Uncapped with Jack Altman
00:02:46 · Jack Altman, Ev Randle
[00:02:46] S0: You mostly could have stitched it together from... If you believe the right things on Twitter. As you always say, Twitter is making us all dumber. But it was almost there. [00:02:53] S1: But it was like, if you really were good at parsing through, it was all basically there. But I think it was another thing entirely to have all the numbers put together in one place. And again, it was on its face. What you want out of a technology business typically is you want immense growth and you want operating leverage. Operating leverage is the sense that you have this big upfront investment in R and D, or sometimes with software businesses, you pay a ton for salespeople upfront, and they take some time to pay themselves back. But you invest all this money upfront, you have fixed costs, and then your revenue grows so much that... And your incremental margins are so good that you become very profitable very quickly as you scale. Yeah. And we don't have to go through every number in stat. Obviously, it's all over x. It's all over the Internet. But I think the most important visual from the S-one, in my opinion, there's a chart that I'm looking at that you guys can't see. [00:03:44] S0: No, no, no. We'll put it up. We have the power of technology.
00:03:44 · Jack Altman, Ev Randle
[00:03:44] S0: No, no, no. We'll put it up. We have the power of technology. [00:03:47] S1: That's true. The power of maybe Some believe it's But the chart is showing the relationship between their revenue and their total compute expenses per quarter. And so it's both the compute expenses for serving customers, but then also training. And in Q1 of twenty twenty five, Anthropic had $400,000,000 ish. They only... They do it in billions, and so it's only 0.4. So it's like $400,000,000 ish of revenue against 900,000,000 of compute and infra expenses. And so that's, like, a point four x ratio where, you know, compute is larger than revenue. Fast forward six quarters. In Q2 of twenty six, they had $11,600,000,000 of revenue against $5,600,000,000 of compute and infra expenses, so a 2.1x ratio of revenue to compute. So that... The entire concern that everyone had is like, Oh, are the labs or all these AI companies selling $1 for zero nine zero dollars or $0.50 Are they ever going to be able to turn a profit? Anthropic, in six quarters, did what it takes a lot of software and technology companies a decade to prove out, which is like, no, these are money printing machines [00:04:58] S0: They're money printing machines. [00:04:59] S1: In a huge way.
00:04:59 · Ev Randle, Jack Altman
[00:04:59] S1: In a huge way. [00:05:00] S0: Yeah, and it's like the revenue just turned out that it's big enough to make up for all these fixed costs of training. And the other surprising thing and this has played out throughout inference and all sorts of areas of the AI stack but price per gigawatt has just surprised everybody. Like, I don't think anybody Like the monetization? Yeah. Yeah. Yeah. And just what the value of the Frontier tokens are [00:05:23] S1: That's right. [00:05:24] S0: Made Has it all work.
00:05:24 · Jack Altman, Ev Randle
[00:05:24] S0: Made Has it all work. [00:05:26] S1: It's made it all absolutely work. And I don't think it I think there was a lot of contention around the quality of the Frontier lab business model that partially answered by the numbers in the s one. Like, you cannot say that, like, Anthropic's business model isn't working on, like, a per unit basis or an aggregate basis. Like, they had 8% operate... Like, GAAP operating margins in q two twenty twenty six, so it's clearly working. I don't necessarily think that means that it's going to be a slam dunk out of the gates in terms of how public investors in the public markets value it and if there's a ton of share price appreciation right out of the IPO. There's two reasons for that. The first is that there's still some battleground topics that people have uncertainty over that I think are somewhat fair. So the first is, you know, Anthropic in q two grew massively. They grew 145 quarter over quarter. So it's like insane sequential growth, but then they didn't grow as much in q three. And there's a lot of pretty good reasons for this, but they grew 45% quarter over quarter, which is still So pretty insane. [00:06:30] S0: Nuts. But, you know, it's like the deceleration from
00:06:30 · Jack Altman, Ev Randle
[00:06:30] S0: Nuts. But, you know, it's like the deceleration from [00:06:32] S1: a 145% growth quarter over quarter to 45% growth. People are like, wait a minute. This doesn't... With SaaS, you go look at the net new per quarter of CrowdStrike or Datadog, it's just consistent upward trajectory. There's very little volatility to the growth rate, and then 80% of the revenue is consumption based. And so there's there's there's not like these... You know, if you had, like, you know, Snowflake or, you know, Databricks or one of these... It's like... A lot of that revenue is, like, multiyear long term contracts. I mean You have more, like, fixed visibility into the future growth. [00:07:03] S0: One of the things that's been surprising to me, which I wouldn't have been... I would not have guessed it, at least at this point, because it's like the last generation of model is pretty good now. But it still just seems that so much turns on who has the very best model at any given point in time. And the leading edge, even if it's by a little bit and even if the last model could do most of the tasks, it just seems like it's worth a crazy amount of momentum. And we saw this go back and forth between the labs all year. And we saw it again in the last month, and then you saw it over the summer. That part is surprising to me, that people will always seem to go for the highest intelligence even with some price and sensitivity. I think it probably is, is there's just so much surplus. And what
00:07:03 · Jack Altman, Ev Randle
[00:07:03] S0: One of the things that's been surprising to me, which I wouldn't have been... I would not have guessed it, at least at this point, because it's like the last generation of model is pretty good now. But it still just seems that so much turns on who has the very best model at any given point in time. And the leading edge, even if it's by a little bit and even if the last model could do most of the tasks, it just seems like it's worth a crazy amount of momentum. And we saw this go back and forth between the labs all year. And we saw it again in the last month, and then you saw it over the summer. That part is surprising to me, that people will always seem to go for the highest intelligence even with some price and sensitivity. I think it probably is, is there's just so much surplus. And what [00:07:50] S1: I mean by that is whether you're paying $0 or $200 for a Claude Max account or you're paying next to nothing with some free service, the value that you can get as an individual [00:08:04] S0: Right. [00:08:04] S1: Is probably in, like, at least in the thousands of dollars, if not tens of thousands of dollars. [00:08:07] S0: This is like a Keith Rabois in that there's, like, you know, two task types. There's the ones where it, like, just needs to be completed and it needs to be right. [00:08:15] S1: Yeah.