Very nice. I recently did a similar exercise, and—because as you note the Epoch data (understandably) doesn’t have estimates of training compute for reasoning models—I had o3 guesstimate “effective training compute” by OpenAI model (caveat: this doesn’t really make sense!). You can see the FLOP by model in the link. And:
By this metric, it’s ~3.5 more OOMs from o3 to 1-month-AGI. If—as was often said to be the case pre-reasoning models—effective compute can still be said to be growing at ~10x a year, then 1-month-AGI arrives around early 2029
“1-month AGI”, of course, on tasks of the type studied by Kwa et al
Very nice. I recently did a similar exercise, and—because as you note the Epoch data (understandably) doesn’t have estimates of training compute for reasoning models—I had o3 guesstimate “effective training compute” by OpenAI model (caveat: this doesn’t really make sense!). You can see the FLOP by model in the link. And:
By this metric, it’s ~3.5 more OOMs from o3 to 1-month-AGI. If—as was often said to be the case pre-reasoning models—effective compute can still be said to be growing at ~10x a year, then 1-month-AGI arrives around early 2029
“1-month AGI”, of course, on tasks of the type studied by Kwa et al
That is also running up against the late-2020s compute slowdown
Along with a bazillion other caveats I won’t bother listing