AI capex – A sharp slide in technology stocks is reviving a familiar worry: hyperscalers are pouring trillions into AI infrastructure, but consumers and businesses don’t appear ready to pay enough to match the costs. From debt-funded data centers to worker layoffs and sk
For the second week in a row, technology stocks have been sliding, and the drop isn’t being driven by one bad earnings call. It’s being driven by a question that feels uncomfortable precisely because it’s simple: what happens after the biggest party—if hardly anyone shows up?
The Nasdaq Composite Index has slipped nearly 3% this week as investors grapple with whether the trillions of dollars flowing into artificial intelligence will translate into the revenue and profit growth needed to justify the cost. Goldman Sachs estimates tech companies will spend $7.6 trillion through 2031 to build thousands of new data centers to power the rise of AI.
Yet the spending rush is colliding with fresh uncertainty about demand—especially whether enough consumers and businesses are willing to pay for the services that infrastructure is meant to deliver. And even as major AI players borrow heavily to build that foundation. the debate is starting to shift from “Will AI work?” to “Will it pay?”.
“There’s concern around how much hyperscalers are turning to debt markets in order to finance the infrastructure buildout. ” Kate Brennan. associate director of independent research institute AI Now. said on CBS News. Brennan pointed directly to the companies driving AI capital spending—Alphabet. Amazon. Meta. Microsoft and Oracle—and argued that the economics aren’t lining up.
“The returns are not coming in, and the claims that are being made, in terms of efficiency or productivity numbers, are not netting out,” she added.
Brennan also flagged skepticism among some consumers and workers about what AI is actually delivering. Americans are increasingly using AI. but. for now. “few appear willing to pay for it.” That reluctance is tied to polling results that show broad public concern: 40% of adults think the technology will be a negative societal force over the next two decades. versus 16% who believe it will be positive. according to Pew Research.
At the same time. more companies are laying off workers and investing in AI instead—an approach that may save money in the short term. but leaves a harsher question hanging over the long term: will it create returns that justify the disruption?. A May study from tech research firm Gartner found that businesses that replace workers with AI agents often fail to generate a return on investment.
For Brennan, the adoption story isn’t only about persuasion. It’s also about inevitability.
“One takeaway is that many consumers are using AI less out of a desire to chat with a bot than because there’s simply no escaping the technology. ” she said. Enter a search query on Google, and an AI response appears at the top of the page. Call a company’s helpline. and the odds are a company will route you to an AI agent with a soothing voice—while fake typing in the background tries to make the experience feel more human than it is.
Brennan framed the push as something driven by finance, not demand.
“The current push for AI adoption that we’re seeing is directly coming from the financial incentives of AI firms. ” she said. Because of the massive capital expenditures. hyperscalers and other AI firms are making a “deliberate push for AI everywhere — no matter whether the demand is there or if customers want it or not.”.
That is the tightrope Wall Street is now watching, and it’s why the selloff has taken on a sharper tone for investors who have already lived through other hype cycles.
Some see the AI moment as analogous to the dotcom bubble of the late 1990s. Back then. many early internet high-flyers flamed out; the ones that survived—like Amazon and Google—eventually became profitable and household names. Now the worry is less about whether AI will reshape tech. and more about whether the current rush in spending and valuations can survive the economic test.
Qian Wang, global head of capital market research at Vanguard, and senior global economist Kevin Khang warned that outcomes are likely to be uneven.
“Some firms may emerge as more profitable and with significant competitive advantages. while others could find their core businesses obsolete in a new AI economy. ” they said this week in a report. “As we continue to learn what the economics of AI look like in practice — the trajectory of AI capital expenditure. how effectively hyperscalers can monetize AI investment. and the size and shape of AI’s addressable market — the market’s sensitivity to the ups and downs is likely to be significant.”.
They added, “Investors should expect a bumpy ride.”
The heart of the matter is the payback test—whether the investment cycle has a realistic path to end-user revenue.
Economist Ed Yardeni of Yardeni Research said the underlying question behind the hyperscalers’ lofty valuations is whether their capital spending plans reflect realistic revenue forecasts. Companies including Alphabet. Amazon. Meta and Microsoft are spending heavily on data centers and chips expecting strong demand for AI services. Meanwhile, large language model developers like OpenAI and Anthropic pay to use data centers.
But Yardeni doesn’t let the story stay abstract. In a note to investors, he described when the ecosystem breaks: “The AI ecosystem falls apart if the expected end-user demand for the AI/LLM products does not materialize or if prices for their offerings fall sharply below expectations.”
His team looked at annualized revenue estimates for OpenAI and Anthropic to judge whether those firms are adding users fast enough to cover their spending commitments with the hyperscalers—what he calls a “capex payback test” for whether these companies can support the industry’s capital expenditures.
“Their conclusion: Not right now, but the picture will improve in several years if current growth forecasts hold.”
Yardeni’s assessment came with a careful caveat. “We find that the AI ecosystem is not fully end-user revenue-backed yet. but it is not entirely speculative either. ” he said. “Expected 2030 revenues make the math look much better. But those forecasts depend on a big assumption: AI revenues must continue to scale. and compute efficiency must improve. or both.”.
And that brings everything back to the same pressure point investors keep circling: the technology may be spreading fast, but the economics still need to catch up—before the next round of capital spending becomes its own justification instead of a response to customers.
AI spending hyperscalers data centers debt markets Goldman Sachs Nasdaq Composite Kate Brennan AI Now Pew Research Gartner study Vanguard Yardeni Research capex payback test OpenAI Anthropic Alphabet Amazon Meta Microsoft Oracle
So basically AI is a scam until someone bills us.
I feel like they keep saying “AI will pay for itself” but then my grocery app is still expensive? Like where is the money coming from, investors just eating losses again.
Isn’t the “who pays” answer just… the government? Like they’ll subsidize it or whatever. Also tech stocks dropping for a week means everybody panic sold, not that AI doesn’t work.
Data centers getting built with debt and layoffs, but they’re acting surprised nobody’s ready to pay? My cousin said his company already got priced out of “AI credits” or whatever and now they’re cutting stuff. Feels like Big Tech is just making bills and hoping we don’t notice.