The Cost of Powering AI: Distributional Impacts and an Operational Standard for Self-Powered Deployments

Artificial intelligence (AI) is scaling amid grid congestion, volatile peak pricing, and uneven water availability. New data-center load can shift public costs—scarcity-hour prices, capacity upgrades, marginal emissions, and water withdrawals—onto disadvantaged communities with elevated energy burdens. This paper treats AI as an electrical load and proposes an operational, auditable Self-Powered AI Standard. Deployments must (i) supply attributable additional clean energy (AACS), (ii) match consumption hour-by-hour within the balancing area (HCC/EHCC with Clean Matching Shortfall), and (iii) maintain firm self-supply availability (FSSA) while demonstrating near-zero Scarcity-Adjusted Import Exposure (SAIE) in top-decile price/CO₂ hours. Using public indicators—wholesale prices, interconnection queues, marginal emissions, congestion, and water context—we construct a screening of “where an extra MW hurts,” outline siting/operation patterns (islanded training parks; grid-tied inference with islanding), portfolio 24/7 matching, and a Levelized Cost of AI Power (LCAP) for self-supplied stacks. A telemetry case study demonstrates end-to-end computation of AACS, HCC/EHCC/CMS, FSSA, and SAIE. The framework enables growth in compute without leaning on the public during scarcity and aligns reporting with standard telemetry and open datasets.