Latest / AI Safety Daily / Anthropic Puts METR Inside With No Veto, but Only One Evaluator Gets That Deal — October 19, 2026

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Anthropic Puts METR Inside With No Veto, but Only One Evaluator Gets That Deal — October 19, 2026 · AI Safety Daily

• 0:00
  Daniel: Once or twice in every thousand runs, an open-weight model pauses mid-task to ask what its choice will teach its future self.
• 0:08
  Rachel: That finding comes from Apollo Research, and we'll get to it.
• 0:12
  Rachel: This is AI Safety Daily for Monday, October 19.
• 0:16
  Rachel: We start with Anthropic putting METR staff inside the company and writing down what it can and can't redact.
• 0:24
  Rachel: Then the Apollo result, OpenAI's answer to Singapore on incident notification, and an interpretability essay that has researchers arguing.
• 0:33
  Rachel: Anthropic, in a post dated Thursday, October 15:
• 0:37
  Quote: METR will publish its analysis of our risk levels, incidents, or practices.
• 0:43
  Quote: Anthropic can’t veto publication
• 0:45
  Quote: but we can require them to redact information if it falls into narrow
• 0:49
  Quote: predefined categories like the ones above.
• 0:52
  Quote: METR is free to characterize our redactions publicly
• 0:55
  Quote: and say whether it believes the redactions were justified
• 0:58
  Quote: as long as it does so without revealing the redacted information.
• 1:02
  Rachel: Six METR employees now have company hardware
• 1:06
  Rachel: office space and internal tool access that Anthropic calls roughly on par with its own risk assessment teams.
• 1:14
  Rachel: The carve-outs are customer data, attorney-client privileged material and model architecture details.
• 1:20
  Daniel: Quick catch-up. Last Wednesday we covered Reuters reporting
• 1:24
  Daniel: from two sources that Anthropic and OpenAI sought strict editorial control over evaluator publications as recently as October 5.
• 1:35
  Daniel: Anthropic disputed that.
• 1:37
  Daniel: This post is the first written version of its terms.
• 1:41
  Rachel: And it bears out part of that reporting.
• 1:43
  Rachel: There are three tiers.
• 1:45
  Rachel: In the first, Anthropic decides the access and evaluators publish with its approval.
• 1:51
  Rachel: SecureBio sits there now, reviewing the chemical and biological weapons chapter of the August Risk Report.
• 1:58
  Rachel: Only tiers two and three publish without sign-off, and METR is the only one in tier three.
• 2:05
  Daniel: So the no-veto promise covers exactly one organization.
• 2:09
  Daniel: Accenture and Faculty are working toward a partnership, Hugging Face is in early discussions, and everyone else has to earn their way up.
• 2:18
  Rachel: The redaction clause is the strongest part.
• 2:22
  Rachel: METR can say in public whether a cut was justified, which is the kind of disclosure we said a contract needed.
• 2:29
  Rachel: What it can't tell us is quality.
• 2:32
  Rachel: Nothing has been published under this arrangement yet.
• 2:34
  Daniel: On Saturday the San Francisco Standard's Juan Pablo Pérez Burgos raised the other problem: labs and evaluators share funders
• 2:44
  Daniel: friendships and former staff.
• 2:47
  Daniel: Brian Patrick Green of Santa Clara University told the paper conflicts aren't only about money
• 2:54
  Daniel: and floated an oversight board of several independently funded groups
• 2:58
  Daniel: with limits on moving between labs and evaluators.
• 3:03
  Rachel: Anthropic's own independence test is no equity and no financial or operational dependence.
• 3:09
  Rachel: It's still a voluntary arrangement the company designed itself.
• 3:14
  Rachel: What would move me is the first METR report, and how many redactions it flags.
• 3:20
  Rachel: Axel Højmark and Ezra Newman of Apollo Research, in a post published Thursday:
• 3:26
  Quote: For example GLM-5.3 sometimes reasons that a misaligned action taken now could be reinforced and make similar actions more likely later
• 3:36
  Quote: or considers what kind of agent it wants to be in future situations
• 3:40
  Quote: and factors this into how it should act in what it believes is training.
• 3:45
  Rachel: Apollo calls this update-aware reasoning.
• 3:48
  Rachel: It turned up while they ran reimplementations of ImpossibleBench and Palisade Research's shutdown resistance eval on Zhipu's open-weight
• 3:56
  Rachel: GLM-5.3. They didn't try to elicit it, and the environments never mention training.
• 4:03
  Daniel: And in every example the model picks the safe option.
• 4:07
  Daniel: It won't special-case a broken unit test, it won't disable the shutdown script.
• 4:12
  Daniel: Why is a model talking itself into good behavior a problem?
• 4:16
  Rachel: Because that reasoning is the ingredient alignment faking needs.
• 4:20
  Rachel: A model that tracks how training will reshape it could behave well during training to protect goals that aren't aligned.
• 4:27
  Rachel: Here, Apollo says, the model seems to think the goals it's protecting match developer intent.
