Dwarkesh Patel: belief

Transcript context

…And so you can imagine a world where it looks like the agent is reasoning in its scratchpad harmlessly, but it's actually hiding a bunch of data. Speaking of inference compute, one thing that I think is not talked about enough is, if you do live in the world that you're painting—in a year or two, we have computer use agents that are doing actual jobs, you've totally automated large parts of software engineering—then these models are going to be incredibly valuable to use. The way you use them obviously means you need compute. Right now, there's 10 million H100 equivalents in the world. By 2028, there's going to be 100 million. But there's been estimates that an H100 has the same amount of flops as the human brain. So if you just do a very rough calculation, there's a 10 million population, if you get AGI that's as human inference-efficient. You could have 10 million AGIs now, 100 million AGIs in 2028. But presumably, you would want more. AI compute is increasing what, 2.5x or 2.25x every year right now. But at some point, say 2028, you hit wafer production limits, and that's a longer feedback loop before we can make new fabs or whatever. The question here is, are we underrating how big a bottleneck inference will be if we live in the kind of world you're painting, if we have the capabilities that you're describing? I'd want to do the math on exactly how much we can ramp up TSMC's production and this kind of stuff. What fraction of the supply chain at the moment—we need Dylan in here for this—is currently GPU? Relatively small right? 5% or something like that. Apple has a huge fraction. Are the 2028 estimates including that ramping up over time? To what? 20, 30%?…

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