's impressive growth numbers and product development strategy.
“And so I know that there's people on the weekend all the time because I'll come on the weekend and I'll post into SF General like, yo.”
Harvey's Rapid Growth Metrics
11:35 to 12:29
Dive into Harvey's impressive growth numbers and product development strategy.
“Nearly 40 % of startups fail because they run out of cash.”
Harvey's Rapid Growth Metrics
12:31 to 12:54
Dive into Harvey's impressive growth numbers and product development strategy.
“That's why companies like NVIDIA, Anthropic, Salesforce, and Gemini partner with Turing.”
Harvey's Business Overview
13:01 to 14:00
Gain insights into where Harvey stands today in terms of growth and customer base.
“Now we're gonna go deep into the business.”
Rapid Growth and Product Development
14:00 to 16:41
Learn about the rapid growth of the company and the importance of product development.
“I think that in the beginning, we had this product roadmap.”
Show all 20 chapters
Challenges of Creating Synthetic Data
16:41 to 17:40
Discover the complexities of generating synthetic data for legal applications.
“now, now you can start actually plus training models and that's how it's expensive.”
Hiring and Organizational Changes
17:40 to 20:08
Understand the significance of hiring practices and organizational changes for company growth.
“Like, what does it take to build an AI forward company?”
Attracting Legal Talent to Tech
20:08 to 21:29
Learn how the company attracts former lawyers to transition into tech roles.
“So what's your framework for prioritization?”
Cultural Shifts in Tech vs. Law
21:29 to 23:28
Explore the differences in job security and performance expectations between law firms and tech companies.
“Oh my god, it was difficult to explain equity in some instances, right?”
Acquisitions and Team Building
23:28 to 25:14
Investigate the strategy behind acquisitions and the importance of team talent.
“I don't know, maybe you still have a billion dollars in the bank.”
Building Companies in the AI Era
26:58 to 28:05
Discover how building companies today differs from the SaaS cycle in terms of pace and product innovation.
“it was ai native from the start so i just want to know like what what is like fundamentally different about building a company today in the AI era versus the SaaS cycle?”
The Importance of Innovation in AI Products
28:05 to 30:04
Learn about the need for constant innovation in AI products and how companies must adapt to remain competitive.
“Like the alternative is Quad, ChatGPT, these other things, like those are great products too.”
Competing Against Larger Models
30:05 to 31:56
Discover how smaller AI companies can differentiate themselves and compete against large models in the legal space.
“What is the thing about Harvey that you cannot copy?”
Understanding ROI in AI Usage
31:57 to 34:24
Explore the challenges of demonstrating ROI in AI applications and the implications for vertical companies.
“And I think every single company on earth is competing against them.”
Benchmarks and Future Collaborations
34:25 to 35:30
Learn about the inadequacies of current benchmarks in legal AI and the potential for future collaborations.
“We're going to be interviewing Gabe after this.”
Transcript
Automatic transcript. May contain errors.0:00Winston Weinberg:The purpose of Harvey, Harvey AI, Harvey AI, if you're reviewing a million documents, is how do you check that its work is accurate? So you were at 100 million in ARR last August, and now you're nearing 300 million. What's been driving that? It's 100 % product. Our token usage in January was$1 trillion, and this month it'll probably be like$12 or$13 trillion. Our DAU over MAU at the beginning of the year was around 36%. Right now it's like 51%, 52%. And then this year, most recently, the main switch we did is just went over to cloud agents. We switched our entire infrastructure to that. And once we did that, usage user just started doubling quarter to quarter.
0:46You asked about a sword. We could start with this.
0:49Winston Weinberg:We could start with this. So I actually, this is probably the best thing. I also personally have a replica of Frostmourne. uh but that's in my apartment i don't know what that is it's it's arthas's sword uh world warcraft got it yeah anyway of course my bad my bad it's an awesome sword but i've seen too many people do sword things and so i didn't end up doing it um but i do have it it's great you guys can okay well that works too you can come through this way it's totally fine um but yeah this thing's fantastic we've actually we had a really small one um there was a kind of like a running joke when we were all in an Airbnb that we had this like little tiny gavel and one of the biggest problems with the product was just like latency um and so whenever we tested like a new feature we would basically just like hammer the gavel and be like make it faster make it faster make it faster which I think annoyed that a lot of people but we've stopped doing that now oh my gosh wow okay that's a good place to start yeah yeah we got a bunch of books got some sculptures and you handpicked all of this stuff just for reference we're on a tour of Harvey right now This is Winston.
