Uncapped #44 | Max Junestrand from Legora

I remember doing this interview in Swedish. There's a saying, like, "Blue Smok." And you know, like, you taste the blood because you worked so hard, yeah? She publishes the article in English. At LaGoura, 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, it's going off? 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 gonna taste the blood. Yeah, they're gonna taste the blood. I love it. Well, this is gonna be a cool new format. I'm here with my new partner, Chathen and Max. And Max, you're the founder CEO of LaGoura, which is an amazing legal tech company that Chathen sits on the board of. And so I just feel really like 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 Chathen, 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 LaGoura's like two years ago. It's a big company already. It's 400 people. You know, but this, 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, Chathen. Like, what was in your head at the moment you invested? Were you thinking about the landscape around like where 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 Max, and 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 software. 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, probably. So there was always this idea that there's opportunity for AI in the legal market. And, you know, there was a player in the market that had already raised an $1 billion 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 of years. And so we hit 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 explain like what was what was that? So I think 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. One, fine tuning doesn't really seem to work, at least on the scale that we were operating. To train the new generational model, you have to put billions of dollars into it. 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. 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 at all? And it was clear that the early LaGoro team, when we invested with the 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, you should share with everyone is because they were embedded in a law firm in a windowless conference from Insta.com. Sounds nice. Sounds great. They had a deeper understanding, I think of like 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, like when we started, I offered to buy a lot of lawyers lunch on LinkedIn, because I wanted to learn. So like literally, cold right now, say, hey, I'd love to meet. I'd love to talk about like IP law. I'll offer to pay you your hourly fee and lunch. And they were all too nice to like make me pay for lunch. They don't have to live enough paying for it, right? But that did as Chatham put it, like I think it allowed us to work with customers from the very beginning. So the founding team at LaGoura were all engineers. The first lawyer didn't 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? So it's actually funny. I mean the founding, and this is a bit of the LaGoura untold. Like the first real, 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 train version called "Sweebert". It was impossible to work with. Not only was it not very intelligent, it was also blatantly racist. Is it, I've been trained like the Swedish like forums, some racist data there, some racist data there. When the LLAMs like 2.5K, that was when the moment shifted. Right. And so we turned this into a company, two of the co-founders left, I joined. And then we basically said, like 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 LLAM that could drag 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 Tuesday. 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 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 visit 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 LaGoura 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 LaGoura 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 banks has it like another big bank doesn't necessarily need it. But was there something about the process of the way work out done or the structure of it that allowed LaGoura's product to drive so much value so fast in a way that it did force that sort of as there's a lot of. 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 LLM's, but they were really hard to solve like pre LLM's. I also think you guys had a great insight early on, which was that there was like a difference in respect to the customer 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 chat GBT and cloud. 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? Microsoft copied the rollout very quickly. Like every law firm in the world is a Microsoft shop. Everybody works with outlook, Microsoft Word and where they store their documents basically. So what have you found 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 guard rail 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. There was a lot of rate limits issues. So you had to like 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 products has moved further away from what the foundation models are and much more into this enterprise wide platform where you know we're going to transact billions of dollars. of legal work on the platform. And we've 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 Cloudbot or whatever it's called these days, where like with Opus 4.5 and Opus 4.6, there was an extraordinary difference in level of intelligence and like 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 capability is improving so quickly, makes us run faster, 'cause 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, like 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 is like we use deeply understand model capabilities and then we need to bring that to our customers in a way that's deeply differentiated, which is you explain to me meant we 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 as a company is 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 where 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 age and framework because like Langchen 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 probably not now either. So we built this new product that we call tabular review, big matrix where you would throw in, you know, tens of thousands of documents and you'd throw in all the problems 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 LaGoura are XYC founders. So our head of engineering, Jake could join, he was a solo founder in YC, our VP product, Adrian, it was also a legal tech founder in YC that happens to be both GC and the lawyer. And so as we progressed, engineering and product has sort of stayed at the core of who we are, 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 LaGoura 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 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 hit the gas on onboarding 1,000 lawyers a day and knowing that the product was going to keep up with that. So we took the early hits of investing in that. Talk more about that period, specifically, 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 we came and we sat the whole, literally, the whole company, which wasn't that big back then. It was like 10 people. Yeah. Like the whole company, the founders, chicken wings and beer. Yeah. And peanuts, actually, those were the three things. 