Y Combinator Startup Podcast
00:44:29 · Jeffrey Morgan, Aaron Epstein, Harj Taggar
[00:44:29] S0: I think security stuff. We're... We have this Ollama thing now. [00:44:32] S1: I think you were catching up with Jared, then I bumped into you on the stairs, and I think you had your t shirt or some swag or something. And I was like, oh, I like it. You guys are Ollama. I was like, know, I... Meeting a rock star or something. [00:44:43] S0: It would have been really hard to time this, but I wish we had taken that leap much sooner. I mean, the best time to do it was during y c. But it was impossible. Didn't exist. Exactly. [00:44:52] S4: Llama Llama didn't launch yet. I guess you were also one of the first GitHub projects that very quickly got to a 100,000 GitHub stars. Right? Do you remember how long it... It was, like, very quick. [00:45:03] S0: Yeah. I can't remember exactly how fast, but it was much faster than Docker and Kubernetes. To your point, things kind of just started working and started taking off. And you're really as a founder just beside yourself because you can't totally explain why. I think it's the best way to explain product market fit. And there are different levels of product market fit. We only started monetizing earlier this year with Ollama Cloud. But just to see people fall in love with the product, it's such a zero to one moment that I wish we had done it during YC, but in some ways it wasn't possible.
00:45:03 · Jeffrey Morgan, Aaron Epstein, Garry Tan
[00:45:03] S0: Yeah. I can't remember exactly how fast, but it was much faster than Docker and Kubernetes. To your point, things kind of just started working and started taking off. And you're really as a founder just beside yourself because you can't totally explain why. I think it's the best way to explain product market fit. And there are different levels of product market fit. We only started monetizing earlier this year with Ollama Cloud. But just to see people fall in love with the product, it's such a zero to one moment that I wish we had done it during YC, but in some ways it wasn't possible. [00:45:36] S1: I also think it's just kind of wild to put into perspective. Like, you sort of went from being in sort of like the cranks on Reddit, like, interested in running their own, like, rigs at home to, like, 85% of the Fortune five hundred in, like, two years or something like that. That's like that's like a pretty [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.
00:45:53 · 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.