“Despite a broader use of AI tools in enterprises and by consumers, that does not mean that AI compute, AI infrastructure in general, will be more evenly spread out,” said Daniel Bizo, research director at Uptime Institute, during the webinar. “The concentration of AI compute infrastructure is only increasing in the coming years.”
For enterprises, the infrastructure investment remains relatively modest, Uptime Institute found. Enterprises will limit investment to inference and only some training, and inference workloads don’t require dramatic capacity increases.
“Our prediction, our observation, was that the concentration of AI compute infrastructure is only increasing in the coming years by a couple of points. By the end of this year, 2026, we are projecting that around 10 gigawatts of new IT load will have been added to the global data center world, specifically to run generative AI workloads and adjacent workloads, but definitely centered on generative AI,” Bizo said. “This means these 10 gigawatts or so load, we are talking about anywhere between 13 to 15 million GPUs and accelerators deployed globally. We are anticipating that a majority of these are and will be deployed in supercomputing style.”
2. Developers will not outrun the power shortage
The most pressing challenge facing the industry, according to Uptime, is that data centers can be built in less than three years, but power generation takes much longer.
“It takes three to six years to deploy a solar or wind farm, around six years for a combined-cycle gas turbine plant, and even optimistically, it probably takes more than 10 years to deploy a conventional nuclear power plant,” said Max Smolaks, research analyst at Uptime Institute.
This mismatch was manageable when data centers were smaller and growth was predictable, the report notes. But with projects now measured in tens and sometimes hundreds of megawatts, finding that much power quickly has become nearly impossible.
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