[UncleOxidant]

I agree that the Jevons paradox can apply here, however, there have been several "breakthroughs" in the last couple of months (R1, diffusion LLMs, this) that really push the amount of GPU compute down such that I think it's going to be problematic for companies that went out and bought boatloads of GPUs (like OpenAI, for example). So while it might not be bad news for NVidia (given Jevons) it does seem to be bad news for OpenAI.

[ithkuil]

I don't quite understand the logic.

Even if you have cheaper models if you have tons of compute power you can do more things than if you had less compute power!

You can experiment with huge societies of agents, each exploring multitude of options. You can run world models where agents can run though experiments and you can feed all this back to a single "spokesperson" and you'll have an increase in intelligence or at the very least you'll able to distill the next generation models with that and rinse and repeat.

I mean I welcome the democratizing effect of this but I fail to understand how this is something that is so readily accepted as a doom scenario for people owning or building massive compute.

If anything, what we're witnessing is the recognition that useful stuff can be achieved by multiplying matrices!