The physical distance of space-based data centers creates significant latency. This delay renders them impractical for real-time applications like crypto mining, where a block found in space could be orphaned by the time the data reaches Earth. Their best use is for asynchronous, large-scale computations like AI training.

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.

Google's "Project Suncatcher" aims to place AI data centers in orbit for efficient solar power. However, the project's viability isn't just a technical challenge; it fundamentally requires space transport costs to decrease tenfold. This massive economic hurdle, more than technical feasibility, defines it as a long-term "moonshot" initiative.

While solar panels are inexpensive, the total system cost to achieve 100% reliable, 24/7 coverage is massive. These "hidden costs"—enormous battery storage, transmission build-outs, and grid complexity—make the final price of a full solution comparable to nuclear. This is why hyperscalers are actively pursuing nuclear for their data centers.

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 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.

Fusion reactors on Earth require massive, expensive vacuum chambers. Zephyr Fusion's core insight is to build its reactor in space, leveraging the perfect vacuum that already exists for free. This first-principles approach sidesteps a primary engineering and cost hurdle, potentially making fusion a more commercially viable energy source.

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.

While powerful, Google's TPUs were designed solely for its own data centers. This creates significant adoption friction for external customers, as the hardware is non-standard—from wider racks that may not fit through doors to a verticalized liquid cooling supply chain—demanding extensive facility redesigns.