It doesn't really matter who was first, it matters who's best. We are moving at such a rapid pace that last week we had Harvey on the show. This week we have their biggest competitor, Legora. And joining me is Mattias Strand, co-founder and CEO of Legora.
In a single day in 2025, we added 7 million of ARR in 24 hours. And that was more than what we did in 2023 and 2024 combined.
There's this conception that Harvey have won the US and that you have won Europe.
I think one of those statements are true.
It's totally a winner takes all. I mean, you know this. Number one will grab 90% and number two to number 10 will share the remaining 10%. What that means for us is you got to run like hell. You got to win. There's no number two. There is only being number one. There's only winning and everything else is losing. Ready to go.
Dude, we did our last show and I have to admit I was so surprised. Not that this sounds awfully rude, but it's the end of the day. [__] it. By how well it did in specifically this incredible founder community where I got pinged by like 300 or 400 founders, which is more than normal actually.
That sounds like a big number.
Yeah, it is pretty solid. And mostly it's just kind of VCs. But thank you so much for agreeing to do a second show with me.
Always. Now happy to be back.
Before I grill the [__] out of you, 60 seconds. What does Legora do? Just to set the scene for people that don't know.
Legora is the platform where legal work happens. I think that's a better pitch than the one that I had last time. And what started to happen more and more is AI is doing more and more parts of legal work. And this has to happen on a centralized platform. And what we started out with was, I mean, simple assistant-based use cases, but this has grown tremendously and it's solving different types of tasks for different types of lawyers. So if you are a transactional lawyer and as part of a due diligence process you need to review the data room and then you need to find the red flags in that data, Legora can do it. If you are a litigator and you are preparing a brief and you are drafting that in Word, Legora can help you do it right. So more and more of these tasks are being bundled into the platform and what we're seeing is that a bigger and bigger part of a lawyer's day is being spent on Legora, which is amazing. That's my favorite data point.
Is that the number one metric you use in terms of product metric?
Yes. Time spent on the platform and number of messages, number of queries, number of actions taken, I think is the best sort of KPI.
When people think AI law, there's a ton of [__] players around the space and around the verticals, but there's you and there's Harvey. And if we're blunt, Harvey is the first name that comes up. When you think about that, why is that?
I don't necessarily think that's the case anymore. And the reason I say that is I just saw this report. I was on Bloomberg a couple of weeks back and they had a big infographic that said that the most deployed generative AI tool in the top 200 law firms in the UK outside of Microsoft Copilot is Legora. Number two was Harvey.
We are moving and the category is moving at such a rapid pace that it doesn't really matter who was first. It matters who's best and it matters who the clients actually are coming back to and want to do more work with. So what often happens, right, is the firms will throw many vendors into a bake off because they're in this kind of luxury position. I mean, basically they're playing VC. They get to bring in all these different vendors and they say we're going to do a bake off and in the bake off it's up to the vendor to display why you are their partner of choice. And I say partner of choice because I don't think that these law firms or big in-house legal teams are buying just a solution. They are buying an outcome today, but they're also buying an outcome tomorrow and they're buying into a vision of what an AI-enabled legal team can look like.
And so when they look across the board and they see all these different companies, they make qualified bets. And more and more and more we're seeing those firms make that bet on Legora. I mean this year alone, actually I went into our HR system before I came in here, we went from 30 to 300 in 12 months exactly in headcount. And this time last year we were working with roughly 50 clients. Now we're with 750. And so you know a name might be associated with a category. I'm pretty sure Google was not the name that you thought of back when AltaVista was the biggest web browser, but now it is synonymous to searching on the web.
Now I want to unpack a couple of elements. You said about like partnership and that being very central to how they think and how they choose. You know, I had Matt Fitzpatrick, the founder of or the CEO of Invisible, which is kind of like a Mercor or a Turing competitor, and he said that essentially it is impossible to sell into enterprise without an FTE model. Do you agree with that? And are you seeing that?
We have a very big team of legal engineers who are ex-practicing lawyers from the top tier best firms, but they're not fully seconded, right? They're forward deployed in the sense that their main job is to make you successful. An example would be a big firm that just went with Legora, White & Case. White & Case now has the challenge of adopting AI across their entire firm. It's a big firm. It's such an enormous change management undertaking to equip all the lawyers across all the different practice areas, across all the offices and across all the skill levels from associate, senior associate to partner with AI proficiency and we need to make them successful because if they are not successful on Legora, you know, a year from now or two years from now it's going to be a really sad conversation. So we invest a ton of upfront manual labor and time and effort in doing the implementation and activation right. So I do think that's necessary for enterprises where you're changing the way they work.
