Data centers powering AI models require massive electricity and water.
Unlike Bitcoin, AI’s energy use is rapidly increasing with global demand.
Sustainable AI development is crucial to reduce its environmental impact.
World Environment Day, observed every year on June 5, focuses on encouraging global awareness and action for protecting the environment. The 2025 theme, “Beat Plastic Pollution,” highlights the danger plastic poses to the planet. But this year, another issue is gaining attention: the increasing energy consumption of Artificial Intelligence (AI) and whether it could soon use more power than Bitcoin.
This article explores how AI is growing rapidly, the impact of its energy use, how it compares to Bitcoin, and what can be done to manage its environmental cost.
Technology is advancing faster than ever, but it comes at a cost. The data centers that power AI models and Bitcoin mining require huge amounts of electricity. As AI continues to grow, its energy needs are increasing quickly. This has raised concerns about AI consuming considerably more energy than the top player in the crypto market.
Bitcoin, the popular digital currency, uses a system called “proof of work,” which involves solving complex math problems to add transactions to the blockchain. This process requires powerful computers running all the time, consuming a lot of energy.
As of 2024, Bitcoin uses about 175 terawatt-hours (TWh) of electricity each year. This is roughly equal to the electricity usage of countries like Argentina or Sweden. Its global power demand is around 10 gigawatts (GW).
On the other hand, AI systems are expected to demand over 20 gigawatts by the end of 2025 — more than double Bitcoin’s current usage. Some researchers even say AI could use as much power as the entire United Kingdom by the end of the year. This means AI might consume between 200 and 400 TWh annually, depending on how fast the technology spreads.
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There are several reasons why AI energy consumption is rising so fast:
AI models, especially large ones like ChatGPT or Google Gemini, require a huge amount of computing power to train. Training a single model can take weeks or months and use electricity equal to what hundreds of homes might use in a year.
After training, these models are used by millions of people for chatting, searching, generating images, and more. Each use, or “inference,” also consumes electricity. With billions of inferences happening daily, the power usage adds up quickly.
AI systems run on massive servers in data centers. These centers not only use power for running computers but also need to keep them cool. Cooling systems, like air conditioners or liquid cooling, use even more electricity and water.
Producing the computer chips used in AI requires mining rare materials and using a lot of energy. As demand for AI chips rises, so does the impact on the environment.
While both AI and Bitcoin consume a lot of energy, they do so for different reasons:
Bitcoin’s energy use is steady and tied directly to the number of miners on the network.
AI’s energy use is growing fast due to increasing demand in industries like health, finance, education, and entertainment.
Bitcoin’s energy usage is easier to track, while AI energy use is often hidden inside cloud computing services run by big tech companies.
Some Bitcoin mining operations use fossil fuels, while tech companies claim their data centers run on renewable energy. However, it’s not always clear how much of this energy is clean.
AI’s growth is already putting pressure on the environment. In addition to electricity use:
Water use is high. For example, training a large AI model can use hundreds of thousands of liters of water to cool the servers.
Electronic waste is increasing. AI hardware gets old quickly, and many devices are thrown away after just a few years.
Carbon emissions could rise. If AI data centers use electricity from coal or gas, more carbon dioxide is released into the air, contributing to climate change.
Tech companies and governments are starting to respond to the challenge:
Some companies are investing in renewable energy, like wind, solar, and nuclear, to power their data centers.
New cooling technologies, such as liquid cooling, are being used to reduce power use and save water.
There are efforts to make AI models more energy-efficient by improving how they are trained and used.
Policies are being discussed to require transparency from companies about how much energy and water their AI systems use.
For example, companies like Microsoft, Meta (Facebook’s parent company), and Google are making long-term deals to buy clean energy. Some are even planning to reopen nuclear power plants to meet energy needs.
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World Environment Day 2025 highlights the need to tackle all kinds of environmental problems, not just plastic. It is a reminder that technology must grow responsibly. Just like people are being encouraged to reduce plastic waste, industries must also reduce the environmental impact of their tools and platforms.
In India, the Bureau of Energy Efficiency (BEE) has launched campaigns promoting energy-saving behaviors that could save over 22 billion kilowatt-hours. These efforts show how simple changes, combined with policy and industry action, can lead to big results.
By the end of this year, AI is very likely to use more energy than Bitcoin. Without action, the environmental cost of AI could grow quickly. However, with the right steps, it is possible to balance the benefits of AI with care for the planet.
Some key ideas to move forward include:
Making AI models more energy- and water-efficient.
Using more clean energy in data centers.
Setting up rules and standards to track and report environmental impact.
Encouraging innovation that helps the environment, such as AI tools that fight pollution or improve energy use in cities.
World Environment Day is a moment to think deeply about how technology affects the Earth. While AI holds great promise, it also brings risks if not handled wisely. The rise in AI energy use is not just a tech issue; it is an environmental issue, too.
The choice is clear: technology should not come at the cost of the planet. With careful planning, smarter systems, and strong environmental responsibility, it is possible to build a future where both AI and nature can thrive.