Inside the AI Data Center Building Boom: Why the World Is Running Out of Power
In Abilene, Texas, a data center campus that did not exist two years ago now draws enough electricity to power a mid-sized city. In Virginia, the world highest concentration of data centers is straining a power grid that was not designed for this load. In Singapore, the government imposed a moratorium on new data center construction for three years because the city-state was running out of power capacity. In Ireland, data centers now consume more electricity than all of the country urban homes combined.
The AI boom has a physical dimension that the coverage of language models and benchmark scores tends to obscure. Behind every query to ChatGPT, every image generated by Midjourney, every hour of AI-assisted coding, there is a physical building full of specialized hardware consuming significant amounts of electricity and generating significant amounts of heat. The scale of those buildings, and the rate at which they are being constructed, has made AI infrastructure one of the largest drivers of energy demand in history. And the demand is accelerating faster than the power grid can accommodate it.
The International Energy Agency estimated in 2024 that global data center electricity consumption would double by 2026, with AI workloads accounting for the majority of that growth. Goldman Sachs research estimated that data centers would consume 8% of US electricity by 2030, up from approximately 3% in 2022. Microsoft, Google, Amazon, and Meta each committed to spending fifty billion dollars or more on data center infrastructure in 2024 alone. The capital expenditure figures for AI infrastructure have reached levels that would have seemed implausible three years ago.
Training a single large frontier AI model: the kind of training run that produces a GPT-4 or a Claude Opus: consumes roughly the same energy as five hundred US homes use in a year. A single training run for a large model requires ten thousand to fifty thousand specialized AI chips running continuously for weeks or months. The electricity cost of a single training run can reach tens of millions of dollars at commercial electricity rates. And the training runs are getting larger, not smaller, as researchers continue to find that scaling produces capability improvements.
Inference: running queries against deployed models rather than training new ones: is lower intensity per query but adds up to enormous totals at the scale of hundreds of millions of daily users. Every ChatGPT conversation, every Copilot suggestion, every Claude query consumes electricity. The aggregate inference load across the major AI platforms is already comparable to significant industrial facilities.
The electricity powering AI infrastructure comes from wherever data centers can get it, which means the carbon intensity varies enormously by location. Data centers in the Pacific Northwest take advantage of abundant hydroelectric power and have relatively low carbon footprints. Data centers in coal-heavy regions of the Midwest or Southeast have much higher carbon intensity for the same compute load. The geography of data center construction is partly driven by electricity availability and cost, but not always by carbon intensity.
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