Goldman Sachs calculates the revenue hyperscalers need to justify $1.7T in AI capex

The hyperscalers are spending money at a pace that makes the dot-com era look like a rounding error. Goldman Sachs now has the receipts, and the core question its analysts are asking is simple: at what point does all this spending actually pay off? The firm’s research breaks the AI infrastructure buildout into three distinct phases. Phase 1, covering 2023 through 2025, totaled roughly $633 billion in capital expenditure across the six major hyperscalers. Phase 2, spanning 2026 and 2027, escalates that figure to approximately $1.73 trillion. Phase 3, projected from 2028 to 2030, could reach around $4.14 trillion. The hyperscalers Goldman tracks are Alphabet, Microsoft, Amazon, Meta, Oracle, and SpaceX. The break-even math is uncomfortable Goldman lays out two revenue thresholds that matter. The first is roughly $300 billion in annual AI revenue, which is what these companies collectively need just to stop losing money on their infrastructure investments. The second, far more ambitious, threshold sits at around $1 trillion in annual revenue, the level required to generate satisfying returns for both infrastructure providers and the application developers building on top of their clou...