Max, how are you this afternoon?
I'm amazing, Ted. It's so great to see you. Yeah, you as well. We've been trying to do this for a while.
Yeah, the schedule has been hectic for all the right reasons, but I'm really excited that we finally got the chance to sit down.
Yeah, so am I. So am I. It's a conversation that's overdue, but I'm really looking forward to it. We have a really good agenda to talk through. Lots to talk through.
Before we jump in, why don't you just do a quick introduction? I'm sure most people know who you are, but for those that don't, just a little background on who you are, what you do, and where you do it.
Of course. So, I'm Max. I'm the CEO and co-founder of Legora. Legora is an AI platform for legal work, and the way that we've really started to think about it is we're building the place where both humans and agents will be completing legal work together. And so, we founded in 2023, just completed our Series D fund raise where we raised over 550 million dollars. We're about 400 people globally across eight offices, and we're having the time of our lives.
It's incredible. It's incredible growth. Like, you and I were on a podcast. I don't know if you remember this, like I do. Yeah, like this that was I you I I think you guys were just starting up. A year ago, a year and a half? I think it was more like two two and a half years ago. I don't even know if I had my podcast then. It was early days. But you had a good setup at least. Yeah, and it's been it man, it's been a lot of fun to watch you know, watch the whole space grow and watch you guys grow and I've seen you've hired a lot of really good people on your team. Um we had Kyle Po on back in the
I like to think that my job at this point, Ted, is to hire people much smarter than myself, and I've certainly had some pinch-me moments where we do a company-wide town hall, and you sort of look out over the group, and you feel like, "How on earth did I manage to sort of lure everybody here, but no, we're grateful. We're super excited and you know, really I think that's a big part of what keeps me going, getting to work with so many inspiring people.
Yeah, you know, have you ever read the book or heard of the book Good to Great by Jim Collins?
Yeah, it's a good It's one worth reading. He talks about he goes back and does a study of, I don't know, hundreds of Fortune 500 companies over really long periods of time and he finds patterns and anti-patterns for success over the long haul. And one of the anti-patterns he calls the model it of a genius and a thousand helpers where you know, one person, you know, may usually the CEO is, you know, kind of the the maestro and the smartest guy in the room and it it creates a lot of friction towards scale. And um yeah, it's a great book, definitely worth I'll I'll I'll pick it up and that's certainly not Legora.
Right. Yeah, it's not InfoDASH either.
Um well, why don't we start off with just talking about like the landscape which is constantly changing and Yeah. you know, we had It's been such a roller coaster with like the Cloud Legal plugin this year and um you know, the whole all this talk of wrappers which I'm surprised that nobody's accused us of being a wrapper on top of SharePoint which which we are essentially. Like we're built on top of SharePoint Online and Azure. Um nobody's called us a wrapper yet, so I guess that's a good thing.
Well, I think we're all we're we're all wrapping something, right? Yeah, you know, I mean, I've heard I've heard the metaphor, well, all apps are just wrappers on a database and I guess that's true to a certain extent, but um how do you how do you see the landscape just broadly and where Legora fits in? Like, you know, how what makes Legora different from its competitors? Why don't we start with that?
Sure. Well, look, it's it's, you know, end of quarter one in 2026 and the models are where they are. I think they made a real sort of step change in their capability and intelligence over Christmas with the Opus 4.5 and 4.6. And what Legora and where we live in the stack is really sort of the central workplace where a lawyer wakes up in the morning, they open up Legora, they might fire off some work to our agents. Our agents will start to perform those things. They will then check in on it. And then we allow the collaboration both internally with the team, but also externally with other firms or with our clients. And in increasingly, we're also seeing large legal departments from sort of insurance companies, banks, large tech companies starting to adopt these tools internally. So, I think there was a lot of initial buzz in 2024, 2025 with, you know, what can this tech do? I think now we've sort of really got into the point where the scale of adoption is moving fast and every new tool and new capability that we add, we can very nicely sort of add to the arsenal of what our agent can go out and do.
