Wedbush analyst Daniel Ives views President Donald Trump’s initiative to shield Americans from higher electricity bills due to data centers as a double-edged sword. His push to curb data-center electricity costs could both ease regulatory pressure and slow expansion, depending on how policy translates into certainty and cost controls. It relieves a political “headache” for the administration while risking a “larger bottleneck” for Big Tech’s rapid data-center expansions.
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Trump signaled this stance on Truth Social (DJT), stating that the administration is working with tech firms to make “major changes” so AI data centers do not drive up electricity costs, with a note that these companies should “pay their own way.”
Big Tech Steps Up in Response
Microsoft (MSFT) quickly responded with its Community-First AI Infrastructure initiative, pledging that its data centers will not raise consumer electricity prices, curb water use, and replenish more water than they consume. Ives expects similar commitments from other major players amid growing federal, state, and local scrutiny of large-scale buildouts.
He noted that the U.S. is at a pivotal moment in the AI revolution, where energy constraints could slow data center growth if policy and cost pressures intensify. Ives noted this marks the federal government’s first explicit position against electricity-cost increases tied to data centers, building on longstanding “pay their own way” demands from state and local governments. Lawmakers must now balance potential tax revenue from these projects against community energy concerns, a tension that could temper expansion pace.
Broader Energy Challenges and AI Race
Ives also highlights an intensifying energy-supply competition with China, which plans heavy investments in power technologies through 2030. In this landscape, Big Tech’s energy strategies will require ongoing negotiations with policymakers, as data centers remain essential to AI dominance.
According to Ives, the AI leadership race will persist through the next decade, but clean energy remains a critical constraint. Ives anticipated AI hyperscalers will pursue partnerships, acquisitions of new energy sources, and scaled contracts with clean-energy providers to fuel their ambitions, while managing costs and availability.
Ives cited Meta’s (META) energy deployment efforts tied to long-term power agreements and nuclear investments, Alphabet’s (GOOGL) push to secure renewable and low-cost energy, and Microsoft’s expanded clean-energy commitments for AI infrastructure. The major takeaway is that energy remains the biggest constraint in the AI arms race, and policy moves around electricity costs will shape how quickly and where data centers can be built.
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