The accelerating global diffusion of generative artificial intelligence has triggered an unprecedented surge in hyperscale data center electricity demand, with leading forecasts projecting United States data center load to reach 6% to 12% of national consumption by 2030. Simultaneously, hyperscale operators have committed to 24/7 carbon-free energy and net-zero targets that cannot be met reliably through variable renewable resources alone, exposing a structural gap between rapidly growing firm clean-power demand and the availability of dispatchable low-carbon generation. Small modular reactors (SMRs) have emerged as a candidate technology to close this gap, yet their economic viability under deep parametric uncertainty remains contested. The purpose of this quantitative, non-experimental technoeconomic study was to evaluate the levelized cost, delivered cost, and comparative economic competitiveness of SMRs for hyperscale applications using a site-anchored modeling framework. A discounted-cash-flow levelized-cost-of-electricity (DCF-LCOE) model was coupled with a 10, 000-iteration Latin Hypercube Sampling Monte Carlo simulation employing Iman–Conover rank correlation, partial rank correlation coefficient (PRCC) sensitivity analysis, first-of-a-kind and nth-of-a-kind (FOAK/NOAK) scenario contrasts, and benchmarking against 25 representative hyperscale sites. Results indicated a deterministic NOAK LCOE of 69. 58/MWh and an 86% probability of NOAK LCOE below 100/MWh, with 13 of 25 candidate sites achieving grid parity under central assumptions. Overnight capital cost and weighted-average cost of capital emerged as dominant PRCC drivers. Findings suggest that NOAK SMRs can be cost-competitive for hyperscale firm clean-power procurement when financing, learning, and licensing risks are jointly managed, offering actionable guidance for operators, vendors, and policymakers navigating the AI-era energy transition. Keywords: small modular reactors; hyperscale data centers; technoeconomic analysis; levelized cost of electricity; Monte Carlo simulation; artificial intelligence energy demand; nuclear economics
Laszlo Pokorny (Wed,) studied this question.