Goldman backs hyperscalers after tech sell-off

Goldman Sachs told investors to buy the pullback in US hyperscalers after a 17% technology sell-off before earnings.

Mateo Fernandez ·

Goldman backs hyperscalers after tech sell-off

Goldman Sachs said the recent slump in US hyperscaler shares offers a buying opportunity before the next earnings round, after a 17% sell-off across technology stocks. The call puts mega-cap cloud and artificial intelligence infrastructure names back at the center of the equity market debate, where valuation pressure has collided with expectations for continued spending on data centers and compute capacity.

Hyperscaler earnings face a July test

The broker call matters because hyperscalers have been a major force behind US equity leadership, with investors treating cloud scale, AI demand and balance-sheet capacity as linked advantages. A 17% decline changes the entry point, but it does not settle the main question: whether earnings can support the capital spending needed to keep AI infrastructure growth intact.

For the companies, the mechanism is straightforward. If revenue growth and margin commentary hold up, the sell-off can be framed as multiple compression rather than a break in fundamentals. If management teams signal slower cloud demand, weaker AI monetisation or heavier spending with delayed returns, the same decline could deepen pressure on valuations.

For the wider sector, the read-through runs beyond the largest platforms. Semiconductor suppliers, data-center operators, power equipment makers and enterprise software vendors all depend on the durability of hyperscaler budgets. A stabilisation in the group would support the broader technology trade; another disappointment would make investors more selective across AI-linked equities.

The dated test is the next 30 days from July 1, 2026, when investors will compare the broker call with company earnings updates and guidance. If guidance confirms demand, global risk appetite may recover through growth equities; if it weakens, the pressure could move from hyperscalers into the wider AI supply chain.

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