1:55And what are we going to see today?
1:57Winston Weinberg:We're going to see the floor that's been renovated. So we have three floors here in San Francisco. And then one, the one we're on now, has been like fully renovated. And then we're doing the sixth and the seventh. And the significance of this painting? Yeah, so there's no significance of this painting. But I mean, I think like one thing about this floor in general is we've tried, and we'll take you up to the speakeasy after, we've tried to do like a combination of kind of like tech plus also like classic um and so you'll see like a lot of sculptures we have a lot of uh like books related to law this is kind of funny because it's john grisham but it looks like it's like oh my god ancient text like hamarabi's code or something like that how did you find them um i actually don't know for these particular ones but in general we've kind of tried to do like a combination of um when we were actually doing our brand in the beginning we were basically like could we do a copy of like the best tech companies and then succession like the show yeah my point was like kind of a combination of like an ode to you know like classics like greek first orators things like that plus like hey we're a tech company um and we've definitely tried to do that a lot with our brand and we've tried to do that throughout the office too.
3:14Seems to be working. It looks amazing.
3:16Winston Weinberg:Thanks. Okay, so we're gonna cover a lot of different things today.
3:24For starters, on this tour I want to talk about the evolution of the company. So you've been around close to four years now. Yeah, it'll be four years in August. Congratulations. Well, not yet. Happy birthday. I don't know. You have nearly a thousand employees?
3:41Winston Weinberg:Yeah, close. It's like 960 something. And this office is one of how many? Twelve. Wow. So we opened up offices pretty quickly, but the office here and then New York are by far the largest. How many employees are at those? So in this one, I think it's around like 350. And then New York is around like 300. Oh my gosh. Yeah, and then the vast majority of EPD is here, but we are growing out the other offices too. That's a really cool vibe. Okay, so when you build out globally, you have 12. Yeah. How do you figure out which cities to go into first? Is it like you have your customers and then you build around them?
4:21Winston Weinberg:Yeah, so it's actually, yeah, so we did it first based off of just like reacting to like big customers. So like we'll sign like Deutsche Telekom and it's like, oh wow, we need an office in Germany, right? Things like that. There's actually something really interesting that we had a problem of in the beginning, which is because we process sensitive data, a lot of the countries we actually needed like an Azure instance in each one, right? So like in Australia, you can't process financial data outside of the country. And so we would set up these offices and then we'd set up like Azure instances too.
4:53Winston Weinberg:And it was almost like we set up an Azure instance and then that would be like a pretty good indicator that we'd have to set up an office pretty soon afterwards just because like customer demand yeah but another way to look at it is kind of like the legal TAM overall too and so we kind of base it off of like you know how many lawyers are there in each country etc and there's how many lawyers in this company yeah in this company we have over 200 yeah and so and then on a commercial side like actually I guess doing what a normal lawyer would do. There's only about like 25. Really? Yeah and then the rest, I mean obviously we use Harvey internally for a lot of stuff so we're trying to scale that up and then the rest of folks either they work on product or they like help with go to market and things like that too.
5:44Winston Weinberg:We don't have this anymore but you used to, if you would have visited us like a year or two ago, you would have been able to tell who's a lawyer and who isn't because. Why? Because they would dress differently? Yeah so there's like a phase. Should we play a game right now? Yeah you won't be able to actually I don't think anyone on this floor actually. But basically like the first week they would wear a full suit and a tie and then about like maybe the next Monday the tie would come off and then like three days after that it's like the suit came off and then it would eventually be like hoodie and sweatpants.
6:20Yeah yeah naturally.
