Uncapped with Jack Altman
00:31:09 · Ev Randle
They have the most aggregated demand. They're the largest, and so they have economies of scale. The data center space that they're buying is cheaper for them on a per unit basis than anyone else because they're the largest purchasers of compute. And if all of these are true, if you have economies of scale, if we are in some weak form of RSI right now where the models are actually designing actual inference efficiencies at any rate that is compounding, that means that you can have a low cost producer of intelligence in the form of an AI lab. And that means that this argument that open source equals cheaper is a misnomer and is not necessarily true, because at the same level of margin, if this was true, a FrontierLab at any point of the intelligence curve, like the capability curve, would be cheaper than a inference provider serving on an open source system. So that's sort of the economic argument for closed source, and then I'll let you take open source.
00:32:08 · Jack Altman, Ev Randle
[00:32:08] S0: Okay. So I mean, there's... I'll start with the economic piece. And obviously, as you know, I mostly mostly buy it. But economically, one difference is that because the... You know, to... There's the model cost, and then you gotta do the inference on it. The model, in theory, has some training that's been amortized into the price. And so to the extent that the labs want to be profitable or have enough margin that they make up for it, they do need to do something to make up for the training costs versus if you're using Kimi. You don't. The model is free. So there's no amortization there. And then, obviously, you still need to run the inference. And it's probably right that on a per inference unit basis, the labs are still going to have the advantage over the inference companies. But maybe in theory, that's made up. The other thing is, do the models care to take their margins down to that level? Is that actually worth it, or is that really expensive for them to do? And so [00:33:09] S1: Then they don't have as much money to train.
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.