Consulting firm estimates that to keep up with computing power demand, global data centers may require approximately $6.7 trillion to $7 trillion in capital expenditures by 2030; of this, about $5.2 trillion is for AI workloads and approximately $1.5 trillion for traditional IT. Global installation demand may nearly quadruple, with about 70% coming from AI.

The U.S. is projected to be the largest recipient of this spending: McKinsey states that over 40% will be invested in the U.S., with domestic demand growing by about 20% to 25% annually. BloombergNEF has revised its forecast for U.S. operational capacity in 2030 to 118 GW, a 52% increase from its December 2025 prediction, and expects it to reach 194 GW by 2035; since December 2025, the U.S. has announced an additional 101 GW in the pipeline.

Six major operators—Amazon, Microsoft, Alphabet, Meta, Oracle, and SpaceX—are estimated by S&P Global to have a combined capital expenditure exceeding $1.3 trillion by 2027. The World Economic Forum cites that the five largest U.S. cloud and AI infrastructure companies have committed to capital expenditures of about $66 billion to $69 billion by 2026, nearly doubling and accounting for about 1.9% of GDP.

Electricity is a hard constraint. NEMA expects data centers to contribute 38% of the net electricity increase in the U.S. by 2037; Goldman Sachs estimates electricity demand will rise by about 165% by 2030, with grid upgrades costing about $720 billion. BNEF states that if only looking at chip shipments, the U.S. could theoretically add 207 GW by 2033, but due to energy constraints, there is a gap of about 63 GW between its capacity forecast and chip implied values. Bank of America expects data centers to add about 125 GW of electricity over the next five years, while utilities plan to add only about 93 GW of reliable capacity, resulting in a gap of over 100 GW, leading developers to turn to on-site gas and storage solutions.

Mechanically, the buyers are large-scale cloud and model companies, while the sellers are chip, power, cooling, and land developers. The event is driven by the rising power density of AI training and inference: cabinet power consumption is shifting from about 227 kW to around 400 kW. Funding is flowing from tech companies' operating cash flows, investment-grade bonds, infrastructure funds, and high-yield debt into cabinets; beneficiaries are owners who secure electricity and turbine scheduling first, while communities in queue with the grid and utilities still planning based on old loads are under pressure.

The publicly predicted range is quite large: in a constrained scenario, the increment is about 78 GW with spending of about $3.7 trillion; in an accelerated scenario, the increment is about 205 GW with spending of about $7.9 trillion. The $7 trillion figure does not represent signed orders.

Source: Public information