Uncapped #44 | Max Junestrand from Legora

I remember doing this interview in Swedish. There's a saying, like, blod smak, and you taste the blood because you worked so hard. Yeah. She publishes the article in English. At Liguara, we wake up with a metallic taste of blood in our mouths, and people in the company go, holy shit, is Max a vampire, or does he just floss badly? Like, what's going on? It's not like a culture that I think would quite work in San Francisco. Like, I don't know if that's something that you can do. Well, when we open our San Francisco office, they're going to taste the blood. Yeah, they're going to taste the blood. I love it. Well, this is going to be a cool new format. I'm here with my new partner, Chathan and Max. And Max, you're the founder CEO of Legora, which is an amazing legal tech company that Chathan sits on the board of. And so I just feel really lucky to be doing this with both of you. So you guys, thank you for making this happen. Thank you so much, Jack. It's great to be here. Okay, so I want to start with the topic of competition. And Chathan, when you invested in the company, there were already competitors out there. This was, I think it was only two years. It's crazy because Lagor is like, it's a big company already. Yeah, almost 400 people. You know, but the seed was two years ago. And at the time of the seed, it was an early market, but there were competitors out there. And so I actually want to start with you, Chetan. Like, what was in your head at the moment you invested? Were you thinking about the landscape around, like, were you just, Max is so special that I don't care? Like, what was going through your head when you did that? The first meeting that we had was with Max, was with me, Peter, and Max, actually in the other room. and interestingly i had invested in two other legal software companies pre-ai oh wow and so there was a shape of the legal market that i intuitively understood because i participated in the market and so i sort of understood the different kinds of lawyers who buy software do in-house lawyers buy software do law firms buy stuff how they sort of there was an intuitive understanding that i had and there's sort of like two things that happen when you've like sold into an industry before, either you end up hating it or you have some strong bias against it.

So there was always this idea that there's opportunity for AI in the legal market. And there was a player in the market that had already raised at a billion dollar valuation. And when Max came in to chat with me and Peter, the thing that immediately jumped out was the clarity of thought that Max had on why the general foundation models had a lot of room to grow in intelligence and how that was going to be a huge boon for the legal profession over the next couple years and so he had this like very strong viewpoint that there was something about legal data that the general models were going to serve in a very unique way Max since you're here can you like explain like what was what what was that so i think it's it's worth to go back to 2023 and 2024 when i think part of the paradigm was you should train your own models and like the general models aren't great and fine tuning is going to be really important for two reasons we were like fuck that right um one fine tuning doesn't really seem to work at least on the scale that we were operating right to train the new generational model you have to put billions of dollars into And secondly, there was so much application that you had to build on top of the models to make them useful in your environment. And back then, I mean, just solving like basic data compliance, privacy, and sort of great file uploads and like great parsing and great chunking and all of these things, that was where the value was. And there was another part of your experience, which was that you were actually embedded in a law firm. Yes. And so you were studying the shape of like what data law firms had in a way that was, you know, Bill talks about this a lot is like, does an entrepreneur strike you as a learn it all? And it was clear that the early Legora team, when we invested with five people, they were just trying to learn everything they could about how the legal profession worked.

And they didn't have any bias towards it. And so the other thing Max said, which you should share with everyone, is because they were embedded in a law firm in a windowless conference room in Stockholm. Sounds nice. Sounds great. They had a deeper understanding, I think, of how a law firm, like the data model of a law firm in ways that most of us didn't. And I mean, just to take you back even further, when we started, I offered to buy a lot of lawyers lunch on LinkedIn because I wanted to learn. So I would literally cold write them and say, hey, I'd love to meet, I'd love to talk about IP law. I'll offer to pay you your hourly fee and lunch. And they were all too nice to make me pay for lunch. And they'd often end up paying for it, right? But that did, as Chetan put it, I think it allowed us to work with customers from the very beginning. So the founding team at Legora were all engineers The first lawyer didn join until nine months into the journey When you had a lawyer join had you already sort of like set the plan and the goal for the company Like, was that done without experts? And was that important to do without experts? This is actually funny. I mean, the founding, and this is a bit of the Legora untold, like the first reveal, the company formation was in 2020. And there were four co-founders. I didn't know that. And I was not one of them. Didn't know that either. Right. They were sort of working on this intersection between AI and law for three years with the early BERT models. And even a Swedish trained version called Swibert. It was impossible to work with. Not only was it not very intelligent, it was also blatantly racist. Because I've been trained on the Swedish forums. Some racist data there. Some racist data there. When the LLM's 3.5 came, that was when the moment shifted. And so we turned this into a company. Two of the co-founders left. I joined.

