AI surge to drive US data centers to use one-fifth of power by 2035

2026-07-22 · Source: Dataconomy · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Fundamental Awareness, quick

Summary

US data centers are projected to consume one-fifth of the nation's electricity by 2035, a fourfold increase from current levels, driven by surging AI compute demand. A BloombergNEF report indicates data center capacity will reach nearly 200 gigawatts over the next decade, with approximately 50% dedicated to AI training and inference. The U.S. is expected to host 64% of AI chips by power demand by 2033. This forecast represents an 83% increase over BloombergNEF's December prediction, with other organizations like EPRI and S&P also revising estimates upward. This rapid expansion is straining electrical grids; PJM Interconnection, serving Virginia to Illinois, will allocate 34% of its electricity to data centers, while ERCOT in Texas will allocate 22%. Grid congestion has led to a 76% rise in electricity prices and challenges for new connections.

Key takeaway

For CTOs and VPs of Engineering planning new data center deployments or scaling AI infrastructure, recognize that escalating electricity demand will significantly impact your operational costs and deployment timelines. You should factor in the 76% rise in electricity prices and potential grid connection delays, especially in regions like PJM and ERCOT. Proactively engage with utility providers and explore energy-efficient AI hardware to mitigate future cost and capacity risks.

Key insights

AI compute demand is rapidly escalating US data center electricity consumption, straining national grids and driving up power costs.

Principles

• AI demand drives exponential data center growth.
• New data centers intensify grid strain.
• Grid imbalance elevates electricity prices.

In practice

• Prioritize early grid connection planning.
• Account for rising electricity capacity costs.

Topics

• AI Compute
• Data Centers
• Electricity Demand
• Electrical Grids
• PJM Interconnection
• Energy Costs

Best for: Investor, Director of AI/ML, Executive, VP of Engineering/Data, CTO, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Dataconomy.