The AI capital expenditure frenzy among hyperscalers is shifting from a debate over "how much to spend" to the core question of "how much can be earned back."
Goldman Sachs' latest research shows that if the six major hyperscalers are to achieve a 15% return on invested capital (ROIC) on approximately $1.73 trillion in AI compute capital expenditure during 2026-2027, they would need to generate cumulative revenue of about $1.42 trillion between 2028 and 2030.
Eric Sheridan, head of Goldman Sachs' internet and technology team, noted in the report that the current market debate has shifted from the scale of near-term spending itself to whether this massive investment round can generate meaningful returns within the next three years. Meanwhile, cloud backlog data provides some support — as of the second quarter of 2026, AWS, Azure, and Google Cloud had a combined backlog of approximately $1.69 trillion, up 152% year-over-year, offering preliminary validation for the achievability of the revenue targets.
However, variables outside Goldman Sachs' framework cannot be overlooked. Approximately $3 trillion in off-balance-sheet commitments have not been included in the ROIC denominator, downward pricing pressure on GPUs and compute has already become apparent, and the return question for the even larger "third phase" capital expenditure (approximately $4.14 trillion from 2028 to 2030) has been explicitly deferred by Goldman Sachs for separate discussion.
Three Phases of Capital Expenditure: Escalating Scale at Each Level
Goldman Sachs divides this AI compute buildout cycle into three phases:
The first phase (2023-2025) involves total capital expenditure of approximately $633 billion, averaging about $211 billion annually, primarily for training infrastructure construction at a small number of foundation model companies;
The second phase (2026-2027) involves total capital expenditure of approximately $1.73 trillion, averaging about $863 billion annually, with the demand curve extending toward inference and compute supply remaining persistently tight;
The third phase (2028-2030) involves total capital expenditure of approximately $4.14 trillion, averaging about $1.38 trillion annually, at which point capital intensity is expected to moderate marginally and hyperscalers may gradually enter a "harvest mode."
It is worth noting that since the beginning of this year, consensus capital expenditure estimates for 2026-2027 across five listed hyperscalers (Alphabet, Microsoft, Amazon, Meta, and Oracle) have been revised up by approximately 66%, a combined increase of about $754 billion, rising from $1.14 trillion to $1.89 trillion. Goldman Sachs stated that its own 2027 capital expenditure forecast remains above market consensus and believes this forecast is "closer to the actual expectations of buy-side investors."
From Capital Expenditure to Revenue Threshold: Goldman Sachs' Calculation Framework
Goldman Sachs' calculation logic is clear: of the $1.73 trillion in second-phase capital expenditure, approximately 70% ($1.21 trillion) is compute investment (servers, chips, networking), and approximately 30% ($518 billion) is infrastructure "shell" (land and buildings). Among the six companies, Alphabet has the highest capital expenditure ($470 billion), followed by Amazon ($424 billion), Microsoft ($327 billion), Meta ($309 billion), Oracle ($165 billion), and SpaceX ($31 billion).
On key assumptions, Goldman Sachs uses a cost of approximately $42 billion per gigawatt of compute, depreciating compute assets over 5 years and infrastructure shells over 15 years, with annual operating costs of approximately $836 million per gigawatt, and assumes utilization rates of 60%, 80%, and 85% for 2028-2030 respectively.
Under a 15% ROIC target, the six companies would need to generate approximately $129.5 billion in after-tax net operating profit (NOPAT) annually, corresponding to approximately $164 billion in annual earnings before interest and taxes (EBIT). Adding annual depreciation and amortization of approximately $276 billion on second-phase assets, this ultimately yields a requirement for annual revenue of approximately $465 billion to $476 billion, or approximately $1.42 trillion cumulatively over three years, corresponding to an EBIT margin of approximately 35%.
Depreciation Pressure: The Underestimated Core Risk
In the above calculations, the most striking figure may not be the revenue target itself, but the scale of depreciation — annual depreciation and amortization on second-phase assets alone amounts to $276 billion, approximately 1.7 times the EBIT these assets need to generate.
This means the economics of this AI investment cycle depend to a large extent on the actual useful life of GPUs. Goldman Sachs uses a 5-year depreciation period; if the actual economic life is only 3 years (a direction indicated by some skeptical analysts and by NVIDIA's own annual product iteration cadence), hyperscalers would be forced to continuously purchase new-generation chips at a higher frequency, and the revenue threshold would rise substantially.
