Image New York Metropolis on a sweltering summer season night time: each air conditioner straining, subway vehicles buzzing underground, towers blazing with gentle. Now add San Diego on the peak of a record-breaking warmth wave, when demand shot previous 5,000 megawatts and the grid almost buckled.
That’s virtually the size of electrical energy that Sam Altman and his companions say might be devoured by their subsequent wave of AI knowledge facilities—a single company undertaking consuming extra energy, each single day, than two American cities pushed to their breaking level.
The announcement is a “seminal moment” that Andrew Chien, a professor of laptop science on the College of Chicago, says he has been ready a very long time to see coming to fruition.
“I’ve been a computer scientist for 40 years, and for most of that time computing was the tiniest piece of our economy’s power use,” Chien informed Fortune. “Now, it’s becoming a large share of what the whole economy consumes.”
He referred to as the shift each thrilling and alarming.
“It’s scary because … now [computing] could be 10% or 12% of the world’s power by 2030. We’re coming to some seminal moments for how we think about AI and its impact on society.”
This week, OpenAI introduced a plan with Nvidia to construct AI knowledge facilities consuming as much as 10 gigawatts of energy, with further tasks totaling 17 gigawatts already in movement. That’s roughly equal to powering New York Metropolis—which makes use of 10 gigawatts in the summertime—and San Diego in the course of the intense warmth wave of 2024, when greater than 5 gigawatts have been used. Or, as one professional put it, it’s near the full electrical energy demand of Switzerland and Portugal mixed.
“It’s pretty amazing,” Chien stated. “A year and a half ago they were talking about five gigawatts. Now they’ve upped the ante to 10, 15, even 17. There’s an ongoing escalation.”
Fengqi You, an energy-systems engineering professor at Cornell College, who additionally research AI, agreed.
“Ten gigawatts is more than the peak power demand in Switzerland or Portugal,” he informed Fortune. “Seventeen gigawatts is like powering both countries together.”
The Texas grid, the place Altman broke floor on one of many tasks this week, usually runs round 80 gigawatts.
“So you’re talking about an amount of power that’s comparable to 20% of the whole Texas grid,” Chien stated. “That’s for all the other industries—refineries, factories, households. It’s a crazy large amount of power.”
Altman has framed the build-out as essential to sustain with AI’s runaway demand.
“This is what it takes to deliver AI,” he stated in Texas. Utilization of ChatGPT, he famous, has jumped 10-fold prior to now 18 months.
Which vitality supply does AI want?
Altman has made no secret of his favourite supply: nuclear. He has backed each fission and fusion startups, betting that solely reactors can present the sort of regular, concentrated output wanted to maintain AI’s insatiable demand fed.
“Compute infrastructure will be the basis for the economy of the future,” he stated, framing nuclear because the spine of that future.
Chien, nonetheless, is blunt concerning the near-term limits.
“As far as I know, the amount of nuclear power that could be brought on the grid before 2030 is less than a gigawatt,” he stated. “So when you hear 17 gigawatts, the numbers just don’t match up.”
With tasks like OpenAI’s demanding 10 to 17 gigawatts, nuclear is “a ways off, and a slow ramp, even when you get there,” Chien stated. As an alternative, he expects wind, photo voltaic, pure fuel, and new storage applied sciences to dominate.
You, the energy-systems professional at Cornell, struck a center floor. He stated nuclear could also be unavoidable in the long term if AI retains increasing, however cautioned that “in the short term, there’s just not that much spare capacity”—whether or not fossil, renewable, or nuclear. “How can we expand this capacity in the short term? That’s not clear,” he stated.
He additionally warned that timeline could also be unrealistic.
“A typical nuclear plant takes years to permit and build,” he stated. “In the short term, they’ll have to rely on renewables, natural gas, and maybe retrofitting older plants. Nuclear won’t arrive fast enough.”
Environmental prices
The environmental prices loom massive for these consultants, too.
“We have to face the reality that companies promised they’d be clean and net zero, and in the face of AI growth, they probably can’t be,” Chien stated.
Ecosystems might come below stress, Cornell’s You stated.
“If data centers consume all the local water or disrupt biodiversity, that creates unintended consequences,” he stated.
The funding figures are staggering. Every OpenAI web site is valued at roughly $50 billion, including as much as $850 billion in deliberate spending. Nvidia alone has pledged as much as $100 billion to again the growth, offering tens of millions of its new Vera Rubin GPUs.
Chien added that we’d like a broader societal dialog concerning the looming environmental prices of utilizing that a lot electrical energy for AI. Past carbon emissions, he pointed to hidden strains on water provides, biodiversity, and native communities close to huge knowledge facilities. Cooling alone, he famous, can eat huge quantities of recent water in areas already going through shortage. And since the {hardware} churns so rapidly—with new Nvidia processors rolling out yearly—outdated chips are continuously discarded, creating waste streams laced with poisonous chemical compounds.
“They told us these data centers were going to be clean and green,” Chien stated. “But in the face of AI growth, I don’t think they can be. Now is the time to hold their feet to the fire.”
