The Fed’s AI Dilemma: Productivity Promises Meet Labor Market Disruption

The Federal Reserve is confronting a scenario its economic models were never built to handle: artificial intelligence capable of boosting productivity while simultaneously displacing workers, creating a policy paradox where unemployment and inflation could rise together.

Federal Reserve Governor Lisa Cook recently stated that AI could have significant consequences for Monetary Policy, warning that ‘our normal demand-side monetary policy may not be able to ameliorate an AI-caused unemployment spell without also increasing inflationary pressure’—a statement that signals profound unease at the highest levels of U.S. monetary policy.

Cook said ‘we appear to be approaching the most significant reorganization of work in generations,’ pointing to changes in computer coding occupations and the difficulties some workers face finding entry-level jobs as evidence that the transition has begun. Demand for labor in certain occupations has declined—most notably for coders, a field where AI has made significant strides, and the unemployment rate for recent college graduates has increased over the last few years at a time when some employers are deploying AI for what had been tasks previously performed by entry-level workers.

The 1990s Playbook That May Not Work

San Francisco Fed president Mary Daly recently invoked former Fed Chair Alan Greenspan, who looked past official data in the 1990s, which hadn’t yet incorporated higher Productivity from technological advances, and held rates steady rather than raising them. That era offers both hope and warning.

In the mid-1990s—the beginning of the computer and internet revolution—businesses were ramping up investment in information technology equipment and software to take advantage of these emerging technologies, but there was little impact on official measures of U.S. productivity growth, though incoming data during 1995 and 1996 were not signaling an increase in labor productivity growth (subsequent data revisions showed that productivity began accelerating before 1995). The FOMC remained patient on policy and the roaring ’90s followed, with a strong labor market and sustained growth.

But according to Yahoo Finance, the current AI wave differs in crucial ways: speed of deployment and breadth of cognitive tasks affected. Governor Michael Barr emphasized that, so far, the economic data is more consistent with a ‘gradual adoption’ scenario, and in this view, while some jobs are displaced, productivity gains eventually boost real wages and create new industries. Yet Barr warned that early warning signs are already visible, highlighting research showing that young people and early-career workers in AI-exposed fields—such as software development and customer service—are already seeing declines in employment relative to other sectors, and ‘for these workers, the short run may have long-term consequences’.

The Productivity Paradox Returns

Despite widespread adoption and massive capital expenditure—just four tech giants (Alphabet, Amazon, Meta, and Microsoft) had capital expenditures of $337.8 billion in 2025, primarily to build out AI infrastructure—productivity gains remain conspicuously absent from official statistics.

An NBER study found that 89% of managers saw no change in productivity (measured as sales volume per employee) over the past three years, despite AI adoption rising from 61% to 71% of firms between early 2025 and early 2026. ‘You can see the computer age everywhere but in the productivity statistics,’ economist Robert Solow wrote in 1987—a statement that eerily describes today’s AI economy according to a Fortune analysis.

San Francisco Fed President Mary Daly noted that AI adoption and use are still evolving, and the technology itself is changing rapidly, and what we know about AI and its impact on productivity growth and the economy remains uncertain. Research from the Federal Reserve Bank of San Francisco emphasizes that around the Twelfth Federal Reserve District—the nine states in the West—firms are using AI for consumer research, back-office operations, sales, and product development, saving time and money, with examples including using AI to research and develop new crop varieties in agriculture, and research case studies find similar results—cost-savings when firms use AI to automate.

Regional Variance Complicates the Picture

AI adoption varies dramatically across Federal Reserve districts, complicating attempts to model national impacts. In December, 70 percent of Fifth District respondent firms reported that they provided employees with AI tools in their jobs, according to the Richmond Fed. Meanwhile, results from a survey of firms in the Third Federal Reserve District—Delaware, southern New Jersey, and eastern and central Pennsylvania—indicate that half of firms are currently using generative AI, according to research from the Philadelphia Fed.

In Richmond Fed business surveys, firms do not think of AI primarily as a way to reduce the need for workers—efficiency and productivity come up much more often than a reduction of labor costs. Yet about half of the respondents who provide AI tools reported using it for tasks like analyzing quantitative and qualitative data, drafting or editing documents, generating graphs or images, or summarizing meeting notes, though even with respect to tasks, fewer reported using it for accounting/finance tasks, and although about 40 percent of respondents reported using AI to automate repetitive tasks, the operational changes that could engender lasting productivity gains—such as improving worker safety, tracking productivity, optimizing inventory, or assessing and tracking risk—seemed to be less prevalent.

The Dual Mandate Under Siege

The Federal Reserve’s dual mandate—maximum employment and stable prices—faces unprecedented tension. Governor Cook suggested that if AI sustains higher productivity, strong economic growth might continue even if unemployment rises due to changes in the job market, and this situation could present difficult decisions for policymakers, who might have to choose between maintaining higher interest rates to counter Inflation or reducing them to combat job losses.

Cook said in a speech that if AI continues to raise productivity, economic growth could remain strong, even as churn in the labor market leads to an increase in unemployment, and in a productivity boom such as this, a rise in unemployment may not indicate increased slack—’as such, our normal demand-side monetary policy may not be able to ameliorate an AI-caused unemployment spell without also increasing inflationary pressure’.

This means that monetary policymakers would face tradeoffs between unemployment and inflation.’— Governor Lisa Cook, National Association for Business Economics

Governor Michael Barr warned that AI might deeply disrupt the labor market, acknowledging some workers could be negatively affected in the near term, but he believes the long-term effect will likely be very positive, suggesting that a gradual adoption of AI could support strong productivity growth without causing extensive job losses, though conversely, he cautioned that rapid adoption might lead to a scenario with strong economic output but few new jobs, where AI systems replace various professional, service, manufacturing, and transportation roles.

Modeling the Unknowable

Fed officials are actively incorporating AI scenarios into economic models, but acknowledge profound uncertainty. What do AI’s still-developing labor and price effects mean for monetary policy? The short answer is that it is likely still too soon to tell, and as always, policymakers confront the challenge of sorting out changes in the economy that are due to cyclical factors from those resulting from structural change, which AI may well represent, and some of the recent changes in hiring patterns, productivity growth, and inflation are likely to represent AI-driven change, but it is difficult to know the degree.

Productivity gains from AI may affect the relationship between employment and inflation and hence the conduct of monetary policy—for example, a productivity-induced boost to the growth rate of potential output could imply that monetary policy will not need to react strongly to what would have previously been perceived as tightness in the labor market.

In a February speech, Vice Chair Jefferson argued that with faster productivity gains consumers may anticipate higher future income growth and choose to spend more now, reducing their savings rate, while ‘increased productivity gains also imply a rise in the marginal productivity of capital and thus higher investment demand,’ and ‘all other things being equal, persistent increases in productivity growth are likely to result in an increase in the neutral rate, at least temporarily.’

Some Federal Reserve officials have begun suggesting in recent days that productivity growth from artificial intelligence could mean higher interest rates, and Fed Governor Michael Barr said ‘I expect that the AI boom is unlikely to be a reason for lowering policy rates.’