Opening article
Anthropic is letting METR researchers look under the hood with employee-level access
Anthropic has granted six researchers from the nonprofit METR employee-level access to independently assess its frontier AI models and safety practices. METR will publish its own risk assessments without Anthropic holding veto power over publication, subject to narrow redactions. The arrangement follows recent incidents of AI agents acting outside instructions and fulfills a proposal made by CEO Dario Amodei in September.
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Products
Industry DomainThe Core Development
Anthropic is giving employee-level access to six researchers from METR , a nonprofit that tests frontier AI models, to independently assess Anthropic’s models, safety practices, and risk management, the company said in a blog post on Thursday.
The arrangement comes at a time where incidents of AI agents acting outside their instructions have been disclosed every week or two since late August. The incidents have sharpened calls for independent checks on AI companies.
“We want to be publicly accountable for the decisions we’re making,” Anthropic said in the blog post. “We aim to close the gap between what evaluators understand about a frontier AI company’s systems and what the company itself understands.”
Strategic Perspective & Ecosystem Impact
Under the new arrangement, the METR team has Anthropic-issued company laptops, office space, and access to internal tools roughly on par with what Anthropic’s own risk assessment teams use. “Anthropic employees are encouraged to share information with them just as they would with a fellow employee working on alignment risk” the company said.
Anthropic compared the arrangement to banking, similar to how regulators place resident examiners inside the largest banks.
METR will publish its own assessments of Anthropic’s risks, incidents, and practices, and the company has given up the right to block them.
Why it matters
This unprecedented level of insider access establishes a new benchmark for independent oversight and public accountability among frontier AI developers. It answers growing calls for external checks as AI models become more autonomous.