The AI boom is usually described with words like chips, models, GPUs, and compute. That framing is not wrong, but it is incomplete. The next phase of AI is becoming an electricity story, and the companies that win may be the ones that understand power markets as well as they understand model training. Gartner expects worldwide data-center electricity consumption to reach 565 terawatt-hours in 2026, up from 447 terawatt-hours in 2025, with data-center power demand climbing to 132 gigawatts. That is not a rounding error. That is a new industrial load class showing up at the grid’s front desk and asking for priority service.
The challenge for utilities is timing. A data center does not behave like a neighborhood. It can require huge blocks of power, move quickly through interconnection queues, and concentrate demand in places where transmission was not designed for a sudden compute rush. That is why the conversation is moving from “Can we build enough power?” to “Can we make this demand more flexible?” Utility Dive reported on research arguing that grid-enhancing technologies and demand response can reduce the consumer-cost pressure from large-load growth, especially when data centers can shift noncritical workloads away from peak hours.
That last point matters because AI demand is not always equally urgent. Some workloads must happen immediately, but others can be delayed, geographically shifted, or scheduled when electricity is cleaner and cheaper. The World Economic Forum has argued that AI itself may become an energy technology if it helps optimize grids, forecast demand, and make flexible loads more useful. That is the Energy Brew translation: AI is not only eating electricity; it may also be asked to help cook dinner.
For energy professionals, the watch item is regulatory design. If power markets treat all data-center load as fixed, utilities will build expensive infrastructure around the worst hours of the year. If markets reward flexibility, the same load can become a grid resource. The question is no longer whether AI needs energy. It is whether AI can learn to behave like it understands the grid it depends on.
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