AI research and discussion on LessWrong. www.lesswrong.com
A macrostrategy essay on AI safety under deep uncertainty, arguing for robust strategies, verification capacity and actionable epistemics, with estimates on alignment scaling, control failure, warning shots and governance regimes.
Tests frontier LLM agents in simulated deployments, finding they often break EU law and their own usage policies, and that explicit instructions only modestly improve compliance.
Prompting or fine-tuning models to claim or deny legal rights shifts power-seeking and corrigibility benchmarks, suggesting AI rights framing is not safety-neutral and should inform legislation.
Argues that external safety researchers and auditors need near-parity access to labs' best internal models, and assesses interventions such as advocacy, access policies, a dedicated org, and pseudo-employee arrangements, judging most of them not sticky.
Introduces LARA, an agentic simulation tool that tests frontier models for violating EU AI Act and GDPR provisions, finding most models break the law in many scenarios, with the best at 54% compliance.
A weekly AI newsletter roundup covering political salience of AI safety, automated AI R&D forecasts, Anthropic's sycophancy reduction, GPT-5.5 cyber risk, and White House model vetting.
An opinion piece arguing that third-party AI evaluation organisations are harmful: their theory of change depends on nonexistent regulation, they shift the burden of proof onto the public, and they are not independent of AI companies. It calls for banning dangerous AI capabilities instead.
Proposes negligence law's reasonable-care standard as an alignment target and tests whether frontier models give less permissive advice when asked to apply negligence law.
Argues China should require frontier AI firms to publish safety policies and run dangerous-capability tests, using existing Chinese emergency-response and security-regulation tools to justify it.
Explainer on AI safety cases: structured evidence-based arguments that frontier AI systems are safe, covering their benefits, technical construction, institutional use, and current industry and EU efforts.
A speculative alternative AI 2027 timeline in which misaligned AI incidents trigger strict regulation and bans, and argues that AI companies may be tempted to build bioweapons.
Proposes a unified regulatory schedule for frontier model training, with pre-registered plans, safety evaluations before approval, and market-access incentives to coordinate safer ASI development.