6:21Winston Weinberg:And you but you would be able to tell like basically like which lawyer had been here for which amount of time and you could always tell which is kind of funny so as we walk through this like how did you set up this new office like there's obviously a lot of conference spaces yeah desks this is a huge communal area do you guys host lots of yeah we we host a lot of hosts like customers and things like that we also i mean it's a little bit past lunch but we were talking about this earlier we have like an insanely loud lunch culture yeah we walked in at like 12 30 and i was like yeah this might be it's it's crazy loud like to the point where i don't schedule meetings during the lunch really like i or i don't do customer meetings during the lunch hour like it's literally like a band like from 12 to 1 30 i just will not do external meetings because it is so loud and you can't it's loud everywhere so people will eat lunch here but it's like loud throughout the whole thing um i think we just have always had that we started in Airbnb and then whenever we moved from office to office like we'd always eat lunch together and it's somehow like spread out to the you know hundreds almost a thousand employees and so we want to keep that I think forever I don't know I think like you need like a couple of those instances where like everyone gets together we've done a handful of these office tours so far and you might have the most employees in office really yeah don't tell them that I mean I think like it's something we care about a lot I think that if you have to try to force it you're probably in trouble right like it's not going to work that well um but I think like the thing that does it the most is stuff like this where it's just like you get to meet everybody from like if you go to here during lunch and again you wouldn't have been able to hear anything so maybe not as helpful but you want you can't be like oh that's the team that works on this that's the team that works on it's like everyone is all mixed together um and so i think people like coming into the office because we have a very like culture of everyone working together and people are really good friends that are like across functions so we have people like like you can come here on the weekend and there will be people in the weekend really yeah all the time yeah yeah and it's it's funny because no one works in offices and so people usually work in like a corner um and then you'll have like just a small group of people in a corner but like I notoriously lose like my card 24-7.
8:43Winston Weinberg:I think I have my living right now. But this is like my 19th. I just constantly go through them. And so I know that there's people on the weekend all the time because I'll come on the weekend and I'll post into SF General like, yo. Can someone let me in? Yeah, and there's no one too. Maybe they think that's a test. Exactly. And so every single time so far I've been able to get in. Yeah. Okay, so what's your favorite room? Oh yeah, good question. um okay favorite room is definitely a speakeasy and i'll show you that in a second i'd say my second favorite is i really like to work actually on the couch like on the couch when people come in and i think part of that is i i used to travel a lot i travel a little bit less now but last year i almost entirely didn't work out of an office i just worked on the couch and it's because i get here early and i like like saying hi to everyone for a couple hours and then i have meetings um But in terms of actual room, it's a speakeasy.
9:35Winston Weinberg:So I can show you that. It's up here, actually. Oh, cool. I'm surprised by being in office. I'll take that, though. It's, I mean, it was a good compliment. Yeah, no, no. I'm trying to think, like, the only other company that was Applied Intuition. Oh, yeah, yeah. They were packed. They're young, though. They're like 40 people, right? No. Oh, no, Applied Intuition, not Applied Compute. Not Applied Compute. I was like, applied computer. Gabe was just there like last week. Okay, so what's the biggest lore here? Biggest lore of this office? This office actually doesn't have, the Airbnbs have like way more.
10:13Winston Weinberg:Oh, I would imagine. Yeah. It was, I mean, so Airbnbs, we went through, I think, eight. So basically what we did is. At what point did you realize you needed to get? 20 people. Okay. So what happened is we basically kept getting a larger Airbnb each month when we just added more people. And then we got to a point where it was like, okay, this is definitely a problem at like 20-ish. What is the deal with people in SF, startups, getting townhouses and houses to build out of? I would like to say that there's like some beautiful reason for this or some smart thing. To be 100 % honest, it's just easier.
11:01Really?
11:01Winston Weinberg:It's just like I can go personally book an Airbnb. Okay. And then you just don't think about it because you're doing so many other things. Is that legal? What? Yes, it is legal to do that. Yeah. And then you basically just like personally book a new Airbnb each time and it's like then you don't have to worry about that overhead. What's the most illegal thing that you've done? I'm not going to say that. I don't care. Nothing. We're a bunch of lawyers. Are you kidding me? Okay, cool. Sorcery is brought to you by Brex, the financial stack trusted by more than 30 ,000 companies, including one in three venture-backed startups in the U.S.
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12:54Winston Weinberg:Visit turing.com slash S-O-U-R-C-E-R-Y. We just did a little walking tour of the office. Now we're gonna go deep into the business. Are you ready? Yeah, let's do it. Okay, so where is Harvey at today? Give me the full stats. Yeah, so we're about four years old, August. That'll be the birthday. I think it is birthday. What's the day? August 4th. It's a Leo. Holy crap. Wow, actually I didn't think about it that way. Yeah. Wow, that puts everything into perspective for me. I gotta take 20 minutes to have deep thoughts on that. I'm a Capricorn, so I don't know if that works. Oh no. Is that a problem?
