Federal Reserve Vice Chair Philip N. Jefferson said on November 13, 2026 that generative artificial intelligence is spreading quickly through the U.S. economy but has yet to deliver a clear payoff in measured productivity, while warning that the technology-driven surge in data center investment is already adding to price pressures. "The evidence so far appears consistent with a contribution to aggregate productivity that is limited, but with the potential to grow quite substantially, broadly in line with historical episodes of technological breakthroughs," Jefferson said in remarks delivered to the University of California, San Diego Economic Roundtable in San Diego, California. "That said, the magnitude and timing of forthcoming productivity gains remain highly uncertain."

The speech, titled "New Technology, Old Questions: Productivity Puzzles and the Macroeconomy," was delivered before an audience of students, faculty, and business leaders. The full text is available on the Federal Reserve's website, and the remarks are also available as a PDF. Jefferson noted that the views expressed were his own and not necessarily those of his colleagues on the Federal Reserve Board or the Federal Open Market Committee (FOMC).

The Vice Chair role is the second-highest-ranking position on the Federal Reserve's Board of Governors, making Jefferson one of the most senior U.S. economic policymakers to lay out a detailed public assessment of AI's effects on growth, prices, and employment.

Lessons from the Computer Age

Jefferson opened with Nobel Prize-winning economist Robert Solow's famous 1987 observation that "you can see the computer age everywhere but in the productivity statistics," suggesting Solow "may have been expecting too much from the computers he saw." While 32 percent of workers used computers at work in 1989, the machines of that era were not very powerful: the processor in a phone today can perform about as many calculations per second as 10 million 1987-vintage personal computers. In aggregate, national spending on computers—shown in figure 1 of the speech—was barely detectable at the time, even as PCs became an important part of the zeitgeist.

Ten years later, computer investment had taken off, and some observers—most notably then-Chairman Alan Greenspan—believed they could see it in the productivity statistics, though the link between the two was only established later. Jefferson said investment in information technology (IT) capital—computers, software, and communications equipment—increased the annual growth rate of labor productivity by about 3/4 percentage point in the late 1990s.

The boom involved more than capital accumulation. Businesses also reorganized their operations around computers, further supporting productivity growth. Advancements in technology changed the cost of creating, storing, and communicating information, giving firms an opportunity to rethink their scope, scale, and production functions. E-commerce, a wave of globalization, and software in the cloud eventually emerged from that process. One sign that reorganization was beneficial and significant was the rapid rise in output per unit of input outside the IT sector in the early 2000s—growth that reflected increases in what economists call total factor productivity, or TFP, rather than purely more capital.

Jefferson noted that the seeds of the 1990s productivity boom were planted long before Solow's 1987 observation. Basic research that laid the foundations for modern computing included the invention of the transistor in 1947 and the Fortran compiler in 1954, followed by decades of applied research by firms that yielded practical applications and relentlessly improved information technology.

That history suggests a simple model of productivity growth. Basic research, conducted without a definite application in mind, expands knowledge about the world. Applied research develops practical industrial applications, which increase productivity and drive down the cost of production. When costs fall, prices tend to fall as well, especially in the presence of competition. Over time, firms intensify their use of lower-priced capital goods produced by higher productivity—a process known as "capital deepening"—and firms using that capital rethink their approach to production, further increasing TFP and perhaps restarting the investment cycle. As the computing example illustrates, Jefferson said, the process takes time for its full effect to be felt and measured, particularly for technologies that require business reorganization.

Applying the Model to AI

Jefferson argued the same model applies reasonably well to the development of AI. Today's generative models can be traced back to basic research that began soon after the transistor was invented. Practical applications emerged many years later—for example, the U.S. Postal Service began using AI to read hand-addressed envelopes in the 1990s. Usage took off, especially in the information and finance sectors, about 10 years later with the emergence of machine-learning models. Finally, after incredible improvements and training on massive data sets—all the text on the worldwide web, by some accounts—large language models are now good enough to support today's popular chatbots, coding assistants, and smart devices, along with a host of less visible applications such as scientific research.

"Today's world mirrors the one Bob Solow observed," Jefferson said. Generative AI dominates news headlines, sits on phones, and pops up in software and customer service calls, and it may have far-reaching implications for workers, firms, and government agencies, including central banks. But the old questions persist: How much are businesses and workers actually using it, and is that use affecting the productivity statistics?

Adoption: Rising but Uneven

Recent surveys find an appreciable and rising share of U.S. firms and workers are using generative AI. Surveys of workers have found that 40 to 50 percent of working adults are using the technology in their jobs, as shown by the blue line in figure 2 of the speech. Among those workers who do use AI at work, however, only a modest fraction uses it every day.

