Jevons Paradox Debunks Deepseek's Clean, Green Claim

DeepSeek has created a powerful open-source, relatively energy-lite model. The company claims it spent just US$6 million renting the hardware needed to train its new R1 model, compared with over $60 million for Meta's Llama, which used 11 times the computing resources.

DeepSeek uses a “mixture-of-experts” architecture , a machine-learning method that allows the model to scale up and down depending on the complexity of prompts. The company claims its model can also store more data and be trained without the need for huge amounts of expensive processor chips.

In reaction, US chip manufacturing and energy stocks plummeted following investor concerns that AI companies would rethink their energy-intensive data center developments. As the world's largest supplier of specialist AI processors, Nvidia saw its share price fall by $589 billion, the biggest one-day loss in Wall Street history.

Paradoxically, as well as upsetting the performance of US tech stocks, improving the energy efficiency of AI platforms could actually worsen the industry's environmental performance as a whole.

With tech stocks crashing, Microsoft CEO Satya Nadella tried to bring a longer-term perspective :“Jevons paradox strikes again!” he posted on X.“As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of.”

The idea that energy efficiency isn't always a good thing for Earth's resources has been around for well over a century. In 1865, a young Englishman named William Stanley Jevons wrote“The Coal Question”, a book in which he suggested that Britain's place as an industrial superpower might soon come to an end, due to its rapidly depleting coal reserves.

But to Jevons, frugality was not the solution. He argued :“It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth.”

According to Jevons, any increase in resource efficiency generates an increase in long-term resource consumption, rather than a decrease. Because greater energy efficiency has the effect of reducing energy's implicit price, it increases the rate of return – and demand.

Jevons offered the example of the British iron industry. If technological advancements helped a blast furnace produce iron with less coal, profits would rise and new investment in iron production would be attracted. At the same time, falling prices would stimulate additional demand. He concluded :“The greater number of furnaces will more than make up for the diminished [coal] consumption of each.”

More recently, the economist William Nordhaus applied this idea to the efficiency of lighting since the dawn of human civilization.

In a paper published in 1998 , he concluded that in ancient Babylon, the average laborer might need to work more than 40 hours to purchase enough fuel to produce the equivalent amount of light emitted by a modern lightbulb for one hour. But by 1992, an average American would need to work for less than half a second to produce the same.

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Throughout time, efficiency gains haven't reduced the energy we expend on lighting or shrunk our energy consumption. On the contrary, we now generate so much electric light that areas without it have become tourist attractions .

Warming and lighting our homes efficiently , driving our cars , mining Bitcoin and, indeed, building AI models are all subject to the same so-called rebound effects identified in the Jevons paradox. And this is why it will be impossible to ensure a more efficient AI industry actually leads to an overall reduction in energy use.

A Sputnik moment

In the 1950s, the US was horrified when the Soviets launched Sputnik, the first space satellite. The emergence of a more efficient rival caused America to allocate more resources to the space race, not less.

DeepSeek is Silicon Valley's Sputnik moment. More efficient AI will probably mean more distributed and powerful models, in an arms race that is no longer made up only of US tech giants.

AI offers superpower status , and the floodgates may now be fully open for the UK and other global competitors, as well as China. What's for certain is that in the long term, the AI industry's appetite for energy and other resources is only going to increase.

Peter Howson is assistant professor in international development, Northumbria University, Newcastle

This article is republished from The Conversation under a Creative Commons license. Read the original article .

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