And then we basically said, we're going to work in the intersection between AI and law. We don't know what that product is, but we're going to run like hell in this direction. And funny enough, the first lawyer who joined was a sort of soon-to-be customer of ours. So he was the CIO at one of the big firms in Sweden that we wanted to sell into. And he had built an early version of like a GPT plus the document management system. So basically like an LLM that could rag into the existing precedent and data that the firm was using. And he basically said, well, these guys are going to run faster than me. And if you can't beat them, I might as well join them. And that turned out to be a good decision. Are you surprised by like how strongly the legal market has adopted AI? If I had thought, you know, in 2023, let's say, or 24, like what's going to really adopt quickly? Like, I don't know if personally I would have seen it coming that lawyers would be near the top of the list. I don't know. I mean, you've invested in stuff before too. So I guess this is for both of you. But like, has it been a surprise over the last two years, the rate of adoption? Yes, it's been like vivid. But second, and maybe more importantly, the law firm market is very interesting because it's like this perfect equilibrium with, frankly, like pretty low differentiation. like if you need to do a VC deal here in the valley like you could go to any you know the top five firms and you're going to get roughly the same thing if one of them starts leveraging Legora to offer a better service at a better price point faster all of them have to adopt it so the equilibrium like shifts down and then everybody has to move so what happened in the law firm market was as soon as one big firm in a market sort of adopted Legora and went public with it everybody else had to do the same. That's not necessarily the same in the in-house legal sector, right? Like if one big bank says it, like another big bank doesn't necessarily need it. But was there something about the process of the way work got done or the structure of it that

allowed Legora's product to drive so much value so fast in a way that it did force that sort of prisoner's dilemma? I just think the legal sector was so underserved with great software for such a long time that there was this like a lot of built up problems that we could easily solve with LLMs, but they were really hard to solve like pre-LLMs. I also think you guys had a great insight early on, which was that there was like a deference and respect to the customers. That lawyers are really smart. They're extremely well-educated. They're tech savvy. They're not programmers, but they're very tech forward. They use like the latest software, they use the latest devices. And so they were all going to be playing with ChachiBT and Claude. And so if you showed up with a legal AI product, it had to be better than the foundation model. Otherwise, they were just going to say, why are you deserving of my dollars? And Microsoft Copilot rolled out very quickly. Like every law firm in the world is a Microsoft shop. Yeah. Like everybody works with Outlook, Microsoft Word, and where they store their documents, basically. What have you found, like to the point of you have to be better than the models, if you had to break down like as a vertical AI application what have been the things that have allowed you to just be so much better than the models that it's worth you know the incremental investment so I think in the beginning there was a lot of just foundational problems with the models like you had to guardrail them very hard to make them useful you had to build citations you had to build good rag systems you had to overcome context window problems and there was a lot of rate limits issues, so you had to juggle different models for different types of tasks. There's just so many incremental basic things to solve. I think as time has progressed, our product has moved further away from what the foundation models are and much more into this enterprise platform where we going to transact billions of dollars of legal work on the platform And we moved from building a lot of the agent work ourselves

and we sort of let the models rip a little bit more, like OpenClaw or Clawbot or whatever it's called these days, where with Opus 4.5 and Opus 4.6, there was an extraordinary difference in level of intelligence and instruction following capability. And so I see our job as let's provide the model the right environment and the right tools and skills to leverage. And then let's build a UI and an interface to the rest of the business so that they can all leverage it comfortably and with a lot of trust. I do think that the model capabilities improving so quickly makes us run faster because we have to be three standard deviations ahead of any general capability. And that's like a very good motivator. as somebody that's invested in a lot of software companies. One of the unique things about an AI software company is that it's tactically built differently than a traditional software company. And I think it's becoming more known now, but when you guys first started and you guys built up this org, the way you designed the org made a lot of sense for the product you were building and what you just described, which was like, we need to deeply understand model capabilities and then we need to bring that to our customers in a way that's deeply differentiated, which, as you explained to me, meant we need to invest heavily in understanding the models, which then would lead to understanding what to build. But as models got better, your features may not matter in six months. And so talk about how that led to an organization that was heavily technical, heavily engineering and researcher-led. and for a company as big as you are, you have very few product people. The number of product people you have essentially rounds to zero. You have like leaders, you have a couple of leaders,