If you just think about a process, right, like let's say you're working in, if we're staying in legal, if you're working with AI contracting, you basically have a contract lifecycle management system. You just send a document somewhere, generate some red lines and then you put it back. That's pretty easy. You don't need a forward deployed engineering or legal engineering model for that. You just deploy the stuff and then you're done. But in our case, I actually like to think of it synonymous to the way that accountants had to learn Excel or architects having to learn CAD, right? Like before you would actually go out to the site, you would draw the building and then you would go back to your office, you would do all the math. But now you just get a picture of the site, you throw it up in CAD, you put in the blueprints for the building that you want and the system AI generates it or generates it and then you look at the math behind it and then you bring taste and you bring design. When architects were learning CAD, I think it was an enormous change management, right? And all the old architects would go, 'Harry, you're going to use that, you know, that computer system to do the hard work for you?' And you would go, 'Yeah, I'm super savvy and I'm going to get to spend more time doing the design or have creative ideas about how to solve my client's architectural problems,' right? And I think that's kind of synonymous to what's happening today.
You said about kind of actually the value not being in the first move for advantage and actually value that comes from being second. What did Harvey not do well that you learned from as specifically as possible?
Some of the things that we observed were spending a lot of effort on fine-tuning models and that always seemed to me at least back in 2023 like a waste of time. Well, so because the general models were improving at such a fast rate that it felt like we should be building boats and then when the tide rises, all of our products just get better. And frankly, my initial team, we were three people. We were three engineers. And so the thesis plays into the team structure, right? And we had $50,000 in angel funding. And so it wasn't really on the question of let's spend $3 million fine-tuning a model. But it happened to be right as well. And we always believed that the majority of the value in our category would come from the application layer.
So you think work being done to fine-tune models does not give you an inherent advantage.
It didn't do that back in 2023 as a starting strategy. I doubt that that's going to be the difference maker today.
Do you think models are plateauing in performance today?
No, I think Opus 4.5 is awesome. It's so good and it continues getting better. I mean, honestly,
Well, it depends on the task in coding. I mean, I think it's gotten to the point now where I think you should just coin it AGI and focus on optimizing the cost pretty much basically. And we are we're
What makes you say that? I'm sorry. I'm naive.
Because I think it's its understanding of my intent and its ability to execute on my intent given the tools that it has available today so good. Like you don't need to sit like with GPT-3.5 and you feel like you're talking to, you know, like a lobotomized system pretty much. Like if you go back, it's crazy bad and this was, you know, two years ago, right? And you would have to give it so many instructions over and over and over again. It felt like managing an employee who wasn't very intelligent. Whereas Opus 4.5 is like a VP. You give it, 'Here's the thing I want. Go execute,' and it just does it and it's amazing.
Has Anthropic won the Claude Code game? Does anyone on your team use Cursor? The question that I'm asking, I'm in
That's a good question. We're in both.
We have both. Yeah. I'm actually not sure what the full team split is, but I know that we're using both very much. But we are pretty diehard Anthropic right now at the company in terms of the models that we deploy in our system.
So initially we were only OpenAI. So 2023, most of 2024 only OpenAI and now we're majority using Anthropic.
No, not 4.5. This was prior to that. I think it was maybe Sonnet 3 or 3.5 that we made the switch. But I think what also happened was you had to start prompting the models quite differently and then there's a question of like where do we actually want to put our effort? Just pick a good model and double down on it and build all the application around it because I feel like our job, frankly, if you think about the pyramid of value, is you sort of have the underlying models and the frameworks, then Legora's responsibility is to build the legal interpretation of those models. So how do we make them the most useful in a legal setting? And a lot of that comes from the models, but 80% comes from building normal software, right? Like enterprise-grade software around the models. So all the scaffolding, all the ways that the users can actually interact with the system. And then at the very very top of that pyramid, we need to enable our partners, our clients to do differentiated things. So our clients and the law firms and the legal teams we work with are very ambitious. They're vibe coding tools internally, they are developing MCP servers and they want to. They've understood that if you compete as a law firm, you used to compete on expertise, on marketing, on your ability to recruit, on, you know, your hourly rates, but increasingly you're competing on tech. Like tech is the main lever for our clients to differentiate from their competition. And so they will use a platform like Legora to get everybody up to, you know, spent so much time in the US now, second base. And then they will, you know, develop things internally to try and get a little bit of a head start against some of their peers.
