Y Combinator Startup Podcast
00:32:29 · Jeffrey Morgan, Harj Taggar
[00:32:29] S0: I think for most customer use cases, there's a level at which a model becomes good enough. And then they can continue using that level of intelligence. Maybe the model will get faster, it'll have better architecture, it'll have new capabilities. But they won't have to reach for the god model. But I do think there are use cases where the most powerful models unlock them and that'll continue to be a thing. It'll be really exciting, sometimes scary as well on what they can do. But for the run of the mill use cases where open models really shine, I think that's where we're hitting a point where you're not solving necessarily the hardest problems within the business. But they're hard enough where it's now unlocked by open models. Will there be an open model that becomes a God tier model? I think it's possible. And we're seeing really exciting developments from Zhipu AI and GLM where some of the tasks they are frontier. And we all saw with the Kimi model how for web development it became the best model. And that sent this new shockwave across the market, which is it's less about a gap and it's more about a head to head competition, which I think makes all of this even much more exciting. [00:33:37] S4: This is a bit of a sensitive question, but what do you think about this and the geopolitics around it?
00:33:41 · Jeffrey Morgan
[00:33:41] S0: I think a lot of the geopolitical angles around this start with where the model's from. And the more we spend time with customers and users, a lot of it's actually how the model's run, where it's run, how it's run. Is it run-in a secure environment? And that starts to matter a lot more. But I do think, look, it's super important that a customer in The US can use a model trained in The US. And we have two kinds of classes of customers we speak to. One is they don't really care where the model's from, they care about where it's run. But for every one of those, there's a customer that's saying, I really care about where the model's from. Because it's data the way it, it's not even just a security issue as much as how does the model speak? We all go through and communicate. And we all go through, a lot of the models go through phases where they sound more robotic, they sound more friendly. And a lot of that matters too. But I think the highest order bet is obviously making sure that you have a model that end to end, you understand where the data's from, which is great from the Nemotron models. Then you can go and introspect what made this model. Because if you're putting in a mission critical task, which people are absolutely using open models for mission critical tasks. There's a post online about how Ollama powers the analytics of a power plant to detect surges in Finland to make sure that the lights stay on. That's where these models, the model origin really matters.
00:35:07 · Jared Friedman, Garry Tan, Jeffrey Morgan
[00:35:07] S3: For for like critical tasks like that, how do you ensure that a Chinese model, even if it's hosted in The US, isn't basically, like, booby trapped to, like, cause problems? [00:35:19] S2: Yeah. The Manchurian candidate problem. Exactly. Have there any been any like known cases of the Manchurian Candidate yet? [00:35:26] S0: I think not that I can think of off the [00:35:28] S2: top of haven't my head. Heard. I feel like I would have heard about it, but... [00:35:31] S0: You know, what you don't see a lot on some of the press articles is how robust some of the the IT and security teams are at the businesses that we know of, the top Fortune 500 businesses. They're really used to this already because open source software, if you think the average application, has thousands of dependencies. This isn't a new problem. And all it takes is one dependency for there to be a major security issue in the entire application. [00:35:55] S2: The blockchain poisoning is insane. [00:35:57] S0: It's a thing. Been a thing for decades. And it's not new in that sense. It's a little more opaque because you can't, like, dig into the model. It's [00:36:06] S3: It it is deterministic. [00:36:07] S0: But it's deterministic. And if you screen the model properly with safety checks, by and large, at least what we're hearing is from customers is that can be solved.
00:36:07 · Jeffrey Morgan, Garry Tan, Aaron Epstein, Jared Friedman
[00:36:07] S0: But it's deterministic. And if you screen the model properly with safety checks, by and large, at least what we're hearing is from customers is that can be solved. [00:36:14] S2: Do you wanna talk about the origins of Ollama? You you know, you guys came up through the Docker ecosystem, and a lot of people watching, you know, would love to be in the position you're in where you have this sort of enduring brand moat that looks like it will extend for, you know, really till the end of time. No. I just... It's it's a very powerful situation to be in. You basically found yourself on top of a giant oil well. Right? For those out there wildcatting, can you tell us that story? You you were actually working with Jared in 2021. [00:36:51] S0: Yeah. You know, my my cofounder and I previously built Docker Desktop while at Docker. So we really got an understanding of what makes a great developer experience. But I have to say the first few years of Ollama as a company was really in search for what's the right problem to solve with this muscle we've built of trying to design a great experience for developers. [00:37:11] S1: You applied to YC with a very different idea, right? [00:37:14] S3: For sure. Do you remember what the tagline was when you guys applied to YC in Winter twenty one? [00:37:18] S0: I think it wasn't well defined. I think we realized, let's go back to building a really great desktop experience for containers and Kubernetes.
00:37:18 · Jeffrey Morgan, Jared Friedman, Aaron Epstein, Garry Tan
[00:37:18] S0: I think it wasn't well defined. I think we realized, let's go back to building a really great desktop experience for containers and Kubernetes. [00:37:28] S3: I remember what I wrote down on the on the application. It was a Kitematic for Kubernetes or a Docker desktop for Kubernetes. [00:37:36] S0: Which was effectively Docker desktop. They had a [00:37:39] S1: great Kubernetes. I [00:37:40] S0: think, you know, it's one of the challenges as a second time founder that, you know, Michael and I have told ourselves. We we tried to overengineer the idea in many ways. And I think even the two to three years after, like Ollama, we did YC in 2021 and Ollama wasn't launched until July 2023. After we raised our series A, after obviously after we had done YC, that journey was one of really in search for a customer problem that could delight a developer. And in some ways it was also a good thing that we tried different ideas and pivoted until 2023 because that's when Llama came out and started the open model wave. [00:38:19] S3: Which is why it's called Ollama. Not necessarily. Okay. [00:38:23] S0: No. Oh, really? Llama means generally from our experience, whether you think of local llama, the subreddit, a llama really just stands for open models. As we were looking through the name, it wasn't necessarily from an existing model. [00:38:37] S2: It's more LLM. Exactly. It's like the animal plus LLM. [00:38:42] S0: Yeah. I think having that character was important. So we're like, what's a good name for a character, a face you can put to the name? Because You need
00:38:42 · Jeffrey Morgan, Garry Tan, Jared Friedman, Aaron Epstein
[00:38:42] S0: Yeah. I think having that character was important. So we're like, what's a good name for a character, a face you can put to the name? Because You need [00:38:48] S2: go to animal mascot sometimes. [00:38:50] S0: Had one. Yeah. GitHub. [00:38:52] S3: Yeah. He still hasn't taken my advice to have llamas come to actual Ollama events. [00:38:55] S2: Oh my god. [00:38:59] S1: I'm curious what your series a pitch was because you raised from Benchmark, like, fantastic investor, but all of this, the future we're in now hadn't quite taken off in 2023. So what was, like, the pitch and the vision back then? [00:39:10] S0: Yeah. And we partnered with Benchmark in 2022. So it was Dolly days, but pre ChatGPT. When it came down to the pitch, I think we weighed so much on like, hey, we're trying to build this great developer experience. We're solving this security problem. And we had known Peter, a partner at Benchmark from our previous lives building a Docker because he was the series A investor in Docker. And so a lot of it was weighted on the people and also why we exist. I think the what, I mean, solving SSO for Kubernetes, which is a real problem, wasn't really our passion. I think we were really lucky to find a partner that could see us for what we stood for and what we were trying to do versus the point in time problem we were solving at [00:39:54] S1: I that see. So you were as the ace or pre pivot, didn't [00:39:57] S0: you? Correct. [00:39:57] S1: Okay. I didn't realize that, actually.