Uncapped with Jack Altman
· Max Junestrand, Jack Altman
[00:00:00] S1: But I think a lot of the things that we have built, we know that we’re going to delete someday. [00:00:00] S0: I guess you need people to opt into that at the front end for that culture to really work. [00:00:00] S1: We’ve also talked about it like this. If we were here today and we started building for the future that’s way over there, 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. We need to take them on the path of being successful. Every iteration cycle now is shorter. Back in 2023, 2024, I think it was slightly longer. You’d have a quarter or two quarters because the models weren’t moving that fast. Every upgrade was pretty incremental. But now it flipped. Opus 4.6 flipped in capabilities. So now we have to revisit a lot of the things that we built. [00:00:00] S0: Do you know what the next flip you’re waiting for is? Is there a thing? [00:00:00] S1: It was funny, I was at the customer advisory board at Anthropic yesterday. I’m wearing my Dario shirt here. [00:00:00] S0: You look like Dario.
· Max Junestrand
[00:00:00] S1: Thank you. Most of that conversation was about how 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. I don’t really think that we’re waiting for new model capabilities anymore. There’s nice things to have. It’s nice to have better context windows, it allows us to do less garbage and context management. When you overflow the context in memory and so on, you have to deal with it to refresh it. So there’s nice-to-haves, but 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’s where all of our focus is. On discovering what the models can do, we thought very early on that evals were going to be important, both building up an exercise of building new evals, but also building out evals for all the use cases that we want to cover. Because in the beginning it was a lot of, “how good is Sonnet? How good is Gemini? How good is GPT?” We had to test them on the different evals. A lot of our customers actually contributed to this. They would give us manual tasks that they used to do, and they’d tell us, “Here’s the evals, and we’re going to call you when we can get to 100% on these evals.” I actually remember. It was a funds-related use case, an LPA key-term review report that a Danish law firm was spending three days on. Basically an associate would spend three days putting together that report. In summer of 2024, we had 60% accuracy on that task. By the end of that summer, we had 100% accuracy. Once you get to 100% accuracy, that task is done, it’s over. I’ve adopted this mentality internally that if AI can do something, it will do it. With our product, we think a lot about solving legal tasks end to end. Once a task is conquered, it’s done. We just strike it out and we’re on this path of solving more and more complex tasks. You start with NDAs, but at some point you get to full-on share purchase agreements, which are very complex. But we’re going to get there.
· Max Junestrand, Jack Altman
The question for these organizations who are maybe more traditional and trying to keep up with the pace of AI is, how do you do that while at the same time doing your normal job? A lot of the organizations that we work with really struggle with keeping up with the technology uplift, even with our developments. We’re struggling by getting all the latest models and turning that into product, and they have to adopt it, and then their customers have to adopt it. [00:00:00] S0: Here’s a question for both of you. As I’m listening to you talk, I can sort of see the hill climb that you’re on. You’ve attacked one part of it and the next one’s coming and the next one’s coming. One of the things I’m thinking about is, for a new startup in legal, what would the right strategy be for them? How do you possibly get into the mix fast enough for all of these things and then— [00:00:00] S1: Exit to Legora. [00:00:00] S0: Sell to Legora, that’s a good one. How urgent is it to grow really big, really fast for Legora, given all of the dynamics around this? Chetan, I’m curious how you think about this. Is it the same urgency as always, or do any of these dynamics mean that getting to real scale is more urgent here than other places?
· Chetan Puttagunta
[00:00:00] S2: We can go back to launch day, October 2024. When they launched, roughly the ARR of the business rounded to a million dollars. If you go back into that moment, there was this exercise of, should we make a budget? What we all decided around the table was there was no reason to make a budget because we don’t know anything about the market. We don’t know if people even like our product. We had instincts, but we just needed to go literally as fast as we could to get the product in as many hands as we could. Because ultimately the whole theory of the company didn’t work until we got product feedback. That was literally the aim. Get this out as quickly as possible into as many hands as possible. One of the things that Max did… It’s cliche to say it’s first-principles thinking, but it is, because the team was unbiased by how to build a software company. One of the things you learned in SaaS was the way you do pilots is you would go in, do a time-trial pilot where you would give them access to the application. 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 Legora is they would go put Legora into your organization and whatever you put into Legora, they would leave behind even if you didn’t want it. So there was this idea that, “Hey, you adopted AI, you did stuff with AI, you built some practices. Whatever skills you built or whatever IP you built, it’s kind of yours. We can leave that behind. It’s not a big deal. It’s your skills, it’s the things that you’ve learned.” Then Max went around and just gave people 30-day pilots, 60-day pilots, whatever they wanted. 90-day pilots.
· Max Junestrand, Jack Altman
[00:00:00] S1: They would run these competitive pilots. They would say, “Okay, there’s a couple of companies on the market. We’re going to want to A/B test all of them because it’s really hard to pick based on the feature set on your website. In those pilots, I think we did an extraordinarily good job of delivering value. When the 30 days were up, if we shut it down, it would be a riot. People would roar and they’d be like, “We’ve never seen software adoption like this in a legal organization. We need this and we need it now.” 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. We hired all these lawyers, who are now called legal engineers. It’s a great term, forward-deployed legal engineers. [00:00:00] S0: I was just going to say, what about FDLE? [00:00:00] S1: That’s right, FDLE. They’re amazing. They’re the most tech-savvy lawyers in different organizations who don’t want to make partner, because that’s one type of life. They want to work in a tech company and now they get to work with their practice that they’re amazing at, and technology. Then they get to work with the best legal organizations in the world and drive that change. [00:00:00] S0: I would think once you’re embedded in these organizations, it’s got to be sticky. [00:00:00] S1: I think Legora is very sticky. We’ve ripped out our competition at many organizations at this point. [00:00:00] S0: What creates stickiness?
· Jack Altman, Max Junestrand
[00:00:00] S0: What creates stickiness? [00:00:00] S1: The stickiness is the use cases and the cadence. If you’ve invested time in building up a workflow that works for you, why would you want to switch? It’s usage stickiness. [00:00:00] S0: It’s not data. [00:00:00] S1: No, not yet. Not any real technical implementation, which is great, because our competition has been deployed in a lot of places with no real usage or very simple use cases. That means that we can go there, show them, and display clearly in a pilot that we deliver much better, and then we can easily swap it. So we actually have a dedicated migration team moving deployments over to Legora.