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, you know, weren't 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're actually showing up. Yeah. Reflecting on it, that was one of the most important things that you did in the company. And the founders that 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. You can't solve 15 things at the same time. And so we had to kill a few doorlings 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 LLGORA be accessible wherever the lawyer is already working. And we were still called Leia back then. Like 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, we're 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 four. 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 4 to 8. Like, whoa, okay, now we're talking. And if it came 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, well, 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 gonna work with all these tools. Like basically agents will leverage the tools that we built. So I remember early when MCP came, our CTO basically went, well, now LaGora has two users. It's human users and agents 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 you 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 and 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 like the main tenants, ideas, cultural concepts that have been important to you just to like make it work in like a fully-AI native world? - Yeah, so I think this idea that J. Thun brought up around, like you have to be willing to like, 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 LaGoura 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 company. and. 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 very painful, totally. Brett Taylor talked about this on this podcast, too, basically that people are going to build something, and six months later, we might just kill that thing, and everybody is become comfortable with that, which I think historically would be a lot of painful internal conversations. Do you have to change, is that a different culture for people to do? I think it's a different culture, completely. I mean, I think the culture is you don't maximize for your function, like you maximize for the company all the ways, and I'm very upfront with every exec who joins LaGoura, that in a way, like 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. I don't know if it was Mark Zuckerberg or somebody talked about like, let's hire people with high-wise slopes and not like high-wide intercepts. I think about that a lot, mostly because I've had to do that. I did not join or start LaGoura 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 LaGoura need to do the same. I think that goes for every function. I think an engineering team that's shipping the amount that we do previously had to be 500 people, and now we can get away with being 50. 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 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. I think the general models have come deferred this encoding, 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 be great if you could dive into. One is LaGoura doesn't really have a long-term roadmap. You guys react and build today. When you first got started, you had this nearly weekly cadence where that's how long you would roadmap to. These days, it feels like you almost roadmap on a daily cadence. Things change tomorrow. You wake up and it's like we have to do something different. Talk about that LaGoura roadmap. And then also the other thing that you've invested heavily in is just understanding model capability and the sort of proprietary e-vell infrastructure you've built where you've had these conversations with the foundation model companies of how you're really able to identify latent model capabilities that they themselves are not aware of. I mean, on roadmap, way back every new model, just unlocked new things. We got our access to GPD 4.5 and you just realized that, holy shit, now it can finally draft an end-to-end thing. And we don't need all of these harnesses and things around it. That's amazing. Let's unleash it in a way that that works. By the way, to do that, you need a low-ego organization because you build all this IP and it's also there. And you're like, okay, now the model can do it. You can't work really hard for six months. We're deleting everything. It's incredible. 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 need people to opt into that at the front end for that culture to really work. We've also talked about it as if we were here today and we start building for the future that's over here. That's too far out. 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 is like, cycle-wise 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 in capabilities. So now we have to revisit a lot of the things that we built. So with 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 a throttake yesterday, which is I'm wearing my like Dorio 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 a trustworthy way. They're seeing that across basically every single vertical and every single company. So I don't really think that we're waiting for new model capabilities anymore. There's like nice things to have. It's nice to have better context windows. It allows us to do less garbage and context management or when you overflow the context in memory and so on, you have to deal with it to refresh it. So there's nice to have. 