The standard for measuring large compute deals has shifted from number of GPUs to gigawatts of power. This provides a normalized, apples-to-apples comparison across different chip generations and manufacturers, acknowledging that energy is the primary bottleneck for building AI data centers.

The race for AI supremacy is not just about models but about securing the underlying infrastructure. The most significant bottlenecks and price appreciation over the next 3-5 years will be in physical assets: land for data centers, permits to build, energy to power them, and the compute itself.

The key constraint in building AI infrastructure has shifted. Previously, the challenge was securing floor space, networking, and then GPUs. Now, the first and most critical step is securing a power contract, as energy accounts for 50% of the cost of serving a token. The goal has become monetizing every available watt.

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.

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.

While chip fabrication is complex, the most binding constraint for AI compute providers is physical infrastructure. The entire industry's growth is bottlenecked by the availability of powered data center buildings, a problem projected to persist for at least another 15-18 months.

The AI buildout faces a multi-gigawatt power shortfall. Consequently, strategic planning has shifted: access to power grids, which can take years to secure, is now the primary factor determining where and how quickly data centers can be built, superseding other logistical or financial considerations.

The AI compute constraint is not just a chip shortage but a systemic bottleneck involving land, permits, electricity, and construction. This environment massively favors incumbent tech giants with huge non-AI cash flows, as they are the only ones who can fund the hundreds of billions in capital expenditures needed to build out supply.