AI Energy And Water The Unseen Drain

Artificial Intelligence might feel like magic, but it's powered by very real, very energy-hungry machines. Training a single large AI model like GPT-4 can emit as much CO2 as five cars over their lifetime. Behind every chatbot answer or AI-generated image lies massive server farms, often powered by non-renewable energy sources.

Artificial Intelligence might feel like magic, but it's powered by very real, very energy-hungry machines. (AI-generated image)

Every time you ask a chatbot for help or generate an AI image, somewhere a vast facility hums to life. It's easy to forget that each "effortless" AI interaction leans on gigantic data centres - buildings full of servers running nonstop, devouring electricity and gulping water.

Today is World Environment Day, so it feels fitting to pull back the curtain on AI's unseen carbon footprint, even as we keep creating content with the help of AI, like the images used in this very article.

The Power Behind The Magic Why AI Needs Huge Data Centres

AI models like ChatGPT and sophisticated image generators don't float on dreams. They rely on colossal data centres packed with racks of servers that rarely sleep.

IAD71 Amazon Web Services data centre (as on July 17, 2024 in Ashburn, Virginia) (Photo: Getty Images)

In 2024, global data centres - including those powering AI - used around 460 terawatt-hours (TWh) of electricity. That's roughly equal to Sweden's entire annual power consumption (International Energy Agency, 2024).

Forecasts suggest this figure could double to 1,000 TWh by 2026, which would almost match Germany's yearly electricity demand.

As per the US Department of Energy, a single data centre can draw over 100 megawatts (MW) of power - enough to run 80,000 average American homes at once.

Perhaps now you can begin to see why AI has become a "sizable" contributor to global emissions. AI's rapid expansion is a big driver behind this surge.

Carbon Cost Of Chatbots Energy Used Per Prompt

Training a large language model isn't a weekend project - it's an electricity marathon.

For instance, powering up OpenAI's GPT-3 used about 1.3 gigawatt-hours (GWh) of electricity, enough to supply roughly 120 average American homes for a year (ScienceDirect, 2023).

Once the model is live, every single prompt you send to GPT-4 still sips power - about 0.0003 kilowatt-hours (kWh). That's like leaving a 60-watt light bulb on for 20 seconds (Epoch AI, 2025).

Now imagine this at scale: ChatGPT answers over 1 billion queries a day. If every Google search worldwide - around 9 billion a day - were replaced with a ChatGPT query, global energy use would spike by an extra 10 TWh per year. That's enough to power all the homes in a city the size of San Francisco for a year (Hugging Face and Carnegie Mellon University, 2024).

Generating a single AI-created image isn't free either. It can use as much energy as fully charging your smartphone once.

The Water Cooling Factor How AI Is Draining Resources

Electricity tells only half the story. Those thousands of servers run hot, and cooling them eats up alarming amounts of water.

In 2024, Google's data centres consumed nearly 6 billion gallons (22.7 billion litres) of water - about one-third of Turkey's drinking water for an entire year, as per its 2024 Environment Report.

During model training, every 10-20 prompts to Google's Bard chatbot reportedly required about 500 ml of water - mostly for cooling.

On average, data centres use about 1.8 litres of water per kWh of electricity consumed (Meta Platforms, 2023). Put another way, a single large data centre can guzzle as much water in 24 hours as a small town.

And that thirst is rising - industry water consumption grows by 8-20% each year, thanks to AI's surging role and stiff competition (University of California, Riverside, and University of Texas at Arlington, 2025).

Whos Doing It Better Green AI Initiatives And Their Limits

Some big names in tech are trying to curb AI's environmental chew. Microsoft, for instance, has cut its water use per computing unit by 39% since 2021, saving about 125 million litres per data centre annually by using reclaimed water and smarter cooling methods (Meta Platforms, 2023).

Alternative cooling strategies - like air cooling or immersing servers in liquid - are also under the microscope. But these options come with their own trade-offs.

Immersion cooling cuts water use but can drive up energy needs for pumps or special fluids. Air cooling can be less water-dependent, yet efficiency dips when temperatures climb (Meta Platforms, 2023).

Switching to renewables helps, but even "green" electricity can't keep pace with AI's relentless demand. A recent MIT Lincoln Laboratory report warned that global demand for clean power is lagging far behind AI's growth rate.

Plus, many companies still don't fully share their energy or water metrics, making it near impossible to verify progress.

The Bottom Line

AI's magic comes at a genuine cost. Data centres powering our digital conversations and image creations now rival entire nations in electricity use. They swig water comparable to small cities.

And as AI continues its meteoric rise, this environmental toll only grows deeper.

On World Environment Day, it's worth asking: how can we keep enjoying the convenience of AI without letting it swallow our planet?

'Green AI' efforts - more efficient hardware, smarter cooling, renewable electricity - are steps in the right direction. But the gap between AI's hunger and our ability to supply green power remains wide.

If we don't face this challenge head-on, our planet will bear the brunt. Every prompt you send, every image you generate, leaves an unseen footprint that's getting harder to erase.