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's good this, how good is Sonnet, how good is Jamani, 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 tell us, here's the eVals and we're going to call you when we can get to 100% on these eVals. And 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, 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 and 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 MDAs. 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 for these organizations who are maybe more traditional and trying to keep up with it 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, you know, 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 tacked, 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 LaGora exit to sell LaGora. That's a good one. How urgent is it to grow really big, really fast for LaGora, given all of the dynamics around? Jason, I'm curious like, how do 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, when they launched, you know, roughly the error of the business was rounded to a million dollars. If you just go back into that moment, you know, there was this exercise of should we make a budget? And you know, what we all decided on 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 and 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 you like a time trial pilot where you would like give them access to the application. And the minute the trial was done, you would turn it off. And then they would have to make a purchasing decision. A big thing that happened with LaGora is they would go put LaGora into your organization and whatever you put into LaGora, 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're You're not like it. Whatever skills you build or whatever IP you build, it's kind of yours. And like we can leave that behind. It's not a big deal. It's like it's your skills, it's your things that you've learned. And then Max went around and just gave people like 30-day pilot, 60-day pilot, whatever they wanted. 90-day pilot. >> I would run these competitive pilots, right? So they wouldn't say, okay, there's a couple of companies on the market. We're gonna wanna A/B test all of them because it's really hard to pick just, you know, based on the features 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 out, like if we shut it down, it would be a riot, right? Like 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, you know, 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 gonna say about FDLE. >> FDLE? >> It's right, FDLE. And they're amazing. Like they're the most tech savvy lawyers in different organizations who don't wanna make partners. It was like, you know, that's one type of life. And they wanna 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 organizations in the world and like driving that change. >> And I would think once you're embedded in these organizations, it's gotta be sticky. >> I think LaGoura is very sticky. We've ripped out our competition at many, or we're saying, what creates stickiness. >> So the stickiness is the use cases and the cadence and, you know, if you've invested time in building up a workflow that like works for you, why would you wanna switch? >> So is it that? Is it, is it, it's usage? >> It's sticky not sticky. >> Not, no, not yet. And not, 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, much better. And then we can easily swap it. So we actually have a dedicated like migration team moving deployments over to LaGoura. And I think this is where we often talked about not only procting 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 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, were 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, one of the largest firms in India wants to buy. So I figured I'd like go give it to him. That's crazy. And so you know this, in SAS, it was like, no, like, do the West Regional, then do the East Regional, then do Western Europe, and then eventually hire an APAC head. And then he was like this thing. 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% of India. And because 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 kind of flight went to India. And the 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 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 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 culture's 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 come into the US will be easier one day. I'm curious your experience on that. I mean, why culminated were in particular the excite that I felt backing a company in Sweden. I remember the first interview with Gustav and his Swedish. I was like a Swedish partner at YC and he goes, so you're going to move to the States, right? And I go, yes, yes, of course. That's the cue to say yes. So you get the invite to go to YC. You're non-gones, man. You guys are pretty sweet now, so YC. 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. Like that was just impossible. But yeah, like the Swedish legal market is smaller than Kirkland and Alice. 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 the first customer we got, manhemers, fighting 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, hey, I just going to change the world. You're going to need a partner. I'm here. Let's work. That sort of made it all start. But then 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? No, no. I had a rule. So there's actually a few Swedish companies that tried to go to the US, but did so unsuccessfully. Like, Clarna, they tried many times before they 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, like, amazing Wall Street firm, and Goodwin and Proctor. And we served them both. We won their business in competitive pilots. And we could serve them from Sweden. Like, we did a lot of flights back and forth. But after they signed, we said, OK, 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 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 fundamentally learn at one of the rules of marketplaces. And so in legal, the market structure is such that the initial adoption will be very slow and hard. But once it unlocks, it really unlocks. There's some kind of 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, LaGora sold to a Swedish firm. Yeah, that makes sense. And a Spanish firm and a Finnish firm. And so yes, they're like laws at the European Union level. Yeah, but like from the beginning, it needs to work for many people. That's right. And if you start in the US, 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. 