13:30Winston Weinberg:I have no idea. I don't either. Leo's like a lion, right? Yeah, fire sign. Fire sign. Jesus Christ. So yeah, about four years old. And we're around 900 folks, 950 people, 960, something like that. We're at around 2 ,000 customers, somewhere around 300 million ARR. So we're growing really fast. I think it's mostly just a crazy experience, how much the product has grown. I think that in the beginning, we had this product roadmap. And our product roadmap has actually really been pretty consistent. The main thing that's collapsed is just like how long it takes to build these things. And so I think the thing that's actually moved the fastest is how much we are starting to basically build a platform for all of these different pieces of the product.
14:20So you were at 100 million in ARR last August, and now you're nearing 300 million. What's been driving that?
14:27Winston Weinberg:It's 100 % product. I'll give you some metrics. Our token usage in January was$1 trillion, like for the month of January. And this month it'll probably be like$12 or$13 trillion. Oh my god! Yeah, so the usage is getting pretty insane. Our DAU over MAU at the beginning of the year was around like 36%. Right now it's like 51, 52%. Our queries per user have basically been doubling up quarter over quarter. Hours spent is doubling quarter over quarter. I think that's better than queries. The problem with queries is as you build a more complex product, you're going to have these outputs that are like crazy long.
15:04Winston Weinberg:And so you might have people like using this like querying less, but the output is like a hundred page document. And so they're like in your system reviewing it and collaborating on it. But like everything is just product improvements. I think like last year, last year we had some moments where we just kind of had to like rebuild a lot of our product. And then this year, most recently, the main switch we did is just went over to cloud agents. We switched our entire infrastructure to that. And once we did that, usage literally just started doubling quarter over quarter. And you've raised over a billion dollars now.
15:39So have you used most of that capital? Are you using it on tokens? Yeah, now we're using it on tokens.
15:47Winston Weinberg:No, we actually haven't used a lot of that money. I think the interesting thing that we always wanted to do was actually do a lot of post-training on the models. And there's a couple problems in legal that makes this really hard. One is the data isn't available. So if you went online and you were like, I want to go find a bunch of documents that are related to a random fund formation by Blackstone. They don't exist. You'd have to go to Blackstone for those documents. right? But the thing that happened with like the last generation of coding models is you can actually take sets of documents and create synthetic docs that are so good that the lawyers can't tell the difference between whether they're created, you know, by an actual lawyer or they're created by their coding models.
16:32Winston Weinberg:And so with that, we've now actually created basically like a pipeline for creating synthetic data sets across like every single legal use case. And because we have that now, now you can start actually plus training models and that's how it's expensive. Wow. And so what are the main categories of your customers now? I think you said it's 42 % is in-house corporates? Yeah, 42 % is in-house corporates. It's growing faster than the law firms technically because I think we're at like almost 70 % of the - Is it easier for them to adopt? Is that why? No, they're just slower to adopt. Yeah, so the corporates adopted like they started like a year after the law firms.
17:09Winston Weinberg:And so, and then our fastest growing of like the, of the verticals in general is financial services is number one for sure. So like banks, private equity, asset management, and then pharma is actually growing pretty fast too. And what we're starting to do is actually like, we're going to verticalize our product too, where we have to actually like have different parts of Harvey that are different for each vertical, right? Because like the compliance and legal needs of a bank are very, very different than even private equity. So I want to go into deeper building the company out today. Like, what does it take to build an AI forward company?
17:43So I reached out to Pat Grady and he said the one thing that he emphasized was that you've been able to reinvent the company over and over again. So what was it like from the beginning to now? And what do you think was like, what are the key unlocks along the way that you think are critical?
17:58Winston Weinberg:Yeah. I mean, the key unlock is hiring for sure. Like that is it. And when I say hiring, I don't just mean hiring new people. I mean, like also development, right? And like promoting people, putting them into different roles, things like that. Or honestly, in some instances, they get outscaled and like figuring out a better role for them. But every six months, I'd say I start to get like this weird feeling of like things are just breaking. Right. And I feel it's like a pressure that like builds up. And then usually so far what has happened is every time that has happened there's been like three big changes that I need to make and I realize I need to make them and then I make them and then like the pressure like flows off.
