The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:42:30 · Harry Stebbings, Harrison Chase
[00:42:30] S1: To me, that number is so central because it is the fundamental determinant of Anthropics enterprise value. If that number goes to 5%, then the $2,000,000,000,000 IPO might be a little bit overpriced. If that number actually goes to 300% like it is in McCall's case, then it's dramatically underpriced. And so that's why I focus so much on it. Do you agree that number underpins Anthropix value? [00:42:54] S0: I think it's probably right that that number underpins Anthropix value. I think the tricky part is that the only place we've seen agents really have effect is in software engineering so far, though. And there are a ton of other places within an enterprise that I I would argue have yet to kind of be affected by by agents. And so I think it's possible. Again, software engineering, think very difficult to predict. But but I think there's basically two levers here. One is like what happens in software engineering? Is it the same as today? Is it more than? Is it less than? But I think undoubtedly for the other domains, it will be a lot more than it is today. And so I think from that perspective, I think that's very bullish on Anthropic and OpenAI and other model providers.
00:42:54 · Harrison Chase, Harry Stebbings
[00:42:54] S0: I think it's probably right that that number underpins Anthropix value. I think the tricky part is that the only place we've seen agents really have effect is in software engineering so far, though. And there are a ton of other places within an enterprise that I I would argue have yet to kind of be affected by by agents. And so I think it's possible. Again, software engineering, think very difficult to predict. But but I think there's basically two levers here. One is like what happens in software engineering? Is it the same as today? Is it more than? Is it less than? But I think undoubtedly for the other domains, it will be a lot more than it is today. And so I think from that perspective, I think that's very bullish on Anthropic and OpenAI and other model providers. [00:43:34] S1: I think another question that a lot of people are asking is about kind of the proliferation of open models and how that changes the landscape. You work with some very large enterprises. One of my dear friends, Jason Lampkin from SASTA says that I'm not gonna do his accent because I'll butcher it, and I've done it before, he got rather... He said it wasn't very good. Do you find that large enterprises are fearful of working with open source Chinese models, or actually they don't mind? [00:43:56] S0: We see that some enterprises are fearful of working with open source Chinese models. We we we see others that don't care. [00:44:02] S1: Seriously, not fear is justified.
00:44:02 · Harry Stebbings, Harrison Chase
[00:44:02] S1: Seriously, not fear is justified. [00:44:04] S0: I don't really think that fear is justified. I think there's different concerns. I think one concern is that the data that it runs on will somehow go back to the Chinese labs. And again, if you're if you're using Fireworks or hosting it yourself, I think that's just ridiculous. I think there's other fears that the stuff that they've been trained on will cause it to respond in a way that's, you know, not consistent with the values of the enterprise or something like that. Think I that's where evals come in handy, I think that's largely unfounded. But, like, sure, maybe for some domains, there's there's some realness there. So I think it's probably largely unfounded, but for some domains, because of that kind of, like, response, like, I could see there being some some nuance there. [00:44:45] S1: Do you think in the next twelve months, we'll have comparable US open source models? [00:44:49] S0: I think in the next twelve months, we'll absolutely have comparable US open source models. I think you look at everything that NVIDIA is doing to kind of, like, work with the ecosystem. I think they they care about it tremendously. You look at... Reflection had a model launch recently. I think it was still a little bit behind some of the Chinese models on some benchmarks, but I think there is a tremendous amount of attention in that domain. And so I think absolutely this will catch up.
00:44:49 · Harrison Chase, Harry Stebbings
[00:44:49] S0: I think in the next twelve months, we'll absolutely have comparable US open source models. I think you look at everything that NVIDIA is doing to kind of, like, work with the ecosystem. I think they they care about it tremendously. You look at... Reflection had a model launch recently. I think it was still a little bit behind some of the Chinese models on some benchmarks, but I think there is a tremendous amount of attention in that domain. And so I think absolutely this will catch up. [00:45:11] S1: When you look at the state of land, say, today in terms of open models, what does the world not see that you see very clearly given your position?
00:45:11 · Harry Stebbings, Harrison Chase
[00:45:11] S1: When you look at the state of land, say, today in terms of open models, what does the world not see that you see very clearly given your position? [00:45:19] S0: I think when it comes to open models, I think there is a lot of usage of them, but I do think they're for more narrow tasks than people think. So I think we see a lot of people using more open source models and more post trained models than than I would have maybe expected a year or a year and a half ago. But I still think they're primarily for these simpler tasks like routing or guardrails or extraction or summarization. I don't think they're really driving the the core agentic loops for a lot of these main agents. And and so I think open source models are are kind of divisive cause you have some people on on one end who are like, great. They're they're good enough. Let's use them for everything. And and I don't think they're quite there. And then I think you have other people who are like, you know, the frontier models are just going to keep on getting better and better and cheaper and cheaper. And like, yes, that's true. But if you really know what you're going to do, so you have to have a high volume use case. And if there are a few components of that that are simple enough tasks, I think it's very good ROI to start kind of like post training models for for those use cases.
00:46:13 · Harry Stebbings, Harrison Chase
[00:46:13] S1: How do you see the inference market playing out? You mentioned that Fireworks very openly. We're a big investor in Fireworks, and I love Lynn and the team. But everyone's trying to do it. You you... We had Chase on the show from Crusoe who also have that as part of their business. You know, friends with the Nebius guys, they also do it as part of their... How does this business play out? And how do you think about the need to do all of the stack versus inference only providers like a Fireworks or a Base 10? [00:46:39] S0: I think when it comes to how things will play out with respect to inference, there's two things that I I think I really underestimated from the start of Lang change now. One is the demand for inference, and two is the demand for, like, coding agents. I think both of those markets are just tremendously bigger than than I thought three, four years ago. I think actually it's it's Eric Visserie at Benchmark who said this on either a tweet or podcast recently where I think he basically said, like, everything just works. Like, this this market is so big. I think Thomas Tungs as well had something around just, the market for inference is just so big that... And I think Eric said it about more broad stuff than just inference, but I think applying it to inference, I think any inference will work. That's like, you know, there's just so much demand for it. And I don't know if it's gonna get less anytime soon. And so, yeah, sure. Fireworks will work. AWS will work. Crusoe will work. I just think there's so much demand for it.