Goldman Sachs' sensitivity analysis shows that within a range of ROIC targets from 0% to 30% and per-gigawatt capital expenditure from approximately $34 billion to approximately $51 billion, the cumulative revenue required by the six companies for 2028-2030 falls between approximately $900 billion and $1.9 trillion, equivalent to annual revenue of approximately $6.2 billion to $18.6 billion per gigawatt. Even in a zero-return scenario, merely covering depreciation and operating costs would require $920 billion in revenue.
Cloud Backlog: The Most Important "Safety Cushion"
The core evidence Goldman Sachs provides for the achievability of the revenue target is cloud backlog data. As of the second quarter of 2026, AWS, Azure, and Google Cloud had a combined backlog of approximately $1.69 trillion, up about 1.5 times from the beginning of the year, with public cloud revenue growth accelerating to over 45% year-over-year.
Measuring only these three companies, their approximately $1.22 trillion in second-phase capital expenditure corresponds to approximately $1 trillion in revenue needed for 2028-2030, equivalent to approximately 59% of the current backlog — and this calculation assumes zero backlog growth thereafter. By company, the required revenue represents approximately 40% of Microsoft's backlog, 68% of Amazon's, and 78% of Alphabet's.
Goldman Sachs characterizes this assumption as "potentially conservative." However, the report also notes that the above backlog data includes non-cloud business commitments and is not strictly purchase orders; in addition, a significant proportion of orders come from a small number of AI labs whose ability to pay is highly dependent on continued access to capital market financing, creating customer concentration risk.
The Other Side from Morgan Stanley: The Financing Gap Is Equally Large
Comparing Goldman Sachs' revenue threshold with Morgan Stanley's research provides a more complete picture of both sides of this issue. According to Morgan Stanley's estimates, GenAI investments can achieve incremental ROIC of 25% to 50% — approximately 31% for GPU leasing, approximately 46% for model APIs based on proprietary infrastructure, and approximately 25% for those based on third-party infrastructure. Morgan Stanley assumes annual GPU leasing revenue of approximately $22.9 billion per gigawatt and model API revenue of approximately $30.4 billion per gigawatt, both higher than the $11.6 billion per gigawatt corresponding to Goldman Sachs' 15% ROIC target. The two institutions' conclusions are broadly consistent: if GPU leasing prices and compute pricing remain near current levels, the revenue targets are mathematically achievable.
However, another Morgan Stanley study previously revealed pressure on the financing side: it estimates that global data center capital expenditure from 2025 to 2028 will be approximately $2.9 trillion, with hyperscalers' own cash flow covering only approximately $1.4 trillion. The remaining financing gap of approximately $1.5 trillion would need to be filled through approximately $800 billion in private credit, approximately $200 billion in corporate bonds, approximately $150 billion in ABS/CMBS, and approximately $350 billion through other channels. Goldman Sachs' latest forecast shows that the combined capital expenditure of just the second and third phases alone amounts to approximately $5.9 trillion, far exceeding the above estimates.
Blind Spots in Goldman Sachs' Framework: Off-Balance-Sheet Commitments and the Third Phase
Goldman Sachs acknowledges in its report the limitations of its own framework. Its ROIC calculations explicitly exclude off-balance-sheet lease obligations, finance leases, special purpose vehicles (SPVs), and third-party compute licensing arrangements.
This is not a minor footnote. According to relevant data, off-balance-sheet commitments in the AI sector have reached approximately $3 trillion and are still growing rapidly. These commitments also need to generate returns or at least be serviced, and credit markets have already begun to react.
Furthermore, Goldman Sachs' framework covers only the second phase. If the same revenue-to-capital-expenditure ratio (approximately 0.82x) is roughly applied to the approximately $4.14 trillion third-phase forecast, hyperscalers would need to generate an additional approximately $3.4 trillion in revenue on top of the $1.42 trillion second-phase revenue target. Peter Berezin of BCA Research went further, noting that if data center spending remains at current levels, as much as $10 trillion in annual AI revenue may be needed to monetize all capital expenditure.
Goldman Sachs maintains a "buy" rating on all six companies and characterizes the recent compression in returns as "a natural consequence of a massive upfront investment cycle, rather than evidence of poor AI economics." But this statement holds equally true in both bullish and bearish narrative frameworks.
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