but that's it. I mean, the founding team, we're three engineers. And so, you know, the most natural hires were, let's grab all the smart engineers that we know from college and let's add them into the org. And in the beginning, you know, we had to build our own agent framework because like Langchain and these things that we initially built on, like couldn't get customized to the level that we needed back in 2024. As we understood more about the model capabilities, but also of the problems we wanted to solve, like let's take due diligence as an example. It's really hard to solve a due diligence task in a chat-based format because you need to review hundreds of documents. And hundreds of documents are never going to fit into the context window of a single model call, at least not back then. And, you know, probably not now either. So we built this new product that we call Tabular Review, big matrix where you would throw in tens of thousands of documents and you'd throw in all the prompts and it started running all of them in parallel. And what we basically did was we just said, okay, three engineers, you're now on Tabular Review. This is your own company, run. Over 10% of the EPD org at Legora are XYZ founders. So our head of engineering, Jake, who joined, he was a solo founder in YC. Our VP product, Adrian, was also a legal tech founder in YC and happened to be both a GC and a lawyer. And so as we've progressed, engineering and product has sort of stayed at the core of who we are and what we do. And I also think that everything else is sort of an expression of that. Like we can only market what we actually build. We can only sell what we actually build. And product lead compounds. I think as you put it in the beginning, we did not show up first. Like Legora was not the first product that many legal teams looked at because there were earlier entrants. So we knew that we had to show up and be best. And if you want to be best, well, then you need to invest in product. You need to invest in engineering. And I think you need to build that culture of like reliability first. We actually had a time period in the company for six months where we didn't sell basically

because we weren't ready to like hit the gas on onboarding a thousand lawyers a day and knowing that the product was going to keep up with that. So we took the early hits of like investing in that. Talk more about that period specifically, you know, the seed round you did with us was in March of 2024. The product went to GA October 1st, 2024. And you called me early September, 2024 and said, you need to come to Sweden because all of us need to sit in a room and just talk about where we are and what we need to do to get this thing out in a month. And, you know, we came and we sat the whole, literally the whole company, which wasn't that big back then, it was only 10 people. Yeah, and so it was like the whole company, the founders, chicken wings and beer. Yeah. And peanuts actually, those were the three things served. And there was a very open dialogue of like, how do we get this thing out in 30 days Because at that point because you were essentially weren facing the market test you were building there were 10 000 things you could build and that the sort of like outcome of that discussion was that we're only going to focus on three use cases yeah that's right so talk about like well one you know you calling me to tell me to come to sweden to have that discussion and you actually showing up reflecting on it that was one of the most important things that you did in the company and the founders that are in the company was at that moment, say we have 30 days to go. We're just going to sprint at these three things, not the 15 things that we could do. Yeah. So I think there was this feeling of like, you got these LLMs, they're so powerful. We learn about all these use cases in the firms and with the clients that we work with. Let's go solve all of them. Like wrong decision. Like you can't solve 15 things at the same time. And so we had to kill a few darlings and we had to like really double down on the stuff that we thought was going to work. And we looked at on the market and we

basically saw a few things that were really working like as a paradigm for LLMs in legal. One of them was this big sort of tabular extraction. Another one was embedding it deeply into Word and Outlook. So basically having Legora be accessible wherever the lawyer is already working. And we were still called Leia back then. This was very early. We took the entire company, we had like a town hall. And I remember showing some numbers where like a particular company that just had one of these features were doing more revenue than us. We were doing like 1.5 million at the time. That felt very painful because we thought that we had a better suite, but we didn't have as much revenue because we were based in Sweden and we were sort of mostly selling to still European firms at the time. So we just said, let's do these three things. Let's do them better than anyone else and it's going to be worth to buy our suite over anybody else's and so i wrote this like very short product manifesto send it out to the entire company and we sort of rallied the troops i think it was off the back of that that we had our first quarter where we doubled revenue so we went from like 1.5 to 4 we're like oh this is like ripping and it's flying off the shelves and then in q1 we had another quarter where we doubled where we went from like four to eight It's like, whoa, okay, now we're talking. And it became time to launch in the US. We hired Patrick and Evan who joined from a competitor and we sort of had our first boots on the ground in the US. And then we felt like, okay, what we have is like a winning formula. So we just need to crunch it out everywhere. And now I think we're at another interesting point in time where we've built all these different tools, But the paradigm from now onwards is humans are probably not going to work with all these tools. Basically, agents will leverage the tools that we built. So I remember early when MCP came, our CTO basically went, well, now Legora has two users. It's human users and agent users.

