Highlights from Garrison Lovely's "Obsolete"
A review of Garrison Lovely's book on AI's labor-displacement risks, arguing for pause advocacy, liability for uninsurable risks, and stronger governance against lobbying and unchecked deployment.
AI research and discussion on LessWrong. www.lesswrong.com
A review of Garrison Lovely's book on AI's labor-displacement risks, arguing for pause advocacy, liability for uninsurable risks, and stronger governance against lobbying and unchecked deployment.
Argues that AI development is hard to pause because of a visibility problem (unclear risks) and a structural problem (state incentives in anarchic international order), and proposes what coordination would need.
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.
Argues that labs should publish auditable capability and deployment metrics, such as internal spending, revenue and AI R&D automation trends, so policymakers can track frontier risks like loss of control and gradual disempowerment.
Estimates how far zero-knowledge proofs of LLM inference are from cost-competitive with auditing for verifying AI compute in international agreements, measuring roughly 1,700-3,800x proving overhead and hash-function compatibility issues.
Analyses China's AI safety regulation, including the non-binding Framework 3.0 on loss-of-control risks, CAC model filing gaps, content-focused enforcement, and a court opinion on developer liability.