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.

The progression from early neural networks to today's massive models is fundamentally driven by the exponential increase in available computational power, from the initial move to GPUs to today's million-fold increases in training capacity on a single model.

The massive energy consumption of AI has made tech giants the most powerful force advocating for new power sources. Their commercial pressure is finally overcoming decades of regulatory inertia around nuclear energy, driving rapid development and deployment of new reactor technologies to meet their insatiable demand.

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.

Unlike the speculative "dark fiber" buildout of the dot-com bubble, today's AI infrastructure race is driven by real, immediate, and overwhelming demand. The problem isn't a lack of utilization for built capacity; it's a constant struggle to build supply fast enough to meet customer needs.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

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.