Despite tech companies making promises about renewable energy, much of this massive AI expansion is still being powered by fossil fuels. It will be at least 2028 before renewables power AI at any real scale, and new nuclear plants are a decade or more away. These fossil-fuel-powered facilities generate fine particulate matter, widely understood to be detrimental to human health. One study estimated the healthcare-related costs for people living near a similar Virginia power plant could be up to $100 million a year.
Even as AI companies have spoken loudly in public about the sustainability of their computing infrastructure, executives at Microsoft have admitted internally that the intermittent availability of renewable energy is not sufficient when data centers need to operate 24/7. Nonstop operation is considered so crucial that Google, Amazon, and most recently, Microsoft now build their campuses in threes to have a backup for the backup in case any facility goes down.
During Hurricane Irma in Florida and Hurricane Harvey in Texas, even as millions of people lost power, some hospitals evacuated patients, and hundreds of thousands of homes and businesses faced damage and destruction. But the data centers in those areas continued to hum along so well that the displaced families of one facility’s employees moved into it for the duration of the natural disaster.
Researchers have shown that data centers create their own heat islands, raising temperatures nearby by as much as 2 degrees Celsius through their heavy use of industrial equipment and energy consumption. AI chips and models are becoming more efficient when it comes to power and heat, but those efficiency gains are being exceeded by overall growth in AI usage, in part as a result of that efficiency gain. This is a well-known phenomenon in technology innovation called the Jevons Paradox which was first described in the 1860s.
The English were concerned about running out of coal, their main source of power. Some argued that as coal-burning plants became more efficient, less coal would be used. But economist William Stanley Jevons showed that increasing efficiency wouldn't save coal, because cheaper, more efficient power would lead to an overall rise in demand for coal. Data centers like Stargate may be getting more efficient, but if the Jevons Paradox holds up, those gains will be erased by the increased demand for AI.
In addition to the land and energy required to support these megacampuses, they also require huge volumes of minerals including copper and lithium needed to build the hardware — computers, cables, power lines, batteries, backup generators. The tens of thousands of high-powered chips inside a data center like this generate enormous amounts of heat, requiring massive cooling systems to prevent failures. Stargate uses a closed-loop cooling system that circulates water to pull heat away from the machines. While these systems usually only need to be filled once, they do require more electricity to run.
The water must be clean enough to avoid clogging pipes and bacterial growth; potable water meets that standard. According to an estimate from researchers at the University of California, surging AI demand will consume 1.1 trillion to 1.7 trillion gallons of fresh water globally a year by 2027, or half the water annually consumed in the UK. Those effects will not be felt evenly. In Global South countries like Chile, it’s often the most vulnerable communities who have borne the brunt of these accelerating economies of extraction.
Elon Musk plans to have SpaceX put data centers into orbit around the world. They would use power from the sun. He has talked about how space has the advantage that it's always sunny. But do we know that a data center in space could actually work and if they are cost-effective? The microchips that AI developers use can get very hot. It's not clear how you would cool those components in a data center in space because space is in a vacuum.
Data centers need a lot of maintenance. How do you get people (or, adequate robots, if not humans) to be where they need to be to continue that ongoing maintenance work that has to happen? And the actual beaming of data across facilities in space is the other issue. It can be very slow, which might not make for the best user experience.
These data centers don't last forever - the chips degrade, and you can burn them out. But it's worse than that. One of the reasons that NVIDIA is worth so much is that every year they are coming out with chips that are substantially superior to the previous chips. The way that they're achieving these improvements is by jettisoning one of the main principles of sustainable product design, which is backwards compatibility with the infrastructure. They're eking out performance gains by going to the absolute limit of the envelope that the chip lives within, even if that breaks backwards compatibility.
Breaking free of path dependency and starting afresh sounds great. But this means that these hard assets are not durable - you're replacing data centers. There have been successive generations of chips where you couldn't go back to the data center and put new chips in it. By the time you finished building the datacenter, you might as well demolish it to the foundation slab because they are so specialized.
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