Artificial Intelligence is no longer just a software challenge. It’s a physical one. In this episode, we explore a convergence most haven’t seen coming—until now.

Across the U.S., AI training and inference are triggering a historic surge in electricity demand, rivaling the rise of air conditioning in the 20th century. By 2030, AI data centers could consume over 9% of total U.S. electricity—an increase of 400–500 terawatt-hours. That’s like plugging in an extra California.

But AI doesn’t just use power. It reshapes it.

AI campuses run 24/7, don’t follow human behavior, and concentrate demand in tight geographies. The result? New “load islands,” rising grid congestion, regional imbalances, and a multi-billion-dollar race to rewire the energy system.

Brandon N. Owens—author of The Five Convergences and Artificial Intelligence and U.S. Electricity Demand: Trends and Outlook to 2040—break down what utilities, regulators, investors, and tech companies must understand about Convergence I: AI as Load.

🔌 Highlights from this Episode:

• Where AI demand is hitting hardest: From Northern Virginia’s “Data Center Alley” to crypto-fueled megawatt spikes in Texas.
• Why traditional grid planning is failing: IRPs are outdated, interconnection queues are jammed, and speculative siting is distorting the market.
• What clean energy advocates need to know: AI could undermine decarbonization—or accelerate it—depending on how we act now.
• How the electricity system is being gamed: Developers are squatting on transmission rights, driving up costs and delaying critical infrastructure.
• What leading utilities are doing: Dominion is charging for reserved capacity. ERCOT is scrambling to keep up. The DOE and FERC are playing catch-up.

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