Adoption also varies widely across industries. While indicators of business adoption differ depending on methodology, the Census Bureau's survey of a nationally representative sample of U.S. firms illustrates the cross-industry differences. As shown in figure 3, the survey indicates that, as of October 2026, roughly 25 percent of all U.S. firms were using the technology in at least one business function as of the middle of this year. That share was closer to 40 percent in knowledge-intensive sectors such as professional services and information, while at the other end of the spectrum it was around 10 percent for the mining and leisure and hospitality sectors.

Compared with more mature technologies like the personal computer, Jefferson said, AI is not truly everywhere—in the sense of being fully integrated into most firms' and workers' workflows across the economy. Rather, the economy appears to be at an earlier stage that is a natural part of the long cycle of innovation and deployment: the new technology has shown its capability, and firms and workers are actively using it and exploring its possibilities, with some industries, firms, and occupations much farther ahead than others.

Productivity Statistics: Too Early to Tell

Is the adoption seen so far showing up in aggregate productivity statistics? Productivity growth has in fact moved up in recent years, but Jefferson said it is too early to say what role AI played in that development. Because productivity measures are so volatile, economists often look through quarterly or even annual swings by examining long trailing averages. Labor productivity growth averaged 2.2 percent between 2020 and 2025—a welcome step up from the modest 1.5 percent average growth rate over the previous business cycle. However, that surge began before the debut of popular AI chatbots in 2022 and likely reflects a variety of factors, including a pickup in new business formation. "Teasing out the contribution of AI to recent productivity growth is not straightforward," Jefferson said. "We'll need to see more data." He noted that research retrospectively tied the 1990s productivity boom to IT, but that was not known for sure in real time.

On the microeconomic side, a growing body of evidence shows the new technology lifting efficiency directly and substantially for some specific tasks, particularly where it can most readily substitute for humans. One influential academic study, for example, found that generative AI tools significantly improved the ability of some customer support employees to resolve issues.

At the same time, evidence about how task-level efficiency gains affect broader productivity appears mixed. Some recent studies have found positive associations between productivity growth and adoption of, or exposure to, AI at the firm level. On the other hand, some surveys that attempt to measure causality more directly have found only modest effects so far. One recent study that surveyed senior managers estimated AI has boosted sales per employee by just 1/2 percent over the past three years.

Jefferson offered two possible explanations for the apparent disconnect between task-level and firm-level gains. First, even among firms that use AI, the scope remains limited as they are still experimenting and learning: one recent study found that among adopting firms, most are using AI in at most a few business functions, most commonly sales and marketing, strategy and business development, and information technology. Second, even for the most natural use cases, efficiency gains in one domain go hand-in-hand with offsetting costs, new demands, or bottlenecks elsewhere—for instance, some firms report that AI-generated code contains more errors, so more debugging work is necessary.

Even so, Jefferson said broader improvements may well materialize over time. Firms and workers will likely broaden and intensify their use of AI and reorganize production and workflows, and successful firms could gain a competitive advantage, enabling them to expand and draw labor and capital away from competing firms.

Macroeconomic Implications

Jefferson's baseline view is that adoption of the new technology will ultimately lift the economy's productive capacity substantially, but gradually. Expectations of those longer-term gains on the supply side of the economy are already increasing aggregate demand significantly. A substantial portion of the rise in equity prices over the past few years can be attributed to enthusiasm around the new technology, and that added wealth appears to be raising household spending. Firms are building and equipping new data centers on a very large scale to support model training and inference, driving rapid growth in aggregate business investment.

That spending is putting upward pressure on resource utilization and the general price level. In addition, because investment demand is concentrated in some sectors where supply has struggled to keep pace—such as memory and logic chips—it has also contributed to steep increases in prices of specific goods. Over the medium term, Jefferson said, the supply of these goods should catch up, and the broader boost to aggregate productivity from adoption should materialize; both developments should ease the upward pressure on prices seen today.

While the new technology should ultimately generate higher output and income in the aggregate, Jefferson acknowledged the transition could be painful for some workers and businesses. Firms that lag in adoption could be at a competitive disadvantage, and their long-term viability could be at risk. Even at firms that successfully adopt the technology, workers who specialize in tasks that AI can perform more efficiently—such as certain aspects of software development—may face job displacement.

