Federal Reserve Governor Michael S. Barr cautioned that the gains from artificial intelligence risk being more narrowly shared than those of earlier technological revolutions, in a speech presenting new evidence that wealth and income inequality in the United States currently stands among the highest levels of the past century.

Delivering remarks titled "American Inequality and the Future of AI" at Yale University in New Haven, Connecticut, on November 12, 2026, Barr argued that inequality is an aspect of every economy and that understanding its extent and evolution in the United States can help form a clear view of the economic outlook and how the economy is likely to react to new developments. Widening inequality may also, depending on many factors, reflect a decline in economic opportunity for many people, which can limit the economy's productive capacity, he said.

The speech built on remarks Barr delivered earlier this year exploring scenarios in which AI could affect economic inequality, for better or for worse. This time, he inverted the focus: first summarizing the current state of U.S. economic inequality using new results from a long-running Federal Reserve household survey, then placing the figures into a century of historical context, and finally examining the forces that have shaped inequality and what they may indicate about how AI is likely to affect the way wealth and income are distributed.

"In my judgment, now is the time for society to begin to consider how to address these potential challenges—while AI adoption is in its relatively early stages—so we can realize the long-term benefits for society," Barr said.

Wealth and Income Concentration Today

The most up-to-date and detailed information on U.S. inequality comes from the latest edition of the Federal Reserve's triennial Survey of Consumer Finances, conducted in 2025 and published in October 2026. Nearly 4,500 single-person households and families were interviewed. The survey confirms that both wealth and income are highly concentrated, and that inequality is greater for wealth than for income.

Households in the top 10 percent by income receive almost one-half of all income in the United States, and the top 1 percent receive about one-fifth. On the wealth side, the wealthiest 10 percent of households hold nearly three-fourths of all wealth, and the top 1 percent hold about one-third—nearly $60 trillion. By contrast, the entire lower half of households, measured by wealth holdings, has only 2 percent of wealth. Low wealth, Barr noted, also makes it harder to get by day to day: the Fed's annual Survey of Household Economics and Decisionmaking found that 31 percent of respondents in 2025 did not have the cash to cover an emergency expense of $500 or more.

Concentration is even more pronounced at the very top. The upper 0.1 percent of households—only 135,000 individuals and families—owns one-sixth of the nation's wealth. A household at the lower edge of the top 1 percent has 75 times the wealth of the median household: $16.3 million compared with $215,000. A household at the lower edge of the wealthiest 0.1 percent has 300 times the wealth of the median household.

Wealth concentration has increased at the very top as well. Combining Forbes magazine's tally of the 400 wealthiest individuals with Survey of Consumer Finances data, Barr offered a rough estimate that their share of total U.S. wealth rose from 3 percent in 2022 to 4 percent in the most recent survey. Forbes counts nearly 1,000 U.S. billionaires.

Business Assets Drive Top-Heavy Wealth

Digging into the sources of widening inequality, Barr said the wealth of those at the top is primarily held in the form of business assets. Research using Survey of Consumer Finances data back to the 1940s shows that returns on business equity have been a major source of widening wealth inequality over the decades. Most income for wealthy households is capital income—interest, dividends, capital gains, and business profits—rather than wages and salaries. For middle-class households, by contrast, most income comes from paychecks, which makes building wealth harder.

Although the largest asset class for wealthy households is typically stocks and other financial assets, a substantial amount of their capital income also comes from ownership of private businesses. These come in two broad types: an actively managed business—such as a plumbing business, retail store, or manufacturing firm—and a passive investment in a business, commonly known as private equity. Owning and running a business has long been a way households become wealthy, but private equity investments are passively run and typically open only to the wealthiest individuals and families. These private investments have delivered returns higher than the typical mix of assets held by middle-income households. In the most recent Fed survey, private equity investments accounted for 12 percent of the assets of the wealthiest 0.1 percent of households.

