USA – WASHINGTON Trends and Developments Contributed by: John Pierce and Patrick Njeim, Kilpatrick Townsend & Stockton
tial for moving electricity from concentrated generation assets such as nuclear, gas or coal- fired plants as well as remote renewable energy sources (such as wind farms in the Midwest or solar plants in the Southwest) to the urban centres where AI data centres are often located. However, developing new transmission lines can take 15 to 30 years (depending on the jurisdic- tion), due to complex permitting, environmental reviews, land-use disputes, and state and fed- eral regulatory hurdles across state lines. These delays are particularly problematic when tech- nology infrastructure expands on much shorter timelines, months or a few years, creating mis- alignments between energy supply infrastructure and digital demand growth. Regionally, the vulnerabilities are even more stark. For instance, in California – despite it being a leader in tech and clean energy – grid conges- tion and substantial permitting delays prevent the swift deployment of new transmission lines and substations. High electricity prices and fre- quent strain alerts from the California Independ- ent System Operator (CAISO) are becoming common, particularly during heatwaves or peak AI training cycles. The electrification of other sectors, such as transportation and heating, adds further stress to the grid, leading to compounding demand pressures just as utilities are being asked to decarbonise and modernise simultaneously. The grid’s growing complexity also introduces reliability risks, including potential blackouts, frequency instability and reduced resilience to extreme weather events – issues that are magni- fied when large data centres operate with tight uptime requirements. Thus, AI is not merely consuming more electrici- ty but is doing so in a way that exposes systemic
weaknesses in the US energy grid. To meet this scenario, the United States (and other countries and regions) must accelerate investment in grid modernisation, inter-regional transmission and flexible energy resources, while rethinking regu- latory frameworks to enable faster alignment between technological growth and infrastructure development. The Great Migration of AI Data Infrastructure: From Coastal Hubs to Interior States As the energy-intensive demands of AI infra- structure continue to surge, major technology firms are increasingly shifting their focus away from traditional data centre hubs on the West Coast. West Coast states, despite their tech- nological dominance, are grappling with deep- rooted infrastructure, regulatory and environ- mental constraints. In response, states such as Nevada, Texas and parts of the Southeast and Midwest are emerging as new epicentres for AI-driven data infrastructure. This migration reflects a broader realignment of digital infra- structure priorities, balancing land availability, energy accessibility, regulatory simplicity and economic incentives. Why AI Firms Are Moving Away From the West Coast Permitting bottlenecks and regulatory overhead In states such as California, the process for sit- ing, permitting and constructing new data cen- tres or transmission infrastructure is notoriously slow. Environmental regulations (eg, California’s CEQA), community opposition, multi-agency review processes, and litigation can delay pro- jects by five to ten years or more. In the fast- paced world of AI, where training models and scaling computer resources often move on a quarterly or annual basis, such delays are unten- able.
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