18:42Winston Weinberg:And that happens like literally every six months I'd say. Maybe three to six months, something like that. And it feels like if you do not constantly change, you are just going to get so behind that you die as a company right now. And I think a lot of that is actually, like, who do you decide to promote? Who do you decide to hire? That is more important than anything else because a lot of people can't do that. Like, it is really hard for every six months for someone to, like, massively change how they operate. It's even simple stuff. Like, when I see somebody, like, mass starting to really break, the first thing I do is I'll go and we'll do, like, a calendar audit of just, like...
19:25Really?
19:25Winston Weinberg:Yeah, seriously, that's like the first thing. I know it's so stupid and it's so simple, but you go in and you do a calendar audit and you come up with like really good ways to be like, do you have to do any of these things? But tell me what is the main prior this week? Are any of these related to that prior? And you will start seeing people, and I do it myself, like you just come up with so many excuses for why you're doing stuff. And at the end of the day, it's like, no, that's not relevant. That's not relevant. That's not relevant. And I think that like finding people that are really good at doing that themselves.
19:53Winston Weinberg:And I think there's a decent amount of people at this company now that have learned how to do that, that's how you scale. That is how you scale at this crazy rapid pace that normally companies need to reinvent themselves maybe every five years, ten years, something like that. I think you have to do it every six months. So what's your framework for prioritization? How do you determine if you can't do something or not? Yeah. So, I mean, for me, I have a bunch of different rules. One of the really simple rules is my chief of staff, whenever she sends me something, I have to write a paragraph for why I would do it.
20:27Winston Weinberg:Not kidding. And you don't use an AI for this? No. It's really important because if you start for the stuff that is really, really, really important and again this is outside of ordinary. I don't do this for product reviews that we do weekly. If you sit down and you can write an entire paragraph about why you're going to do something, you definitely are going to do it. If what happens is you go, it's so annoying that I have to write this down. I'm like, why would I ever need to do that? You probably don't need to do it. It's not that important, right? And then what I've found is the stuff that is really important, I could write like 100 pages about what I'm doing, right?
21:03Winston Weinberg:And I know it's like kind of a small, silly thing, but it helps a lot to get a massive unlock for just blocking things. So you have a quarter of your company as previous lawyers, former lawyers. How did you convince them to come to a tech company that's completely different than a traditional career path? It was really, really hard in the beginning and now it's like I think quite easy. You show them equity? What works? Oh my god, it was difficult to explain equity in some instances, right? Like what is the value of equity and things like that. I think really like we hired in the beginning, we hired a lot of people who I think really wanted to like, they loved law.
21:44Winston Weinberg:Like they loved the practice of law, but they didn't love working in big law. And so they wanted to do something adjacent. and I think that was really really attractive and then over time like we have so many lawyers that are now just full-time PMs like literally they just like transferred in other PMs we have so many different like career opportunities that I think that now it's a really attractive place to be because you could have a legal background and then you end up doing something else right like you don't actually have to be practicing law or anything like that here what's the biggest complaint of people leaving law firms?
22:18Winston Weinberg:Oh, I'll tell you, by far, like the biggest thing that they need to get used to here is, and maybe that's your question, if it isn't, I'm gonna answer that one anyway. Because I think this is actually super interesting. Law firms very rarely fire people, very rarely. Okay. And so actually, I think one of the most interesting things to get used to is that like tech is very fast moving and I try to make it a meritocracy. And so if you aren't doing well, we're gonna have to let you go. And I think that's really hard for some people that have been in a situation where you get promoted every single year and that's how it works in BigWaft.
Read the full transcript
23:01Winston Weinberg:You get promoted every single year to exactly the same level as everyone else, despite your performance. And you don't really get pushed out, right? And I actually think that's one of the weirdest things. And I remember in the beginning, there were a couple of people that we had to let go and things like that. And people freaked out. And it's like, you know, from the tech world, that's like very normal. But I remember that being like a huge issue in the beginning. Thinking of how fast you're growing the company. I don't know, maybe you still have a billion dollars in the bank. How are you thinking about buying or sorry, building out the company itself or buying?
23:41There's like a now there's like an onslaught of M &A within all these AI companies and buying up smaller startups for talent and all this kind of increased competition. How are you thinking about that?
23:51Winston Weinberg:Yeah, devalue, like higher value on team and lower value on what they've built in terms of like, I do not believe that it is a good idea right now to go around and buy legacy technology. I don't. I think it is a much better idea to buy really, really good teams, regardless of if they worked in your space. It doesn't matter. If you look at the aqua hires that we have done, they actually haven't been in the legal AI space or legal tech. They've been outside of it. But they're really, really good teams that could work on a problem that we have. That doesn't mean that I won't do legal tech acquisitions in the future.
