The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:11:27 · Sameer Dholakia, Harry Stebbings
[00:11:27] S0: Look. I'm I'm a I'm a genius of and on this one versus the tyranny of the or. This... It is and not or here. As I I come back to my my comments about just the scale of the market for intelligence. We just can't get our heads around it. I mean, you think about this, like, Harry, when we invested, my bullish bull case was that we could get to some massive valuation. And and I'll just say that I missed the mark on how big of a of a business could be created at the frontier by factors. And so when you're in a market that big, predicting with any precision the mix between closed tokens and open tokens is irrelevant. You just... It's gonna be big. You're in... You... Right? Do you understand what I'm saying? Like, when it's that large of a market, the ratio is actually not not not a concern. And and I really do believe that there are all those different use cases from consumers up through enterprise and government, and they're all gonna want intelligence of different types and different flavors. And that is why we're both... We're investors in both on Anthropic and and in fireworks and the closed tokens and the open tokens. We'll get... We'll have exposure as a as a fund and as a firm to both, but I think both are gonna be huge. [00:12:35] S1: Do you think US enterprises are willing to use open source Chinese models? [00:12:40] S0: I think that once that Chinese model is hosted by Fireworks, it's no longer a Chinese model first.
00:12:40 · Sameer Dholakia, Harry Stebbings
[00:12:40] S0: I think that once that Chinese model is hosted by Fireworks, it's no longer a Chinese model first. [00:12:47] S1: Do you think they see it like that? Me me and you both know that to be true. But when you speak to CIOs, they go, I don't want it. If the providence is China, I don't want it. And you're like, it's downloaded code on your local drives. [00:13:00] S0: Yeah. No. I... Listen. I've talked to some CIOs who who share that opinion for sure. There's no question that is out there. And by the way, I do think that we should have a great open weight model that is is US based, and I think that would be awesome. I think there's a big old map market. [00:13:15] S1: Is that is that a great business? [00:13:17] S0: It can be. For sure, can be. [00:13:19] S1: But if you look at, like, a thinking... You know, if you look at the thinking machines at a $40,000,000,000 price today, is there a $500,000,000,000 open model US to be built, or is just open a much harder business model to monetize, and therefore, it won't be that large?
00:13:19 · Harry Stebbings, Sameer Dholakia
[00:13:19] S1: But if you look at, like, a thinking... You know, if you look at the thinking machines at a $40,000,000,000 price today, is there a $500,000,000,000 open model US to be built, or is just open a much harder business model to monetize, and therefore, it won't be that large? [00:13:32] S0: Oh, no. I I believe it absolutely can build a a big business. Whether it's that particular scale, I don't know. But you can build a very large business and get venture returns. If you are the leading provider of a open source solution... I mean, you just look at history across, you know, all these other categories, whether you're going from Red Hat to, know, you fill in the blank in the database market. I mean, you can build you can build a very... You can find ways to monetize it for sure. And most enterprises don't wanna be responsible for the open source. They're gonna want some provider with the right talent behind it. All the reasons that open source businesses have been great businesses for for decades now. [00:14:06] S1: If you were to present a bear case for frontier models, what would it be?
00:14:06 · Harry Stebbings, Sameer Dholakia
[00:14:06] S1: If you were to present a bear case for frontier models, what would it be? [00:14:11] S0: Do I think there's a bear case for the frontier labs? I really don't because I believe that the... At the frontier, intelligence solving problems that were heretofore unsolvable or solving them at a pace that was heretofore unimaginable. I just think there's no shortage. Humans have so many problems to solve, Harry. We got a lot of problems in this world. We have a lot of them, and we... We're we're nowhere... It's not like we're at the 90... We're on the nine... You know, ninety ninth percentile of problem solved, and we're just trying to get to the last one. We have a lot of problems to go solve, and that's what these things will help us solve. [00:14:45] S1: The majority of people on the pod come on and say, we'll see 99 of token traffic flow through open models, but we'll see 99% of dollars flow through closed models. Do you think that's apt, or do you think that's kind of a bullshit throwaway comment? [00:14:58] S0: I think that I think that ratio is wrong. I really do. I don't think that's quite the mix. But to my previous comment, I don't think we have to get the mix per... Precise. I think everybody just has to understand that you just have to look at the size of them... Of the TAM.
00:14:58 · Sameer Dholakia, Harry Stebbings
[00:14:58] S0: I think that I think that ratio is wrong. I really do. I don't think that's quite the mix. But to my previous comment, I don't think we have to get the mix per... Precise. I think everybody just has to understand that you just have to look at the size of them... Of the TAM. [00:15:11] S1: 15,000,000,000. It seems now ridiculously cheap. How could you? That's amazing. 15,000,000,000 at the time was freaking wild. I got brought open now at 30,000,000,000. And to my stupidity, I said, oh my god, if this is 100,000,000,000, I like just three x it, not including dilution. Oh my god. The small mindedness that I had was just unbelievable. [00:15:33] S0: Harry, get out of my mind, man. I went through the exact same thing. [00:15:36] S1: So how did you get over it?
00:15:36 · Harry Stebbings, Sameer Dholakia
[00:15:36] S1: So how did you get over it? [00:15:37] S0: It was because in the intervening time... Right? So you remember that that OpenAI opportunity that I also passed on. I had the exact same thought pattern that you did, and that was right after the ChatGPT moment. Right? So the conversation was in December. And then in that intervening time, that little OpenAI went from 40,000,000 by the... To the time that we then looked at that first round of Anthropic. OpenAI had gone from, like, 40,000,000 to about... I wanna say, I'd have to go back and look at the numbers. You could fact check me, but it was something well north of a billion, billion and a half, maybe approximating two. And clearly, we got it wrong. And that is one of the both... I think one of the most humbling things about this job is that you will get them wrong. And sometimes you'll get them wrong on really, really important decisions, and you have the opportunity to fix it. It's not a once in a lifetime opportune... You could go back and fix it. And that was when Anthropic came along. The imagination was stretched. To your point, like, were... Our imaginations were capped at a certain even if it got to a 100,000,000,000. And now, obviously, you know, you could you could see the growth potential of how big this market could be because of what OpenAI had done in in calendar twenty three. [00:16:45] S1: In a world of $2,000,000,000,000 assets, do you care about price anymore? Like, is LP focused on price misplaced?