A new survey from the National Bureau of Economic Research (NBER) suggests that the real-world business impact of artificial intelligence may still be more limited than the market narrative implies. Among 6,000 executives across the U.S., U.K., Germany, and Australia, nearly 90% said AI had not produced a significant effect on employment or productivity over the past three years.
That finding stands in sharp contrast to what is happening inside the technology sector. In the first quarter of 2026, the industry recorded 78,557 layoffs, with 47.9% linked to AI adoption and workflow automation. The disconnect is striking: many companies are restructuring teams around AI before they can clearly demonstrate that the technology is generating measurable productivity gains.
An AI version of the Solow Paradox
This tension is drawing comparisons to the “Solow Paradox” from the computer era, when investment in new technologies surged but broad productivity gains took longer to appear in the data. AI may now be following a similar path, with spending racing ahead of visible economic returns.
According to the report, around $250 billion has already been invested in AI. Yet PwC’s 2026 Global CEO Survey found that only 12% of CEOs reported financial gains from AI so far. That suggests most businesses remain in an early implementation phase, where experimentation, integration, and operational change are still underway.
Benefits may arrive later than workforce changes
Experts cited in the source say AI’s true productivity impact may take another 6 to 12 months to emerge. Companies adopting AI often need time to redesign workflows, train employees, and adjust internal systems before efficiency improvements show up in financial or operating metrics. During that transition, automation-driven layoffs can arrive sooner than the benefits.
For now, the data points to a complicated reality: AI is already influencing hiring and workforce decisions, but its promised gains in productivity and profitability have yet to be broadly confirmed. The next few quarters may prove critical in determining whether AI can turn heavy investment into measurable business value.