24:32Winston Weinberg:but I do think that right now if you're making an acquisition like the number one thing you should be looking at is just talent Because you can build things so much faster now, right? That it should literally just be talent. Like are you buying a team that? Is really really good and are they gonna align with your cultures because the other problem is like We're for you not even four years old if we go and absorb a bunch of teams Like our culture is still being built, right? And so you're gonna just like collapse yourself, right? And so I think it's a really bad idea to go out and buy a bunch of companies and then they end up being like half of your company is that.
25:07Winston Weinberg:That's a terrible idea. I do think it is a good idea to go out there and acquire some companies for really good talent. Today's episode is sponsored by VCX by Fundrise, the public ticker for private tech, allowing investors of all sizes to invest in venture capital. Learn more at GetVCX.com. Some of you may not have heard this yet, but our sponsor Public just launched something called Generated Assets, and it brings AI into investing in a way I've honestly never seen before. Here's how it works. You type in an idea like AI-powered supply chain companies with positive free cash flow or defense tech companies growing revenue over 25 % year over year.
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26:23Paid for by public investing.
26:25Winston Weinberg:Full disclosures in the description. Enterprise AI runs on Merge, the AI infra platform for integrations, agent tooling, and model orchestration. So your teams ship product, not plumbing. Mistral, Dropbox, and Drada already trust Merge in production. Start building at Merge.dev. founders scale faster on deal set up payroll for any country in minutes hire anyone anywhere get visas handled fast and get back to building visit deal.com slash sorcery that's d-e-e-l.com slash sorcery so you started this company post-covid it's nearly four years old and it was ai native from the start so i just want to know like what what is like fundamentally different about building a company today in the AI era versus the SaaS cycle?
27:16Winston Weinberg:I mean, I think pace is really important. And one thing with pace that I think people struggle with is like, you have to just assume that most things are two way doors. And people really hate this. Like they really like to kind of like think about a decision, get to like 90 % certainty, and then make a decision. The reality is like, you're probably going to have to make a bunch of decisions at like 51 % certainty, which means you're being wrong, right? And can you deal with being wrong and then quickly pivoting, right? And I think you have to do that way more than you used in the past, A. B, I think there's a huge difference on enterprise, like massive difference on enterprise, which is you cannot get away with like going in a room, building some product, and then selling it and then never improving the product over the next like XYZ years.
28:10Winston Weinberg:The users matter so much. Like the alternative is Quad, ChatGPT, these other things, like those are great products too. And so I think that the product bar for enterprise is astronomically higher than it used to be, where like you 100 % have to be constantly innovating and creating the best product for the end user. And I think in the past you could kind of get away with doing some things like really long sales cycles and things like that. And token usage is compressing margins. So how are you financing 13 trillion tokens? Yeah, I mean so there's a lot of... Okay, a couple things. One, I think every single company is going to sell intelligence.
28:56Winston Weinberg:Like that is gonna be the core of the company. And so like eventually like we were selling legal intelligence. Whereas not legal intelligence to lawyers, right? But it's still like selling intelligence. And so what I mean by this is like, we need to get to a point where we have models that perform as well, if not better than the frontier models, but cost significantly less. Like I basically think of this as like an intelligence allocation problem, where if you look at like a model, you know, the new models coming out, it's like Mythos, like 5.6, all these guys, right? Those models come out and they're incredibly expensive and they're general.
29:29Winston Weinberg:and if you use them across every single legal task, like maybe they do decently well, although on our benchmarks, like a lot of them fall apart, but they're insanely expensive. And so the better thing is, can you actually create models that like do diligence, do change of control review, do contracting, do these different things at the level, if not, or better than the frontier models, and they cost a hundred times less, right? And so I think that every single company is going to sell tokens. and I think that they are basically going to be selling intelligence. So you'll have the product layer and everything like that, jubble up your data, and then you're going to be selling intelligence as well.
30:04How do you compete with those large models that are offering competitive services? What is the thing about Harvey that you cannot copy?
