Why smarter AI models could drive up compute prices 10x
In a Nutshell
Frontier labs are closing the gap between 10× revenue growth and 3× compute growth by raising inference margins and pushing compute prices higher, with spot prices already up >40 % and deals like Google’s $900 M/mo GB200/300 contract showing 2× premiums over spot. As models become human-level engineers, the same H100-equivalent hardware will generate 15×+ more revenue, letting frontier labs outbid everyone else for scarce tokens and pricing out weaker users and applications. With Moore’s Law, fab capacity, and wafer re-allocation all maxed out, compute supply cannot scale fast enough, so the pre-singularity regime will see sustained, sharp price increases until robotics eventually makes chips abundant again.
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Anthropic's revenue has 10x'd year over year for three consecutive years and is likely to do so again. They ended last year with nine billion in revenue and are projected to reach one hundred billion to one hundred and fifty billion dollars this year. For this trend to continue, Anthropic would need to reach one trillion dollars in revenue by the end of next year. Whether this occurs depends on AI capabilities and whether models become sufficiently useful.
Lab compute only 3x's year over year. To maintain 10x revenue growth with only 3x compute growth, one of three things must happen: lab margins increase, compute prices increase, or the percentage of compute spent on inference versus training increases. All three are already occurring. Anthropic's inference margins went from forty percent in the middle of last year to upwards of eighty percent now. Spot prices for compute are more than forty percent higher than the February trough. OpenAI spent just a quarter of its compute on inference in 2024 according to Epoch, with that number likely closer to fifty percent now.
Labs prefer not to increase the share of compute spent on inference. Inference revenue is viewed as a means to secure investor funding for training the next model. Spending most compute on inference would signal that AI progress has stalled and that the lab is now a cloud provider, which is less compelling than building AGI. Labs believe they will build models within a year that make current ones look extremely poor and need to allocate the majority of compute to training and experiments for the next model.
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