And every new feature that we build has to be able to cater to both. And now we're seeing more people like basically use our agent that uses the tabular grid or like our agent who uses our word editing capabilities than humans actually going and using those features at all. Chase made a cool point to me recently, which is that because you, we were talking about how, you know, companies that are pre-AI and companies that are just fully AI native just have to be built differently in various ways. And the fact that you didn't build a pre-AI company, I think, gives you sort of like, you know, this unshackled mind to like, you're not even trying to think about some past alternative. You're just like, given what's in front of me, what should a company look like? And, you know, you talked about how like having YC founders inside the company has been helpful. And I'm sure there's like a lot there. But I'm curious about like, what are like the main tenants that you've observed? You know, because now you've probably hired a lot of people who did, you know, work and build companies pre-AI. What do you think are the main tenants, ideas, cultural concepts that have been important to you just to make it work in a fully AI-native world? Yeah, so I think this idea that Chetan brought up around you have to be willing to kill the stuff that you've done in the past is very important. Because I think in more traditional software, you had to build the foundations and then you build the stuff on top of it and you sort of kept building the stack. and in that world it was also very good to have like a technical architecture where one feature would rely on the same microservices as like other features but the problem is in ai like maybe that feature now needs to scale really really quickly and the cost of writing software is so low that it's basically better to build your own stack for like each thing and now that we hire you know finance professionals or even like lawyers internally to legora or we just hired our first like tax person. I think they come with a set of ideas of like, oh, this is how I used to do it in my old

everybody's forced to relearn, I think, and also question what their value is on top of the general model capabilities in a way, which is like very painful. Totally. Brett Taylor talked about this on this podcast too, basically that like people are going to build something and six months later, we might just kill that thing and everybody needs to be comfortable with that, which I think historically would be a lot of painful internal conversations. Like, do you have to change? Is that like a different culture for people? I think it's a different culture completely. I mean, I think the culture is you don't maximize for your function. You maximize for the company always. And I'm very upfront with every exec who joins Legora that in a way, you're joining with an expiration date. And you have to continuously prove that you scale out of that in a way because the company is scaling so exponentially. and I don't know if it was Mark Zuckerberg or somebody talked about like let's hire people with high y slopes and not like high y intercepts I think about that a lot mostly because I've had to do that right like I did not join or start Legora with a lot of experience but I've proven that at every new point in time I've scaled with the business and so other people in Legora needs to do the same and I think that goes for every function and you know I think an engineering team that shipping the amount that we do previously had to be like 500 people and now we can get away with being 50 and i think there's even a question of like do we need to be more than 100 engineers or is the bottleneck here really knowing what to build and building it in the right way and designing an experience that works for you know hundreds of thousands of people that we now have on the platform. I think the paradigm is like shifting all the time. What's nice about our work is that engineering is sort of a roadmap of what's going to happen in other industries too.

Like I think the general models have come the furthest in coding, but also those organizations are very quick to adopt and shift. And so like engineering orgs are today looking slightly different. And I think we can expect the same in legal organizations. Two things you brought up that you should, it'd be great if you could dive into. One is, Legora doesn't really have a long-term roadmap. Like, you guys react and build today. And, you know, when you first got started, you had this, like, nearly weekly cadence where that's how long you would roadmap to. And these days it feels like you almost roadmap on a daily cadence. And so, like, things change tomorrow. Like, you wake up and it's like, we have to do something different. talk about that lack of roadmap and then also the other thing that you've invested heavily in is just understanding model capability and the sort of proprietary eval infrastructure you've built where you've had these conversations with the foundation model companies of how you're able to identify latent model capabilities that they themselves are not aware of i mean on roadmap like way back every new model just like unlocked new things right and when we got early access to gpd like 4.5 and you just realize that holy shit like now it can finally draft like an end-to-end thing and we don't need like all these harnesses and like things around it that's amazing like let's unleash it in a way that that works by the way to do that you need sort of like a low ego organization because you build all this ip and all this software yeah and you're like okay now the model can do it yeah delete it all worked really hard for six months yeah we're deleting everything. Yeah, it's incredible. Yeah. But I think a lot of the things that we have built, we know that we're going to delete someday. And I guess you kind of need people to opt into that at the front end for that culture to really work. We've also talked about it as like, if we were, if we're here today, and we start building for the future that's over here, like, that's too far out. Like, our customers are not going to adopt that they don't understand it yet. So we need to take

