Goldman Sachs projects $1.2T in AI infrastructure capex by 2027 as energy becomes key bottleneck

Goldman Sachs strategists are projecting that the five biggest US hyperscalers will pour roughly $1.2 trillion into AI infrastructure capital expenditures in 2027. That figure represents a 50-54% jump from an estimated $800 billion in 2026, and it blows past Wall Street’s consensus forecast of around $1.1 trillion. The research note, led by strategist Ryan Hammond, goes further: an upside scenario puts the number closer to $1.4 trillion. And looking at the longer horizon, Goldman estimates cumulative AI infrastructure spending could hit $7.6 trillion from 2026 through 2031. The hyperscaler arms race The five companies driving this spending tsunami are Amazon, Alphabet, Microsoft, Oracle, and Meta. Each is racing to build out the compute and data center capacity needed to train increasingly powerful AI models and run inference at scale. Goldman’s team has been revising its projections upward throughout 2026, citing strong quarterly capex results from hyperscalers in Q2 and Q3 as evidence that previous consensus figures were too conservative. AI infrastructure spending grew nearly 100% in 2026. Goldman expects that rate to slow to 54% in 2027 and then to 12% in 2028, when total spend...