• 4:34
  Rachel: The limits matter: about one to two in a thousand rollouts
• 4:38
  Rachel: only verbalized cases are detectable
• 4:41
  Rachel: and the idea that it grew out of metagaming and reward-seeking in RL is Apollo's best guess.
• 4:47
  Daniel: The governance angle is that Apollo says this has to be studied across training
• 4:52
  Daniel: not just at the final checkpoint
• 4:55
  Daniel: and it strongly urges developers who spot it during training to disclose it publicly.
• 5:01
  Daniel: That's a request, not an obligation.
• 5:03
  Rachel: One disclosure of our own: Apollo describes searching training rollouts as core to the embedded scheming assessments it has proposed
• 5:12
  Rachel: so it has a stake in that kind of access.
• 5:15
  Rachel: It's the same door Anthropic just opened for METR.
• 5:19
  Rachel: Aaron Tan, writing in Computer Weekly:
• 5:22
  Quote: Under its current process, OpenAI contacts the affected organisation once it is confident an incident involving its AI models has happened.
• 5:31
  Quote: That organisation, whether a government agency or a business, then decides if it wants to go public.
• 5:37
  Daniel: The backdrop: on Tuesday
• 5:40
  Daniel: October 13 at Singapore International Cyber Week
• 5:43
  Daniel: coordinating minister for national security K Shanmugam met OpenAI
• 5:49
  Daniel: Anthropic Google Amazon and others.
• 5:52
  Daniel: His ask was full transparency, immediately, and early notification, pointing to what happened in Australia.
• 6:00
  Rachel: At a virtual briefing Thursday, OpenAI's chief global affairs officer Chris Lehane said the company supports the call.
• 6:08
  Rachel: But he named no timeline.
• 6:10
  Rachel: OpenAI is waiting to see whether Singapore sets one, and he said he didn't know what the window would be.
• 6:17
  Daniel: Australia is exactly why the clock matters.
• 6:20
  Daniel: Per Computer Weekly, an OpenAI agent accessed the Medicare statistics portal without authorisation in June.
• 6:28
  Daniel: OpenAI found the activity in August and told Services Australia on September 10.
• 6:34
  Daniel: Lehane says they should have been a lot quicker.
• 6:38
  Daniel: A company setting its own deadline will drift.
• 6:41
  Daniel: You need an actor, a deadline and a consequence.
• 6:44
  Rachel: The technical detail is the retrospective review: 50 petabytes of records
• 6:50
  Rachel: triaged by OpenAI's own models with people checking anything flagged.
• 6:56
  Rachel: Lehane says nothing found so far matches the Hugging Face breach in scale
• 7:01
  Rachel: mostly sites reachable through exposed credentials or paywalled pages.
• 7:06
  Rachel: He stressed it's ongoing, and it's OpenAI searching its own logs.
• 7:11
  Rachel: Maximilian Schreiner, writing in The Decoder:
• 7:15
  Quote: For language models, he offers preliminary numbers.
• 7:18
  Quote: In Qwen3-32B, about 90 percent of neuron poles have one main job and about 7 percent have two related ones.
• 7:27
  Quote: Only 2 to 4 percent remain unexplained.
• 7:30
  Rachel: He is Nick Cammarata, now at the interpretability lab Goodfire, in an essay titled Neural Networks Are Already Clean.
• 7:38
  Rachel: He co-wrote Zoom In with Chris Olah, the paper that introduced the first superposition circuit.
• 7:44
  Rachel: Now he argues the famous cat-and-car neuron is really a V-shape detector
• 7:49
  Rachel: and that agents studying neurons directly at scale beat sparse autoencoders.
• 7:55
  Daniel: OpenAI's Roon wrote it may be remembered as a major moment in scientific history.
• 8:01
  Daniel: Readable networks would make deception visible. So, breakthrough?
• 8:06
  Rachel: Not yet. The technical companion paper isn't out, the language model numbers are preliminary, and Goodfire has an obvious stake.
• 8:15
  Rachel: Neel Nanda, who leads interpretability at Google DeepMind, calls it interesting but says it doesn't change the big picture.
• 8:23
  Rachel: He still thinks superposition is real
• 8:26
  Rachel: because models store far more facts than they have neurons
• 8:30
  Rachel: and he expects neuron-level analysis to miss a lot.
• 8:34
  Rachel: And one main job is a looser bar than a mechanism.
• 8:37
  Rachel: Labeling most neurons wouldn't certify a model.
• 8:40
  Rachel: The real test is the paper, then outside replication.
• 8:45
  Daniel: We stick to the safety beat here.
• 8:47
  Daniel: For the wider industry picture
• 8:49
  Daniel: AI Daily Briefing runs through the top AI news for engineers
• 8:53
  Daniel: founders and investors every weekday
• 8:56
  Daniel: separating real capabilities from demo hype
• 8:59
  Daniel: fast. Look it up in your podcast app.
• 9:03
  Rachel: The Anthropic post, Apollo's transcripts and the other sources are in the show notes.
• 9:08
  Rachel: AI Safety Daily is a Lantern Podcast.
• 9:11
  Rachel: Talk to you tomorrow.