them on the journey. And we need to take them on the path of being successful, which I think every iteration cycle now is shorter like back in 2023 2024 i think it was like slightly longer like you'd have a quarter or two quarters because the models weren't moving that fast every upgrade was pretty incremental but now it's like flipped opus 4.6 flipped uh in capabilities so now we have to revisit a lot of the things that we built do you know what the next flip you're waiting for is like is there a thing so actually i don't think so it was funny i was at the customer advisory board at anthropic yesterday, which is I'm wearing my like Dario shirt here. You look like Dario. Thank you. Most of that conversation was about the models are now intelligent enough where they're no longer the bottleneck. The bottleneck is all of the software around putting the models in an environment where they can execute and do work and humans can review that work in like a trustworthy way they seeing that across basically every single vertical and every single company so i don really think that we're waiting for new model capabilities anymore there's like nice things to have like it's nice to have better context windows it allows us to you know do less garbage and context management or when you overflow the context in memory and so on you have to like deal with it to refresh it So there's nice to haves, but I think we're at a point now where we just have so much building in front of us in terms of bringing the model capabilities into our world that that's where all of our focus is. I think on sort of discovering what the models can do, we thought very early on that evals were going to be important and both building up like an exercise of building new evals, but also building out evals for all the use cases that we want to cover for. Because in the beginning, it was a lot of like, oh, how good is Sonnet? How good is Gemini? How good is GPT? And so we had to test them on the different evals. And a lot of our customers actually contributed this. So they would give us manual tasks that they used to do.

And they'd tell us, here's the evals, and we're going to call you when we can get to 100% on these evals. And I actually remember it was a funds-related use case, an LPA key term review report that a Danish law firm was spending like three days on, basically. Like an associate would spend three days putting together that report. In summer of 2024, we had like 60% accuracy on that task. By the end of that summer, we had 100% accuracy. And once you got to 100% accuracy, I mean, that task is done. Like it's over. I've adopted this mentality internally that if AI can do something it will do it and so our product like we think a lot about solving legal tasks end to end and once a task is conquered it's done like we just like strike it out and we're on this path of solving more and more complex tasks like you start with NDAs but at some point you get to full-on share purchase agreements which are very complex, but we're going to get there. I think the question for these organizations who are maybe more traditional and trying to keep up with the pace of AI is how do you do that while at the same time do your normal job, right? I think a lot of the organizations that we work with really struggle with keeping up with the technology uplift, even like our developments. And so we're struggling by getting all the latest models and then we're turning that into product and they have to adopt it and then their customers and it's, yeah. There's a question I think for both of you. As I'm listening to you talk, I'm sort of like, you know, I can sort of see the, you know, the hill climb that you're on where you've like attacked, you know, one part of it and the next one's coming and the next one's coming. And, you know, one of the things I'm thinking about is for, let's say, a new startup in legal, what would the right strategy be for them? Like, how do you possibly get into the mix fast enough for all of these things? And then exit to Legora.

Exit to sell Legora, that's a good one. How urgent is it to grow really big, really fast for Legora, given all of the dynamics around? Chetan, I'm curious, like, how you think about this? Like, is it the same urgency as always? Or do any of these dynamics mean that, like, getting to real scale is more urgent here than other places? We can go back to sort of launch day, October 2024. So when they launched, roughly the ARR of the business was rounded to a million dollars. If you just go back into that moment, there was this exercise of should we make a budget? And what we all decided around the table was there was no reason to make a budget because we don't know anything about the market. We don't know if people even like our product. We had instincts, but like we just needed to go literally as fast as we could to get the product as many hands as we could. Because ultimately the whole theory of the company didn't work until we got product feedback. And so that was literally the aim. The aim was like get this out as quickly as possible in as many hands as possible. And I think one of the things that Max did, again, this is like it's cliche to say it's first principles thinking. but it is because it's like the team was unbiased by how to build a software company and so one of the things that you learned in sas was the way you do pilots is like you would go in do like a time trial pilot where you would like give them access to the application and the minute the the trial was done you would turn it off and then they would have to make a purchasing decision a big The thing that happened with Legora is they would go put Legora into your organization and whatever you put into Legora, they would like leave behind, even if you didn't want it. And so there was this idea that like Hey you adopted AI you did stuff with AI you built some practices but you not like whatever skills you built or whatever IP you built it kind of yours And like we can leave that behind It not a big deal It like it your skills it your things that you learned

And then Max went around and just gave people like 30-day pilot, 60-day pilot, whatever they wanted, 90-day pilots. They would run these competitive pilots, right? So they would say, okay, there's a couple of companies on the market. We're going to want to A-B test all of them because it's really hard to pick just based on the feature set on your website. And in those pilots, I think we did an extraordinarily good job of delivering value. And so when the sort of 30 days were up, like if we shut it down, it would be a riot, right? People would roar and they'd be like, we've never seen software adoption like this in a legal organization. We need this. And we need it now. And in those pilots, we would demonstrate much better than any other company the value that the product and the service around the product could bring. So we hired all these lawyers who are now called legal engineers. It's a great term, I think. Forward deployed legal engineers. I was just going to say, what about FDLE? FDLE. That's right. FDLE. FDLE. And they're amazing. Like they're the most tech savvy lawyers in different organizations who don't want to make partner because like, you know, that's one type of life. And they want to work in a tech company. And now they get to work with their practice that they're amazing at and technology. And then they get to work with the best legal organization in the world and like driving that change. And I would think once you're embedded in these organizations, it's got to be sticky. I think Ligure is very sticky. We've ripped out our competition at many organizations. What creates stickiness? So the stickiness is the use cases and the cadence. And if you've invested time in building up a workflow that works for you, why would you want to switch? So is it that? It's usage. It's not data? No, not yet. Not any real technical implementation, which is great. because our competition has been deployed in a lot of places

