The artificial intelligence industry is scrambling to reduce its massive energy consumption through better cooling systems, more efficient computer chips, and smarter programming — all while AI usage explodes worldwide.<\/p>\n
AI depends entirely on data centers, which could consume three percent of the world’s electricity by 2030, according to the International Energy Agency. That’s double what they use today.<\/p>\n
Experts at McKinsey, a US consulting firm, describe a race to build enough data centers to keep up with AI’s rapid growth, while warning that the world is heading toward an electricity shortage.<\/p>\n
“There are several ways of solving the problem,” explained Mosharaf Chowdhury, a University of Michigan professor of computer science.<\/p>\n
Companies can either build more energy supply — which takes time and the AI giants are already scouring the globe to do — or figure out how to consume less energy for the same computing power.<\/p>\n
Chowdhury believes the challenge can be met with “clever” solutions at every level, from the physical hardware to the AI software itself.<\/p>\n
For example, his lab has developed algorithms that calculate exactly how much electricity each AI chip needs, reducing energy use by 20-30 percent.<\/p>\n
– ‘Clever’ solutions –<\/p>\n
Twenty years ago, operating a data center — encompassing cooling systems and other infrastructure — required as much energy as running the servers themselves.\u00a0<\/p>\n
Today, operations use just 10 percent of what the servers consume, says Gareth Williams from consulting firm Arup.\u00a0<\/p>\n
This is largely through this focus on energy efficiency.<\/p>\n
Many data centers now use AI-powered sensors to control temperature in specific zones rather than cooling entire buildings uniformly.<\/p>\n
This allows them to optimize water and electricity use in real-time, according to McKinsey’s Pankaj Sachdeva.<\/p>\n
For many, the game-changer will be liquid cooling, which replaces the roar of energy-hungry air conditioners with a coolant that circulates directly through the servers.<\/p>\n
“All the big players are looking at it,” Williams said.<\/p>\n
This matters because modern AI chips from companies like Nvidia consume 100 times more power than servers did two decades ago.<\/p>\n
Amazon’s world-leading cloud computing business, AWS, last week said it had developed its own liquid method to cool down Nvidia GPUs in its servers – – avoiding have to rebuild existing data centers.<\/p>\n
“There simply wouldn’t be enough liquid-cooling capacity to support our scale,” Dave Brown, vice president of compute and machine learning services at AWS, said in a YouTube video.<\/p>\n
– US vs China –<\/p>\n
For McKinsey’s Sachdeva, a reassuring factor is that each new generation of computer chips is more energy-efficient than the last.<\/p>\n
Research by Purdue University’s Yi Ding has shown that AI chips can last longer without losing performance.<\/p>\n
“But it’s hard to convince semiconductor companies to make less money” by encouraging customers to keep using the same equipment longer, Ding added.<\/p>\n
Yet even if more efficiency in chips and energy consumption is likely to make AI cheaper, it won’t reduce total energy consumption.<\/p>\n
“Energy consumption will keep rising,” Ding predicted, despite all efforts to limit it. “But maybe not as quickly.”<\/p>\n
In the United States, energy is now seen as key to keeping the country’s competitive edge over China in AI.<\/p>\n
In January, Chinese startup DeepSeek unveiled an AI model that performed as well as top US systems despite using less powerful chips — and by extension, less energy.<\/p>\n
DeepSeek’s engineers achieved this by programming their GPUs more precisely and skipping an energy-intensive training step that was previously considered essential.<\/p>\n
China is also feared to be leagues ahead of the US in available energy sources, including from renewables and nuclear.<\/p>\n
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