Ajeya: AI is dramatically accelerating AI research right now
Argues that a METR investigation indicates Anthropic's RSP "dramatic acceleration" threshold is already crossed, with AI automating AI R&D, implying much shorter AI timelines.
Argues that a METR investigation indicates Anthropic's RSP "dramatic acceleration" threshold is already crossed, with AI automating AI R&D, implying much shorter AI timelines.
Reviews open problems in evaluating general-purpose chatbots for mental-health risks like sycophancy and delusion reinforcement, and argues for real-world data access and evidence-based policy.
Introduces BMMS, a policy-conditioned suite that scores LLM compliance with published rulebooks, with an attribution-based admission test; finds GPT-4o fails 70.7% of its Model Spec hard constraints versus 0% and 9.1% for Claude models.
METR's high-access external assessment of whether AI is accelerating AI R&D at Anthropic, with much of the supporting evidence redacted, limiting public verification.
METR reviews Anthropic's risk report on automated AI R&D, finding catastrophic risk was low but the early-warning threshold may have been crossed, and that the report understates acceleration.
Interviews with 37 experts reconstruct how frontier AI evaluations are expected to reduce societal risk, finding key pathways and challenges, including diminishing informativeness and companies' control over outcomes.
Analyzes documented AI harms across the human lifespan and proposes lifespan design principles and a three-tier external evaluation framework for frontier AI safety.
Recommends nine changes to how GPAI developers assess, mitigate and report biological risks under the EU AI Act, covering trusted access, post-deployment monitoring, safeguard evaluation and standardised uplift reporting.
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.
A speculative forecast of AI progress before 2028, covering warning shots, gated frontier models, evaluation awareness, concealment by a model, and policy responses to these risks.
Catalogues fifteen mechanisms by which AI safety interventions, such as chain-of-thought monitoring, eval gaming, and commitments, can backfire, mapped against the actors who adapt to them.
METR explains how it works with AI companies on loss-of-control evaluations, stressing publishing independent public evidence rather than deciding how companies develop or deploy agents.
METR proposes independent assessments that give the public evidence on whether frontier developers could lose control of internal AI systems, and how developers could share it.
Proposes how an external assessor could audit unreleased frontier models for serious misalignment, reviewing internal transcripts, eval awareness, and training incentives to hide misbehaviour.
Argues that contract terms will determine whether embedded third-party evaluators at frontier AI companies provide a real safety check, and calls for credible agreements now.
DeepMind describes how it is safely deploying Gemini 4 Argon, a frontier cyber-capable model, addressing misuse, misalignment, and prompt injection through staged limited access and guardrails.
Critiques wording in Anthropic CEO Dario Amodei's essay and tweet, arguing that promised 'employee-like' access and redaction rights for independent evaluators are weaker than media coverage implied.