1000 Years of AI Safety
Models how much AI safety research a multi-year pause among top labs could produce, including verification bottlenecks and Plan A scaling tradeoffs, using a semi-endogenous growth framework.
AI research and discussion on LessWrong. www.lesswrong.com
Models how much AI safety research a multi-year pause among top labs could produce, including verification bottlenecks and Plan A scaling tradeoffs, using a semi-endogenous growth framework.
Estimates how many frontier AI companies would need to pause for a given pause length, using ECI-based capability lags behind OpenAI and a staircase model of competitors catching up.
Proposes a taxonomy for verifying and enforcing international AI pauses via compute monitoring, estimating that pauses are enforceable for training runs above roughly 10k H100-equivalents absent concealment.
Argues that a global AI pause is nearly impossible because treaties come after warning shots, AI is dual-use, there is no capability ceiling, defection is hard to detect, and no clear warning shot may occur.