Hyperscalers need $300 billion in annual AI revenue to break even: Goldman

Investing.com -- In a note to clients this week, Goldman Sachs told investors that it estimates the largest U.S. AI hyperscalers need to generate about $300 billion in annual AI revenue in the coming years to break even on their heavy spending.

Goldman analyst Ryan Hammond said the hyperscalers are on track to spend $800 billion on capital expenditures in 2026.

He notes that consensus expects that to rise to $1.1 trillion in 2027. Goldman's baseline is for 2027 spending to beat consensus, but for both the pace of growth and the size of upside surprises to slow.

“Hyperscaler cloud revenues have accelerated sharply this year, annualizing roughly $70 billion above the pre-AI trend in Q2 2026, and announced revenue backlogs exceed $1.5 trillion,” wrote Hammond.

Goldman estimates AI users would need to spend about $1 trillion a year on AI applications for the hyperscalers to earn solid returns. That compares with about $1.5 trillion in global software spending this year.

"Many market participants are skeptical about the durability of AI infrastructure stock earnings, and that view is at least partially reflected in market pricing," Hammond added.

At current prices, earnings at some semiconductor companies would have to fall substantially for those stocks to trade at their historical average P/E ratios. Goldman said the hyperscalers also trade at low multiples, and signs of returns on investment, along with slower growth in spending, should support the shares.

Beyond infrastructure, Goldman expects AI software and services stocks to keep producing winners and losers.

"Enterprise adoption is still in early stages but the recent acceleration in enterprise AI spending suggests the impact of AI on corporate earnings should become increasingly visible in coming quarters," Hammond added.

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