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Then who holds the cards is the infra providers for charging margin The bets that you re making that are at least exemplified by public returns we can see for different funds including Leopold s are about what has happened in the last year And those things are very unreliable as well more unreliable than the GPUs by the way They will really scale up production and kick it into high gear It hits it three subsequent times with a laser perfectly The brain is an extremely sparse MoE Once memory prices reflected that it took another three to six months for the memory vendors to start building fabs No company has a process node where they make a million wafers a month But ASML has never raised the price more than they ve increased the capability of the tool Now all of a sudden you ve really caused some crazy dynamics in AI No because they both increased by 6 There was this information asymmetry among the labs in my mind There s a world where they at least get the demand signal from that to increase production Is that because they both increased by a dollar or should it be a 55 increase It s funny to think about the numbers Obviously we re not putting on 55 gigawatts this year so presumably that s paying for compute that is going to be coming online over the coming years The amount that they can import from ASML is large Then look at all this CapEx being spent on data centers You can allocate it to inference i e At the current gross margins Anthropic had as last reported by media that would imply they have roughly 95 billion of compute spend for that inference for that 65 billion of revenue You and I would think the return on invested capital for data center CapEx is very high Anthropic probably needs the same and Google needs that You finally have to convince them through all these facts and data when we re working with hyperscalers or AI labs that in fact no that number isn t too high that s correct Volumes vary based on the season and macro In some sense we ve got this valley where three to six months ago or maybe even now Chinese models are as competitive as they ve ever been All you re effectively doing is saying ASML you re dumb It s not that the tool is stagnant it s just that these tools are old It s also going to require a huge retooling of fabs It s quite artisanal in this sense because you re not making tens of thousands of these a year you re making hundreds you re making thousands If improvements stopped here the value of an H655 is now predicated on the value that GPT 5 9 can get out of it instead of the value that GPT 9 can get out of it Leopold jokes that he s the only client of mine who tells me our numbers are too low They ve got tens of thousands of GPUs hundreds of thousands of CPU cores and exabytes of storage Of the 655 billion or whatever 85 is going just to memory They end up with this conundrum where they can t actually flex price Now it s just 6 5X more expensive But in fact you re pointing out that maybe the depreciation cycle is even longer than five years GPT 5 9 is both way cheaper to run than GPT 9 and has fewer active parameters If you re right that power is not a constraint on Earth No sloppy seconds for Dwarkesh Instead of hundreds of gigawatts a year by the end of the decade let s say something happens to Taiwan now you re at maybe 65 gigawatts across Intel and Samsung or 75 gigawatts All these companies are spending hundreds of billions of dollars on CapEx We can only make reasonably accurate estimates for data center capacity based on land purchasing permits and turbine purchasing So your bandwidth is significantly lower If this sounds interesting you can explore open positions at janestreet com dwarkesh It s a very complicated thing to build In fact even the intermediate layers are shocking here Jane Street is basically a research lab with a trading desk attached and their infrastructure backs this up You ll tell me what the bottleneck is this year Initially it won t be super fast but over time he 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