I think the big crux here is what sort of pause we implement. If we thoroughly paused AI research, I think your logic goes through; only the hardware improves meaningfully. But that’s pretty unrealistic.
If it’s only a pause in training the largest models, and all other research continues unabated, then I think we get 90% of the problem with overhang. Algorithmic efficiency and hardware availability have increased almost as much as they would otherwise, as Logan argues. There’s a bit less research into AI, but almost the same amount since current LLMs are already economically valuable.
(There’s even a curious increased reason to invest in AI research: the effective lead of OAI, GDM and Anthropic is lessened; new companies can catch up to their current knowledge to race them when the pause is lifted, and to pursue other directions that aren’t paused).
I very much agree that demand for new hardware from training frontier models will not change dramatically with a pause. The limited supply chain and expertise is the limiting factor in hardware improvements, with demand changes playing a smaller role (not insignificant, just smaller).
Since a limited pause on training large models is the only one we can even really hope to get, I’d have to say that the overhang argument for pause remains pretty strong.
You make some excellent points.
I think the big crux here is what sort of pause we implement. If we thoroughly paused AI research, I think your logic goes through; only the hardware improves meaningfully. But that’s pretty unrealistic.
If it’s only a pause in training the largest models, and all other research continues unabated, then I think we get 90% of the problem with overhang. Algorithmic efficiency and hardware availability have increased almost as much as they would otherwise, as Logan argues. There’s a bit less research into AI, but almost the same amount since current LLMs are already economically valuable.
(There’s even a curious increased reason to invest in AI research: the effective lead of OAI, GDM and Anthropic is lessened; new companies can catch up to their current knowledge to race them when the pause is lifted, and to pursue other directions that aren’t paused).
I very much agree that demand for new hardware from training frontier models will not change dramatically with a pause. The limited supply chain and expertise is the limiting factor in hardware improvements, with demand changes playing a smaller role (not insignificant, just smaller).
Since a limited pause on training large models is the only one we can even really hope to get, I’d have to say that the overhang argument for pause remains pretty strong.