The full quote is: > Most of the money is being spent on incredibly expensive GPUs that have a 1-3 year lifespan due to becoming obsolete quickly and wearing o

The full quote is: > Most of the money is being spent on incredibly expensive GPUs that have a 1-3 year lifespan due to becoming obsolete quickly and wearing out under constant, high-intensity use. So it isn’t entirely tied to the rate of obsolescence, these things apparently get worn down from the workloads. In terms of performance improvement, it is slightly complicated, right? It turns out that it was possible to do ML training on existing GPGPU. Then there was spurt of improvement as they go after the low-hanging fruit for that application… If we’re talking about what we might be left with after the bubble pops, the rate of obsolescence doesn’t seem that relevant anyway. The chips as they are after the pop will be usable for the next thing or not, it is hard to guess.