30:12Winston Weinberg:Yeah, I mean, the best way to think about this is there's the product side and then there's the intelligence or model side. And you could say on the product side, it's like the harness side. I think that's conflating things a little bit too much. On the product side, you just go very vertical, right? So you build solutions that are really good at like diligence in a particular space, like all of those things. And I think it's going to be hard for the labs to get to that level of specificity on the product side. On the model side, you actually do a similar thing, which is you basically build a bunch of models that are really good at specific legal tasks.
30:46Winston Weinberg:And then you optimize them for cost, right? Like there's a world in which GPT-10 is more expensive than a lawyer. That's like a very possible world, right? And so where we have is basically you can think of like the frontier models here, commoditized models here. I think a lot of the economy is actually in between these things, right? Because the frontier model might be like too expensive to basically put in terms of every single piece of work. And so you have this massive space that is like all these vertical companies. In terms of other competitors, Legora is fast moving behind you. How do you think about, I mean, you are global from the beginning, but they are dominating Europe.
31:27So how do you close that gap?
31:29Winston Weinberg:Yeah, I mean, I would say in Europe, I think our win rate is like over 70%. So I don't think it's as much dominant there. To be honest, I think like our main competitor is the labs. I mean, you saw a clock for legal. There's a rumor that like codecs for legal, right? I think that the reality is like the labs have an incredible amount of resources, right? And so it is a race for how quickly can we build the best product that is verticalized and then how do we build the best vertical models, right? And I really do think at the end of the day, it is a race against the labs. And I think every single company on earth is competing against them.
32:02Do you think they're going to start acquiring?
32:05Winston Weinberg:Like in this space? I think that they will start acquiring in any space where they see a significant amount of traction. Have they tried to acquire you? One thing that I think is crazy, I'm not going to answer that. One thing that I think is that people forget about is the more success that any of these verticals have, they'll just keep entering them again and they'll re-enter them. And so I think people, you know, Clock for Legal came out recently. And I think a lot of people were like, oh, okay, well, it was this release and now that's kind of it or whatever. The better we do, the better other companies do, the more the labs will be like, oh, okay, we're gonna put more resources into that and so it's not like a one and done thing it is like a constant competition what do you think the biggest question right now is that people are not asking by far i think the biggest thing that people are not paying attention to is what i was talking about earlier which is do you need frontier intelligence for every single task i mean this is the best example of this is like what happened with uber recently right and it's like we are going to get to this point this is actually a weird situation where um i'm sure you know what billable hour is because people like talk about that a lot right yeah weirdly the billable hour problem is the same problem that i think the entire world is about to run into like the billable hour for for those that don't know what it is it basically is when you get a bill from a law firm it says in six minute increments what people did and then the hourly rate or the six minute increment for that for that task right why do they do that it is because they're trying to show roi They're like, hey, your legal bill is 100 grand.
33:45Winston Weinberg:We're gonna break it down into six minute increments. This is kind of like the main problem that I think the entire world is about to hit, which is I just spent a billion dollars on tokens. Where's my ROI, right? And I actually think that like, there aren't enough companies that I know of that are starting to think about how do I actually show ROI in every single vertical use of these things? And I think that vertical companies are going to have a huge advantage here where you can start to get to the point where you basically can show every single token and what the ROI was of that token for your particular task in vertical.
34:23So before we started recording, I asked you what you're most excited about. And you said benchmarks.
34:28Winston Weinberg:Yeah. We're going to be interviewing Gabe after this. What question do you think I should ask him? I think that the best question actually is why are the current benchmarks for most verticals bad? And if you look at the benchmarks that have existed for legal for a long time, I mean, half of them are like, can it pass the bar? Multiple choice questions on community property law and things like that. And I think that we haven't actually until now had, here is a very good set of data that doesn't illegal tasks from end to end. And we're missing this in most articles other than coding. Basically, coding is the only one that has a good saturated benchmark.
35:13Wow. Okay. When are you going to do a collaboration with Kim Kardashian?
35:17Winston Weinberg:Soon. She didn't pass it. She gave up. I think Kanye passed it. Or maybe that was a joke. I don't know. I think it was a joke. I think it was a joke. Damn. I think it was a joke. I think it was. I'm not sure. Well, thank you so much. Yeah, of course. Thank you. Because it lives. Why do you have a hippo? Well, because they both live on land and in the water. So they see both sides of the world. Oh my God. That kind of works. Ducks fly and then ducks fly. Ducks do fly. And then hippos eat people. Hippos have eaten a few people. Yeah, so that's not as good. I don't know why you want... Runaway jury, that's legal.
