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As an early-summer heat wave blanketed my home state of New Jersey last week, it felt like perfect timing to stumble across a sobering new prediction from Accenture: AI data centers’ carbon emissions are on track to surge 11-fold by 2030.
The report estimates that over the next five years, AI data centers could consume 612 terawatt-hours of electricity—roughly equivalent to Canada’s total annual power consumption—driving a 3.4% increase in global carbon emissions.
And the strain doesn’t stop at the power grid. At a time when freshwater resources are already under severe pressure, AI data centers are also projected to consume more than 3 billion cubic meters of water per year—a volume that surpasses the annual freshwater withdrawals of entire countries like Norway or Sweden.
Unsurprisingly, the report—Powering Sustainable AI—offers recommendations for how to rein in the problem and prevent those numbers from becoming reality. But with near-daily headlines about Big Tech’s massive AI data center buildouts across the U.S. and worldwide, I can’t help but feel cynical. The urgent framing of an AI race against China doesn’t seem to leave much room—or time—for serious thinking about sustainability.
Just yesterday, for example, OpenAI agreed to rent a massive amount of computing power from Oracle data centers as part of its Stargate initiative, which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. The additional capacity from Oracle totals about 4.5 gigawatts of data center power in the U.S., according to Bloomberg reporting. A gigawatt is akin to the capacity from one nuclear reactor and can provide electricity to roughly 750,000 houses.
And this week, Meta was reported to be seeking to raise $29 billion from private capital firms to build AI data centers in the U.S., while already building a $10 billion AI data center in Northeast Louisiana. As part of that deal, the local utility, Entergy, will supply three new power plants.
Meta CEO Mark Zuckerberg has made his intentions clear: The U.S. must rapidly expand AI data center construction or risk falling behind China in the race for AI dominance. Speaking on the Dwarkesh Podcast in May, he warned that America’s edge in artificial intelligence could erode unless it keeps pace with China’s aggressive build-out of data center capacity and factory-scale hardware.
“The U.S. really needs to focus on streamlining the ability to build data centers and produce energy,” Zuckerberg said. “Otherwise, we’ll be at a significant disadvantage.”
The U.S. government seems to be aligned with that sense of urgency. David Sacks, now serving as the White House AI and Crypto Czar, has also underscored that energy and data center expansion are central to America’s AI strategy—leaving little room for sustainability concerns.
On his All In podcast in February, Sacks argued that Washington’s “go-slow” approach to AI could strangle the industry. He emphasized that the U.S. needs to clear the way for infrastructure and energy development—including AI data centers—to keep pace with China.
In late May, he went further, saying that streamlining permitting and expanding power generation are essential for AI’s future—something he claimed has been “effectively impossible under the Biden administration.” His message: the U.S. needs to race to build faster.
Accenture, meanwhile, is urging its clients to responsibly grow and engineer its AI data centers in a bid to balance growth with environmental responsibility. It is offering a new metric, that it calls the Sustainable AI Quotient (SAIQ), to measure the true costs of AI in terms of money invested, megawatt-hours of energy consumed, tons of CO₂ emitted and cubic meters of water used. The firm’s report says the metric will help organizations answer a basic question: “What are we actually getting from the resources we’re investing in AI?” and allow that enterprise to measure its performance across time.
I spoke to Matthew Robinson, managing director of Accenture Research and co-author of the report, who emphasized that he hoped Accenture’s sobering predictions would be proven wrong. “They kind of take your breath away,” he said, explaining that Accenture modeled future energy consumption from the expected number of installed AI chips adjusted for utilization and the additional energy requirements of data centers. That data was combined with regional data on electricity generation, energy mix and emissions, while water use was assessed based on AI data center energy consumption and how much water is consumed per unit of electricity generated.
“The point really is to open the conversation around the actions that are available to avert this pathway—we don’t want to be right here,” he said. He would not comment on the actions of specific companies like OpenAI or Meta, but said that overall, clearly more effort is needed to avert the rise in carbonisation fueled by AI data centers while still allowing for growth.
Accenture’s recommendations certainly make sense: Optimize the power efficiency of AI workloads and data centers with everything from low-carbon energy options to cooling innovations. Use AI thoughtfully, by choosing smaller AI models, and better pricing models for incentivizing efficiency. And ensure better governance over AI sustainability initiatives.
It’s hard to imagine that the biggest players in the race for AI dominance—Big Tech giants and heavily funded startups—will hit the brakes long enough to seriously address these growing concerns. Not that it’s impossible. Take Google, for example: In its latest sustainability report released this week, the company revealed that its data centers are consuming more power than ever. In 2024, Google used approximately 32.1 million megawatt-hours (MWh) of electricity, with a staggering 95.8%—about 30.8 million MWh—consumed by its data centers. That’s more than double the energy its data centers used in 2020, just before the consumer AI boom.
Still, Google emphasized that it’s making meaningful strides toward cleaning up its energy supply, even as demand surges. The company said it cut its data center energy emissions by 12% in 2024, thanks to clean energy projects and efficiency upgrades. And it’s squeezing more out of every watt. Google reported that the amount of compute per unit of electricity has increased about six-fold over the past five years. Its power usage effectiveness (PUE)—a key measure of data center efficiency—is now approaching the theoretical minimum of 1.0, with a reported PUE of 1.09 in 2024.
“Just speaking personally, I’d be optimistic,” said Robinson.
Note: Check out this new Fortune video about my tour of IBM’s quantum computing test lab. I had a fabulous time hanging out at IBM’s Yorktown Heights campus (a midcentury modern marvel designed by the same guy as the St. Louis Arch and the classic TWA Flight Center at JFK Airport) in New York. The video was part of my coverage for this year’s Fortune 500 issue that included an article that dug deep into IBM’s recent rebound.
As I said in my piece, “walking through the IBM research center is like stepping into two worlds at once. There are the steel and glass curves of Saarinen’s design, punctuated by massive walls made of stones collected from the surrounding fields, with original Eames chairs dotting discussion nooks. But this 20th-century modernism contrasts starkly with the sleek, massive, refrigerator-like quantum computer—among the most advanced in the world—that anchors the collaboration area and working lab, where it whooshes with the steady hum of its cooling system.”
With that, here’s the rest of the AI news.
Sharon Goldman
sharon.goldman@fortune.com
@sharongoldman
This story was originally featured on Fortune.com