For households above the median but below the 90th percentile of wealth, the most prevalent assets are housing and stocks, though the value of housing equity has grown more slowly than that of business equity. About three-fourths of households in this group have stock holdings, mostly through retirement accounts. While ownership of publicly traded companies has become more widespread across the income spectrum as 401(k) plans have become common over the past few decades, stock ownership by value remains highly concentrated among the wealthiest households: households in the 50th through 90th percentiles of wealth hold only 17 percent of all public stocks by value.

Finally, on net, households in the bottom half of the wealth distribution hold just over 2 percent of all wealth, an average of $52,000. About 40 percent of them have equity in homes, and nearly 40 percent own business equity, either through a retirement account or as their own business—but the values are low compared with households in the upper half.

A Century of Widening and Narrowing

By many estimates, today's inequality levels are among the highest in the past 100 years. But wealth and income inequality in America have changed significantly over the country's history, widening and at times narrowing, driven by booms and busts in financial assets, structural changes in the economy, and changes in tax and regulatory policy.

In the Gilded Age, roughly 1870 to 1910, incomes rose strongly for most people, but it was also the era when large fortunes were accumulated by industrialists and their families. With no income tax or estate tax on the wealthiest and limited public education for everyone, the gap between those at the top and most others widened.

Over the subsequent decades, especially during the New Deal, legal and institutional change strengthened worker power, and the introduction and broadening of progressive income and estate taxes helped limit the concentration of income and wealth. Inequality narrowed in the decades after World War II, an era of fast economic expansion that benefited many households, supported by improvements in public education including the GI Bill, income tax changes that made rates more progressive, and growing unionization that raised wages for workers.

Beginning in the late 1970s, however, wealth and income inequality began to increase again, in a period of slower economic growth and sometimes high inflation. Top income tax rates declined, and fewer households were required to pay the estate tax. As in the 1910s and 1920s, business equity grew as a share of total assets, helping the wealthiest households become wealthier. Housing—the typical middle-class asset—generated wealth but at a slower pace than business assets, a gap exacerbated by the decline in housing wealth during the Global Financial Crisis. Over the same period, real wages grew slowly, and minimum wage laws failed to keep up with inflation and the growth of the economy.

The lesson from this history, Barr said, is that the level of inequality in American society is not immutable—it changes over time. These historical patterns suggest policy choices play a crucial role in shaping inequality levels, though economic growth, technological change, education, and globalization also play key roles.

Labor's Shrinking Share of National Income

Some of these trends are most clearly seen in the decline in the share of national income going to labor over the past 25 years. Because lower- and middle-income households depend heavily on wages and salaries, the split between labor earnings and capital income—dividends, interest, and private business earnings—is a useful lens on inequality, even though households across the spectrum hold some business equity.

After World War II, the labor share of national income was fairly steady between 60 percent and 65 percent, but it began falling in fits and starts in 2001. The decline accelerated after the pandemic and has been especially steep in the past year, reaching a record low of 52.8 percent in the second quarter of 2026. Barr acknowledged that some of the steep post-pandemic decline reflects the relative rise of less labor-intensive industries, and that measurement issues associated with how income is allocated between labor and capital over time play a role—one research paper estimates the decline between 1986 and 2017 is overstated by approximately one-third as a result of the shift to pass-through firms. But even after adjusting for those factors, he said, there is a clear downward trend.

Economic research points to three causes of the decades-long shift from labor toward capital income, each highly relevant to the potential effects of AI on inequality: technological innovations that have reduced demand for labor and decreased wages; the concentration of market power in many industries with a small number of firms; and the related reduction in the bargaining power of workers.

Technology, Concentration, and Worker Power

Technology replacing labor has been going on for centuries, but new technology can also augment the productivity of existing jobs and create new jobs, including those that are hard to envision when the technology is initially adopted—a point Barr said he has noted in a number of speeches. The internet and the businesses it created reduced employment in some occupations, such as travel agents, and eliminated others, like video store workers. Yet new technologies have expanded jobs in other areas: millions of full-time social media creators now earn a living in the United States, something unimaginable a decade ago.