that sees no real usage or very simple use cases, which means that we can go there, show them, and display clearly in a pilot that we deliver much better, and then we can easily swap it. So we actually have a dedicated migration team moving deployments over to Liguara. And I think this is where we often talked about not only product engineering velocity, which came naturally to the founders here, because they were engineers, but also this idea of velocity of customer interaction, which was like, if a customer wanted to buy a certain way, wanted to do a pilot, whatever, just don't add friction. That was actually the key unlock, which was like, there was this idea of let's just go get this in everybody's hands and not to have any bias. And so one of my favorite stories about Max is that he came to San Francisco to sell a bunch of clients. And then, you know, he texted me and he was like, are you free for dinner? So we met for dinner. And then he asked for a ride to the airport. And I casually asked, where are you going? Expecting to say Seattle or LA or something. And he was like, I'm going to New Delhi. I was like, why are you going to New Delhi? He was like, well, like one of the largest firms in India wants to buy. So I figured I'd like go give it to them that's crazy and so you know this in sas it was like no like do the west regional yeah then do like the east regional yeah then do like western europe and then eventually hire an apac head and then it was like this thing yeah and because this company didn't by the way there's going to be like a year of engineering work to be even kind of ready to 100 and because like this company and this team had never built a pre-ai software company they didn't know they weren't supposed to go sell in india early like one quarter into like selling the product so max got on a flight went to india and a customer in india bought so it was one of those things where like because they didn't have the patterns they were able to get big globally in parallel you

You know what I also wonder on this? We talked about this a little bit, that like being a Europe-based company means that you are multinational from the beginning. You have to. And I think this, I'm sure some of this is pre-AI. And I think it, I mean, a lot of it is. I also think there's a thing where like if you started in Europe, you've already learned how to sell to 10 countries. And you know that there's differences in the way that the cultures work and the way they purchase software and what the rules are and the regulations and these things. And so I'm curious if you thought about that when you invested and you're like, well, actually, maybe coming to the US will be easier one day. I'm curious your experience on that. I mean, Y Combinator weren't particularly excited about backing a company in Sweden. I remember the first interview with Gustav and he's Swedish, like a Swedish partner at YC. And he goes so you going to move to the States right And I go yes yes of course Like the cue to say yes so that you get the invite to go to YC Noggins You guys are opening a suite enough And then I came to YC, and I left three days later, because I had so much business going on in Sweden, and I couldn't do work between 1 a.m. and 10 a.m. That was just impossible. But the Swedish legal market is smaller than Kirkland and Ellis. So, of course, you have to expand. and naturally we went to Finland and then we went to Denmark. Then I was like, well, I think we got the hang of it. And the most important thing was like the first customer we got, Mannheimer Svartling, the big firm in Sweden, their managing partner had such a good relationship with the other firms in other non-competitive countries that he would just introduce me and I would fly down and I'd say the same thing as I told him, like AI is going to change the world and you're going to need a partner. I'm here, let's work. you know that sort of made it made it our all start but then the the move to the uk and the us was like when we really started ripping how different was coming to the us versus going to

finland not all i i had a rule so there's actually a few swedish companies that tried to go to the us but did so unsuccessfully like klarna they tried many times before they like actually made it work and my rule was if we can serve two of the biggest clients in the world or in the us from Stockholm, then we're ready. And then we'll open an office here. So Cleary Gottlieb, amazing Wall Street firm, and Goodwin and Procter. And we served them both. We won their business in competitive pilots. And we could serve them from Sweden. We did a lot of flights back and forth. But after they signed, we said, okay, amazing. Now we're ready. Let's open an office here. So one thing about the market structure of legal that we knew about here at Benchmark, ahead of investing is that legal has this unique market feature that it's a services industry. And in services industries, technology adoption is slow at first and then rapid later. So if you just look at any marketplace idea in a services area, it's like the marketplaces are usually like supply constrained. And then the minute supply unlocks, all of the supply comes online into the market and then you become demand constrained. And so if you study marketplaces, especially marketplaces around services, this is something that you just like fundamentally learn as like one of the rules of marketplaces. And so in legal, the market structure is such that like the initial adoption will be very slow and hard, but once it unlocks, it like really unlocks. There's some kind of like exponential viral coefficient that happens there. That's one part about the legal industry that's really interesting. And then how it overlays into software in legal is that if you look at the most successful legal software companies, they were all started in Europe. Pre-AI too, by the way. I had a hypothesis that part of the reason why you get that way is that you're used to selling the multi-geography and multi-rule systems

