While AI chips represent the bulk of a data center's cost ($20-25M/MW), the remaining $10 million per megawatt for essentials like powered land, construction, and capital goods is where real bottlenecks lie. This 'picks and shovels' segment faces significant supply shortages and is considered a less speculative investment area with no bubble.

While custom silicon is important, Amazon's core competitive edge is its flawless execution in building and powering data centers at massive scale. Competitors face delays, making Amazon's reliability and available power a critical asset for power-constrained AI companies.

The two largest physical costs for AI data centers—power and cooling—are essentially free and unlimited in space. A satellite can receive constant, intense solar power without needing batteries and use the near-absolute zero of space for cost-free cooling. This fundamentally changes the economic and physical limits of large-scale computation.

Despite a massive contract with OpenAI, Oracle is pushing back data center completion dates due to labor and material shortages. This shows that the AI infrastructure boom is constrained by physical-world limitations, making hyper-aggressive timelines from tech giants challenging to execute in practice.

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."

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.

Satya Nadella clarifies that the primary constraint on scaling AI compute is not the availability of GPUs, but the lack of power and physical data center infrastructure ("warm shelves") to install them. This highlights a critical, often overlooked dependency in the AI race: energy and real estate development speed.

The primary constraint for scaling high-frequency trading operations has shifted from minimizing latency (e.g., shorter wires) to securing electricity. Even for a firm like Hudson River Trading, which is smaller than tech giants, negotiating for power grid access is the main bottleneck for building new GPU data centers.

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.