What matters for inequality, Barr argued, is how returns to labor compare with returns to capital from the adoption of new technology. Available evidence from across the globe suggests advances in digital technology have lowered the price of investment in technology relative to labor. When capital is cheaper relative to labor, firms use relatively more of it, and capital can substitute for labor—for example, through the adoption of automation and robots. Some evidence shows that firms that adopt robots may become more productive and expand their labor force, but in this expansion labor is outpaced by the expansion in capital, seen in rising profits and equity appreciation, which widens inequality. Barr said it will be important to watch these developments closely given the immense capital investment going into the AI sector.

Market concentration is another driver of the declining labor share. Researchers estimate concentration has increased significantly in three-quarters of U.S. industries since 2000, and since that time wages have grown at a slower rate than labor productivity. Because larger firms are often more productive, new technologies can in turn contribute to market consolidation, producing so-called "superstar" firms. These firms have lower labor shares of income because they can establish higher product markups—the difference between their costs and what they can sell their product for—where there is less price competition and fewer alternatives for customers. At the same time, even with higher markups, their ability to deploy new technologies means these firms may be able to charge lower prices than before, which could serve to further increase their size and may result in lower costs for consumers.

Together, Barr said, technological change, market concentration, and declining bargaining power for workers may have contributed to the declining share of labor income over time—and all these changes have tended to favor those with the highest incomes and wealth, driving greater wealth inequality.

How AI Could Reshape the Labor Share

The discussion of superstar firms, Barr observed, likely brings to mind the handful of companies dominating AI: the hyperscalers and frontier AI labs. The AI sector has seen tremendous investment, with announced plans suggesting capital deepening in the sector will escalate further. AI could turn out to be what some researchers call capital-biased innovation, in which returns go primarily to capital rather than to labor.

In previous remarks, Barr has explored scenarios for how AI could evolve as a technology and how it could affect labor markets. Key questions include the extent to which AI will substitute for human workers or largely complement them, the degree to which AI will create new jobs, and how market competition and the bargaining power of workers will affect the labor share of income. The nature of AI—particularly its ability to learn how to complete complex, nonroutine tasks that require extensive knowledge difficult for humans to achieve—raises the question of whether AI's effects on the labor market will be stronger than those of other technologies. Having previously explored whether labor displacement could be larger than in the past, Barr said he considered in this speech the risk that the benefits of this technological revolution could be more narrowly shared than those that came before.

The Concentration Scenario

Some researchers believe AI has characteristics that, in past examples of high-tech advances, reinforced the concentration of market power. AI depends on access to data, model improvements, and computing power, so it benefits from economies of scale and scope. AI itself is a powerful tool to train and accelerate the development of new AI models, including through self-reinforcement learning. The extent of the advantage possessed by AI leaders may prove to be unprecedented.

If a small number of AI firms dominate the market and receive the lion's share of investment returns, the wealth of the managers and owners of these firms will surge, Barr said. Large businesses could afford AI investments and continue to grow, while smaller firms would see declining market share. In this scenario, the vast majority of businesses and their employees would have limited access to ever-improving AI resources, meaning they would see smaller returns. Overall, the labor share of income would decline, market concentration would increase, and inequality would go up.

The Broad-Access Scenario

Alternatively, competition and distributed innovation could make AI a cheap and ubiquitous commodity, with gains widely shared. In that case, most businesses would have access to AI resources sufficient to support improved productivity and further innovation, and AI could democratize access to the skills and information needed for small businesses to thrive. Some believe many businesses will find that most of their AI needs can be fulfilled by smaller-scale language models that would not need to be on the cutting edge of technology—and in that case, benefits would be more broadly shared.

So far, Barr said, there has been relatively little displacement of workers by AI, along with some significant evidence that the transition to AI is enhancing the productivity of some workers, increasing demand in some sectors, and even creating new occupations. Vigorous competition among AI firms has meant access to AI remains fairly broad. But as fast as AI has grown, its implementation is still limited, and no one knows the degree to which its benefits will be shared.