35:52Winston Weinberg:John Grisham. How many... I've noticed you have over 1 ,200 books in this office. Have you read all of them? Every single one of them. Okay. From front to back. and I could recite any part of the book. Even the culinary cookbooks? Mostly those actually. I'm like more well-versed than that. It's been a long time since I cooked. I am really bad. I'm a chicken and rice DoorDash guy and it's actually the same, it's the place called Cholita Linda and it's around here. And there was actually a time I was interviewing someone and I asked them like, hey, do you want lunch? And I pulled out my phone to do DoorDash and they'd seen that I ordered it 467 times.
36:29Winston Weinberg:Yeah, it said like the little number. You ordered multiple times a day? Yeah, twice. Lunch and dinner. Oh, yeah. So you're one of those types. You just eat the same thing every day. Well, so, and then breakfast, there's a Bluestone on like Folsom. And I get there exactly when they open. Like it's 7.01. And I get, it's like this keen green smoothie that's really good with extra almond butter. And I look like a psychopath because I'm like, I have a spoon and I'm literally eating. You eat it with a spoon? Yeah, it's kind of terrifying. I eat these with forks so you're okay. How do you even do that?
37:03Winston Weinberg:Wait, actually. I'm literally not even kidding. How? Well, I like to put like granola on top so I eat it with a fork. But then you, so is it like you fork out the granola? So it's like granola doused in smoothie. Okay, so it's granola doused in smoothie and you do that with the fork and then you put a straw in and finish it. It's like a smoothie parfait. Yeah, that actually kind of makes sense. You know? That's not that bad. No. A spoon seems like it would work better, though. But I'm making you feel less bad about the whole spoon thing. I guess that makes sense. All right, are we walking or what are we doing here?
37:33Yeah, let's start. Okay, so.
37:35Winston Weinberg:I like the duck, though. We do. The duck was great. I mean, might as well. Hey, it's Molly. If you enjoy our interviews, check out our newsletter, Sorcery.bc, where we deliver a once-a-week top deals and tech headlines email and also go deeper on our podcast interviews. Subscribe to Sorcery today. and don't forget to subscribe to the podcast on YouTube, Spotify, Apple, or wherever you listen. Link in description to sign up.
From the publisher
Winston Weinberg is the CEO and co-founder of Harvey, the $11 billion AI platform now used by 2/3 of the AmLaw 100 and 500+ in-house legal teams including HSBC, Bridgewater, Carvana, and Blue Owl.
"I think every single company is going to sell intelligence."
In this episode, Winston walks me through Harvey's San Francisco HQ and then sits down to break down the state of the business: roughly $300M ARR (up from $100M last August), 2,000 customers, 960 employees across 12 global offices, and token usage that jumped from 1 trillion in January to a projected 12-13 trillion this month.
"It feels like if you do not constantly change, you are just gonna get so behind that you die as a company right now."
Winston covers the shift to cloud agents, why Harvey's real competition is the foundation labs (not other legal tech companies), how to reinvent a company every 6 months, the future of vertical models, the billable hour problem coming for every AI buyer, and why every company will eventually sell intelligence.
Winston Weinberg: https://x.com/winstonweinberg
Molly O’Shea: https://x.com/MollySOShea
Sourcery: https://x.com/sourceryy
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𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒
(00:00) Winston Weinberg, Co-Founder & CEO at Harvey
(00:45) Inside Harvey HQ
(03:17) Harvey by the numbers
(04:11) How Harvey expands globally
(05:16) Why Harvey employs 200+ lawyers
(06:30) The philosophy behind Harvey's office
(07:35) The loudest lunch culture in tech
(09:03) Winston's favorite room
(10:04) Eight Airbnbs before a real office
(12:58) Inside a $300M ARR company
(14:20) Tripling revenue in under a year
(15:35) What $1B+ unlocks
(17:36) Building an AI native company
(21:10) Convincing lawyers to join tech
(22:14) The biggest adjustment for lawyers in tech
(23:26) Build vs buy
(26:59) What SaaS got away with that AI can't
(28:39) Financing 13 trillion tokens
(30:05) Why the labs can't just copy Harvey
(31:18) Competing with OpenAI and Anthropic
(32:03) Will the labs start acquiring?




