Y Combinator Startup Podcast
00:32:00 · Gustaf Alstromer, Max Junestrand
[00:32:00] S1: Yeah. A lot of news on models this week or last two weeks. [00:32:05] S0: It's gonna be like a great episode for whoever makes the documentary. [00:32:08] S1: I'm I'm hoping in some ways we're making a documentary right now about everything that's going on. I'm not sure if someone is, but I hope they are. Open Weight Small is very much the topic right now. And when we spoke on stage a couple months ago, you talked about that you're not attached to one specific model. You can work with many different models. Have you updated your view based on this week? But, like, how do you think about... So, like, I'm sure there was a moment, like, a year and a half ago when it seemed like it was just one or two models that were really sufficient to do... To power Legora, and now there seems like a lot more.
00:32:46 · Max Junestrand
[00:32:46] S0: Yes. Which I think will be great. Yeah. And and, I mean, you know, clearly there will be applications of different models in different use cases, and so if you take customer support, you're optimizing for, you know, speed of turnaround, latency to the customer, and cost on solving a particular ticket. I think Fin used to charge like $1 per solved customer ticket. In law, you actually want the most amount of intelligence quite often, because the fraction of token spend or software spend compared to like, you know, human expertise applied to the problem is really tiny. If you're solving complex litigation, you're gonna wanna throw the most brain that you can on that problem. And so we have really sort of two different camps, like one group of our users are asking for the ability to loop really complex models just to work on problems for a long period of time, like racking up big LLM costs, and another group of our users are saying, can we get access to open source? That is gonna be cheaper on our consumption. And I think the answer is somewhere in between. I think ultimately, one of the core IPs and muscles that I encourage as many of you as possible to build is the ability to eval new models and to eval new use cases, because that is the superpower that then allows you to route things effectively. We did a lot of that early on because we hired a lot of lawyers into Legora, and we made part of their job customer facing, but part of their job was also to build out use cases that we would throw our evals on. And the frontier of like, you know, cost intelligence and where that sort of frontier lies, you know, yet to be seen. I think there's also a compute problem where when we lock in big contracts with the labs and with our compute providers, that is actually a huge thing. We cannot have our system go down when, you know, a number of percentage points of all the lawyers in the world are working with it, and their clients rely on it.
00:35:14 · Gustaf Alstromer, Max Junestrand
[00:35:14] S1: Are there specific benchmarks you look at for how... Yeah. I'm sure you are, Ben, basically. [00:35:18] S0: Well, we we look at the Legora bench. So we actually announced and released that on Friday last And it has been that has been work for for the past three years, and we've kept that internal up until Friday. And the reason for that was it was basically OpenAI or or the Anthropic models. Mhmm. And so it wasn't like that interesting to publish benchmarks on. Yep. And we would only make those two models available in our system and the user could pick. Now, the smorgasbord of available options is a lot larger. And actually we found, to our surprise, that SpaceX and Grok was one of the best performance models, especially given the cost Yep. On our benchmarks. And we don't even have a a... Have them on our data processing agreement. And so we actually can't yet offer them to our customers, but now we will start to. [00:36:11] S1: And do you see your customers having having strong opinions about which models they wanna use? Or is it sort of like they just want the best, whatever is [00:36:18] S0: the best? Some of them have an... They have a strong... Especially like banks, you know, big law firms have a... They don't want Chinese models.
00:36:18 · Max Junestrand, Gustaf Alstromer
[00:36:18] S0: the best? Some of them have an... They have a strong... Especially like banks, you know, big law firms have a... They don't want Chinese models. [00:36:27] S1: Yeah. Makes sense. So I want to go back to just a little bit earlier, like when you decided you want to build a startup. A lot of people in the audience here. I've had two dinners in the last two days with attendees at Startup School. We talked about when should you start a startup? People are working at DeepMind, people are working at internships or jobs right now. Often, you're one or two years into it. What would you say was the trigger for you? To say, I'm just gonna do this and not do McKinsey or the other options that you So [00:36:59] S0: my my dad had two companies during the dot com boom, both of which went to zero after raising too much venture capital. Maybe some [00:37:11] S1: Has he been a good mentor for you? [00:37:13] S0: Yes. Yeah? Absolutely. [00:37:17] S1: How And... Often do you speak about... [00:37:18] S0: Oh, it's probably every day or every other day. Balagora. It's great. Yeah. About lots of stuff. I used to... Now, I'm, like, too tired to walk home often, so I have to, like, take the train or like take a cab, but I used to walk home [00:37:32] S1: Mhmm. [00:37:32] S0: Then I would call him, and that would be a lot of fun. And sometimes we call him senior adviser. We have actually... One of our biggest customer pitches, we we were stretched for resources. So he came in and did the PowerPoint for for that client. [00:37:49] S1: So he knew everything at this point.
00:37:49 · Gustaf Alstromer, Max Junestrand
[00:37:49] S1: So he knew everything at this point. [00:37:50] S0: Yeah. Yeah. He knows most of it. So he had two companies in the .com, both went to zero, and then he had a boat business in Archipelago when I grew up. And so I've always been surrounded by entrepreneurship, and always felt like I wanted to prove myself in that arena. I'm very competitive. I used to play video games. I used to play DOTA two, like more than I went to school. And then I think building a company is like the optimal... Like it's the biggest arena that you And can you'll either leave victorious or you'll leave defeated or you'll be okay. And I was very decided in that I should probably learn more about the world, which I thought McKinsey was gonna be a great place to start at, which I still think it is. Yep. I think it really teaches you like how to work. You learn work ethic there. But I did an internship, and so I think I I like got a bit of that even during my studies. And then we had this like pivotal moment with GPT. Yeah. And so we were talking about this, like, you weren't as as excited about investing a few years back, like, pre LLMs because there wasn't a lot of... [00:39:09] S1: There wasn't a lot going on. Like like, I think the the last year or two before COVID, there was not a lot of new ideas.
00:39:16 · Max Junestrand
[00:39:16] S0: Right. And I remember sitting down pre GPT, like Yeah. Here are all the startup ideas I can think of. Most of them were pretty boring. I I don't think most of them would have worked out. Yep. Yep. And then, like, small companies got a really big advantage versus big companies. Mhmm. And I mean, I think you saw this in just like the way that Microsoft would build copilot. It didn't work for a really long time. Yep. And I remember when that came out, we were like, oh my god, we're so screwed. You know, every lawyer already works in Word and Outlook, and now they're just gonna use Copilot, but turns out like it didn't work. And the ability for us then to still build a lot of value was huge. I think now reflecting back on that, you learn so much by having to figure it out. And I really think that the amount of things you learn is a function of the amount of discomfort that you are willing to endure. When we started Legora I was super introverted, I really didn't feel comfortable talking to customers, I wanted to code as much as possible. You know, when you have to go separate ways with an employee for the first time, like all of this is so hard. But you realize that startups is one of the places where you can learn the most in a short amount of time. I think, you know, if I were to build a company again or join a company, I still feel like I learned a lot working at other startups. I actually worked with YC startups before starting my own. And then I would say you should think really deeply about who you started with and the problem space. And I think we picked a space more than we picked a problem, which I think is like two different ways of going about it. It was completely obvious that legal and AI was going to be a thing. Yep. How it was going to be a thing was like very uncertain. And so we just said, let's march in this general direction, and then we'll figure it out. That's one way of doing it. I guess the other way of doing it is like you find a specific problem, you solve that very clearly, you generate ROI, and then you expand from that.