Goldman Sachs: hyperscalers Need $300B Annual AI Revenue to Break Even

Goldman Sachs calculates that hyperscalers must generate roughly $300 billion in annual AI revenues within a few years to break even on 2026-2027 capital expenditures; reaching that level would imply approximately $1 trillion in annual end-user AI spending economy-wide. Cloud revenues for Amazon, Alphabet, Microsoft, and Oracle accelerated from 25% year-over-year growth in 2024 to 48% in Q2 2026, annualizing roughly $70 billion above pre-AI trends, but Goldman Sachs places these figures below the break-even threshold. Revenue backlogs across hyperscalers exceed $1.5 trillion. The five largest hyperscalers collectively hold over $3 trillion in future data center commitments, with $1.3 trillion in lease form, and Goldman Sachs and Visible Alpha project combined capex across eight major tech companies reaching $1.5 trillion in 2027 and approximately $1.75 trillion by 2030. To finance the buildout, the largest hyperscalers issued nearly $200 billion in debt in 2026, with Ancora projecting AI-related debt through 2030 at an estimated $4 trillion.