“As these companies go public and are accountable to investors — say they want to go from 60% of compute on training to 70% — their investors are going to say: if you're generating $100 billion per gigawatt, you're basically saying no to $200 billion of revenue in order to increase your training compute. What the fuck? You're already spending so much on training, why are you spending even more?”

Right. Yeah, maybe there's like some slowdown in AI progress or the deployment of AI that means that the revenue per gigawatt can be lower. But that's the only way I could see it being only 100 per megawatt by the end of next year.

Yeah, I mean, as long as the model gets better, the value generated out of it gets better, obviously who captures the value is still up for debate. But ultimately, everyone's going to raise their prices because they can. And it's super inflationary, especially if the method of regulation is right now so far. It's just don't release the models. But more and more, the method of regulation is New York's banning data centers. Texas is holding moratoriums. Ohio is saying you have to, or at least trying to say you have to like pay everyone's property tax in a certain radius. These sorts of things are going to decrease supply and increase cost. And that's going to get passed on as well. So you start to end up in a spot where progress does slow, at least in the external sense. Even if the models internally keep getting better and better. I see no reason why, like, you know, again, like in a takeoff scenario, why would Anthropic not have their best model six months ahead of what is externally available because of safety and regulation, but also, you know, the competitive advantage. And then that six-month difference, if progress accelerates, is actually a bigger differential. So that's the thing that would cap revenue per megawatt gains to a much lower growth than we've seen in the first half this year.

yeah so so this is sort of what i i personally believe that the labs are going to allocate less and less compute to inference over time which i think is very non-consensus right everyone's sort of the standard belief of most people is oh most compute will go to inference um most of it will go to forward passes for training not maybe necessarily revenue generating inference but ultimately you end up with if they're generating you know 30 40 million dollars per megawatt today you allocate 40 to inference if you now get to generating 60 70 million dollars per megawatt do you still allocate 40 to inference and generate all this profit and then do dividends and share buybacks or do you go build a giant and i think the obvious answer from anthropic and open ai and not just at the executive level but also their board is go build a gi because it's way more profitable right um and and so ultimately you're going to see them ratchet up their percentage of compute dedicated to training

while each increment of compute is getting more and more profit generating if they had dedicated to inference

right and and so the whole point is well okay if i'm gener if i'm selling tokens is anthrop is open ai releasing ultra fast mode for just external or are they doing it internally too and it turns out no actually i'm going to allocate it to internal and external because my internal you know value that i'm generating from super fast ai or the best ai model is way more than what someone externally is and so ultimately sure i could generate a hundred million dollars per megawatt but if i turn that towards ai research what is the incremental progress that i get and then what does that do towards my future earnings potential the discounted cash flows of whatever the hell i've done right um and so you know they're not going through that calculation but ultimately it's it makes more sense to dedicate more and more compute internally and the only reason to you know have inference compute be so large is so you can grow your training fleet right