That only explains why the price of those GPUs at resale is high, but it doesn't invalidate the fact that those GPUs are still very useful way past a 2 to 3 years depreciation schedule advocated by Michael Burry et al.

Older GPUs can still be used to run model inference, albeit more slowly than newer GPUs. We already see frontier labs turning this insight into an opportunity to charge a premium on token delivery speed, when models run on newer hardware. As AI becomes more widely spread throughout the economy, not all workloads will require fast inference, and there will be many use cases for previous generation hardware. The only exception is if the latest batch of specialised chips become ubiquitous very quickly, but then price of compute AND intelligence per watt will both go down dramatically, which would reduce the need for sustained high capex and opex

Yes it is a supply-side crunch (chip manufacturing), but IMHO, it is not temporary. Only two things will remove the supply constraint.

- Demand drops like a stone: raw TFLOPS / per model. This will happen if someone figures out insanely fast and power-efficient math (specifically, multiplication) for existing chip architectures, that makes it dirt-cheap to keep re-computing ever-better SOTA LLMs, as well as to on-line compute inference with ever-bigger models (scaling laws rule). These are different workloads, AFAIK.

- Moore's law re-starts, because someone figures out entirely new transistor designs that can deliver at least up to 100x per-core frequency gains over Si transistors (which would be nothing considering that Si transistor tech delivered ~1Mx gains since invention) and which can be manufactured at similar bin sizes, at similar scale (i.e. equivalent per-die yield), price-competitively (no more 10x the equivalent in SOTA Silicon compute).

I'd say that a math breakthrough might be more accessible than the transistor-manufacturing breakthrough.