The idea came to Amy Webb in fully formed fashion during her long Sunday bike ride—the ride she takes when she's not training for a race. As she recently posted on LinkedIn, every CEO she speaks with is buying abundance, and none are budgeting for what abundance will cost. Talking to CEOs is, after all, what she does every day for a living.

Webb, 51, leads the Future Today Strategy Group, the foresight and consulting firm she founded in 2006 after a career in data journalism sparked her interest in machine learning. She also teaches at NYU's Stern School of Business and is the author of The Big Nine, which identified nine American and Chinese tech giants as the forces likely to dominate a world shaped by artificial intelligence and a full-scale tech cold war. She is accustomed to being ahead of her time—and to being frustrated that the world lags behind the schedule she carries in her head.

So when Webb tells Fortune that she sees something resembling a bust coming for corporate AI spending, the precise dynamic she describes deserves attention. "AI is making production cheap," she said, "but it's making everything else in companies much more expensive." The corporate world, she added, is living through something akin to the dating-app fatigue familiar to millennials and Gen Zers.

"The best thing [for a dating app] is to never get married," Webb said, and she sees the same logic playing out in the endless string of generative AI pilots. Executives tell her they are stuck in "pilot purgatory": juggling unending pilots and "enormous productivity but [they're] not sure what to do with that." The phenomenon is not unique to her clients: a widely cited 2025 report from MIT's Project NANDA found that roughly 95% of enterprise generative AI pilots produced no measurable return, a finding that ignited debate across corporate America about how much of the current spending wave is actually reaching production.

Venture capitalist Marc Andreessen, for his part, said in March that large companies are overstaffed by as much as 75% and are using AI as a "silver bullet excuse" for cuts that reflect pandemic-era overhiring. A separate analysis by Oxford Economics found that AI-cited layoffs accounted for a mere 4.5% of total U.S. job losses, despite the outsized headlines—a gap that underscores how much of the AI story so far is about perception and internal reorganization rather than measurable labor-market shifts.

'It feels like you're buying abundance'

Webb, who speaks with between 100 and 150 CEOs a year, says her chief concern is a bubble distinct from what is happening on Wall Street: the strange way AI is deforming work without actually changing it very much.

"It feels like you're getting a lot when you invest in AI," Webb said. "It feels like you're buying abundance. But that abundance ends up costing much more down the road." Nor is this the classic trade-off between long-term investment and short-term gains. "This is immediate satisfaction, followed by: can I productize this? Can I put it in a workflow?" AI gives companies the sensation of winning—a feeling of "I'm getting away with it"—which is fueling much of the current enthusiasm and adoption.

At the same time, Webb said she can count on one hand the companies that have found a sustainable way to run this kind of experimentation. One of her clients has run 14 or 15 generative AI and agent pilots since the start of the year, applying Amazon's famous two-pizza rule—no team larger than what two pizzas could feed. None of the pilots scaled. "They've gone through a lot of pizza."

Part of the problem, she noted, is that pilots frequently run without integration into legal and IT, so executives never embed them into their infrastructure and instead restart from zero each time. "That costs a lot of money," she said. It is a pattern familiar from earlier enterprise technology cycles—cloud migrations and digital transformation programs a decade ago were similarly dogged by stalled rollouts—except that generative AI's low upfront cost makes the pilot treadmill far easier to enter and harder to notice.

The data reflect the pattern. A Bain & Company survey of 951 global companies published in June found that nearly 40% of companies that measured their AI cost savings came in below 10%, despite having targeted returns of 11% to 20%. Consistent with Webb's argument, the shortfall has not slowed spending: 90% of surveyed companies said they are increasing their AI budgets anyway.

Drowning in decks

Beyond pilot purgatory, there is the drowning-in-decks problem. Webb recalled one executive who recently said their direct reports were suffering a kind of decision paralysis—not from too little information, but from being buried under more analysis than they could process. Another described the issue to Webb as "insta-decks": presentations that once took a week to build now take a day, but the same team now receives five times as many of them. It doesn't help, she added, that "Claude has a little bit of a verbosity problem," producing 10 pages when only one is needed.

"The more a company uses these tools," Webb said, "the more generic ideas are spit out." That is not the same thing as AI slop, she clarified—it is something different. "It's fine with me if something was not written necessarily by a person, if the rest of the information is useful."

Webb said she asks nearly every CEO she meets the same question: if AI freed up 10% of your total capacity tomorrow, where would you deploy it? "So far, I haven't gotten an answer." Of all the time being saved, she said, no one appears to have the job of harvesting the productivity gains. "I'd bet at most companies, people are prioritizing speed over creating new ways of thinking. And then you're not learning anything."

Psychologists have begun studying "cognitive offloading"—delegating mental work to a tool rather than doing it yourself—and recent research finds that when AI takes over core reasoning tasks, people's sense of ownership over the resulting work declines. It is "automating something that people very much feel they have ownership over," Webb said, noting that the same tension is visible in debates in Hollywood and the media over where true creativity is headed. From a business standpoint, she stressed, "AI is cheap to get started with," but the costs then compound in ways that quickly become "shockingly uncomfortable."

When will the reckoning happen?

Webb expects the reckoning as early as next year. "Right now, very few companies are in a position to show an actual measurable change in the next two quarters," she said, calling it the "second chapter" of the story now unfolding. "We might start to see cracks happen with missed targets" as Wall Street begins asking about all the generative AI pilots in the enterprise and demanding results.

At the same time, she resisted the tidy bubble framing. "This is not a normal dotcom bubble and burst," she said. "All of this abundance and productivity comes at a new cost that people aren't factoring in."

Part of what is colliding here, Webb added, is generational and emotional, not merely financial. She was working in journalism when the commercial internet first switched on and remembers how "a lot of people were demoted to digital" during that earlier transition, with little intentional planning behind the decision. She sees a similar dynamic today: the people steering the AI revolution are not the ones equipped to really know what they are dealing with.

A big part of the problem, Webb explained, is that the boards and executives under pressure to adopt AI—and to do it yesterday—have spent the past several decades building expertise in fields that have nothing to do with it. "No CEO was hired because they're an expert in artificial intelligence," she said. "These people are heads of organizations because they're excellent executives," and AI is nearly the worst possible technology for them to grapple with. "AI is not one technology, it's an umbrella for many technologies," she said, and "planning requires data — you can't just go with your gut on this stuff."

Every new technology wave feels disorienting in the moment, she added, and those who do not feel fluent in it tend to resist it. The people most likely to feel that way are the older, more expensive workers who are also the most likely to be laid off.

If this mismatch between pressure, inexperience, and deceptively expensive technology persists, Webb warned, the outcome could be far worse than a market crash. "This isn't like the economy takes a hit. It's like the economy makes weird decisions." When Fortune asked whether she was describing an AI hallucination on an economywide scale, she laughed and said, "I'm going to start using that in class."

Her own business, for the record, has never been doing better. "When there's horrific uncertainty out there," she said, "uncertainty is what we do."

This story was originally featured on Fortune.com.