Uncovering AI & water usage

Key numbers mentioned

- Sam Altman claims an average ChatGPT query uses about 0.000085 gallons* of water, roughly *1/15 of a teaspoon.

- Morgan Stanley projects AI data centres’ annual water use (cooling + electricity generation) could reach about 1,068 billion litres (~1 trillion litres)* by **2028**, an *11× increase from 2024 estimates.

- Both numbers can be “true” because they measure different parts of the lifecycle* and use *different boundaries for what counts as “water use”.

Why the numbers diverge so much

1. What’s included in the calculation?

- Per‑query figures* (like Altman’s) typically count only the *on‑site cooling water tied to that single inference.

- System‑level projections (like Morgan Stanley’s) include:

- Scope 1: On‑site data centre cooling.

- Scope 2:* Water used in *electricity generation (especially thermoelectric power plants).

- Scope 3:* Water for **semiconductor manufacturing**, which can require up to *5 million gallons of ultrapure water per day.

2. Training vs inference

- A large share of AI’s resource use comes from training* models, not just running queries. Training can account for around *50% of total resource use for a model.

- Per‑query stats often exclude training entirely, even though the model couldn’t exist without it.

- There’s also ongoing and future training for newer models, which is part of the build‑out driving the 2028 projections.

3. Power‑plant water accounting

- Thermoelectric power plants (coal, gas, nuclear) account for about 40% of all freshwater withdrawals in the US.

- Some AI water estimates attribute a share of this intake-and-return water to AI based on electricity demand, which massively inflates the “water use” number compared to counting only evaporated/consumed water.

- Only about 2–3% of that power‑plant water is lost to evaporation; the rest is returned to the source, albeit warmer.

Types of water matter

- Municipal (potable) water used for data centre cooling competes with other urban uses and relies on treatment infrastructure.

- Ultrapure water for chip manufacturing is far more energy‑intensive to produce than drinking water, even if the volume is smaller.

- Power plants and some data centres often use non‑potable water taken directly from rivers, lakes, or the sea, which is a different stressor than tapping municipal supplies.

Context: AI vs other water uses

- US corn production uses around 20 trillion gallons of water per year*, roughly *80× more than estimated global AI data centre use (~260 billion gallons).

- About 40% of US corn* goes to *ethanol for fuel, not direct human food, meaning tens of trillions of gallons are evaporated annually to make transport fuel.

- Residential lawn irrigation in the US also consumes trillions of gallons of (often municipal) water each year.

Key takeaways

It’s easy to understate AI water use by:

- Counting only per‑query cooling,

- Excluding training, chip manufacturing, and power‑generation water.

It’s also easy to overstate it by:

- Counting all water flowing through power plants (most of which is returned) without clarifying consumption vs withdrawal.

Photo by Patrick Pahlke on Unsplash