Unpacking so many things just doing it kind of chronologically there. How promiscuous do you think you'll be with model usage over time? You mentioned you're pretty much exclusively Anthropic now. When you look in the next 12 to 36 months, will you switch between them as they improve in model efficiency or do you think there'll be a continuing loyalty towards Anthropic?
We will be very promiscuous and that's a clip.
That's a real That's the intro right there.
Right there. Well, I think it's our responsibility to be that because our clients have entrusted us to be their AI partner and to deliver them the outcomes that they need based on everything that you can do with AI and we need to deliver them the best possible thing at the best possible price using all the tools available to us. And so if Gemini is better, we will switch immediately. Or if OpenAI is better, we will switch immediately. Or if a new model comes out that's better, we will switch immediately. You know, proven that the EVA is better. But then in specific workflows, you could also let the users pick, right? So let's say they have a very deterministic thing that they want to run and they always want to run it on this specific model to not break the system, then you could also allow that.
Can you help me in 24 months? Rank the model landscape for me.
I'll bet based on what I've seen in the last sort of three to six months. I think for our type of work it will either be Claude or Gemini that is the top model and it will be dependent on if context window is a very important factor or not. So far it is not because we've built so much architecture around handling a lack of context window that we still prefer the Claude models or the Anthropic models. And it seems to me like OpenAI is going down the, you know, let users fine-tune models like that type of journey a bit more which so far I don't have a lot of reason to believe in.
Okay. So, we're going for Anthropic, Gemini, and then OpenAI,
I think. So, I also think it's it's um
And we're not going to throw Grok in there at all.
No, we're not going to throw Grok in there at all.
He said it. Don't kill me. It was not me.
We I also think there's a difference between solving enterprise needs and solving B2C needs. And to me there's a split happening or a perceived split at least from my vantage point which is that Anthropic is going more enterprise and OpenAI is going more B2C.
Yeah, I and we're an enterprise class type of system thus we should benefit more from their models.
Yeah. And Jason Lemkin, a dear friend of mine, and you said you know the shows with Jason Roy. He said on a show recently that we're going to see inference running 24/7 for a portion of the knowledge worker economy. And how that really is going to be the defining theme of the year. Do you agree with that? And if so, what ramifications do you think that has?
So basically that you sort of are you're continuously running tasks at all times.
Continuously 24 hours a day. Exactly. When lawyers leave the office, they're going to still have inference running for the projects that they have.
For what it's worth, I don't think that we're there yet when we have tasks that take that much time to run, right? Like we don't have any task in Legora that would take 12 hours to run yet. But when you can put the models in loops and it gets better and better and better the more loops it takes then you can for sure allow that. I do think that we're going to move into a world where we, you know, sort of start a lot of things as we go to bed and we wake up in the morning and it's done like for sure. I think I already started doing that with Deep Research when that came out for the first time and it would take 25, 30 minutes to run. And it was magical. It was so cool. But you so quickly get used to our new shiny toys.
What do you think we don't talk about enough or don't see in the model environment/landscape today that more people should see or talk about?
One of the things that's very impressive with Claude Code and do they call the new thing coworker?
I think it's maybe not so much about that tool itself but about the paradigm that it has shown is useful and probably the right thing to go directionally. And the more we were working with Claude Code and Cursor in our engineering team the more we just thought, hey, let's apply the same principles to the way that Legora works, right? Basically having the Legora agent access all the other tools available in our ecosystem as well as any MCP servers that the client brings and then basically kind of letting it roam and you just give it this again overarching task. It gives you back its plan and then you say that looks awesome, go execute. And it's pretty much the way that a partner would work with a senior associate and a senior associate would work with an associate and I think this is like adding another layer in that, you know, hierarchy basically with AI in the bottom but then that's available to everyone 24/7. So like one of the themes that I've noticed is that partners at these firms that we work with are starting to think that the technology is so good that as they give a task to their team member they will simultaneously give that task to Legora and then very often it's like the quality that they get back is pretty good and that has real implications.
We're going to go to the structure of law firms in the future. I just want to unpack another thing that you said earlier which I don't want to forget is you said I'm spending more and more time in the US. There's this kind of perception when I speak to especially US VCs that Harvey have won the US and that you have won Europe.
I think one of those statements are true.
One part of that statement. My worry with you is your lack of confidence, my friend. Why is that not true? Because they seem to have the magic circle in the US.
