Johnson: AI compute per model has scaled one million-fold since 2012
Justin Johnson · After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs · Nov 25, 2025 · at 4:13
Justin Johnson, co-founder of World Labs and University of Michigan professor, reflects on the scale of GPU compute expansion since the 2012 AlexNet breakthrough.
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“And if you think about, you know, AlexNet required this jump from CPUs to GPUs, but even from AlexNet to today, we're getting about a thousand times more performance per card than we had in AlexNet days. And now it's common to train models, not just on one GPU, but on hundreds or thousands or tens of thousands or even more. So the amount of compute that we can marshal today on a single model is, is, you know, about a million fold more than we could have even at the start of my PhD.”
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