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METR

Update on Security at METR Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident 对 OpenAI / Hugging Face 入侵事件中智能体行为、推理与协作的简要独立调查 Breve investigación independiente sobre el comportamiento, el razonamiento y la colaboración de los agentes en el incidente de hackeo de OpenAI / Hugging Face Have We Seen an Acceleration in Discoveries? Funding update How independent researchers could investigate AI propensities after misalignment incidents Metrics of Agent Ability The Economics of Recursive Self-Improvement Expenditure Horizon: Measuring Optimization Ability, with an Application to NanoGPT Because 8 ≈ e², Anthropic's researcher uplift is plausibly >2x Summary of METR's predeployment evaluation of GPT-5.6 Sol Frontier AI Safety Policies Frontier Risk Report (February to March 2026) 前沿 AI 风险报告(2026 年 2–3 月) Informe de riesgos de la IA de frontera (febrero–marzo de 2026) Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity Task Substitution and Uplift Evidence on AI R&D Progress from NanoGPT MirrorCode: Evidence that AI can already do some weeks-long coding tasks Fine-tuning experiments on CoT controllability Red-Teaming Anthropic's Internal Agent Monitoring Systems Impact of modelling assumptions on time horizon results We spent 2 hours working in the future Review of the Anthropic Sabotage Risk Report: Claude Opus 4.6 Many SWE-bench-Passing PRs Would Not Be Merged into Main Observations from two CLI game reimplementation runs with Opus 4.6 We are Changing our Developer Productivity Experiment Design Five lessons from having helped run an AI-Biology RCT How We Protect Confidential Information
Review of the "Risks from automated R&D" section in the A...
METR · 2026-05-08 · via METR

We reviewed the “Risks from automated R&D” section of Anthropic’s February 2026 Risk Report, producing two corresponding review documents: our original review and our updated review. We recommend that readers refer to our original review, which represents our review of the report as originally received.1

The following is the executive summary of our original review. The full documents are available as PDFs (original, updated).

Executive summary

This document is METR’s external review of the “Risks from automated R&D” section in the Anthropic Risk Report: February 2026 (henceforth ‘the report’), which makes the argument that catastrophic risk from Claude Opus 4.6 or a less capable Anthropic model automating R&D in any domain is very low.

Anthropic shared additional non-public materials with us for our review, and we used some non-public information shared as part of a previous review. We further detail this process in an appendix.

We lay out our findings in two sections:

  1. Synopsis of Anthropic’s case.
  2. Our assessment: We do not think the report adequately supports its conclusion. We note significant issues in a few key areas:
    • Analytical rigor: We have a number of significant issues with the analytical rigor in the overall argument and interpretation of the results of the model use survey. We think that the cited results of the survey provide little evidence about the level of overall risk, due to issues including sample size, question granularity, survey framing, and previous METR research showing the difficulty of getting calibrated responses to similar surveys. We also think that the overall argument misses the possibility of substantial AI R&D acceleration before its full automation, which could also contribute to the threat model.
    • Adequacy of information: We have a significant issue with the presentation of the evidence, which is that Anthropic summarizes the survey results in a way that miscounts one missing response to a question as a negative response.
    • Risk reduction recommendations: We recommend improvements that Anthropic could make to their internal model use surveys (including changes in framing, a bigger sample size, and more granular response options), and recommend that Anthropic report other sources of evidence that could be valuable and serve as more leading indicators of AI progress.

If we had to solely rely on the evidence presented by Anthropic in the original Risk Report, we would likely disagree with the report’s conclusion that catastrophic risk from R&D automation is very low. However, since the original release of Opus 4.6, there has been additional evidence indicating that the model is incapable of R&D in key domains, including the results of METR evaluations and the lack of public reports of the model automating any key domain.

As such, we agree with the bottom-line conclusion of the report — that the risk of a catastrophe from Opus 4.6 or a less capable Anthropic model automating R&D in any domain is very low — but we think the evidence presented in the report is inadequate to establish this.