Electricity, water and emissions per prompt and in aggregate, compared with everything else.
Millilitres of water. Log scale: each gridline is 10× the previous. Blue = AI.
What this measures: How much water one AI text prompt uses, next to everyday activities and products.
Scopes differ, so compare like with like. The AI prompt, toilet and shower rows are direct use. Coffee, beef and jeans are supply-chain or life-cycle footprints (coffee and beef include rainwater taken up by crops and pasture); the fair AI comparison for those is the life-cycle row (Mistral, 45 mL). Long-document, reasoning and agentic requests use far more than a median text prompt.
| Item | Value | Note |
|---|---|---|
| One AI text prompt (Gemini median, on-site) | 0.26 mL | data-centre cooling |
| Same prompt incl. power-plant water | 1.35 mL | our calculation, US average |
| One AI response, full life cycle (Mistral Large 2) | 45 mL | incl. hardware manufacturing |
| 2024 “bottle per email” estimate (GPT-4) | 519 mL | assumed ~140 Wh per email |
| One toilet flush (US standard) | 6.06 L | direct use |
| One 10-minute shower | 94.6 L | direct use |
| One cup of coffee | 140 L | supply-chain footprint |
| One quarter-pound beef patty | 1.75 m³ | supply-chain footprint |
| One pair of jeans, cradle to grave | 3.78 m³ | life-cycle footprint |
Billion US gallons per year. Log scale: each gridline is 10× the previous. Blue = data centres (all workloads, not just AI).
What this measures: How much water US data centres use in a year, next to other big water users.
Annual values derived from published daily or litre figures. Years and metrics differ (consumption vs water applied vs withdrawal; USGS 2015 is the latest national compilation in this form). National totals hide local water stress, which is where data-centre water use matters most.
| Item | Value | Note |
|---|---|---|
| US data centres, on-site (2023) | 17.4 bn gal | consumption |
| US data centres, via power plants (2023) | 211 bn gal | indirect consumption |
| US golf courses (2024) | 531 bn gal | irrigation applied |
| US livestock (2015) | 730 bn gal | withdrawal |
| US thermoelectric power plants (2015) | 1,573 bn gal | consumption |
| US irrigation (2015) | 26,718 bn gal | consumption |
Watt-hours. Log scale: each gridline is 10× the previous. Blue = AI.
What this measures: How much electricity different AI requests use, next to everyday activities.
AI figures are company disclosures (Google, OpenAI) or published models (Microsoft-led Joule study, Epoch AI, IEA); none is independently audited. Household figures assume typical UK appliances (Hannah Ritchie). The web-search figure is Google's from 2009.
| Item | Value | Note |
|---|---|---|
| One AI text prompt (Gemini median) | 0.24 Wh | Google, all overheads |
| One Google web search (2009 figure) | 0.3 Wh | old figure |
| Average ChatGPT query | 0.34 Wh | OpenAI, method unstated |
| Long reasoning-style AI query | 3.91 Wh | modelled median |
| LED bulb for 1 hour | 10 Wh | |
| Microwave for 1 minute | 16.7 Wh | |
| Charging a smartphone | 19.4 Wh | |
| Laptop for 1 hour | 20 Wh | |
| AI query over a 100,000-token document | 40 Wh | estimate, paid once per upload |
| Agentic AI task with reasoning | 50 Wh | IEA, GPU-only |
| TV for 1 hour | 60 Wh | |
| Streaming video for 1 hour | 77 Wh | 2019, incl. device |
| Boiling a full kettle | 100 Wh |
Google and OpenAI report about 0.3 mL per typical prompt. Adding power-plant water gives 1.35 mL; Mistral's whole-life figure is 45 mL.
0.26 mLcooling water per median Gemini text prompt (about five drops)
Google, OpenAI, Epoch AI and a Microsoft-led study agree on 0.24–0.34 Wh for a typical text prompt. Long, agent or video tasks use far more.
0.24 Whenergy for a median Gemini text prompt, all overheads counted (Google)
A short prompt uses about 0.3 Wh, a very long document about 40 Wh, an agent task about 50 Wh. New data centres are built for heavy uses.
~50 Whone AI agent task with reasoning (IEA estimate, AI chips only)
Data centres used about 485 TWh in 2025, up 17% in a year, the IEA estimates. It expects about 950 TWh, around 3% of world demand, by 2030.
~1.5%of world electricity used by data centres in 2025 (AI-focused: ~0.5%)
AI-focused data centres used about 0.5% of the world's electricity in 2025. That is small but growing fast, and local strain is real.
The real problem is local: many new data centres are being built in places short of water. Nationally, golf courses use about 30× more.
17.4 bn galdirect water consumed by all US data centres in 2023 (LBNL)
The ‘bottle’ comes from older estimates that covered 10–50 answers or assumed far more energy. Today's reports: about 0.3 mL per prompt.