Your AI footprint
Set the sliders to your usage to estimate the electricity, water and carbon behind a year of it.
Claude Code, Codex, and similar tools work very differently from chat: one session churns through hundreds of thousands of tokens, so a single task costs about as much as 140 chat prompts.
Electricity / year
24 kWh
about 1,604 full phone charges
Water / year
24 L
about 48 half-litre bottles
Carbon / year
8.8 kg CO₂e
like driving 46 km in a petrol car
Text prompt ≈ 0.3 Wh (Google, OpenAI, Epoch AI, 2025); median coding session ≈ 41 Wh across ~600k tokens (Couch, 2026). Both already account for prompt caching: re-reading cached context costs about a tenth of processing it fresh; without caching, agent sessions would run roughly ten times higher.
Water assumes typical evaporative cooling (~1 mL per Wh); air-cooled and closed-loop data centers evaporate far less on site but draw more electricity, and the water behind power generation is often larger still. Carbon uses US average grid intensity. Excludes model training; image, video, and long “reasoning” requests cost far more.
For most people the result is small. A year of text chat uses less electricity than a refrigerator does in a week, and a full-time developer running coding agents uses roughly half a refrigerator's annual electricity.
The large numbers are at the industry level: training runs, chip manufacturing and the data centers being built ahead of demand. Those are covered in the company data and on the site map. Assumptions behind this calculator are listed in the methodology.