The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:40:55 · Harrison Chase, Harry Stebbings
[00:40:55] S0: That's guesstimate. Yeah. [00:40:56] S1: A guesstimate. Totally fine. What is that in two years?
00:41:00 · Harrison Chase
[00:41:00] S0: I think the hard part about estimating coding agent spend is that orgs are just changing so rapidly. So what I mean by that is I think there's a future where orgs are much smaller and coding agent spend is, you know, the same as human salary or or or more or an order of magnitude more. And you have a small number of people that are driving and executing incredibly rapidly and incredibly performantly. And I and I think that's an enticing vision in terms of of of what you think about. I don't think many companies, including us, are are probably there. I think we still see that we need a bunch of different people with a bunch of different backgrounds and a bunch of different expertises collaborating on things, and there's a bunch of communication and overhead and review. And, like, yes, it would be ideal to have one person driving a 100 agents and just, like, driving everything that, like, makes up, you know, a large part of the product. But I think we just see that it's still a very human component to it. And so I spend a lot of time thinking about kind of, like, org design. Is it possible to have, like, small teams, one person teams, two person teams that are kind of, like, driving agents? I think it's very tempting. And in that situation, I would say, like, you know, agent spend, maybe three x the cost of... But then also, you know, the engineers who can do that are incredibly valuable. And so maybe it actually will just stay the same because the people who can actually do that and who can drive all those agents, there's fewer people, but you're paying them a lot more. And so the spend actually stays the same. So I think org design is very, very interesting right now. And so because of that, I think it's actually really hard to answer that question.
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?