AI agents use roughly 600 times more energy than a

What happened

Google and OpenAI have been reassuring users with low-energy consumption figures per AI query. Climate scientist Zeke Hausfather's Claude Code data tells a different story. 24 watt-hours (Wh), less energy than nine seconds of television. 34 Wh, roughly on par with a Google search from 2009. Both data points were published last year. Even then, it was clear the numbers misrepresent actual AI usage.

On his most intensive day, when several parallel agents were grinding through an extensive geodata analysis, he estimates 11 kWh were consumed. S. household, according to Hausfather. S. household uses annually. S. electricity mix, that translates to about 370 kg of CO₂ equivalents per year.

That's slightly more than running an electric clothes dryer for a year (262 kg) and about half of a round-trip economy flight from San Francisco to New York (700 kg), though Hausfather notes the flight figure covers only direct CO₂ emissions. S. gas-powered car and roughly two percent of the average American's yearly carbon footprint.

"This is simultaneously a large emissions source and a relatively modest part of my total carbon footprint," Hausfather writes. Hausfather doesn't argue for sacrifice or guilt. Personal restraint by the small group of heavy users "is not going to bend any curves," he says, adding that routing simple tasks to smaller models does make sense, since they use five to seven times less energy per token than frontier models.

Why it matters

They don't account for reasoning models that generate many times more tokens from a single chat query, multimodal processing across various formats, multi-agent systems, code generation, scaling across billions of uses, and plenty of other factors. Google's "study" in particular massively downplayed real energy consumption. Climate scientist Zeke Hausfather has now calculated just how big that gap is in an analysis on The Climate Brink.

One major blind spot remains: nobody outside AI labs knows the actual energy cost per token of a state-of-the-art model. Keep in mind that Hausfather's numbers are reasonable estimates, but still just estimates. They're also based on current usage patterns. AI labs are already working to scale agent-based systems that run autonomously on tasks for days, weeks, or even months. That could drive an exponential jump in energy consumption.

Hausfather tracked his own usage in detail over eight weeks. Claude Code stores complete local logs of every session, including exact token counts reported by the API for each model call. His 1,138 typed prompts triggered over 14,000 model calls, an average of twelve per prompt. 9 million tokens. For comparison, a typical chat exchange without reasoning or web searches runs about a thousand tokens. 2 billion tokens.

Of those, 96 percent were cache reads because at each of the 14,000 steps, the agent re-reads its entire accumulated context. 4 percent of all processed tokens. His best estimate for total consumption comes to about 170 kWh of data center electricity over eight weeks, with an uncertainty range of 70 to 330 kWh.

Hausfather directly measured only the token counts from his Claude Code logs, and the conversion to electricity values relies on three independent methods with different assumptions. Per prompt, that works out to roughly 150 Wh, about 600 times as much as a median chat prompt.

"A 'prompt' is ultimately not a unit of AI use any more than 'trips' is a measurement of driving; it’s how far you go that matters," Hausfather writes. 6 kWh, fifty times the electricity needed to charge a phone. 9 kWh), more than the daily draw of two refrigerators.

What to watch

But the biggest lever is the carbon intensity of the electricity itself, he argues. If the same workload ran on largely clean power, the carbon footprint would drop by about 90 percent. S. data centers runs on natural gas. That's where Hausfather sees an opening. AI companies bring enormous capital and unusual urgency.

If that money flows into clean energy, grid expansion, and advanced technologies like geothermal or nuclear power, the AI boom could leave the grid cleaner than it found it.