Published: October 16, 2026 · Last updated: October 16, 2026
TL;DR: Anthropic is giving researchers from the AI safety nonprofit METR employee-level access to look inside its systems and evaluate Claude’s capabilities and risks firsthand, rather than through the usual limited API testing. Critics are already pointing out that METR has financial ties to Anthropic, which complicates the independence the move is supposed to demonstrate.
Most outside evaluation of frontier AI models happens through an API, the same interface any developer uses. You send prompts, you get outputs, and you try to infer what’s happening underneath from the pattern of results. It’s a bit like trying to evaluate a car’s safety by only ever looking at it through the windshield while someone else drives. Anthropic announced this week that it’s giving researchers from METR, a nonprofit focused on evaluating dangerous AI capabilities, something closer to the keys: employee-level access to its internal systems.
METR, formerly known as ARC Evals, has built a reputation over the past two years as one of the more rigorous third-party testers in the AI safety world, running evaluations that try to measure whether a model could autonomously pursue harmful goals, replicate itself, or acquire resources without human oversight. Giving that team deeper access than a typical API relationship allows means they can observe training processes, internal tooling, and model behavior in ways that black-box testing simply can’t capture.
On its face, this is the kind of transparency move that AI safety advocates have been asking frontier labs to make for years. Most capability evaluations happen under NDA, with limited scope, and on a timeline the lab itself controls. Opening up employee-level access is a genuinely bigger step than most of Anthropic’s peers have taken publicly, and it’s reasonable to read it as the company trying to differentiate itself on safety credibility at a moment when regulatory scrutiny of frontier AI labs keeps climbing.
But the announcement landed into an existing skepticism that’s worth naming directly. METR isn’t a fully independent government regulator or an academic lab with no stake in the outcome. It has received funding connected to Anthropic in the past, and critics, including commentary that circulated widely on social platforms this week, have pointed out that an evaluator whose own operations are partly financed by the company it’s evaluating isn’t the same thing as a fully arm’s-length auditor. That doesn’t mean METR’s findings are compromised. It does mean the word “independent” is doing more work in Anthropic’s framing than the funding relationship fully supports.
The core tension isn’t whether METR is competent, researchers broadly agree it is, it’s whether any evaluator funded even partly by the lab it audits can be described as independent without an asterisk attached.
This is a problem the entire AI safety ecosystem is going to keep running into as it matures. There currently aren’t many well-resourced, fully independent organizations capable of doing this kind of deep technical evaluation that aren’t funded, directly or indirectly, by the handful of labs with the money to fund safety research in the first place. Until government bodies or genuinely arm’s-length institutions build that capacity themselves, “independent” third-party evaluation is going to keep meaning something closer to “less dependent than internal review,” which is real progress, just not the full thing the word implies.
Anthropic’s move is still worth more scrutiny than cynicism alone. Employee-level access is harder to walk back than a one-time audit, and it creates an ongoing relationship that’s at least theoretically more accountable than a single press release claiming a model passed safety testing. Whether that translates into findings the public actually gets to see, especially findings that are inconvenient for Anthropic, is the part nobody can verify yet.
Related: Microsoft’s CEO has been making a related argument publicly, that every AI model should be treated as potentially compromised from the start, and Netflix’s new AI-driven homepage is facing its own transparency backlash.
Bottom Line: Giving outside researchers deeper access is a real step forward for AI safety transparency, but the word “independent” needs an asterisk until the people doing the evaluating aren’t also partly funded by the people being evaluated.
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