Well, that's not true. When we started the year, we were zero boots on the ground in the US. Now we are 50 people. We're opening up our new office on Manhattan this week. It's going to be 150 people. We've got three more offices in the US opening this year. And the US has by revenue become our biggest market.
Wow. Revenue wise you have more.
It's the biggest country by revenue. Yeah.
Wow. Do you have more in US than you do in Europe?
In total? No, we have so much in the Nordics actually because we basically work with all the big firms. But it will be I think by the end of Q1.
Yeah. Super interesting. So by the end of Q1, you'll have more in the US than you will in Europe.
Yeah. And the cool thing, right, is that if you look at the Am Law 200 and by the way, like many of these clients that we're working with in the US, it's not the mom and pop shops, right? We work enterprise. And so when we came to the US, we had a strategy which was there's so many Nordic or European companies that have launched in the US and failed very close to home. I think Klarna tried to launch in the US like a couple of times before it really worked. And so I had this heuristic which was if we can sign and serve two of the Am Law 200 law firms from Europe, we are ready to open in the US. So clearly got, you know, white shoe Wall Street firm Goodwin Procter, one of the best VC firms in the world. And I think both are in the top 20 law firms in the US. We were able to work with both of them and give them confidence that we could support them better than anybody else. And that gave me the confidence to go to the US and hire a team. And the awesome thing about building a team in the US is it takes two weeks for people to leave. We can talk about some of the differences between, you know, US Europe but I think the termination period in the US versus in let's say Sweden is actually one of the structural benefits of having a big office in the US.
So how does it compare two weeks to leave in the US versus three months on like everybody has three months basically and for you as a founder that is a night and day difference in terms of ramp.
Well, we've doubled in size every quarter and the minute I know that I need somebody, if they wait a quarter, we're a different company, right? It's wild. Like, so you just need to, well, for one, I need to try and predict our headcount plan much more diligently in Europe than I do in the US because there it's like pa, but really awesome people can just turn up in two weeks.
So your biggest advice to founders on scaling in the US without committing large resources would be that you can do a premium and test it from Europe.
Yes, for sure. I think well I think we could so why can't you? And we're very enterprise that might be different, right? We did not need to invest a ton in marketing or like B2C content in the US. I could just get on demos and get on a few flights. You know, demo the product, run a few pilots, you know, always competitive pilots and then again on the partnership level show that we were willing to work with these firms on their ambition level because it's very high. The firms that we work with are not treating AI as a check the box exercise. It's not oh let's buy, you know, this thing, let's roll it out and we're done. We want to be the firm that dominates our market because we understand AI and technology better than any other firm.
Totally get that. Do you just going back to the US and the expansion there? Do you regret waiting as long as you did?
No. Did I tell you that I took a decision to not sell the product for 6 months?
So our first board meeting after we so to give you some context, we were a YC, we raised $10 million from Benchmark, a month later we raised another 25 million from Redpoint and we had our first board meeting and we were like 12 people at the time. First board meeting, it's Benchmark, Redpoint and the three founders and we sit down and I tell them that we are not going to sell at all for the next 6 months. And Redpoint sort of looked at me and they were I think a little nervous that they had met me for basically an hour and 45 minutes and given me paid price and I was showing up and I said we're not going to sell.
What price was the Redpoint round?
that's interesting. Do you regret taking I'm not saying Redpoint but doing that round?
Because that's a lot of dilution. 25 at 150 when you didn't need the money 2 months after a Benchmark.
Yeah. But you couldn't know that you didn't need the money. And if I remember correctly, one of our competitors did another round quite quickly after. So I think it was good to solidify that there's interest in Legora stock. And now I just get like, you know, at this point I'm just archiving emails because I'm getting too much inbound. But back then,
which is why I send WhatsApps.
Yeah. But back then, that's a good thing. That's a good thing. Back then they were, you know, I turned around and said, 'Hey, we're not going to sell.' We need to get to the point because we only have one shot. We only have one shot with these lawyers because they are very impatient. If it doesn't work, they're not going to come back. And that's why activation and getting that like the time to value is so important in the product. But to go back, we took six months, calculated the time and said we're not going to sell. We have to solve our infrastructure, our reliability, the scalability of the product and we need to rebuild and refactor a lot of it because it has just been quickly put together. And what we told all the clients were we were lucky, you know, it was summer because we could say, 'Oh, it's, you know, summer in Europe, so we're not working. And we're going to wait to onboard you after summer.' And there's so much demand that we're going to have to do it in October because September is like completely full. We can't onboard more clients. But October 1st, 2024, we were ready to onboard a thousand lawyers a day comfortably on the product. And we got to that point and then we started like then we started to do RIP. And I'm very proud that I had the guts to tell the investors that that was the right plan because I think if we had continued to push we would have just churned everything.
