While AI chips represent the bulk of a data center's cost ($20-25M/MW), the remaining $10 million per megawatt for essentials like powered land, construction, and capital goods is where real bottlenecks lie. This 'picks and shovels' segment faces significant supply shortages and is considered a less speculative investment area with no bubble.

While currently straining power grids, AI data centers have the potential to become key stabilizing partners. By coordinating their massive power draw—for example, giving notice before ending a training run—they can help manage grid load and uncertainty, ultimately reducing overall system costs and improving stability in a decentralized energy network.

The long queues for connecting projects to the power grid are misleadingly large. They are often inflated by multiple speculative applications for the same project. The real, viable projects are backed by investment-grade tenants, while many others are merely "PowerPoints" that will never actually be built.

The most critical component of a data center site is its connection to the power grid. A specialized real estate strategy is emerging where developers focus solely on acquiring land and navigating the multi-year process of securing a power interconnection, then leasing this valuable "powered land" to operators.

Despite staggering announcements for new AI data centers, a primary limiting factor will be the availability of electrical power. The current growth curve of the power infrastructure cannot support all the announced plans, creating a physical bottleneck that will likely lead to project failures and investment "carnage."

Unlike typical diversified economic growth, the current electricity demand surge is overwhelmingly driven by data centers. This concentration creates a significant risk for utilities: if the AI boom falters after massive grid investments are made, that infrastructure could become stranded, posing a huge financial problem.

For years, the tech industry criticized Bitcoin's energy use. Now, the massive energy needs of AI training have forced Silicon Valley to prioritize energy abundance over purely "green" initiatives. Companies like Meta are building huge natural gas-powered data centers, a major ideological shift.

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.

Today's complex data center financing structures (ABS/CMBS) are not new inventions. They directly apply the same securitization technology and principles previously used for financing cell towers and residential solar projects, adapting them for data center leases and long-term cash flows.