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For a few weeks this summer, Wall Street had a crisis of faith. The biggest names in tech were spending money at a scale that made even seasoned investors nervous, and the stocks responded accordingly. Then the earnings came in, and the mood shifted fast.
AI-related chip stocks shed more than $1 trillion in combined value during July 2026, as investors grappled with a simple question: how much infrastructure spending is too much before it stops being an investment and starts being a liability?
The numbers that spooked the market
The anxiety had a clear trigger. Alphabet revised its full-year capital expenditure guidance for 2026 to between $195 billion and $205 billion, a figure that landed well above what analysts had penciled in. Its second-quarter capex alone came in at $44.9 billion, surpassing forecasts by a meaningful margin.
Amazon was equally aggressive. The company raised its own full-year capex forecast to $220 billion, with AI infrastructure cited as the primary driver. Put those two together with Microsoft, Meta, and Oracle, and the combined spending projection for major hyperscalers across 2026 sits somewhere between $550 billion and $700 billion.
Semiconductor stocks bore a disproportionate share of the pain, given their direct exposure to the capex cycle. A slowdown in hyperscaler orders, or even a pause for reassessment, translates quickly into revenue risk for chipmakers. The $1 trillion drawdown across AI-adjacent chip names captured just how tightly those fortunes are now linked.
Why earnings changed the conversation
The recovery began as Q2 results started rolling in. Microsoft reported substantial growth in its Azure cloud business, signaling that enterprise demand for AI compute was not just holding steady but accelerating. Alphabet and Amazon both exceeded earnings expectations, providing concrete evidence that the spending was generating returns rather than simply being absorbed into overhead.
US stock markets rebounded in both late June and late July, with tech leading the recovery. Analysts began suggesting that an extended rally for Big Tech could be forming, driven by the combination of strong AI-related revenue growth and improving sentiment heading into the second half of 2026. The earnings season that kicked off in early August reinforced that view, with the underlying demand metrics continuing to outperform what the market had priced in during the July turbulence.
What this means for the AI infrastructure buildout
The competitive logic driving the spending is also worth keeping in mind. No major hyperscaler can afford to under-invest relative to its peers during a period of foundational infrastructure build. Falling behind on compute capacity today means ceding market position in AI services for years. That dynamic makes the spending commitments somewhat self-reinforcing: each company’s aggressive capex announcement creates pressure on the others to match it, which is part of why the combined 2026 figure has grown so large.
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