"One risk to my baseline outlook is that the new technology could displace workers much faster than they can adapt and retrain, which could result in an increase in unemployment," Jefferson said. In that scenario, a key question for monetary policy would be how much of the increase is structural—reflecting a persistent mismatch between worker skills and firms' needs—and therefore likely to persist even when the economy is operating close to its full potential. Monetary policy can affect cyclical conditions, such as a temporary downturn in employment and spending, but it cannot address the structural factors that determine long-run rates of employment.

Jefferson also flagged the potential effects on the longer-run neutral rate of interest, often called r*. If the neutral rate rises, then for any given level of the federal funds rate, policy effectively becomes more accommodative; conversely, if the neutral rate declines, policy effectively becomes more restrictive. If the new technology leads to permanently higher levels of productivity growth, it may increase firms' desire to invest and hence their demand for funding, raising interest rates. Higher productivity growth may also discourage household saving and raise consumption by increasing expected future income—conditions under which, to reconcile increased investment with reduced saving, the neutral rate would likely rise. Predicting changes in the neutral rate is challenging, however, given the historically noisy relationship between productivity growth and real interest rates. Policymakers, he said, must remain attentive to real-time and potential future developments to ensure the stance of monetary policy remains appropriately calibrated.

The signposts, by Jefferson's own account, are whether firms widen their use of AI beyond the few business functions where it is concentrated today, whether supply in constrained segments such as memory and logic chips catches up with data center demand, and whether enough data accumulate to isolate AI's contribution to aggregate productivity.

Economic Outlook and Policy Stance

Turning to the broader economy, Jefferson said it has gained momentum this year, expanding at a pace roughly in line with its potential—notable given a cascade of shocks, including an energy shock and significant changes in trade policy, in addition to the technology buildout. GDP expanded at a 3.4 percent pace in the third quarter, as shown in figure 4 of the speech. Business investment has been quite strong, and consumer spending has remained resilient. Jefferson said he expects the expansion will continue roughly in line with potential output next year.

A broad range of recent labor market data shows conditions have stabilized. The unemployment rate has largely moved sideways this year, settling at 4.1 percent in October, as shown in figure 5. Layoffs remain low and job openings have moved a bit higher. While job creation has been volatile, payroll gains have broadened to many sectors in recent months—something Jefferson finds encouraging. He expects the unemployment rate to stay around its current level in the near term, a level he views as consistent with maximum employment. The risks to his output and employment forecasts are roughly balanced: an increase in uncertainty tied to Middle East tensions may weigh on activity and hiring, while a further surge in investment spending could support stronger growth.

On the price-stability side of the Fed's dual mandate, inflation remains notably above the FOMC's 2 percent target. The 12-month change in the personal consumption expenditures price index, shown by the blue line in figure 6, was 3.6 percent in September. Higher energy costs are one of the drivers of elevated inflation: oil prices have been quite volatile, and global energy supplies have come under renewed strain amid heightened geopolitical tensions. Jefferson said he remains concerned about the risk of higher energy prices leading to a persistent rise in inflation more broadly.

The 12-month change in core PCE inflation, which excludes volatile food and energy prices, was 3.1 percent in September. Indirectly, higher energy costs are showing through in areas including airfares and transportation, and the boom in demand for new technologies has been driving unusually strong cost increases for consumer goods and services that depend on the same inputs, such as memory. Data received so far for October remain consistent with well above-target inflation.

Jefferson is also paying close attention to inflation expectations. Survey-based, short-term measures are elevated, mainly reflecting higher energy prices, while most measures of longer-term inflation expectations have remained stable at levels consistent with 2 percent inflation.

At the FOMC's policy meeting last month, Jefferson joined the majority of his colleagues in voting to hold the federal funds rate steady, as shown in figure 7. Data received since that meeting have been consistent with economic activity and labor market conditions remaining broadly solid, while inflation readings show persistent price pressures. Accordingly, Jefferson said he sees the trajectory of inflation as providing the more important signal for determining the appropriate stance of monetary policy. Going forward, he believes further policy adjustments should be determined by carefully examining trends in the data, the evolving outlook, and the balance of risks.

Conclusion

In closing, Jefferson returned to his central theme: new technologies cause a revisit of old but very important questions. The paths from innovation to adoption, adaptation, and durable productivity gains can be uneven and unpredictable—puzzling, even. New technologies command attention long before they affect measured productivity, and they force a rethinking of how the economy works, making monetary policymaking exciting and challenging. "Thankfully, through it all, our mission at the Fed is clear," he said: deliver on the dual mandate given to the Fed by Congress—maximum employment and price stability. "These are the goals of every action we take."

