In 1987, Nobel Prize–winning economist Robert Solow wrote, “You can see the computer age everywhere but in the productivity statistics.” The same could be said about AI today.
Gartner projects worldwide AI spending will hit $2.59 trillion in 2026, a 47% jump over last year, with the US accounting for at least half that amount. But in the US, utilization-adjusted total factor productivity grew just 0.07% over the four quarters ending in the first quarter of 2026 — a near-standstill by historical standards.
Some 95% of enterprise generative-AI pilots have produced no measurable effect on the bottom line. A report published this week found that even Meta, one of AI’s loudest boosters, has fallen short in its plan to replace workers with AI. Explaining that gap has become the question of the decade.
One idea: Blame the users
A working paper posted to SSRN by University of Pittsburgh business professor Mark Ma and colleagues makes a sweeping claim: The productivity shortfall is caused by employees who resist AI out of fear for their jobs.
Over a five-year period, the team looked at millions of Glassdoor reviews, thousands of financial reports, hundreds of AI-investment and layoff announcements by US public companies, and some 10,000 earnings-call transcripts.
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They found a wide divide between managers and employees.
Managers tend to be true believers in the promise that AI will deliver sky-high productivity, while employees worry that AI-driven gains will cost them their jobs. Companies, the authors claim, are caught in a doom loop in which they lay off employees, citing productivity gains. But fear of layoffs causes workers to resist the technology, which sabotages the very gains the companies were counting on. Executives see that lack of productivity and conclude that more layoffs will help.
It’s a tidy narrative.
There’s just one problem — while parts of this study are backed by verifiable data, two key elements are not. The study fails to support the assumption that fear of layoffs causes employees to resist using AI, and also that productivity gains would be higher if employees were less resistant. The authors never establish causation in their data. It’s a correlation.
There are reasons to doubt the employee-foot-dragging theory. For starters, the notion that rank-and-file employees are broadly resisting AI isn’t entirely true.
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A Columbia Business School survey of 1,400 US employees found that 31% of individual contributors expressed enthusiasm about adopting AI. Many of the non-enthusiastic are being required to use it anyway. More than half of US workers now use the technology. If AI were a significant driver of productivity, measurable gains should already be showing up.
A better idea: Blame AI overload
The more likely explanation involves another factor altogether. The time and trouble of producing a business report, proposal, plan, slide deck, or budget used to limit how large, how complex, and how frequent such documents were. Now, thanks to AI, people can churn out incredibly complex business communications, ideas, and proposals in a few minutes.
Using AI makes the person generating such documents super productive. But then it burdens everyone else who has to sift through those documents, teasing out hallucinations, problems, and irrelevancies, and struggling to grasp ideas that even the so-called creator hasn’t taken the time to understand.
One person’s productivity is everyone else’s information overload, lowering a company’s overall productivity. “AI can make an organization extraordinarily busy without necessarily making it more productive,” said Justin Greis, CEO of consulting firm Acceligence, as quoted in that report.
This pattern is playing out everywhere. AI-pilled chatbot enthusiasts who believe the technology is solving all their problems are judging AI through a narrow personal lens. Likewise, analysts and AI companies look at one person’s productivity gains, extrapolate across thousands of employees, and wrongly conclude that the gains will scale without considering the impact of that output on the productivity of others.
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Understanding the problem at scale
The idea that productivity-enhancing AI might reduce productivity sounds paradoxical, so consider this oversimplified thought experiment.
Suppose AI enables you to write three times as many emails as before (say, 30 a day instead of 10). Your email-writing productivity has tripled, making you more valuable to the company. The problem is that every additional email you send lands in someone else’s inbox. Your colleagues, who once got 10 emails from you daily, now get 30.
Multiply that across an organization: if 10 people triple their email output, the team gets 300 emails a day instead of 100. If 100 people do the same, that figure blows up to 3,000, three times the reading burden.
The AI that makes email writing easy makes email reading hard for everyone else. Of course, this effect doesn’t apply to every use of AI, every industry, or every employee. But on the macro level, this is clearly happening. And it helps explain what Deloitte called the “paradox of rising investment and elusive returns.”
AI isn’t “bad.” But shortsighted and delusional thinking about it is. AI is like nearly every powerful technology since the Industrial Revolution: It rewards individuals for behavior that collectively exhausts a shared resource — in this case, human attention. What’s called for is a wholesale redesign of workplace AI. We need tools built not to make the individual user more “productive,” but to make the organization more productive, magnifying individual ability without dumping needless work on everyone else.
