Investors are intensifying scrutiny of hyperscaler AI capex guidance ahead of Q2 2026 earnings reports.

Investors are entering the second-quarter earnings season focused on whether heavy artificial intelligence spending by major technology companies will begin translating into stronger revenue growth, according to Wedbush Securities analysts.

Wedbush wrote that recent weakness in large-cap technology stocks reflects growing investor concerns over the timing of returns from record AI infrastructure investments rather than a deterioration in the long-term outlook. "We are going through another 'gut check' few weeks ahead for the tech trade as tech investors await a very important Q2 earnings season in July to further validate the AI Revolution buildout," the firm wrote.

Companies including Microsoft Corp, Alphabet Inc, Amazon.com Inc, Meta Platforms Inc, Nvidia Corp, Oracle Corp, and Palantir Technologies Inc have come under selling pressure as investors question when elevated capital expenditures will begin generating meaningful revenue growth. "We are in an 'air pocket stage' right now where the $700 billion of Big Tech cap-ex this year is fueling the AI buildout," Wedbush wrote. The firm added that investors are becoming increasingly impatient as companies such as Microsoft and Meta continue investing heavily while waiting for broader monetization of AI initiatives.

The firm argued that the current period represents a transition phase, with data center and computing capacity expanding ahead of expected growth in enterprise AI adoption. It described the environment as "short-term pain for long-term gain" and maintained that the recent pullback has created buying opportunities.

Wedbush also pointed to rising compute and memory costs as another source of market concern, particularly after recent price increases announced by Apple Inc raised questions about the affordability of large-scale AI deployments. However, the firm wrote that those cost pressures should ease over the coming year as AI hardware, enterprise applications, and physical AI deployments expand, adding that the current uncertainty is part of a longer investment cycle it views as being in "Year 3 of a 10-year AI buildout."