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 as 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 of global scale because they were forced to, because they couldn't serve the Stockholm legal market. Totally. Those two things just meant from the launch of 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. And it was global from day one. And now, I mean, LaGua, I think, has become a hub in, like the technology hub in Europe. 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. 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 whole world. When you came back from the LaGua, if it was recently you were like, oh my god, they are so good. It's like something's going on there that I haven't seen before. It sounded different. I don't know if it's LaGua or specific, or if it's like something that is, you know, 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 threat amongst everybody. There 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. It's like what did it feel like to you in 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 they're going to 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 you know, 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 writing 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 that disadvantaged of Stockholm has now become lagour as 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 Stockholm. I think our competition is, you know, has remote days, like three days in office. Everybody lives at six, like from from very early on, like we served dinner at eight every day. A lot of people in in our region are sort of tired about like all these big American winners. And we know that we have the talent and the grid and the prerequisites to bail the generational company. Like yeah, we had to go to the US to raise money because we want to work with the best businesses in the world. But like there is 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 US? I think we have. I think we have a very unique culture in our New York office. Is it different or is it very different? Like people? Well, it's not different from Stockholm. Oh, okay. But it's that we seated it with the, you know, the culture carriers from Sweden that 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 wants to talk. So you live in New York, you're going to join the New York office. You're going to Stockholm. Yeah. People who joined Sydney have to go on a 24 hour flight. On board 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, see that. To those offices to set a cadence. It's like it's all going to be the same as Stockholm. And like the Germans who joined the Gora, like they have to move to Stockholm and they'll work here a year and then they can move back to open the the German office. Yeah. Okay. I have to get it right. It's 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 the Gora 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 dinner time, which is APM. Like that's when they have guests is like APM. 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'd have dinner at it. So I was like, I guess that's how you do it. Totally. And so like in some ways doing doing the US 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 like a great tech company. And you know, it's 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 the fact that now we just open in Houston and we're opening in Chicago. It's all the big legal hubs. Is this correct? Did you do a reference with Daniel Iack? 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. 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 is to be vastly different outcomes. Oh yeah, completely. And it doesn't actually matter if that's the case or not. That's the way I meant. It gives you the right mindset. 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 smog. And you like, you taste the blood and 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. It's like I'm like, I have some fire. And then she publishes the article in English. Yeah. And the saying doesn't make any sense in English. So it's like, you know, it's like the LaGora founders make wake up at LaGora. 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? And what do you feel about that now? Now it's become this thing. Like the Americans are hashtag blood smog. Like it's, everybody's in on it. It's amazing. It's a cult. I mean, I can like feel the energy of it. Like I don't know if that's something that you can do uniquely. Well, and we open our separate Cisco office. They're going to taste the blood smog. 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 of all, just to give you a bit of insight into the round, like every round at LaGoura since Trayton has been a preempted round. I don't think I've ever actually gone out to like fundraise since the round. Yeah, it's been very pleasant. It's been very pleasant. We actually also have a history of taking the like lowest term sheets. I remember taking, like we were actually, so this is funny. Like we were negotiating the number of shares that Trayton 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 go, I live 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. It was like the removable objects. It makes the force. And so we like just put on Excel. We write down the exact number of shares. And we start going decimal by decimal. Wow. Until we're both that's such a leap. That is so legal coded. Yeah. Just the nerdy Excel. It was wild. It's perfect. You end up investing like 9.521. And we're both equally unhappy or happy. It's great. I think we're both happy. Yeah. Of course. 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 like pick the song you want to walk out to. And he goes, Max, I want monster by Kanye West. And I go, okay, dude. And it's like the lights are off. And I'm like, I have a big surprise for you, everyone. Like David is drawing us our CFO. And like the speakers just explode with this like, and I don't know if you heard the song. Yeah. Yeah, of course. And it's like, it's like, holy shit, what's going on. It comes up on stage. And he's just like so much energy. And in the reference, as people refer to him as a CFO, 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 was a lot. It was crazy. But we're super thrilled about Excel coming in and leading it. Some great participation from Manlo and Bane. Well, 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. Jathan, thank you as well. It's great. Thank you so much, check.