American corporations have poured hundreds of billions of dollars into artificial intelligence over the past three years, yet a growing body of evidence suggests that the promised productivity bonanza remains stubbornly elusive. A new working paper from the National Bureau of Economic Research and a mounting chorus of CEO frustrations are raising a familiar specter from economic history: the so-called productivity paradox, first articulated by Nobel laureate Robert Solow in 1987 when he quipped that computers could be seen “everywhere but in the productivity statistics.”
Nearly four decades later, the same conundrum appears to be playing out with generative AI. Despite breathless forecasts from consulting firms and technology vendors projecting trillions in economic value, the hard numbers tell a more complicated story—one that should give pause to boards, investors, and policymakers betting heavily on AI as the engine of the next great productivity surge.
A New Paper Puts Numbers to the Disconnect
A working paper published by the National Bureau of Economic Research (NBER) offers one of the most rigorous examinations to date of how AI adoption is translating—or failing to translate—into measurable productivity gains at the firm level. The researchers analyzed data across a broad cross-section of industries and firm sizes, tracking both the intensity of AI investment and subsequent changes in output per worker, revenue efficiency, and total factor productivity.
The findings are sobering. While firms that adopted AI tools reported improvements in certain narrow task-level metrics—such as the speed of generating first drafts of documents or the volume of customer service inquiries handled per hour—these micro-level gains have not aggregated into statistically significant improvements in firm-wide productivity. The paper identifies several structural reasons for this gap, including the substantial overhead costs of implementation, the reallocation of worker time toward AI supervision and error correction, and what the authors describe as “productivity displacement”—the tendency for efficiency gains in one area to be offset by new inefficiencies elsewhere in the organization.
CEOs Voice Growing Frustration Over Returns
The academic findings echo a sentiment that has been building in corporate boardrooms. As Fortune reported in February, a growing number of chief executives are privately expressing disappointment with the returns on their AI investments. The publication cited a survey of Fortune 500 CEOs in which a majority acknowledged that their companies had yet to see meaningful productivity improvements from generative AI deployments, even as spending on the technology continued to accelerate.
One CEO quoted in the Fortune piece described the situation as “a lot of demos and not a lot of P&L impact.” Another noted that while individual employees were enthusiastic about tools like ChatGPT and Microsoft Copilot, the organization as a whole had not figured out how to convert that enthusiasm into measurable business outcomes. The frustration is compounded by the fact that AI spending is not discretionary for many firms—competitive pressure and investor expectations have made it feel mandatory, regardless of near-term returns.
The Solow Paradox Returns, With a Twist
The parallels to the information technology boom of the 1980s and 1990s are impossible to ignore. When Solow made his famous observation in 1987, businesses were spending heavily on personal computers, networking equipment, and enterprise software, yet aggregate productivity growth in the United States was actually slowing. It took nearly a decade—and a wholesale reorganization of business processes around digital technology—before the productivity gains finally materialized in the late 1990s.
Economists who study this earlier episode point out that the lag was not accidental. As Erik Brynjolfsson of Stanford, who has written extensively on the topic, has argued, general-purpose technologies require complementary investments in organizational redesign, worker training, and process reengineering before their full benefits can be captured. The NBER paper makes a similar argument about AI, noting that firms which simply layered AI tools on top of existing workflows saw the smallest productivity effects, while the handful that undertook more fundamental restructuring showed more promising—though still modest—results.
The Hidden Costs That Rarely Make the Pitch Deck
One of the most striking findings in the NBER research concerns the hidden costs of AI adoption that are frequently omitted from vendor projections and internal business cases. The paper documents significant expenditures on what it terms “AI maintenance labor”—the human effort required to review AI outputs for accuracy, correct hallucinations and errors, manage prompt engineering, and handle the edge cases that automated systems cannot resolve.
In customer-facing applications, for example, the researchers found that while AI chatbots could handle a larger volume of initial inquiries, the rate of escalation to human agents actually increased in several cases, as customers grew frustrated with incorrect or irrelevant automated responses. The net effect on cost per resolved inquiry was, in some firms, negligible or even negative. Similarly, in knowledge work settings such as legal research and financial analysis, the time saved by AI-generated first drafts was partially consumed by the additional review and fact-checking those drafts required. Senior professionals reported spending less time writing but more time editing—a reallocation of effort rather than a reduction.
