Suppose uplift at the start of 2026 was 1.6x as in the AIFM’s median
Where are you getting this 1.6 number?
With respect to the rest of your comment, it feels to me like we have such little evidence about current uplift and what trend it follows (e.g. whether this assumption about a % automation curve that is logistic and its translation to uplift is a reasonable functional form). I’m not sure how strongly we disagree though. I’m much more skeptical of the claim that uplift can give much tighter confidence intervals than that it can give similar or slightly better ones. Again, this could change if we had much better data in a year or two.
Our median value for the coding uplift of present-day AIs at AGI companies is that having the AIs is like having 1.6 times as many software engineers (and all the staff necessary to coordinate them effectively).
As for the rest, seems reasonable. I think you can’t get around the uncertainty by modeling uplift as some more complicated function of coding automation fraction as in the AIFM, because you’re still assuming that’s logistic, we can’t measure it any better than uplift, plus we’re still uncertain how they’re related. So we really do need better data.
I think you can’t get around the uncertainty by modeling uplift as some more complicated function of coding automation fraction as in the AIFM, because you’re still assuming that’s logistic, we can’t measure it any better than uplift, plus we’re still uncertain how they’re related. So we really do need better data.
But in the AIFM the coding automation logistic is there to predict the dynamics regarding how much coding automation speeds progress pre-AC. It doesn’t have to do with setting the effective compute requirement for AC. I might be misunderstanding something, sorry if so.
Re: the 1.6 number, oh that should actually be 1.8 sorry. I think it didn’t get updated after a last minute change to the parameter value. I will fix that soon. Also, that’s the parallel uplift. In our model, the serial multiplier/uplift is sqrt(parallel uplift).
In my model it’s parallel uplift too. Effective labor (human+AI) still goes through the diminishing returns power to get to serial uplift, which I estimate as between roughly 0.1 and 0.3.
Where are you getting this 1.6 number?
With respect to the rest of your comment, it feels to me like we have such little evidence about current uplift and what trend it follows (e.g. whether this assumption about a % automation curve that is logistic and its translation to uplift is a reasonable functional form). I’m not sure how strongly we disagree though. I’m much more skeptical of the claim that uplift can give much tighter confidence intervals than that it can give similar or slightly better ones. Again, this could change if we had much better data in a year or two.
I got it from your website:
As for the rest, seems reasonable. I think you can’t get around the uncertainty by modeling uplift as some more complicated function of coding automation fraction as in the AIFM, because you’re still assuming that’s logistic, we can’t measure it any better than uplift, plus we’re still uncertain how they’re related. So we really do need better data.
But in the AIFM the coding automation logistic is there to predict the dynamics regarding how much coding automation speeds progress pre-AC. It doesn’t have to do with setting the effective compute requirement for AC. I might be misunderstanding something, sorry if so.
Re: the 1.6 number, oh that should actually be 1.8 sorry. I think it didn’t get updated after a last minute change to the parameter value. I will fix that soon. Also, that’s the parallel uplift. In our model, the serial multiplier/uplift is sqrt(parallel uplift).
In my model it’s parallel uplift too. Effective labor (human+AI) still goes through the diminishing returns power