AI workloads reshape US hyperscaler data center strategies

McKinsey & Company outlines how AI-driven workloads are forcing US hyperscalers to redesign data center strategies, power sourcing, and campus architectures while rapidly scaling capacity.

• AI demand is expected to expand US data center power capacity from ~30+ GW (2025) to 90+ GW (2030, ~22% CAGR), with inference workloads growing at 35% CAGR to >90 GW and training at 22% CAGR to >60 GW, driving shifts toward high-density, liquid-cooled, AI-ready campuses, modular builds, and tier 2 markets where power, land, and permitting are more accessible and faster.
• Hyperscalers are restructuring capital and infrastructure models, including JVs, special-purpose vehicles, lease‑to‑own deals, behind‑the‑meter power (e.g., New APR Energy’s 100 MW+ mobile gas turbines), and hydrogen-powered microgrid campuses, while retrofitting existing sites at $4–7M/MW for co‑locators and $20–30M/MW for hyperscalers to support GPU‑intensive AI and consolidating into multifacility campuses projected to represent ~70% of deployments by 2030.