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 clouds.

For context, hyperscaler cloud revenues are currently running about $70 billion above the pre-AI trend line as of Q2 2026.

Using a 15% annualized return on invested capital as the benchmark, Goldman estimates the six hyperscalers would need to generate approximately $1.42 trillion in cumulative revenue across the three years from 2028 to 2030. That works out to roughly $11.6 billion per gigawatt of compute capacity per year during that window.

The spending projections themselves are staggering. Goldman sees AI infrastructure capex hitting around $800 billion in 2026 alone, climbing to $1.2 trillion in 2027, and continuing higher to roughly $1.4 trillion in 2028. Those figures sit well above Wall Street’s current consensus estimates.

The backlog argument, and its limits

Combined contract backlogs for AWS, Azure, and Google Cloud reached approximately $1.69 trillion in Q2 2026, a 152% increase year over year.

Supply constraints in compute resources have been a persistent bottleneck, and Goldman notes these constraints are part of what’s driving the accelerating expenditure.

What investors should be watching

The central tension Goldman is surfacing is a timing one. The capital is going out the door now, in enormous quantities, while the revenue needed to justify it is expected to materialize between 2028 and 2030.

Goldman’s framing positions this capex cycle against historical technology waves, implying the current buildout exceeds any prior infrastructure investment period in absolute terms.

Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.