From a first-principles perspective, space is the ideal location for data centers. It offers free, constant solar power (6x more irradiance) and free cooling via radiators facing deep space. This eliminates the two biggest terrestrial constraints and costs, making it a profound long-term shift for AI infrastructure.

Digital computing, the standard for 80 years, is too power-hungry for scalable AI. Unconventional AI's Naveen Rao is betting on analog computing, which uses physics to perform calculations, as a more energy-efficient substrate for the unique demands of intelligent, stochastic workloads.

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 narrative of energy being a hard cap on AI's growth is largely overstated. AI labs treat energy as a solvable cost problem, not an insurmountable barrier. They willingly pay significant premiums for faster, non-traditional power solutions because these extra costs are negligible compared to the massive expense of GPUs.

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).

Instead of relying on hyped benchmarks, the truest measure of the AI industry's progress is the physical build-out of data centers. Tracking permits, power consumption, and satellite imagery reveals the concrete, multi-billion dollar bets being placed, offering a grounded view that challenges both extreme skeptics and believers.

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.

Microsoft's massive $17.5 billion investment is justified by a single projection from its subsidiary, GitHub: India will host the world's largest developer community by 2030. This data point transforms the country from a promising growth market into the indispensable future hub for global software talent.

Many AI startups prioritize growth, leading to unsustainable gross margins (below 15%) due to high compute costs. This is a ticking time bomb. Eventually, these companies must undertake a costly, time-consuming re-architecture to optimize for cost and build a viable business.