How AI Is Sparking a Change in Power
Energy companies increasingly cite AI as a major driver of electricity demand. But the grid could hold everything back.
Elon Musk recently made a bold prediction about artificial intelligence—and not the one about it being an existential threat to humanity.
Musk said rising demand for power-hungry AI chips could soon lead to an electricity shortage. “Next year, you will see that they just can’t find enough electricity to run all the chips,” the Tesla CEO said at the Bosch ConnectedWorld conference late last month.
While AI’s surging demand may not lead to mass electrical outages, the AI boom is already changing how data centers are built and where they’re located, and it’s already sparking a reshaping of U.S. energy infrastructure.
Energy companies increasingly cite AI power consumption as a leading contributor to new demand. AES, a Virginia-based utility, recently told investors that data centers could comprise up to 7.5% of total U.S. electricity consumption by 2030, citing data from Boston Consulting Group. The company is largely betting its growth on the ability to deliver renewable power to data centers in the coming years.
Sempra Energy, which operates public utilities in California and Texas, has cited AI as a major factor in its growth, alongside the electrification of the oil and gas industry.
New data centers coming on line in its regions ”represent the potential for thousands of megawatts of new electric load—often hundreds of megawatts for just one project,” Sempra told investors on its earnings call last month.
According to Boston Consulting Group, the data-center share of U.S. electricity consumption is expected to triple from 126 terawatt hours in 2022 to 390 terawatt hours by 2030. That’s the equivalent usage of 40 million U.S. homes, the firm says.
Much of the data-center growth is being driven by new applications of generative AI. As AI dominates the conversation, it’s likely to bring renewed focus on the nation’s energy grid.
Siemens Energy CEO Christian Bruch told shareholders at the company’s recent annual meeting that electricity needs will soar with the growing use of AI. “That means one thing: no power, no AI. Or to put it more clearly: no electricity, no progress.”
The technology sector has already shown how quickly AI can recast long-held assumptions. Chips, for instance, driven by Nvidia have replaced software as tech’s hottest commodity. Nvidia has said that the trillion dollars invested in global data-center infrastructure will eventually shift from traditional servers with central processing units, or CPUs, to AI servers with graphics processing units, or GPUs. GPUs are better able to power the parallel computations needed for AI.
For AI workloads, Nvidia says that two GPU servers can do the work of a thousand CPU servers at a fraction of the cost and energy. Still, the better performance capabilities of GPUs is leading to more aggregate power usage as developers find innovative new ways to use AI.
The overall power consumption increase will come on two fronts: an increase in the number of GPUs sold per year and a higher power draw from each GPU. Research firm 650 Group expects AI server shipments will rise from one million units last year to six million units in 2028. According to Gartner, most AI GPUs will draw 1,000 watts of electricity by 2026, up from the roughly 650 watts on average today.
Ironically, data-center operators will use AI technology to address the power demands. “AI can be used to improve efficiency, where you’re modeling temperature, humidity, and cooling,” says Christopher Wellise, vice president of sustainability for Equinix , one of the nation’s largest data-center companies. “It can also be used for predictive maintenance.” Equinix states that using AI modeling at one of its data centers has already improved energy efficiency by 9%.
Data centers will also install more-effective cooling systems. Vertiv VRT , a leading provider of power and cooling infrastructure equipment, says that AI servers generate five times more heat than traditional CPU servers and require ten times more cooling per square foot. AI server maker Super Micro SMCI estimates that switching to liquid cooling from traditional air-based cooling can reduce operating expenses by more than 40%.
But cooling, AI efficiency, and other technologies won’t fully solve the problem of satisfying AI’s energy demands. Certain regions could face issues with their local grid. Historically, the two most popular areas to build data centers were Northern Virginia and Silicon Valley. The regions’ proximity to major internet backbones enabled quicker response times for applications, which is also helpful for AI. (Northern Virginia was home to AOL in the 1990s. A decade later, Silicon Valley was hosting most of the country’s online platforms.)
Today, each region faces challenges around power capacity and data-center availability. Both areas are years away making from the grid upgrades that would be needed to run more data centers, according to DigitalBridge, an asset manager that invests in digital infrastructure.
DigitalBridge CEO Marc Ganzi says the tightness in Northern Virginia and Northern California is driving data-center construction into other markets, including Atlanta; Columbus, Ohio; and Reno, Nev. All three areas offer better power availability than Silicon Valley and Northern Virginia, though the network quality is slightly inferior as of now. Reno also offers better access to renewable energy sources such as solar and wind.
Ultimately, though, Ganzi says the obstacle facing the energy sector—and future AI applications—is the country’s decades-old electric transmission grid.
“It isn’t so much that we have a power issue. We have a transmission infrastructure issue,” he says. “Power is abundant in the United States, but it’s not efficiently transmitted or efficiently distributed.”
It’s one more problem AI will need to solve.