Why smarter AI models could drive up compute prices 10x

Today, I wanna talk about what the compute situation for the labs will look like over the next few years. For the last three consecutive years, Anthropics revenue has 10 x to year over year, and it's likely to do so again this year. So they ended last year with 9,000,000,000 in revenue. I think they'll probably end this year with somewhere between 100,000,000,000 to a $150,000,000,000 in revenue. Now for this trend to continue, Anthropic would need to make $1,000,000,000,000 in revenue by the end of next year. Of course, there's no deep reason why this has to be true. It's a very wild conclusion, and it's ultimately a question of AI capabilities. Does AI get that useful by the end of next year? But suppose the trend does continue. Well, I wanna think through what happens in that world. Now the other big trend in AI is that lab compute only three x's year over year. For a lab to keep 10 x ing revenue year over year while compute only three x's, one of the following three things needs to happen or some combination of the three needs to happen. One, lab margins have to increase. Two, the price of compute has to increase. Or three, the percentage of compute that labs spend on inference rather than training has to increase. My understanding is that basically all three of these things are already happening. With regards to the margins, Anthropics inference margins reportedly went from 40% in the middle of last year to upwards of 80% now fable. With regards to compute, the spot prices for compute are more than 40% higher than they were in the February trough that we had earlier this year. And with regards to the share compute that goes to trading versus inference, in 2024 according to epoch, OpenAI was spending just a quarter of its compute on inference and that number is likely closer to 50% if not higher now. Now labs would prefer not to do this final thing of increasing the share of compute they spend on inference. The way that labs see the world, the whole point of inference revenue is to help convince investors to give you more money in order to train the next bigger better model. And if you're spending most of your compute on inference, you're basically declaring that AI progress has stalled, and you're just now in the business of being a cloud provider. Now this is a less compelling business than building AGI. And so the labs do not want to be in this business nor do they think they're in this world. They think that within a year, they'll have built models that make the current ones look extremely shitty. But they need to invest a lot of their compute, the majority of their compute into doing the training and experiments that are necessary to build the next model. So that leaves only two options for how you can get out …