from day zero. So for example, Legora sold to a Swedish firm and a Spanish firm and a Finnish firm. and so yes they're like laws at the european union level like from the beginning yes needs to work for many people that's right and if you start in the u.s what you end up designing is that there's the federal legal system there's a state legal system and there's regional but it's not as bifurcated as like literally different countries and different languages and different languages and so you build all this stuff on day zero that you don't if you start in San Francisco. And one of the interesting things that Max showed us in the prototype in the first meeting is he had multi-language support already built and he had like multi-legal framework support already built. I remember I demoed Sweden and Spain. That's right. And that was like remarkably impressive because it was a company with five people thinking global scale because they were forced to because they couldn't serve the stockholm legal market totally those two things just meant from they launched the product they got a bunch of people to sign that immediately it was like let's go get the two big firms in every geography because we have to it was global from day one and now i mean laguerre i think has become a a hub in like a technology hub in europe like people from germany from the netherlands from spain from italy they're all moving to stockholm even in the winter to come and work with us. So talk about the culture part of it, which I think stands out a lot. And so it's hard to describe to people what it's like to visit the Ligora office. I mean, when you came back from a Ligora visit recently, you were like, oh my god, they are so good. Like something's going on there that I haven't seen before. It sounded different. I don't know if it's Ligora specific or if it's like something that happens in Sweden that can't happen in America, but like you were affected by it. It's true. Initially, even in the

group of five or in that group of 10 in September of 2024, or group of 15, however big the company was, there was like a common thread amongst everybody. They were like deeply technical, deeply intense, and a desire to win. And they were like thinking globally from like day zero. And because they were in Stockholm, they also decided to recruit all over Europe from day zero to bring people to the Stockholm office. And so what ended up happening is I think you end up becoming a magnet for anybody that wants to build at the forefront of AI with a level of intensity and determination, this idea of like wanting to win. So like, what did it feel like to you on your recent trip? There's like a few hundred people in there. Like, what did that feel like? The level of engagement and buy-in to the company mission was truly unique. And I think the company has done a great job with this idea of building for the company. And I really do think building an AI company is like a real test in ego. It's like you literally can't have an ego because you have to have this idea that AI is just going to do this. It's going to be better than us at everything at some point. And it's just going to do this. The foundation model will do this capability. And I'm like puzzling through this and it's really hard and it's an amazing feature. And we have these high bars of quality and polish. So we're going to like ship fast, work really hard, build this amazing feature, and it's going to disappear within 12 weeks requires an extreme amount of buy-in and an extreme amount of humility that like, we're just riding this massive wave and we don't know where it's taking us. But like every day we solve today's problems and we don't worry about tomorrow because it's a different world. There's a different type of energy buy-in cadence that comes with that culture and i think that it's really interesting the disadvantage of stockholm has now become lagora's advantage of being in stockholm which is that their talent population that they that they

get to hire from is not just in stockholm it's all over europe and now it's like all over the world because anybody that has that attitude is welcome to come join stock i think our competition is you know has remote days like three days in office everybody lives at six Like from very early on like we serve dinner at eight every day A lot of people in our region are sort of tired about like all these big American winners. And we know that we have the talent and the grit and the prerequisites to build a generational company. Like, yeah, we had to go to the US to raise money because we want to work with the best VCs in the world. but like there is a a level of like we can also do it right and we have spotify just on the street how are you going to get this level of fervor in the u.s i think we have i think we have a very unique culture in our new york office is it different or it's very different like people well it's not different from stockholm oh okay but it's we seeded it with the you know the culture carriers from sweden i came to new york and i think tactically this was a really cool thing they did which was i think like you should tactically talk about how you make everybody interview in stockholm yeah and then they have to onboard in stockholm everybody wow so you live in new york you're going to join the new york office you're going to stockholm yeah people who join in sydney have to go on a 24-hour flight to onboard in stockholm so you can't onboard anywhere else but stockholm yeah and then when they first opened the first international office which is new york and actually london too you did this with which is like people that were based in stockholm moved yes to those offices to set a cadence it was like it's all gonna be the same as stockholm and like the germans who joined like where like they have to move to stockholm and they'll work

