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.

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.

The primary bottleneck for scaling AI over the next decade may be the difficulty of bringing gigawatt-scale power online to support data centers. Smart money is already focused on this challenge, which is more complex than silicon supply.

The insatiable demand for power from new data centers is so great that it's revitalizing America's dormant energy infrastructure. This has led to supply chain booms for turbines, creative solutions like using diesel truck engines for power, and even a doubling of wages for mobile electricians.

Facing immense electricity needs for AI, tech giants like Amazon are now directly investing in nuclear power, particularly small modular reactors (SMRs). This infusion of venture capital is revitalizing a sector that has historically relied on slow-moving government funding, imbuing it with a Silicon Valley spirit.

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.

The massive energy requirements for AI data centers are causing electricity prices to rise, creating public resentment. To counter this, governments are increasingly investing in nuclear power as a clean, stable energy source, viewing it as critical infrastructure to win the global AI race without alienating consumers.

To secure the immense, stable power required for AI, tech companies are pursuing plans to co-locate hyperscale data centers with dedicated Small Modular Reactors (SMRs). These "nuclear computation hubs" create a private, reliable baseload power source, making the data center independent of the increasingly strained public electrical grid.

Most of the world's energy capacity build-out over the next decade was planned using old models, completely omitting the exponential power demands of AI. This creates a looming, unpriced-in bottleneck for AI infrastructure development that will require significant new investment and planning.