The Numbers Come First
Researchers at the Federal Reserve Bank of St. Louis analyzed nearly 490,000 corporate earnings call transcripts from 5,198 publicly traded U.S. firms between 2000 and 2025. Their finding is blunt: artificial intelligence has not yet produced a measurable bump in aggregate productivity.
The share of productivity commentary tied to AI rose from near zero before ChatGPT's late-2022 debut to roughly 15% of all productivity discussion by the end of 2025. Yet approximately 95% of those AI-related sentences describe gains executives expect in the future, not gains already realized — a share that has held steady since 2023. When executives do characterize AI's effect, they are almost uniformly bullish: 95% describe productivity as rising, compared with 75% for non-AI commentary.
The Abundance Paradox
Economist Serdar Ozkan, one of the paper's authors, offered a more unsettling explanation for the statistical silence. 'Some things are going to become more abundant,' Ozkan said. 'That means they're also going to become probably less valuable.' The mechanism is straightforward: when AI makes output radically cheaper to produce, that output simultaneously loses value, and the productivity math cancels itself — gains in one column erased by falling prices in another.
Co-author Aakash Kalyani, who has separately studied diffusion patterns across general-purpose technologies, said the profession has largely reached consensus after the initial post-ChatGPT excitement faded: 'The aggregate gains will be in the future, whereas what you see right now is a lot of investment and a lot of excitement and optimism for the future.' He noted that technology diffusion across regions, occupations, and firms is 'extremely slow,' typically unfolding over 20 to 30 years, and compressing AI's lag to just three to five years 'would be a huge change' from historical precedent.
A Century-Old Pattern
Ozkan invoked economist Robert Solow's famous observation that 'you can see the computer age everywhere except in the productivity statistics,' drawing a direct line to electrification, which he said took 'several decades' to reorganize factories, retrain workers, and change workflows before its productivity payoff showed up in the data. Stanford economist Erik Brynjolfsson called this the 'productivity paradox' in a 1993 paper for MIT and has described the current AI moment as the modern sequel.
The pattern aligns with other 2026 Fed research. A Kansas City Fed analysis found the recent productivity pickup in official data is 'not yet broad-based,' with a small set of industries accounting for most of the gains. Fed Chair Kevin Warsh told Congress in July that AI 'hasn't displaced workers' so far and has made them 'a bit more productive,' but cautioned that 'the long term can be quite far out.' Previous St. Louis Fed research estimated generative AI represented only a 1.1% increase in productivity by late 2024 relative to 2022 — modest against the 2.3% and 1.6% overall productivity growth the economy posted in 2024 and 2023, respectively.
The researchers also note that firms discussing AI positively have increased R&D, capital expenditures, and investment — a correlation that has strengthened since their earlier 2024 work. A related San Francisco Fed study found AI-positive firms saw substantially higher investment and R&D growth by 2025, concentrated among the largest technology firms building AI infrastructure.
CEO Times Read
For investors and executives, the honest takeaway is this: the capital is flowing, the commitment is real, and the historical analogy — electrification, the PC revolution — is not flattering to the impatient. Free markets are pricing in a transformation that the productivity statistics have not yet confirmed. That gap is not fraud; it is the ordinary friction of a general-purpose technology working its way through an economy of 330 million people.
The risk is not that AI fails to deliver. The risk is that Washington, watching the lag, intervenes with regulation or industrial policy before the compounding begins — turning a natural diffusion curve into a bureaucratic obstacle course. Capital rewards clear rules and patience. The data will catch up; the question is whether the administrative state will let it.