here a year and then they can move back to open the german office like you have to get it right it's a fascinating thing where you know i've been part of many companies that have many offices and every office tends to take its own character. And I remember the founders of Legora saying, we want every office to feel the same, which was itself a different way of thinking. Every time Max has had me visit the company, I visit during dinnertime, which is 8 p.m. Like that's when they have guests is like 8 p.m. And that's been the case in every office. And so that's another like thing that happened at this company. And it's interesting to me that it continues to scale, which is like you can continue to onboard in Stockholm because like every... The 400 people that joined before you onboard in Stockholm. So you should too. I mean the only reason why we have that rule was because I did an internship at McKinsey and we have dinner at it So I was like I guess that how you do this Totally It also like in some ways doing the U office in New York Obviously, it's not like it's some outsider city, but from a tech perspective, there's a lot of people in New York like, I want to work at a great tech company. And there's obviously been more there than in Stockholm, but it's still different than San Francisco. And so I think you could probably bring some of that cultural thing there as a result of that too. Now we just opened in Houston and we're opening in Chicago. So it's all the big legal hubs. Is this correct? Did you do a reference with Daniel Ek? Yes. Yeah. And I think I heard this from you. You asked him about like, what is the culture at Leia? And I think he said something like, they're pretty intense. That's right. We were very upfront with that even in interviews and not intense to the point where it's like not fun, but like coming, like showing up as number, like being number two in this space is like not an outcome worth fighting for. Like, then we might as well go do something else. Like, we're only going to play here to win. You think number one, number two will just be vastly different outcomes?

Oh, yeah, completely. And it doesn't actually matter if that's the case or not. But that's the way I meant. Gives you the right mindset. Yeah, yeah. Like, I think everybody's dialed into that. And I remember doing this interview in Swedish. And there's a saying that you like, like, blood smock. And, you know, like, you taste the blood because you worked so hard. And I basically told her in Swedish that, yeah, you know, sometimes I wake up and, you know, it's a Swedish saying. I'm like, I'm so tired. And then she publishes the article in English. And the saying doesn't make any sense in English. Like Max is bloodthirsty. No, it's like the Legora founders wake up. At Legora, we wake up with a metallic taste of blood in our mouths. And people in the company go, holy shit, is Max a vampire? Or does he just floss badly? And what's going on? How do you feel about that now? Now it's become this thing. The Americans are hashtag blood smoke. Everybody's in on it. It's amazing. It's a cult. I mean, I can feel the energy of it. It's not like a culture that I think would quite work in San Francisco. I don't know if that's something that you can do uniquely. Well, when we open our San Francisco office, they're going to taste the blood. They're going to taste the blood. Blood smoke. Yeah, they're going to taste the blood. I love it. All right. My last question is you just raised a big round, which is awesome. Congrats. What does this mean for the future? What's coming? Well maybe first off just to give you a bit of insight into the round like every round at Legora since Chathan has been a preempted round i don think i ever actually gone out to like fundraise since since yeah it been it's been very it's been very pleasant we actually also have a history of taking the like lowest term sheets um i remember taking like we were actually so this is funny like we were negotiating the number of shares that chathan was going to buy on excel in front of us and he goes I've never ever bought a company where I didn't get 20%. And I go, well, I'm never going to dilute more than 17.5%.

And we sort of look at each other and go, well, I guess we're in a bit of a stalemate here. It's like the immovable object meets the force. And so we just put on Excel. We write down the exact number of shares. And we start going decimal by decimal until we're both. That is so legal coded. Yeah. Just the nerdy Excel. It was wild. So you end up investing like 9.521. And we're both equally unhappy or happy. That's great. I think we're both happy. It's a beautiful way to start. We're both happy. But the Series D has been really great because it's the first time I've done it together with someone else. So David, our CFO, who just joined from Vanta, he's an absolute monster. It was funny. We had our company-wide kickoff and you get to pick the song you want to walk out to. and he goes max i want monster by conya west and i go okay dude and it's like the lights drop and i'm like i have a big surprise for you everyone like david is joining us our cfo and like the speakers just explode wow it's like and i don't know if you heard the song yeah yeah of course it's like starts and everyone's like holy shit what's going on and he like comes up on stage and he's just like so much energy and in the references people refer to him as a cf go I was like, that's amazing. So he and I did the round. It was super fun. It was the first time we went out to actually do a fundraise. We had a deck this time. And it was wildly oversubscribed. I think we ended up having like 1.5 billion in demand for the round. It's a lot. It was crazy. But we're super thrilled about Excel coming in and leading it. Some great participation from Manlo and Bain. It's awesome. It's a huge testament to what you've done and super exciting. and I think you're just getting started. So Max, thank you for doing this. Chetan, thank you as well. And really enjoyed it. Thank you so much, Jack.