Capital Markets Are Starting to Ask Harder Questions
The productivity paradox is not merely an academic curiosity; it has real implications for the investment thesis underpinning the AI boom. Technology companies have committed more than $300 billion in capital expenditures on AI infrastructure in 2025 and 2026, according to estimates from multiple Wall Street analysts. Cloud providers, chipmakers, and enterprise software firms have all justified elevated valuations on the assumption that AI will drive a step-change in corporate efficiency and, by extension, willingness to pay for AI-powered services.
If the productivity gains remain diffuse and difficult to measure, the willingness of enterprises to sustain—let alone increase—their AI spending could come under pressure. Already, some analysts have begun drawing comparisons to the fiber-optic buildout of the late 1990s, when massive infrastructure investment preceded a painful period of overcapacity and write-downs. The comparison is imperfect—AI capabilities are advancing far more rapidly than bandwidth demand did in that era—but the underlying concern about the gap between investment and realized value is structurally similar.
Where the Gains Are Actually Appearing
It would be misleading to suggest that AI is producing no value whatsoever. The NBER paper identifies several areas where productivity improvements are both real and measurable. Software development stands out as one domain where AI coding assistants have demonstrably increased output per developer, particularly for routine tasks such as writing boilerplate code, debugging, and generating test cases. Customer support operations at very large scale—think millions of interactions per month—have also shown genuine cost reductions, though primarily in tier-one triage rather than complex problem resolution.
The Fortune report similarly noted that CEOs in the pharmaceutical and materials science sectors were more optimistic about AI’s near-term impact, citing applications in drug discovery, molecular simulation, and supply chain optimization where the technology is being applied to well-defined problems with clear metrics. The common thread among the success stories is specificity: AI appears to deliver the strongest returns when applied to narrow, well-structured tasks with abundant training data and low tolerance for ambiguity, rather than as a general-purpose productivity enhancer across the enterprise.
The Organizational Challenge May Be the Binding Constraint
Perhaps the most important insight from both the NBER research and the CEO surveys is that the binding constraint on AI productivity is not technological but organizational. The technology itself is advancing at a remarkable pace—large language models are becoming more capable, inference costs are falling, and new modalities are expanding the range of tasks AI can address. But organizations are struggling to redesign their workflows, incentive structures, and management practices to take full advantage of these capabilities.
This is a pattern that has repeated with every major general-purpose technology, from electrification to the personal computer. The firms that eventually captured the largest productivity gains from electricity in the early 20th century were not those that simply replaced steam engines with electric motors in the same factory layout. They were the ones that redesigned their factories from the ground up to take advantage of the flexibility that distributed electric power provided. The analogy to AI is direct: bolting a chatbot onto an existing customer service operation is the equivalent of swapping a steam engine for an electric motor without changing the floor plan.
What Comes Next for the AI Investment Cycle
History suggests that the productivity paradox is not permanent. The question for investors, executives, and workers is how long the lag will persist and how painful the intervening period will be. The optimistic view, articulated by some of the researchers behind the NBER paper, is that the current period of disappointing returns is a necessary phase of experimentation and learning, and that the organizational adaptations required to unlock AI’s full potential are already underway at leading firms.
The more cautious view is that the gap between AI hype and AI reality could trigger a correction in spending and valuations before the productivity gains arrive. If CEOs continue to report underwhelming returns, boards may begin to question the pace of investment, particularly in an environment of elevated interest rates and tightening capital budgets. The technology will almost certainly prove transformative over a longer time horizon—but as Solow’s paradox reminds us, the gap between “almost certainly” and “right now” can be wide enough to swallow billions of dollars in shareholder value.
For now, the data suggest that the AI productivity revolution is real in theory and elusive in practice. The firms most likely to bridge that gap will be those willing to undertake the difficult, unglamorous work of organizational redesign—rethinking not just which tasks AI can perform, but how entire business processes, team structures, and performance metrics need to change to accommodate a fundamentally different kind of tool. That work is harder to sell in a keynote presentation than a flashy demo, but it may ultimately be what separates the winners from the also-rans in the AI era.
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