Uncapped with Jack Altman
00:33:11 · Jack Altman
[00:33:11] S0: They don't have as much money to train. And also, it's like if they're really focused on the real frontier, and they're like, you know what? Use your open source for the taxes. Use it for recommending which iPhone you should buy and should you get the Duo or not. And we're going go solve biology. We're going go do robotics. And it's just not worth it to go optimize two models ago to go do the basic stuff. And so there's some there's basically some argument along the lines of we're going to get saturation on many tasks. And as open source of the future is better than OPUS 5.5 and Astra are today, that's going to be good enough for most tasks. And the frontier labs are not going to care to go reduce their margins, optimize two models ago. They just need to keep their eyes on the frontier. And so maybe there will be some space for that. But what I would say probably is more than the money pieces. And I wish I actually had because to your point, the specific numbers on the margin are where it all turns on. So I don't have those on hand. But I think more important is, do you want to own your own intelligence? And this is like the Alex Karp stuff of just like, you cannot trust as an enterprise, you cannot trust any company to own all of your data and to be fully reliant on any company. And you need to own your own models. You need to own your intelligence. And I think there is an argument to be made there. And I think for certain industries, that trade is going to be worth it. And particularly as the open source models get good enough, even if you're not saving any money, you're in control. And that's got something.
00:34:52 · Ev Randle, Jack Altman
[00:34:52] S1: Then even think about the branding of let's say all your tokens are Anthropic tokens, and all of a sudden, Anthropic enters your space. And then your customers are like, well, what model are you using? You're like, 100% of our models that we're using is Anthropic. We're like, well, then why don't we just go to Anthropic? Yeah. If you're Eli Lilly no special sauce. [00:35:12] S0: If you're Eli Lilly and all of your data lives in Anthropic [00:35:15] S1: Yeah. [00:35:16] S0: And then Anthropic's like, hey. By the way, BTW. We got some drugs coming. You're like, Okay, well, don't feel so good now. So I think there are some reasons that people won't want to do that. And then I think there's situations where you can post train on open source, and that can be useful. And if you've got a lot of data, that data can outperform the extra intelligence that you might get from the next model. And so you'll see some of that, too.
00:35:16 · Jack Altman, Ev Randle
[00:35:16] S0: And then Anthropic's like, hey. By the way, BTW. We got some drugs coming. You're like, Okay, well, don't feel so good now. So I think there are some reasons that people won't want to do that. And then I think there's situations where you can post train on open source, and that can be useful. And if you've got a lot of data, that data can outperform the extra intelligence that you might get from the next model. And so you'll see some of that, too. [00:35:38] S1: I feel like we hear that pretty consistently from founders that aren't even selling an AI product but just have a big product corpus, and they're using AI throughout their products. Where they're like, look, if you really spend the resources to fine tune and you have really good trace data from the task that you're trying to do, yes, maybe Vanilla, Kimi k three, or whatever isn't quite as good as whatever the model, but you can actually get a really small model, like a really, really small open source model. And if you fine tune the hell out of it, gets to a k three or a Sonnet level performance, and it is dirt cheap. It is dirt cheap. Think we had a large public CEO come in and talk to us about that. It's like there's clearly all these use cases where that's true.
00:35:38 · Ev Randle, Jack Altman
[00:35:38] S1: I feel like we hear that pretty consistently from founders that aren't even selling an AI product but just have a big product corpus, and they're using AI throughout their products. Where they're like, look, if you really spend the resources to fine tune and you have really good trace data from the task that you're trying to do, yes, maybe Vanilla, Kimi k three, or whatever isn't quite as good as whatever the model, but you can actually get a really small model, like a really, really small open source model. And if you fine tune the hell out of it, gets to a k three or a Sonnet level performance, and it is dirt cheap. It is dirt cheap. Think we had a large public CEO come in and talk to us about that. It's like there's clearly all these use cases where that's true. [00:36:23] S0: Yeah, but then it's like the nice thing is we know the answer. The market's telling us the answer. And the answer is that the inference companies in combination are doing mid single digits worth of total revenue. That's a reasonable proxy for open source because that's where it's running. And that's whatever percent of the labs companies. And you can argue for that there'll be some mix shift. But what we're basically seeing is that open source might five, it might be 12. It'll be some percentage like that unless there's some change of the overall spend, and that's what will be there.
00:36:23 · Jack Altman, Ev Randle
[00:36:23] S0: Yeah, but then it's like the nice thing is we know the answer. The market's telling us the answer. And the answer is that the inference companies in combination are doing mid single digits worth of total revenue. That's a reasonable proxy for open source because that's where it's running. And that's whatever percent of the labs companies. And you can argue for that there'll be some mix shift. But what we're basically seeing is that open source might five, it might be 12. It'll be some percentage like that unless there's some change of the overall spend, and that's what will be there. [00:36:57] S1: Yeah, so I think the synthesis between these two is that again, shout out Eric Vitrea. It all works. It all works. But also, the way it all works is, actually, they're probably not even rivalrous to each other. And I don't know if this would be the case, but also, I'm sure the Frontier Labs, they're probably even happy about there being an open source ecosystem because it probably creates all... Like, from an antitrust perspective [00:37:21] S0: 100%. [00:37:22] S1: If it's like, oh, they're getting too powerful, it's like, no, there's a healthy open source ecosystem where people can also use. And I think that's probably also healthy. Have no idea if that's an actual thing that they think about. But right now, this will be excluding the Yeah. [00:37:35] S0: I'm not sure the scalars. But... Yeah.
00:37:35 · Jack Altman, Ev Randle
[00:37:35] S0: I'm not sure the scalars. But... Yeah. [00:37:37] S1: Yeah. But if you look at... So, like, right now, if you look at the... So we'll exclude the hyperscalars because we have no idea how much open source inference they're doing. But if you look at just the inference clouds, as we've talked about, and then you look at just OpenAI and Anthropic this is very imperfect, but it gives us some Okay proxy. By the end of this year, I think the top three or four inference clouds will probably do like $6,000,000,000 of run rate ish. And then let's say OpenAI and Anthropic will do like $170,000,000,000 of run rate ish if their growth extrapolates from public disclosures. So that's like three ish percent thus far between the inference clouds and the top two frontier labs. If the growth continues [00:38:18] S0: Yeah, and maybe if you add the clouds in, maybe it's double at most or something like that. [00:38:22] S1: That's right. So we can say 5% to 10% in terms of revenue for open source versus closed. But if you have the growth that the labs have had and that people expect again, this isn't a guarantee, so this isn't a projection but if you do have the growth for three to four years on the frontier side, and the labs combined are at a trillion dollars and open source takes 10% of that, that is like $100,000,000,000 of revenue. And the inference market has consolidated. There's really only three players. [00:38:49] S0: We