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There is a real possibility that local, free models keep trailing a few years behind the frontier. If that does happen, then the math for building out all this capacity is that you need to make a profit in that window. Can they recoup a trillion dollar data center capex in 2-3 years?

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Doesn't that require local hardware to trail a few years behind as well? I don't see consumer hardware being on the same level as a cluster of A100s for a very long time - mostly due to form factor. No one wants a laptop that's two inches thick and liquid cooled. :)

That’s true, but models have been getting more efficient over time as well.

I find the smaller Qwen models and waiting 30 seconds are actually quite tolerable.

You can't train new models on consumer hardware.

Right, so the consumer use of LLMs is really just covering for the commercial training of LLMs.

If training slowed down by 2/3rds would consumers be that much worse off?

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