even a pause which completely stops all new training runs beyond current size indefinitely would only ~double timelines at best, and probably less
I’d emphasize that we currently don’t have a very clear sense of how algorithmic improvement happens, and it is likely mediated to some extent by large experiments, so I think is more likely to slow timelines more than this implies.
shrug
I think this is true to an extent, but a more systematic analysis needs to back this up.
For instance, I recall quantization techniques working much better after a certain scale (though I can’t seem to find the reference...). It also seems important to validate that techniques to increase performance apply at large scales. Finally, note that the frontier of scale is growing very fast, so even if these discoveries were done with relatively modest compute compared to the frontier, this is still a tremendous amount of compute!