People underrate how big a bottleneck inference compute will be. Especially if you have short timelines. There's currently about 10 million H100 equivalents in the world. By some estimates, human brain has the same FLOPS as an H100. So even if we could train an AGI that is as inference efficient as humans, we couldn't sustain a very large population of AIs. Not to mention that a large fraction of AI compute will continue to be used for training, not inference. And while AI compute has been growing 2.25x so far, by 2028, you'd be push against TSMC's overall wafer production limits, which grows 1.25x according to AI 2027 Compute Forecast. ht @EgeErdil2, @EpochAIResearch's "Can AI Scaling Continue Through 2030?", AI-2027 compute forecast