AI share of data-center power capacity

Methodology

Source-backed values are seeded for all seven charts: global data-center electricity consumption (IEA Energy & AI, historic vs base-case scenario, corroborated by S&P Global), the AI vs non-AI share of data-center power capacity (McKinsey base case, corroborated by JLL), the PJM capacity-market clearing price by delivery year (PJM Base Residual Auction reports, corroborated by Utility Dive), the US data-center demand-vs-supply gap to 2028 (Morgan Stanley; Deloitte), per-site power draw vs a city, US data-center power demand (2025 vs 2028), and the projected electricity-price impact by region/scenario. Every numeric point carries a sources[].ref and a value_basis. The 2028 available-capacity figure is derived from Morgan Stanley’s ~45 GW shortfall estimate against ~100 GW demand (labeled in the value_basis). Demand figures trace to CRS data-center energy analysis; the Virginia (+57% by 2030) and national wholesale (+6% to +29% by end of decade) price impacts trace to Fortune / public-opinion reporting on data-center power costs. ESTIMATE: the AI-share chart uses McKinsey’s published AI-vs-total capacity trajectory (AI ~44 GW in 2025 → ~156 GW of ~219 GW total by 2030); the 2028 point is interpolated along that trajectory and the non-AI slice is a deduction (total − AI). It is a published-estimate split, not a measured per-year megawatt count. CAVEAT: price-impact figures are forward scenarios, not realized prices, and depend on how fast new generation is added. Ranges are shown as separate low/high bars rather than a single point. Re-verified 2026-07-17.