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.
Argues that an ASI developer's commitment to the common good matters for utopia, and that authoritarian capture of such organisations could make even a shutdown-based off-ramp lead to dystopia.
Uses a person-affecting decision model to argue that accepting substantial AI catastrophe risk can still favour early superintelligence development, with brief pauses for safety progress, and warns that poorly implemented pauses could cause harm.
Research note proposing cryptographic and memory-exhaustion protocols to verify that compute owners run only permitted computations, motivated by enforcing a possible AI pause treaty.
Proposes and reviews a conditional international AI safety treaty with compute thresholds, mandatory model audits, and AISI-led pause authority to address loss-of-control risks from advanced AI.