When power (watts) is the primary constraint for data centers, the total cost of compute becomes secondary. The crucial metric is performance-per-watt. This gives a massive pricing advantage to the most efficient chipmakers, as customers will pay anything for hardware that maximizes output from their limited power budget.

The common belief is that AI decisions are driven by compute hardware. However, NetApp's Keith Norbie argues the critical success factor is the underlying data platform. Since most enterprise data already resides on platforms like NetApp, preparing this data structure for training and deployment is more crucial than the choice of server.

Beyond the well-known semiconductor race, the AI competition is shifting to energy. China's massive, cheaper electricity production is a significant, often overlooked strategic advantage. This redefines the AI landscape, suggesting that superiority in atoms (energy) may become as crucial as superiority in bytes (algorithms and chips).

In a power-constrained world, total cost of ownership is dominated by the revenue a data center can generate per watt. A superior NVIDIA system producing multiples more revenue makes the hardware cost irrelevant. A competitor's chip would be rejected even if free due to the high opportunity cost.

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.

Beyond capital, Amazon's deal with OpenAI includes a crucial stipulation: OpenAI must use Amazon's proprietary Trainium AI chips. This forces adoption by a leading AI firm, providing a powerful proof point for Trainium as a viable competitor to Nvidia's market-dominant chips and creating a captive customer for Amazon's hardware.

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 factor for siting new AI hubs has shifted from network routes and cheap land to the availability of stable, large-scale electricity. This creates "strategic electricity advantages" where regions with reliable grids and generation capacity are becoming the new epicenters for AI infrastructure, regardless of their prior tech hub status.

AWS CEO Matt Garman's emphasis on "customer choice," combined with Jeff Bezos's philosophy of being customer-obsessed rather than competitor-obsessed, suggests AWS might offer Google's TPUs in their data centers if customers demand them, despite the direct competition.