Yeah, it it really does seem like the world changed in December in terms of, you know, I think the to your point, Opus 4.5 and then Opus 4.6, it really feel it felt like there was a seismic shift in terms of capability with these foundation models. How has that how has that affected your business? Um obviously, you're built on top of these models, Yeah. but is there a, you know, a mindset of you got to stay one step ahead of the sheriff so to speak in terms of building on on top of that? Do they ever overlap in terms of capabilities that you have in existence? How do you think about that?
Well, look, when we founded Legarra in 2023, there was really just one model to work off of. It was GPT-3.5, which was being hosted on Azure like you mentioned. And um you know, from that point we've seen models sort of come and go. And every quarter we get new capabilities to work with. I think one job of of our team here at Legarra is how do we bring the latest and greatest and how do we sort of squeeze the most juice out of the latest models for our use cases and for our customers. Because as you said Ted, it's moving really fast. These are large organizations that we work with, large law firms who are adopting Legarra's enterprise-wide. And that also means that we need to bring those capabilities for different practice areas. So, the way that you work with Legarra in litigation is different from M&A and it's different from in-house. And I think the models themselves are a sort of equalizer. Everybody has access to them, right? It's sort of like the new electricity. But you still need to put that electricity in a in a great car um or you know, sort of get get the most out of them. And the way that I like to think about Legarra is like a Formula 1 car. And then we can hot swap the engine anytime we want. So, the capabilities that the models themselves solve, I think is ever increasing. That just allows us to build more on top of them.
Interesting. And then the you know, SAS-pocalypse as they call it, um the Claude legal plugin, which I have a lot of thoughts on that I've some of which I've shared on previous podcast. I did a podcast with Ilta recently where we um myself, Cat Casey, and Dan Zeebo um did a deep dive into what the meaning of it really is and, you know, my theory on why there was such a swift market reaction to really, there were no new capabilities as part of the legal plug-in, like absolutely nothing new. The plug-in architecture existed. It's some really simple markdown with, you know, six skills that are extremely basic. There were no new capabilities. So, why did it have such an, um, a huge impact on the markets? My theory on that, and I'd be curious to get yours, my theory on that is, um, the plug-in was a release valve for a lot of anxiety in the marketplace over a lot of capital investment, high valuations, uncertainty about how, you know, how AI is going to impact certain industries. And you saw companies impacted that shouldn't have been, like, you know, TR and Lexus with all of the proprietary data they have. You know, LegalZoom got hit hard, and that made more sense to me. I actually see more impact to a LegalZoom business model than I would to TR or Lexus. Um, but I it it felt like a release valve, um, and an excuse for this anxiety to manifest itself. I don't know, what is your take on the legal plug-in and its impact on the market?
Well, I think you have traditional SaaS companies that are more or less well equipped to execute on what an AI native stack and future will look like. I think that's independent or sort of, you know, set aside from, you know, whether, uh, Claude can work with a legal skill or not, right? So, I think you're somewhat right in that, you know, maybe it was a a marketable moment that became an opportunity to or a reason to sort of, you know, pivot a certain way. Now, I think generally, the sauce that has been built up, like the cost of writing software, is obviously going down. But, the opportunity space and the amount of greenfield problems you can go and solve have actually significantly increased with the model capability. So, we had this really funny moment because we were out racing our series D at the time of this release. And you had a public, you know, stock sell-off, and we experienced astronomical demand in our own private fundraising round. And so, although there's may maybe a a lack of of trust and excitement around what the public SaaS companies can do in terms of executing on this AI-native future, there was a real excitement about uh ours and a few, you know, other companies' ability to go and execute on that. So, you know, I think there's trade-offs. Um and I am again, sort of very happy that the models keep developing because that uh is also playing into the way that our strategy is, which is, let's sort of build, you know, every functionality in Logora like a boat, and when the tide rises, when the underlying capabilities improve, every single feature that we have gets better. And I also tried to tell the team tell that we need to build with a very low ego. You might spend a lot of time building something, you know, nine months ago that made a lot of sense back then, but it won't make so much sense now that we're in a world where agents will be executing and doing work on our behalf. Right? I think we've moved from the sort of co-pilot, you know, let's iterate very tightly to I'm actually going to let the agent go and work for a bit and then review the output that it's coming back with.
