Ev Randle walking through a chart from Anthropic's S-1 comparing quarterly revenue to total compute spend, including training. His point: the ratio flipped from 0.4x to 2.1x in six quarters, which undercuts the worry that AI labs sell a dollar for 50 cents.
We have the power of, you know, maybe. Unbelievable.
Maybe we'll use AI.
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 2025, Anthropic had 400 million-ish. They do it in billions, and so it's only 0.4. So it's like 400 million-ish of revenue against 900 million of compute and infra expenses. And so that's like a 0.4x ratio where compute is larger than revenue. Fast forward six quarters, in Q2 of 26, they had 11.6 billion of revenue against 5.6 billion of compute and infra expenses. So a 2.1x ratio of revenue to compute. So like that, like the entire concern that everyone had is like, oh, are the labs, are all these AI companies, you know, selling a dollar for 90 cents or 50 cents? Like, 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 like a decade to prove out, which is like, no, these are money printing machines. They're money printing machines. In a huge way. 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 you know, this has played out throughout inference and all sorts of areas of the AI stack, but the price per gigawatt has just surprised everybody.…
made it all absolutely work. And, you know, I don't think it, like, I think there was a lot of contention around the quality of the Frontier Lab business model that was somewhat that was partially answered by the numbers in the S1. Like you cannot say that like Anthropic's business model isn't…
which is still insane.