This seems to make the simplifying assumption that the R&D automation is applied to a large fraction of all the compute that was previously driving algorithmic progress right?
If we imagine that a company only owns 10% of the compute being used to drive algorithmic progress pre-automation (and is only responsible for say 30% of its own algorithmic progress, with the rest coming from other labs/academia/open-source), and this company is the only one automating their AI R&D, then the effect on overall progress might be reduced (the 15X multiplier only applies to 30% of the relevant algorithmic progress).
In practice I would guess that either the leading actor has enough of a lead that they are already responsible for most of their algorithmic progress, or other groups are close behind and will thus automate their own AI R&D around the same time anyway. But I could imagine this slowing down the impact of initial AI R&D automation a little bit (and it might make a big difference for questions like “how much would it accelerate a non-frontier lab that stole the model weights and tried to do rsi”).
Yes, I think frontier AI companies are responsible for most of the algorithmic progress. I think its unclear how much the leading actor benefits from progress done at other slightly behind AI companies and this could make progress substantially slower. (However, it’s possible the leading AI company would be able to acquire the GPUs from these other companies.)
Maybe distracting technicality:
This seems to make the simplifying assumption that the R&D automation is applied to a large fraction of all the compute that was previously driving algorithmic progress right?
If we imagine that a company only owns 10% of the compute being used to drive algorithmic progress pre-automation (and is only responsible for say 30% of its own algorithmic progress, with the rest coming from other labs/academia/open-source), and this company is the only one automating their AI R&D, then the effect on overall progress might be reduced (the 15X multiplier only applies to 30% of the relevant algorithmic progress).
In practice I would guess that either the leading actor has enough of a lead that they are already responsible for most of their algorithmic progress, or other groups are close behind and will thus automate their own AI R&D around the same time anyway. But I could imagine this slowing down the impact of initial AI R&D automation a little bit (and it might make a big difference for questions like “how much would it accelerate a non-frontier lab that stole the model weights and tried to do rsi”).
Yes, I think frontier AI companies are responsible for most of the algorithmic progress. I think its unclear how much the leading actor benefits from progress done at other slightly behind AI companies and this could make progress substantially slower. (However, it’s possible the leading AI company would be able to acquire the GPUs from these other companies.)