U.S. Utilities Move Early on Quantum Computing to Avoid Repeating the AI Power Shock
With AI already straining U.S. grid interconnection queues, utilities are building quantum computing expertise now before the technology creates a second load surprise.
Lawrence Berkeley National Laboratory analysis published on Monday (2026-08-17) put U.S. data center electricity consumption on a path to between 325 and 580 TWh by 2028, up from 176 TWh in 2023. That 330-TWh planning range shows how poorly the power sector has managed to pin down AI's actual demand trajectory, and utilities are now treating it as a warning about quantum computing.5
According to Utility Dive reporting on Friday (2026-08-14), the sector's primary motivation for engaging with quantum now is to avoid an identical scramble. Utilities were caught off guard as AI data centers multiplied, straining planning and interconnection timelines. The goal is to understand quantum's load profile, grid impact, and integration requirements before the technology reaches commercial scale, not after grid capacity is already committed elsewhere.4
McKinsey has told business leaders that quantum is not something utilities can put off. The advisory firm stopped short of a deployment timeline but characterized early engagement as necessary, according to the same Utility Dive report. The message targets executives who might otherwise wait for quantum to prove itself commercially before committing planning resources. Getting that timing wrong carries the same kind of catch-up cost the sector is now paying for with AI.4
The applications utilities are actively studying include grid management optimization, materials discovery for improved battery storage, and load forecasting. Power Magazine's interview on Wednesday (2026-05-20) with a quantum computing specialist described potential use cases across those areas while noting that current hardware performs best on narrow, specific problem types. The gap between those potential capabilities and live commercial deployment remains large.1
The AI buildout provides the reference scale for what quantum might eventually require. Goldman Sachs Research has forecast global data center power demand growing by as much as 165% by 2030 against 2023 levels. McKinsey separately put AI data center capital expenditure at roughly $5.2 trillion over the same period, according to OilPrice.com analysis from Wednesday (2026-07-08). Quantum carries no equivalent demand forecast yet, which is the planning gap utilities want to close before the load materializes.2
Transmission infrastructure compounds the problem. Berkeley Lab data found that more than 70% of U.S. interconnection requests are withdrawn because the grid cannot absorb additional load. Any future wave of quantum data centers would enter that same constrained queue, competing with AI projects already in the pipeline. Grid operators have no additional headroom to grant without substantial capital investment in transmission, and that investment moves on multi-year timelines.2
Private capital has already made directional bets on where power-hungry computing gravitates. Amazon paid $650 million for a data center campus co-located directly with the Susquehanna nuclear station in Pennsylvania, reflecting how much value technology companies now place on direct, reliable power access outside the standard interconnection process, according to OilPrice.com analysis from Wednesday (2026-07-08).2
The June 2025 cybersecurity executive order, a June 2026 AI directive, and a dedicated quantum order issued on June 22, 2026, marked three consecutive federal computing priorities in roughly twelve months, according to War on the Rocks published on Monday (2026-07-20). Washington's sequential focus on cybersecurity, then AI, then quantum indicates the federal government regards each as the next layer of the same national computing infrastructure challenge.3
Utility Dive noted on Friday (2026-08-14) that quantum carries what it described as a "curious load profile," distinct from AI servers but unquantified at commercial scale. Whether that profile arrives smoothly or in sharp spikes will shape grid planning in ways that are currently impossible to model. No utility has the data to answer it yet, and that information gap is exactly what the sector failed to close in time with AI.4