Title: Read | AI and the Environment [15 - 20 mins]
Course: AI Literacy > Section: Module 2: Ethical Considerations in AI: Impact & Perspectives

Module 2 · Read

AI and the Environment

AI can feel weightless, just text on a screen, but it runs on very physical infrastructure. Behind every chatbot reply are data centers full of computer chips that draw electricity and use water to stay cool. As AI use has exploded, so has attention to its environmental cost. This page looks at what we know, what is still debated, and how to think about it.

Why AI Has an Environmental Cost

Two stages use resources. Training a large model is a one-time but enormous computation across thousands of specialized chips running for weeks. Inference, the everyday use when millions of people send prompts, adds up across billions of requests. Both run in data centers that:

- Draw electricity to power the chips, much of it still generated from fossil fuels in many regions.
- Use water to cool the equipment, and indirectly through the power plants that supply the electricity.
- Require hardware whose manufacturing and eventual disposal carry their own footprint.

The Big Picture: A Fast-Growing Footprint

The clearest part of the story is the aggregate trend. As AI scales up, its share of global energy and water use is rising quickly:

~15-20%
of data-center electricity demand came from AI by the end of 2024 (IEA estimate)
~945 TWh
projected global data-center electricity use by 2030, roughly double the mid-2020s level (IEA)
billions of liters
of water tied to AI each year, with projections rising sharply toward 2030 (UN; peer-reviewed estimates)

The exact figures vary by study, but the direction does not: training and running AI at global scale uses a large and growing amount of power and water, enough that some new data centers are straining local electricity grids and water supplies.

What About a Single Prompt?

Here is where the numbers get noisy, and where your AI literacy really matters. You may have seen the claim that one AI prompt "drinks a bottle of water." Before you read any numbers, make a prediction and commit to it.

How much water do you think one short text prompt to an AI actually uses?

There is no penalty for guessing. The point is to notice your own assumption first.

💧About a few drops 🥤About half a small glass 🚰About a full 500 mL water bottle

Here is the catch: credible sources genuinely disagree, and learning to ask why is the real skill.

"About five drops"

Google reported in 2025 that a median text prompt to its Gemini assistant used about 0.24 watt-hours of energy and 0.26 milliliters of water, roughly five drops.

Source: Google, 2025 (its own data)

"Half a bottle per few dozen queries"

A 2023 University of California, Riverside study estimated that every 20 to 50 ChatGPT queries used about a 500 mL bottle of water, because it counted cooling water and the water used by power plants.

Source: Li & Ren, 2023

Why such a gap? The studies count different things (just the data center, or also the power plant), measure different tasks (a short text prompt versus a long or image-generating one), and report differently (a typical "median" use versus a heavy one). A company sharing its own favorable number also has an interest in looking efficient. None of these is automatically "the truth," and learning to ask what was measured and by whom is exactly the skill this course is about.

Key insight: a single prompt's footprint is small and genuinely debated. The large, less-disputed cost is the aggregate: billions of prompts plus massive training runs, multiplied across the whole industry.

It Is Not Shared Equally

Environmental costs do not land on everyone the same way. Data centers are often built where land and power are cheap, and some draw water from regions already facing drought. The communities living near a data center may carry the local cost of its energy and water use without seeing much of the benefit. This is part of why AI's environmental impact is also an equity question, connecting back to the fairness themes in this module.

How to Think About It

You do not need to feel guilty about a single question to an AI tool; one prompt is a small thing. But the totals are real, and worth keeping in view:

- Be aware, not paralyzed. Use AI where it genuinely helps, and skip it where it does not add much. Choosing not to use AI remains a legitimate position.
- Bigger tasks cost more. Generating long videos or many images uses far more than a short text question.
- Efficiency is improving, and providers are reporting more openly than they used to, partly because students, researchers, and the public have pushed them to.
- Read claims critically. When you see a dramatic "AI uses X water" headline, ask what was measured and who is reporting it.

Knowledge Check

Select an answer to see feedback. Each option explains why it is or is not correct.

Question 1 of 3

Why does using AI have an environmental cost at all?

A) It does not; AI has no physical footprint B) AI runs in data centers that use electricity and water for cooling C) Only training uses energy; everyday use is free D) The only cost is from making phones

Question 2 of 3

Estimates of how much energy or water a single AI prompt uses vary widely. What is the best way to read that?

A) The scariest number is always the right one B) They differ because methods differ; check what was measured and by whom C) Since the numbers disagree, the real impact must be zero D) Only the company's own figure should be trusted

Question 3 of 3

What is the clearest, least-disputed part of AI's environmental story?

A) Every single query boils a gallon of water B) The overall demand for data-center electricity and water is large and growing fast C) AI has no measurable environmental footprint D) Renewable energy already powers all data centers

Sources

International Energy Agency. (2025). Energy and AI. iea.org

Google. (2025). Measuring the environmental impact of AI inference. Google Cloud Blog

Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making AI less "thirsty": Uncovering and addressing the secret water footprint of AI models. arXiv:2304.03271

United Nations University. (2025). The environmental cost of AI's energy use: carbon, water, and land footprints. unu.edu