1) If you haven't read AI as Normal Technology, these annotated slides are probably the easiest way to get a high-level overview. https://ww…

If you haven't read AI as Normal Technology, these annotated slides are probably the easiest way to get a high-level overview. https://www.cs.princeton.edu/~arvindn/talks/icml-2026-annotated-slides/ 2) If you're already familiar with the core ideas, Part 1 of the talk is largely a summary of what I and @sayashk have already written, while Parts 2 and 3 have new ideas. There are a lot of unexamined assumptions in the discourse about Recursive Self-Improvement and I hope you find my pushback interesting. 3) I'm really grateful to the team (@steverab @sayashk @PKirgis & Felix Chen) for feedback on the talk. In my first version, Part 2 was about 3x too long and I was super frustrated with myself. They encouraged me to cut it down ruthlessly and turn the full version into essays on the newsletter, so that's what I plan to do! (https://www.normaltech.ai/) 4) I've received a few requests for the video. There's a video on the ICML website, but it is login-walled https://icml.cc/virtual/2026/invited-talk/67274 (I assume it's for ICML registrants only). Last year's videos are public, so presumably @icmlconf will make it public at some point. I had the honor of giving a keynote at the International Conference on Machine Learning in Seoul last week titled “What will be left for us to work on?” I addressed the widespread anxiety about how we should adapt as AI capabilities increase. I was thrilled by the talk’s receptio…

Source: Yann LeCun (X) | 2026-07-14

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