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Jevons paradox debunks DeepSeek’s clean, green claim

AI burns through a lot of resources. And thanks to a paradox first identified way back in the 1860s, even a more energy-efficient AI is likely to simply mean more energy is used in the long run.

For most users, “large language models” such as OpenAI’s ChatGPT work like intuitive search engines. But unlike regular web searches that find and retrieve data from anywhere along a global network of servers, AI models return data they’ve generated from scratch. Like powering up a nuclear reactor to use a calculator, this tailored process is very inefficient.

One study suggests the AI industry will be consuming somewhere between 85 and 134 terrawatt-hours (TWh) of electricity by 2027. That’s a similar amount of energy as the Netherlands consumes each year. One prominent researcher predicts that by 2030, over 20% of all electricity produced in the US will be feeding AI data centers (huge warehouses filled with computers).

Big tech firms have always claimed to be heavy investors in wind and solar energy. But AI’s appetite for 24/7 power means most are developing their own nuclear options. Microsoft even plans to revive the infamous Three Mile Island power plant, the scene of America’s worst-ever civil nuclear accident.

Despite Google’s ambitious target of being carbon neutral by 2030, the company’s AI developments mean its emissions have climbed 48% in the past few years. And the computing power needed to train these models increases tenfold each year.

However, Chinese start-up DeepSeek claims to have created a fix: a model that matches the performance of established US rivals like OpenAI, but at a fraction of the cost and carbon footprint.

An environmental game changer?

DeepSeek has created a powerful open-source, relatively

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