So when you look back at the timing of the US expansion, do you not think you could have gone sooner and not seeded so much ground?
Well, for what it's worth, I don't think we conceded a lot of ground and we are winning back a lot of ground if you put it that way.
So customers aren't loyal.
Well, for what it No, I think everybody is still treating this as an extended pilot and an option on AI. It's like a call option, right? They're not doing five-year contracts. They're doing one to three-year contracts. And in law firm time, that's a blink. Many of these firms have been around for 200 years. So two years might be half of our lifetime but for them it's a short time.
Okay. So when we look at the numbers like bluntly the retention for a Harvey is it's 98% logo retention, 178% net revenue retention. Do you have as good numbers?
So for both those numbers, yes. But on NRR, I don't think that's a fair number for me to comment on because there's so much of our growth is not about renewing contracts from 2024, right? Like we did a in a single day in 2025 in December, we added 7 million of ARR like one day in 24 hours and that was more than what we did in 2023 and 2024 combined. And so NRR and logo retention, you know, it's up to 2026 to determine where those, you know, real numbers will be. And I think for what it's worth, the ability of these products to go quite broad will be very interesting because to some extent, there's initial use cases that you can solve with AI that we target and then the more time we spend with our clients, the more problems and opportunities we see. And so what's happening to the products is they're growing quite a lot. And so what I think will happen is that this will be like a suite like a platform kind of play that just becomes more and more of the central system where they do their work.
So on an NRR standpoint, do you charge on a per seat basis or on a per task basis or on a volume per task basis?
We charge on a per seat basis.
I think that's optimal for the buyer. I don't think that's optimal for us. Yeah. And so,
and is that not like solving for a historical norm, not a future optimization?
It is. I actually don't think it's the right pricing model. I think it should be consumption based.
Because you can have individual users racking up such big LLM costs that it basically becomes unsustainable on a per user basis. The reason why we have that is you need to make it easy for the buyer, right? If they don't know how to manage a consumption based pricing model, you can't have it. And I think that will pivot. And I'm unsure exactly what that timing is. I think the timing is more around when the clients are ready versus when we are ready. And so task expansion is incredibly useful for retention, not for revenue optimization.
In other words, the more they do, it's the more likely they are to retain, but it doesn't actually help your dollars, right? But another
No, actually cost a lot more. Yeah. I mean, so that's it's a bad thing the more they use the product, but
I respect the honesty of that answer.
Yeah. Right. It's not SaaS margins and I think it will take time to get there.
Will it get there do you think or is
Yes, I do. I actually think it might you know to some extent. Yes, I think it will get there. Not only do I think it will get there, but I think your ability to price versus traditional SaaS products will be insanely high.
Because well, because you used to use a lot of these products to do very narrow parts of the work and they were all disconnected. To give you an insight into the life of a lawyer, it's like you have this product over here that you use to compare two contracts. You have this product over here that you use to extract relevant data from contracts. You have this other product over here where you go and look up legislation. You have this other product over here where you go and look up case all all these different things. So you as the human had to sit there comb through all these different systems and like aggregate the stuff yourself. But now you're just going to similar to Opus 4.5, you're just going to send the task to Legora and it can be pretty arbitrary and then you let it figure it out and it just goes and does all the work or at least a big portion of the work in a much much much much shorter time at a very high quality level. And when you do that, you are not being priced against your the other SaaS products. You're being priced against, you know, what would I pay a lawyer to go out and actually do this work.
So in 3 years time will you still have seat-based pricing?
When our clients are ready to buy on consumption.
Why do you sound so confident that will be within 3 years respectfully? You know, Cursor's consumption.
A lot of other enterprise tools are for consumption. I just think legal takes a little bit more time, but within three years... well, look, you've got to understand my vantage point. Three years is longer than I've been CEO at Legora. So three years for me is a very long time going forward. On the margin optimization side, is it a little bit like, you know, obviously we're ambassadors in Lovable where they're able to do model selection dependent on task and optimize margin because of that?
You can do that, of course, which we do to some extent, but it's also... I don't think we're in the model margin optimization time yet. You know what I mean? Like...
You're in the land grab time.
Yes, that's the right way to phrase it.
Yeah. And in the land grab time, what is the biggest challenge that you face?
