AI’s biggest labs are suddenly calling for a slower pace of development. Is it because AI risks are catching up with the technology, or because the economics of the race are getting harder to justify?
For years, the defining characteristic of the artificial intelligence race has been speed.
Build a bigger model. Spend more on compute. Release it. Rinse and repeat, with litte regard for the unknown unknowns.
The average “p/doom” (probability of AI eventually going catastrophically wrong) among AI researchers was estimated to be between 15% and 20% in 2024.
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