Yeah, and you know, that was another theory that was put forward in terms of why the sell-off took place, which is with how good agents have gotten even automating a UI, right? That it's going to put pressure on the per seat licensing model. If you can license an agent to do what three people can do, you now need 1/3 the licenses. And I you know, I like, okay, but we can adapt to that. We can That That's not the only licensing model that's available in the universe, right? We can that you can get it You can get around that. Um how do you see the you know, agentic phenomenon impacting per user licensing cuz I would imagine you're on a per seat license model as well. Is that accurate?
Yeah, we're on a per seat then and sometimes consumption as well. And I think look, you you've had um tools for engineers that have been out for a for a time if you look at cursor or or sort of the first um really agentic tools that came out and they've always been consumption based pricing. And so I think that as a paradigm makes sense, for sure. Um and then I think there's the question of how do we as a market, you know, work with that so that we can control that in an effective way, right? Like um if you're leveraging a lot of AI in an e-discovery process, you're already used to paying something on a consumption based model because you could run, you know, hundreds of millions of documents. And so I think there's a there is an an understanding of that. And as we move more and more into these gigantic sort of, task completions, as you explained Ted, you are using more and more tokens. And so, in order for that margin profile and for that work to actually make sense, I think there has to be some level of consumption pricing associated with it.
Is is outcome based or deliverable based pricing in in the near term or is that still further away?
I think that's possible, but I it's it's it's harder because then you need you need to build out the taxonomy of the task that you can solve, right? To a very granular level and the different types of tasks that you can solve with Legora at least across the different practice areas is really broad and really varied. And you know, we're also seeing firms moving to that from the billable hour, right? In certain practice areas and in certain geographies. And so, I think all of these developments, the waterfall will sort of happen slowly, but sort of all together. And then, I um I'm sure you heard of about the the post on X. I think it was Zach Shapiro who wrote about the Claude native firm. Um and there's been a lot of back and forth on LinkedIn about you know, how how realistic um some of what was put forward actually is in terms of how do you how how do you explain to someone what the difference is between leveraging just, you know, Claude and a bunch of prompts and skills versus leveraging a Legora. How do you differentiate it to people how how what those different paths look like.
Um I I've I've spent enough time with lawyers to learn to say that it depends, but it really depends on the problem that you're trying to solve, right? Let's say you're looking to solve um contracting in an enterprise. The way that you actually set that up is you want the entire, you know, company to be able to leverage the same set of playbooks and and work produced by the legal team. And then you want an agent to go and review all those documents, create markup, and sort of be ready to send back to the other party. And you need example language, you want different fallbacks for different types of documents. There's a lot of routing, intake, the way that you actually work with that. Claude or the model as the engine, again, I think makes a lot of sense. It's the electricity. But then it's the house or the car that you actually build around that that becomes valuable for an enterprise to deploy. And if you look on an M&A transaction, the way that you work with the data room and the way that you sort of walk through that, the way that you then take the IP part and the employment part and the finance or tax part and then you bundle all of that together into a single due diligence report, like all the work around where the model is actually working is the stuff that we do. And then we work really hard to as as I said, like get the maximum juice out of the model so that you can trust that the output that you get back is based in the context with citations. You can mark it, you can red flag it, you could green light it, and then everybody can collaborate around that at the same time. And I think the more time goes on, the bigger and bigger difference, you know, Legora has from the foundational models, to be honest with you.
How how do you think law firms are going to differentiate themselves in this post in the post-transformation era like historically differentiation uh like I actually spun up Claude co-work and as input gave it the Am Law 100 list and had it go and list out how firms frame their differentiation narrative and um eight out of 100 even mentioned technology. Right? Pretty low percentage. Historically how law firms have differentiated themselves is you know, their people, their brand, their their wins, their you know, um their history. And as we get into a taken a tech-enabled world, technology is going to be part of that differentiation equation, but buying off-the-shelf tools isn't differentiation it isn't differentiating by itself. So, how do you think you know, again, kind of post transformation and I don't know when that is going to be, but we're probably going to be transforming for a very long time, but how are firms going to differentiate themselves from each other? If everybody's running Legora, how how am I different than from the firm down the street?
