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.

Pat Gelsinger contends that the true constraint on AI's expansion is energy availability. He frames the issue starkly: every gigawatt of power required by a new data center is equivalent to building a new nuclear reactor, a massive physical infrastructure challenge that will limit growth more than chips or capital.

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.

While the world focused on GPU shortages, the real constraint on AI compute is now physical infrastructure. The bottleneck has moved to accessing power, building data centers, and finding specialized labor like electricians and acquiring basic materials like structural steel. Merely acquiring chips is no longer enough to scale.

Unlike the dot-com era's speculative approach, the current AI infrastructure build-out is constrained by real-world limitations like power and space. This scarcity, coupled with demand from established tech giants like Microsoft and Google, makes it a sustained megatrend rather than a fragile bubble.

While chip production typically scales to meet demand, the energy required to power massive AI data centers is a more fundamental constraint. This bottleneck is creating a strategic push towards nuclear power, with tech giants building data centers near nuclear plants.

Even if NVIDIA and TSMC solve wafer shortages, the AI industry faces a looming energy (watt) bottleneck. The inability to power new data centers could cap AI growth, shifting the primary constraint from semiconductor manufacturing to energy infrastructure and supply.

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.