From Idea to $650M Exit: Lessons in Building AI Startups

**Jake Heller** (0:00)
What we're going to talk about today is how my company built an AI app that was so good, we're able to bring it to an exit for $650 million, and how you can do that too. So really, we're talking about three big ideas today. The first is, what ideas to pick? How do you decide what to pursue? Second is how you actually build it. And third, and honestly often overlooked, is how you take that thing that you built and market and sell it successfully in the market. Before we dive into this, a little bit about me, so you know who's talking to you. I grew up a coder. I've been building stuff since as long as I can remember. It's probably the same as basically everybody here. A bit of a side quest for me, but I fell in love with law and policy, and I became a lawyer. And I had a pretty conventional, though brief, legal career. Law school, clerkship, big law firm, etc. I think like anybody who builds stuff and then goes to one of these old professions like law or accounting or finance or whatever, the first thing you find out is I cannot believe that they are doing it this way. And so I immediately left that and founded a company called Casetext in 2013, when I think a lot of you are about turning eight. And maybe as a side note, that's about how long it takes sometimes for these companies to be successful. So I know you're 18, 19, 20, 21, 22, whatever you're old right now. Be ready to sign up for one of the most amazing adventures of your life when you start a startup and it takes time. At Casetext, we've been focused for the vast majority of our experience on a deep conviction that AI when applied to law can make a huge difference. And by the way, it wasn't even called AI when we started focusing on it. It was called natural language processing, maybe machine learning. But one of our AI researchers who is here today, Javed, saw an early application. As soon as the BERT paper came out, Attention is All You Need, etc. This is like seven years ago of how AI technology could apply to making lawyers' lives better, for example, making search a lot better. Because we were so focused on large language models and we're researching deeply in this space, we got really early access to GPT-4 like summer 2022 We were like $20 million in revenue. We were doing great. I had like 100 people and we stopped everything that we were doing and said, we're going to build something totally new based on this new technology. That became a product called CoCounsel, which was the first ever and I think still the best AI assistant for lawyers. For reasons I'll go into the rest of this talk, we were acquired by Thomson Reuters about two years ago for $650 million in cash. By the way, that feels like a big number, but I think for a lot of folks in this room, you're going to look back at this talk and be like, I can't believe that was a big number back then. You guys are going to build things that are so much more valuable. I really believe that. I think that's because what AI is going to unlock for all of you, is the ability to build amazing stuff for this world. So how do you pick an idea? How do you know what to work on? It's actually one of the hardest and most consequential problems. Since the beginning of YC, they've had this saying, make something people want. The reason they had that saying is because it's genuinely difficult to know what people want, especially in the old world of building software. You have to build something, get it in users' hands and try and fail a lot of different times, and you just hope that it's something that people actually want to use. So that's why the saying for Y Combinators, make something people want. I actually think it just got a lot easier. Because what do people want? Well, what do people want, for example, what things they're paying for right now? People are currently paying people to do tasks, right? In this case, it's going to be very unhappy, like customer support people or something like that. But we already know what people want because they're paying people to do it. This includes a lot of work like customer support or insurance adjusters or paralegals or things you do in your personal life, like personal trainers or executive assistants or whatever. That is what people want. And so the problem of choosing what people want just got a lot easier. Because now, you just have to look at what are people paying other people to do. For a lot of those problems, either traditional AI like LLMs can solve many of the problems that people work on right now. And if not that, then robotics can solve a lot of things that people are working on in the physical world. And what I think you're going to see as you decide what you're going to build, you first pick an area to target, it really kind of falls under three different categories. One is like assistance, where say a professional needs help accomplishing a task. That's what we built with CoCounsel. Lawyers need a lot of help reading a lot of documents, doing research, reviewing contracts, marking them up, making red lines, sending them to opposing council. So that's one big category, is assisting people doing their work. The second big category is just replacing the work altogether. People currently hire lawyers, what if we just became a law firm powered by AI? People currently hire accountants and financial experts and physical therapists and, and, and, you know, people to fold your laundry, whatever it may be, right? You can just replace that task using AI. And finally, the third category is you can do things that were previously unthinkable, right? Like, for example, at law firms, they would have hundreds of millions of documents, and they would never think in a million years, I should have people read over every single document and categorize it in certain ways and summarize it and index it, etc. It would just be insane, right? It would cost them millions and millions and millions of dollars. But now that AI is here, you can have thousands of instances of Gemini 2 flash or whatever, read over every document. The previously unthinkable is now thinkable. These are basically the three categories of ideas to choose. And what I think is incredible about this is the amount of money to be made with these new kind of categories each has gone way up. It used to be that what's called the total addressable market, which is basically how much money you can make from your product, was the number of professionals, for example, number of seats you can sell, times the dollars, like $20 per month or whatever. And by the way, a lot of many billion dollar companies are built selling seats to X number of professionals. But today, the actual amount of money that we already know people and companies are willing to spend is the combined salaries of all the people they're currently paying to do the job. And that number is like 1000X bigger. You pay $20 a month to solve a problem, for example, a typical SaaS kind of subscription. But you might pay $5,000 or $10,000 or even $20,000 a month to certain professionals to solve problems for you. So the amount of money that you can make with your new applications, with AI, has gone up by a factor of 10, 100 or even 1,000 compared to what it used to be. I want to take a quick moment, because that might sound like pretty dystopian. Like we're talking about taking all these salaries and these become, you know, your addressable market. I think it's kind of the opposite. I think it's beautiful. I think the future is beautiful for two reasons. The first is that you're going to unlock a future. When you replace or substantially assist certain jobs, like people used to, Sam Almond wrote about this in a recent essay, people used to have a job called lamp lighters, where we didn't have electricity and lights. So people would go around with a matchstick, lighting all the lamps at night on and then turning them off at night by putting out the candles. That's what things used to be. And we couldn't even imagine the kind of stuff we're doing now, because that's what we were stuck doing in the past. So you're going to unlock a future that we can't even imagine today, when we move past the roles that we're currently doing right now. It will feel antiquated ten or fifteen or a hundred years from now to do the kind of things we're doing today, because you're going to help us move past that. But as importantly, what I think some people don't think about with this stuff, which is very true, is you're going to democratize access to things that were used to be really, really hard or very expensive. In the field we worked in in law, over 85% of people who are low income don't get access to legal services.