AI, Data Centers, and Energy: Where the Bottleneck Really Is
Flex Capital – Investment Perspectives | December 2024
Founder's Note: At Flex Capital, we focus on identifying constraints before they show up in prices. Cycles are shaped less by narratives than by bottlenecks, and disciplined capital earns its edge by positioning where demand becomes inelastic and supply cannot respond quickly.
For a long time, data centers felt predictable. Cloud adoption drove steady demand, power was assumed to be available, and capital flowed freely. If you believed in cloud, the rest largely took care of itself. That framework broke the moment AI entered the system—not because of hype, but because of physics.
AI workloads are fundamentally more energy intensive. Traditional enterprise data centers typically operate at 5–10 kW per rack. AI training clusters now routinely exceed 40–80 kW per rack, with leading-edge deployments pushing beyond 100 kW. A single hyperscale AI campus can require 300–500 MW of continuous power—roughly equivalent to the load of a mid-sized city.
The constraint is no longer compute. It is energy, land, and time. In the U.S., power interconnection queues now exceed 2 terawatts of proposed generation capacity—more than double the country's installed base. In core data center markets such as Northern Virginia, Dallas, and Phoenix, vacancy has fallen below 2%, yet new supply is constrained not by capital availability, but by grid access, transmission timelines, and permitting friction. Power has become the gating factor.
This shift is already reshaping energy markets—particularly natural gas. As AI-driven electricity demand accelerates, natural gas has emerged as the marginal and most reliable fuel source capable of scaling on data-center timelines. According to the EIA, over 40% of U.S. electricity generation already comes from natural gas, and incremental load growth through 2030 is expected to be met disproportionately by gas-fired generation due to its dispatchability, lower capital intensity, and permitting advantages relative to nuclear or large-scale renewables. Hyperscalers increasingly rely on gas-backed grids or direct gas-to-power arrangements to ensure uptime, even when renewable power purchase agreements are layered on top. This dynamic is beginning to tighten regional gas markets, particularly near major load centers, and is shifting capital back toward long-life, low-decline natural gas basins where supply reliability matters more than spot pricing. In other words, AI is quietly converting natural gas from a cyclical commodity into a strategic infrastructure input—supporting sustained drilling activity and midstream investment even in a volatile price environment.
This changes asset evaluation fundamentally. Energy costs, historically around 20% of operating expenses for data centers, now represent 40–60% for AI-oriented facilities. Power is no longer just an operating input—it is the asset itself. Markets with available substations, transmission redundancy, firm gas supply, and regulatory alignment are capturing pricing power, while others remain stalled despite overwhelming demand.
At Flex Capital, we view this as an infrastructure repricing rather than a technology cycle. When demand becomes inelastic and supply is slow-moving, value migrates toward the bottleneck. AI models will continue to improve efficiency over time, but infrastructure timelines do not compress. That mismatch—between exponential demand growth and linear supply response—is where durable, risk-adjusted returns are being created.
Happy Investing,
Flex Capital