Speech Notes and References

1. The views expressed in the speech are Jefferson's own and are not necessarily those of his colleagues on the Federal Reserve Board or the Federal Open Market Committee.
2. Recent technological advancements discussed in the speech are sometimes referred to as super intelligence, or SI.
3. Robert Solow (1987), "We'd Better Watch Out," New York Times, July 12.
4. The Intel 386 microprocessor, a high-end option for PCs at the time, performed about 200,000 floating point operations per second (FLOPS). See Richard Grehan, Tom Thompson, Curtis Franklin Jr., and George A. Stewart (1988), "Introducing the New BYTE Benchmarks," BYTE, June. The Apple A19 chip, used in the iPhone 17, performs 2.07 trillion FLOPS. See Nanoreview.net (2026), "Apple iPhone 17: Specifications and Benchmarks,"
5. The small share of computers in the capital stock was noted as a resolution of the paradox at the time. See Stephen D. Oliner, Daniel E. Sichel, Jack E. Triplett, and Robert J. Gordon (1994), "Computers and Output Growth Revisited: How Big is the Puzzle?" Brookings Papers on Economic Activity, no. 2, pp. 273–334.
6. Alan Greenspan (1998), "The Implications of Technological Changes," speech delivered at the Charlotte Chamber of Commerce, Charlotte, N.C., July 10. For evidence that the step-up in productivity and information technology were linked, see Stephen D. Oliner and Daniel E. Sichel (2000), "The Resurgence of Growth in the Late 1990s: Is Information Technology the Story?" Journal of Economic Perspectives, vol. 14 (4), pp. 3–22.
7. Daniel E. Sichel, Stephen Oliner, and Kevin J. Stiroh (2007), "Explaining a Productive Decade," Brookings Papers on Economic Activity, Spring, pp. 81–152.
8. Timothy F. Bresnahan, Erik Brynjolfsson, and Lorin M. Hitt (2002), "Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm-Level Evidence," The Quarterly Journal of Economics, vol. 117 (1), pp. 339–76.
9. Sichel, Oliner, and Stiroh, "Explaining a Productive Decade" (see note 7).
10. James Moor (2006), "The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years," AI Magazine, December 15, pp. 87–91.
11. Brian Tanton (2007), "Becoming More Clear," Mailing Systems Technology, January 29.
12. Alexander Bick, Adam Blandin, David J. Deming, and Tyler R. Schumacher (2026), "What Work Does Generative AI Do?" NBER Working Paper Series 35677 (Cambridge, Mass.: National Bureau of Economic Research, August).
13. Because large firms account for a disproportionate share of total employment, adoption rates weighted by employment are even higher than the Census figures discussed. See Ivan Yotzov, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Chanya Chawla, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Samil Oh, Katia Garcia Loredo, Monique Reid, Pawel Smietanka, Gregory Thwaites, and Ben Zhe Wang (2026), "Firm Data on AI," NBER Working Paper Series 34836 (Cambridge, Mass.: National Bureau of Economic Research, February; revised October 2026). This paper finds that the employment-weighted adoption rate among U.S. firms is 95 percent.
14. Erik Brynjolfsson, Danielle Li, and Lindsey Raymond (2025), "Generative AI at Work," The Quarterly Journal of Economics, vol. 140 (2), pp. 889–942.
15. Tania Babina, Alex X. He, and Renhao Jiang (2026), "Canaries in the Gold Mine: Early Productivity Gains from Artificial Intelligence Creating Organization Capital," NBER Working Paper Series 35684 (Cambridge, Mass.: National Bureau of Economic Research, August).
16. Yotzov et al. (2026), "Firm Data on AI" (see note 13). This estimate is an employment-weighted average over survey respondents.
17. Kathryn Bonney, Cory Breaux, Emin Dinlersoz, Lucia Foster, John Haltiwanger, and Aditya Pande (2026), "The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks," Center for Economic Studies Working Paper No. CES-26-25 (Washington: U.S. Census Bureau, April).
18. A substantial share of this investment demand, especially for IT equipment, is satisfied with imports, reducing the direct effect on GDP. That said, capital deepening raises labor productivity, whether the capital was imported or not.
19. In particular, memory chip manufacturers have announced a massive increase in production capacity, which will ease price pressure for products using DRAM in coming years.

Source: Federal Reserve Board, speech by Vice Chair Philip N. Jefferson, "New Technology, Old Questions: Productivity Puzzles and the Macroeconomy," November 13, 2026, https://www.federalreserve.gov/newsevents/speech/jefferson20261113a.htm