SpaceX shares fell more than 10% in premarket trading Wednesday after the company reported a sixfold surge in capital expenditures, driven largely by AI infrastructure spending, that overshadowed otherwise solid quarterly results. The stock closed Tuesday at roughly $125, below its $135 IPO price and well off its post-listing high above $200.
Investor unease over whether big tech can justify massive AI investments has spread to SpaceX. The company's capex hit $18.4 billion in the second quarter, far ahead of analyst estimates, with the bulk going to Nvidia chips and computing capacity that SpaceX rents out as an alternative cloud provider.
Spending vs. returns
SpaceX CFO Bret Johnsen tried to reassure investors during the earnings call, saying the company has been "efficient" with spending. "On the AI compute side, we're able to deploy capital in such a way that we're getting less than a one-year payback," Johnsen said.
Still, the market remained skeptical. Steve Westly, founder of The Westly Group and a former Tesla board member, told CNBC: "SpaceX wants to tell the story they're the market leader ... But people still have these questions: how quickly can they grow? How big are the costs going to be before this thing gets to profitability?"
Narrowing losses and a new revenue target
The share drop came despite SpaceX narrowing its losses and Elon Musk moving up his revenue forecast. Musk now says the company will hit $1 trillion in annual revenue by 2030, a year earlier than his previous prediction. Investors must also contend with Thursday's expiration of insider lock-ups, which will allow company insiders to sell shares for the first time.
SpaceX's AI models are considered behind competitors like OpenAI and Anthropic, but the company is betting on its cloud business built with Nvidia chips to generate returns quickly.
Why this matters for finance professionals
For analysts and investors, the SpaceX episode highlights how AI spending can rattle markets even when a company meets or beats expectations. Understanding the relationship between capital allocation, payback periods, and investor sentiment is essential for evaluating tech stocks. Professionals can deepen their knowledge through resources such as AI for Finance training, which covers financial analysis of AI investments. For executives assessing strategic spending, courses like AI for Executives & Strategy offer frameworks for decision-making in capital-intensive AI projects.
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