While currently straining power grids, AI data centers have the potential to become key stabilizing partners. By coordinating their massive power draw—for example, giving notice before ending a training run—they can help manage grid load and uncertainty, ultimately reducing overall system costs and improving stability in a decentralized energy network.

A recent Harvard study reveals the staggering scale of the AI infrastructure build-out, concluding that if data center investments were removed, current U.S. economic growth would effectively be zero. This highlights that the AI boom is not just a sector-specific trend but a primary driver of macroeconomic activity in the United States.

The International Energy Agency projects global data center electricity use will reach 945 TWH by 2030. This staggering figure is almost twice the current annual consumption of an industrialized nation like Germany, highlighting an unprecedented energy demand from a single tech sector and making energy the primary bottleneck for AI growth.

Despite staggering announcements for new AI data centers, a primary limiting factor will be the availability of electrical power. The current growth curve of the power infrastructure cannot support all the announced plans, creating a physical bottleneck that will likely lead to project failures and investment "carnage."

Before AI delivers long-term deflationary productivity, it requires a massive, inflationary build-out of physical infrastructure. This makes sectors like utilities, pipelines, and energy infrastructure a timely hedge against inflation and a diversifier away from concentrated tech bets.

The U.S. has the same 1.2 terawatts of power capacity it had in 1985. This stagnation now poses a national security risk, as the country must double its capacity to support AI data centers and reshoring manufacturing. The Department of Energy views solving this as a "Manhattan Project 2.0" level imperative.

The massive capital rush into AI infrastructure mirrors past tech cycles where excess capacity was built, leading to unprofitable projects. While large tech firms can absorb losses, the standalone projects and their supplier ecosystems (power, materials) are at risk if anticipated demand doesn't materialize.

While semiconductor access is a critical choke point, the long-term constraint on U.S. AI dominance is energy. Building massive data centers requires vast, stable power, but the U.S. faces supply chain issues for energy hardware and lacks a unified grid. China, in contrast, is strategically building out its energy infrastructure to support its AI ambitions.

Most of the world's energy capacity build-out over the next decade was planned using old models, completely omitting the exponential power demands of AI. This creates a looming, unpriced-in bottleneck for AI infrastructure development that will require significant new investment and planning.