Arvind Narayanan(@random_walker):This is one of the central points in my ICML keynote, with a lot of detail — see part 2. https://t.co/ZA5TBDyX4T Note: I take RSI seriously! One of our big empirical projects is evaluating agents' ability to do open-ended AI research. But it doesn't imply much about superintelligence, labor displacement, or doom.

2026.09.17 21:10

This is one of the central points in my ICML keynote, with a lot of detail — see part 2. Note: I take RSI seriously! One of our big empirical projects is evaluating agents' ability to do open-ended AI research. But it doesn't imply much about superintelligence, labor displacement, or doom.

Ramez Naam@ramez

2026.09.16 18:04

We frequently conflate RSI with a fast-takeoff to super-intelligence (ASI). They are not the same. RSI means a model can improve on itself. That does not mean that it can do so more at each iteration, which is what would be required for a singularity. Far more likely that diminishing returns inherent to AI improvement (everything is sub-linear, governed by power-laws or log-scale diminishing returns) mean that each iteration of an RSI loop gives less relative uplift to the model than the previous, and that, after an initial boost, the system settles back to something like its previous improvement rate. RSI != Singularity, fast-takeoff, or ASI.