The expansion of artificial intelligence infrastructure no longer follows a simple supply-demand curve; it behaves more like a financial system quietly accumulating liabilities. Data centers, hyperscale clusters, and accelerated compute environments are deploying capacity at a pace that power systems were never designed to match. Energy, once treated as a stable input, now acts as a constrained variable that introduces latency into scaling decisions. Grid operators, utilities, and regulators face mounting pressure as demand forecasts shift from incremental to exponential within a few planning cycles. This disconnect is not immediately visible in quarterly reports or deployment announcements, yet it is structurally embedded in how AI infrastructure is financed and deployed. The result is an emerging form of “energy debt,” where present growth borrows from future grid stability without fully accounting for repayment timelines.