Y Combinator Startup Podcast
00:45:53 · Garry Tan, Jeffrey Morgan
[00:45:53] S2: That's the Homebrew Computer Club to broad computer adoption and, like, speed run that took ten years for the PC. Yeah. It took, like, eighteen months, twelve months. [00:46:02] S0: Yeah. And that's one of the things that surprised us the most. Because I think, look, I think open models, the original users, very much hobbyists just tinkering, oh my god. This is even possible. But very quickly because, you know, two things. One is they're free to get started with and you could run them anywhere. That is incredibly helpful to a Fortune 500 IT developer team. They don't have to ask for permission to use it. And so it just happened what was really good for a hobbyist user translated very quickly to a developer within a business. It just happened to be a case where that was it. For example, databases, we saw some of this too where databases started for devs like MongoDB very quickly also moved to enterprise. But because LLMs are stateless, it made for such an easy transition. Now moving to the cloud, there's a lot more in play. There's an economic question if you're a customer. There's obviously security, where's the model running? But what's beautiful about open models that both hobbyists and IT developers loved is you could just get started, you didn't need permission.
00:47:02 · Jared Friedman
[00:47:02] S3: Can we talk about the monetization angle? Because this is interesting too. So like in 2023, Llama two takes off. All of a sudden, you've got all these users, a 100,000 GitHub stars, like, you've clearly found something. But it's basically like Reddit cranks who are using it. You're making no revenue, and there's no obvious path for how you will ever make any revenue from all these, like, cranks on Reddit. It was two years before you actually figured out a business model for it, which, funny enough, is exactly the position that Docker was in. Like, how did you think about it during those two years? Were you worried about it? Were, was the team asking like, what's the business model gonna be? How did you think about like figuring out how to make money from it? I think
00:47:40 · Jeffrey Morgan
[00:47:40] S0: there's always two ways that we saw open models being able to monetize in a way that's great for the company, great for the developer, and great for the customer. And one of them was a privacy focused AI product, which Ollama started, really started with that and its open source incarnation. But we always felt that there was this moment where you weren't using Llama with the Llama models, for example, tool calling right away when they came out. So there were use cases where it was still reserved for the frontier models. And again, the risk of overthinking it, we kind of saw that there wasn't the level of product market fit with open models that closed models had. And in some ways philosophically we want to align with when that happens, we want to be there to capture that. I think it happened this year with coding agents running with open models. Because you had the largest consumption of AI being matched with finally open models being able to service that. There are lot of opportunities along the way to do it privately, securely, a lot of Fortune 500 have already adopted Ollama. But we really asked ourselves what would be the most important problem we could solve for a customer. And the local piece while an important part of that story never felt like the whole story, which was how do you access open models for the hardest problems? And so in some ways waiting, we knew we had to wait a little bit for the market to mature. At the same time, what are the risks of waiting? Well you build a culture if not careful, and we had learned a lot of this from our Docker days, where you don't think about monetization, it's not a priority. I think from our previous battle scars as a team, we knew about that. But I think the other component which is really important is making sure you keep in touch with your customers. Like one of the biggest risks of having an open source project that takes off is you consider your user base and customer base, like your customer, just a blob on the internet, which is a really risky way to think about customers. Because you want to meet them, figure out their needs. What are they doing? What do they want to do in six months? What's their story? And I think that's the thing I wish we had done a little more in the last few years. And we're doing a ton of that now.
00:49:43 · Harj Taggar
[00:49:43] S4: One thing I'm curious, when you went through YC, you guys were second time founders. I'm curious what got you to decide to do YC actually.
00:49:51 · Jeffrey Morgan
[00:49:51] S0: We went back and forth on this for a lot, which we shouldn't have. We should have just said, of course, we're doing YC. But by and large, starting a company is a really lonely experience. Even if you have a great co founder, and Michael, my co founder was the co founder of my first company, he was my college roommate at University of Waterloo, but it's still lonely. And I think just having a set of peers, even though we did it during the pandemic, just talking to Jared and like five other groups of founders every week really helped you feel less lonely. And I think that's such an important part of it. And then of course, when we finally moved down here and there was no more COVID, the network was just incredible. And the fact that we could meet founders building on open models, building on any kind of AI, you know, we kinda knew that was gonna happen because we had known so many founders from the University of Waterloo who had done YC pre COVID. And they were like, it's really about getting together. And like that was a big part of it. And we knew that was there. And I think that's what made it a no brainer. But also just I think there are a lot of mistakes you can repeat that you don't have to. And what I love about the YC community is how transparent founders are with each other about those. And I still keep in touch with the founder of Docker, who's an investor in our company. And we're able to talk about some of these challenges we saw in the previous generation of companies that we don't necessarily have to repeat or things that worked and we can bring into the future.
00:51:18 · Garry Tan, Jeffrey Morgan
[00:51:18] S2: Yeah, if you just don't repeat one of those mistakes, sometimes is the mistake that would have killed the company. [00:51:24] S0: Potentially, yeah, yeah. Our famous saying, a bunch of our team is from companies that ended up working great and Docker's doing phenomenal now. But whether it's some of our team was early, early at VMware. And there are always ups and downs. And I think just having a group of people around the table who have a collection of those, and also what worked... Actually, what worked is actually even more important. And just being able to, like, have that muscle memory is a big part of it.