I overall agree that things seem to be going slower than AI 2027 (and my median was longer when it came out).
However as mentioned in the caption, the green curve is a simplified version of our original timelines model. Apologies about that, I think it’s reasonable to judge us based on that.
FWIW though, the central superexponential Mar 2027 trajectory from our original model certainly is not strongly contradicted by GPT-5, both with and without an AI R&D speedup interpolation issue fixed.
The original model, filtered for superexponential (pre-AI-R&D-automation) trajectories that reach superhuman coder in 2027:
With AI R&D speedup bug fixed, also filtered for superexponential pre-AI-R&D-automation (backcast looks much better, GPT-5 prediction slightly worse):
Either way, we’re now working on a much improved model which will likely have an interactive web app which will provide an improvement over this static graph, e.g. you’ll be able to try various parameter settings and see what time horizon trajectories they generate and how consistent they are with future data points.
Note also that the above trajectories are from the original model, not the May update model which we unfortunately aren’t taking the time to create for various reasons, we think it would likely look a little worse in terms of the GPT-5 fit but might depend how you filter for which trajectories count as superexponential.
I overall agree that things seem to be going slower than AI 2027 (and my median was longer when it came out).
However as mentioned in the caption, the green curve is a simplified version of our original timelines model. Apologies about that, I think it’s reasonable to judge us based on that.
FWIW though, the central superexponential Mar 2027 trajectory from our original model certainly is not strongly contradicted by GPT-5, both with and without an AI R&D speedup interpolation issue fixed.
The original model, filtered for superexponential (pre-AI-R&D-automation) trajectories that reach superhuman coder in 2027:
With AI R&D speedup bug fixed, also filtered for superexponential pre-AI-R&D-automation (backcast looks much better, GPT-5 prediction slightly worse):
Either way, we’re now working on a much improved model which will likely have an interactive web app which will provide an improvement over this static graph, e.g. you’ll be able to try various parameter settings and see what time horizon trajectories they generate and how consistent they are with future data points.
Note also that the above trajectories are from the original model, not the May update model which we unfortunately aren’t taking the time to create for various reasons, we think it would likely look a little worse in terms of the GPT-5 fit but might depend how you filter for which trajectories count as superexponential.