Research / Sources
AI research and discussion on LessWrong. www.lesswrong.com
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.
An essay defending Anthropic CEO Dario Amodei's proposal for coordinated frontier AI slowdown and regulation, arguing for embedded independent evaluators and distributed trust rather than trust in labs.
Argues that coordination among AI developers to avoid building dangerous AI deserves serious, concrete discussion like a real geopolitical negotiation, and that leaders should explain why they consider it infeasible.
Proposes a low-trust reference architecture for verifying AI compute, using network taps, air-gapped or ZKP-based evaluation, and workload evidence capture to check treaty compliance.
A humorous allegorical tour of the AI safety landscape, covering access controls, red lines, reward misspecification, jailbreaks, field-building and race dynamics, using a children's story about a cookie tree.
Argues that open-weight models undermine the defenses proposed for near-future AI risks, since guardrails can be fine-tuned away, use cannot be monitored, and bad actors have access by default.
A workshop report on building hardware-based verification mechanisms, a datacenter 'lie detector', to check AI compute use for international agreements and reduce arms-race risks.
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.