Uncapped with Jack Altman
00:26:02 · Jack Altman, Ev Randle
[00:26:02] S0: Well, again, if you are an AI application company buying tokens and then doing something to them and then giving them to somebody else, somebody still needs to make people's lives better. Like, at the end of the day, somebody needs to turn the token into a better life in a way that you couldn't have done without the token or at least not as well. [00:26:19] S1: That's right. Then the other side of the argument that I think also has a lot of merit is, but look, the AI companies that are also buying a lot of these tokens, creating software factories, they're going to become massive companies of the future because they're going to outcompete all the companies not doing this. And so when you look at Stripe, Stripe didn't get... Now they work with a lot of old economy companies, but they didn't get big because they worked with old economy companies. They got big because they worked with DoorDash and Shopify and Instacart Lyft. And Lyft and, like, these... And Uber. You know, a lot of the sort of, like, YC era Yeah. Internet native companies got big. That got massive. And, like, that's what... And then once they were massive, then there... It was much easier to attract Yeah. Old economy companies because they were so... Like, they're like, hey. We just helped DoorDash Yeah. You know, kick the hell out of you. It's, like, you should probably use this as well. [00:27:09] S0: Yep.
00:27:09 · Jack Altman, Ev Randle
[00:27:09] S0: Yep. [00:27:09] S1: And so I think that... Like, I I have no problem. I I I don't really have any problem with AI companies buying AI tokens, like the circularity, because I'm like, look, these are companies. They're just at the frontier. And just like Internet native companies were at the frontier of the Internet and older economy companies didn't really buy into what they were doing until it was too late, those companies were very large, I think the same thing's going to happen in AI. Yeah, that's probably right. [00:27:33] S0: How do you feel about the closed versus open source stuff? We've talked about this a bunch. And obviously, we are exposed to a lot of the open source world through Fireworks and other things. What we've seen in general, I think, has been a rise in open source this year. At the same time, obviously, the frontier labs have gotten massive. And the price per token is very different. The use cases are similar but different. So anyway, what's your latest on open, closed stuff?
00:27:33 · Jack Altman, Ev Randle
[00:27:33] S0: How do you feel about the closed versus open source stuff? We've talked about this a bunch. And obviously, we are exposed to a lot of the open source world through Fireworks and other things. What we've seen in general, I think, has been a rise in open source this year. At the same time, obviously, the frontier labs have gotten massive. And the price per token is very different. The use cases are similar but different. So anyway, what's your latest on open, closed stuff? [00:28:09] S1: Yeah. Mean, if if you'll if you'll entertain me, I I I feel like we probably have a similar synthesis of what we feel now. But I think what would be interesting to do is, like, maybe we steel man each side of the argument. Great. And then like that... But I think once you have the steel man for each argument... Because I think there's a sort of like a lot of like... There's a lot of talk online about open source versus closed source, which I think is sort of off track Right. From the reality on the ground. So I think if you steel man both sides, you can actually understand, okay, well, what's the synthesis that probably blends the two steel man? So I'll do closed source. You do open source. [00:28:44] S0: You go first.
00:28:45 · Ev Randle
[00:28:45] S1: So the steel man of the closed source argument... And what we mean by this is why would a company get inference from a closed source frontier API rather than from an open source API inference provider like a Fireworks using Kimi K3 or any of these other models, US or Chinese open models. And people say, well, look, intelligence is fungible, or it's sort of a commodity. And if we take that seriously, that idea that a unit of intelligence is this fungible thing, There's a lot of history and a lot of good literature about who wins those types of markets and... When you're when you're optimizing for the cost of a fungible output. And it's... Oftentimes, the winner is something we call a low cost producer. And so the defining feature of one of these low cost producers is that you are structurally, as a company, you have a lower cost to deliver a comparable outcome. And so you can either charge lower margins, or you can charge lower prices at the same margin as competitors, or you can earn higher margins than your competitors at the same price. And so two famous examples in tech. SpaceX is probably the easiest one to understand. SpaceX has the lowest cost to them in order to launch a kilogram into space Yep. In the world. No one can launch a kilogram into space as cheaply as SpaceX can, and that gives them a pseudomonocaly on launch, at least at at the same level... Unit level of of capability. A previous one in computing is AWS. They had massive aggregated demand that created economies of scale. They were able to amortize a lot of investments over a massive customer base, which gave them lower cost relative to competitors in the CPU cloud business. So for AI, the equivalent here would be how do you become the low cost producer of a unit of intelligence? So we could say this is like a given level of capability, latency, context length, and reliability for some task. Like, who can produce that task at a given level of capability the lowest, reliably? And I think proponents of the closed source argument would say the leading frontier labs have the best models in the world, many of which are not released to the public or not released via API, and they're using those models and pointing them at their infrastructure to improve their serving efficiency and co design their serving infrastructure with the actual models that they're training.
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.