Contrary to popular belief, recent electricity price hikes are not yet driven by AI demand. Instead, they reflect a system that had already become less reliable due to the retirement of dispatchable coal power and increased dependence on intermittent renewables. The grid was already tight before the current demand wave hit.

The primary bottleneck for scaling AI over the next decade may be the difficulty of bringing gigawatt-scale power online to support data centers. Smart money is already focused on this challenge, which is more complex than silicon supply.

AI companies are building their own power plants due to slow utility responses. They overbuild for reliability, and this excess capacity will eventually be sold back to the grid, transforming them into desirable sources of cheap, local energy for communities within five years.

The massive energy consumption of AI data centers is causing electricity demand to spike for the first time in 70 years, a surge comparable to the widespread adoption of air conditioning. This is forcing tech giants to adopt a "Bring Your Own Power" (BYOP) policy, essentially turning them into energy producers.

The U.S. has plenty of power for the AI boom, but it's in the wrong places—far from existing data centers, fiber networks, and population centers. The critical challenge is not generation capacity but rather bridging the geographical gap between where power is abundant and where it is needed.

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."

Contrary to the common focus on chip manufacturing, the immediate bottleneck for building new AI data centers is energy. Factors like power availability, grid interconnects, and high-voltage equipment are the true constraints, forcing companies to explore solutions like on-site power generation.

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.

The primary constraint on the AI boom is not chips or capital, but aging physical infrastructure. In Santa Clara, NVIDIA's hometown, fully constructed data centers are sitting empty for years simply because the local utility cannot supply enough electricity. This highlights how the pace of AI development is ultimately tethered to the physical world's limitations.