Users praise the ICML keynote slides on AI as normal technology for offering useful reframings of concepts like AGI and reliability along with thoughtful perspectives on adaptation.
Based on 12 visible X reactions from 25 accounts; directional sample.
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This is a fantastic framing, @random_walker. The "Normal Technology" framework feels spot on, especially when we look at the massive gap between the rate of lab innovation and the rate of enterprise adoption. Even if a breakthrough happens tomorrow, established companies have massive compliance, legacy, and release cycles that act as a natural buffer. The friction of real-world implementation is a huge reason why we won't see an overnight cliff.
@random_walker Very informative talk! Really appreciate the ambition to elegantly decompose what key terms actually mean (AGI, reliability, automation) -> to expose where unfounded causal leaps are made. Complements Messy Jobs' use of economic abstraction -> explain AIs impact on tasks.
@random_walker @sayashk Thanks for sharing I’d call this one of the best perspectives on AI for anyone in AI or wanting to cut through the haystack of hype.
@random_walker I like the talk, decided to follow you after it
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://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.
The Princeton professor says progress dimensions are distinct and bottlenecked.
This is a fantastic framing, @random_walker. The "Normal Technology" framework feels spot on, especially when we look at the massive gap between the rate of lab innovation and the rate of enterprise adoption. Even if a breakthrough happens tomorrow, established companies have massive compliance, legacy, and release cycles that act as a natural buffer. The friction of real-world implementation is a huge reason why we won't see an overnight cliff.
@random_walker Very informative talk! Really appreciate the ambition to elegantly decompose what key terms actually mean (AGI, reliability, automation) -> to expose where unfounded causal leaps are made. Complements Messy Jobs' use of economic abstraction -> explain AIs impact on tasks.
@random_walker @sayashk Thanks for sharing I’d call this one of the best perspectives on AI for anyone in AI or wanting to cut through the haystack of hype.
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://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.
Users praise the ICML keynote slides on AI as normal technology for offering useful reframings of concepts like AGI and reliability along with thoughtful perspectives on adaptation.
Based on 12 visible X reactions from 25 accounts; directional sample.
Ask a question below.
Published answers will appear here.