Signs of labor substitution are already emerging in some sectors, especially among younger, less-experienced workers. Tasks that are easily automated, with clear guardrails and predictable outcomes, might see rapid labor substitution, while tasks requiring human judgment, management, coordination and relationships, creativity, or outputs that are hard to measure might see more labor augmentation.

The pace of change matters, Barr said. If labor market changes happen quickly, it will be hard for workers to adjust and dislocations might be large, whereas a more gradual adoption might permit more orderly adjustments. Still, society should be prepared for the possibility of serious short-term disruptions in the labor market that need to be managed effectively to ensure the benefits of AI are realized over the long term. If AI proves capable of work that displaces humans, the extent of the disruption will depend in part on whether society undertakes the investments needed in new job creation and worker training, connecting workers to new jobs, and other efforts to mitigate adverse effects over the long term.

Barr concluded that the concentration of market power may help drive a widening of inequality that would strain the American Dream, and that society should begin considering how to address these potential challenges now, while AI adoption is in its relatively early stages, so the long-term benefits can be realized.

Notes

Barr emphasized that the views expressed in the speech are his own and are not necessarily those of his colleagues on the Federal Reserve Board or the Federal Open Market Committee.

Selected references cited in the speech:

• Michael S. Barr (2026), "Will Artificial Intelligence Broadly Raise Living Standards or Drive Income and Wealth Inequality?" speech delivered at "Next-Gen Financial Inclusion," the third annual Financial Inclusion Conference hosted by the Federal Reserve Board, Washington, July 14 — federalreserve.gov
• Board of Governors of the Federal Reserve System (2026), Economic Well-Being of U.S. Households in 2025, Washington, May
• Chase Peterson-Withorn and Matt Durot (2026), "Forbes 400: The Definitive Ranking of America's Richest People 2026," Forbes, September 15,
• Moritz Kuhn, Moritz Schularick, and Ulrike I. Steins (2020), "Income and Wealth Inequality in America, 1949–2016," Journal of Political Economy, vol. 128, pp. 3469–3519
• Emmanuel Saez and Gabriel Zucman (2016), "Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data," Quarterly Journal of Economics, vol. 131, pp. 519–78; Vincent J. Geloso, Phillip Magness, John Moore, and Philip Schlosser (2022), "How Pronounced Is the U-Curve? Revisiting Income Inequality in the United States, 1917–60," Economic Journal, vol. 132, pp. 2366–91
• Peter H. Lindert and Jeffrey G. Williamson (2016), Unequal Gains: American Growth and Inequality since 1700, Princeton University Press
• Jesse Bricker, Joseph Briggs, Sarah Friedman, and Kamila Sommer (2025), "The Business Cycle Dynamics of the Wealth Distribution," Journal of Political Economy Macroeconomics, vol. 3, pp. 343–81
• Ann Harrison (2024), "Disentangling Various Explanations for the Declining Labor Share: Evidence from Millions of Firm Records," NBER Working Paper Series 32015 — nber.org
• Richard Audoly, Miles Guerin, Srinidhi Narayanan, and Rachel Schuh (2026), "The Post-COVID Decline in the Labor Share," Federal Reserve Bank of New York, Liberty Street Economics (blog), June 24 — newyorkfed.org
• Matthew Smith, Danny Yagan, Owen Zidar, and Eric Zwick (2022), "The Rise of Pass-Throughs and the Decline of the Labor Share," American Economic Review: Insights, vol. 4, pp. 323–40
• Michael S. Barr (2026), "What Will Artificial Intelligence Mean for the Labor Market and the Economy?" speech delivered at the New York Association for Business Economics, New York, February 17 — federalreserve.gov
• Mark Schaefer (2023), "How Big Is the Creator Economy? Three Times Larger Than We Thought!" LinkedIn, November 14,
• Loukas Karabarbounis and Brent Neiman (2014), "The Global Decline of the Labor Share," Quarterly Journal of Economics, vol. 129, pp. 61–103
• Daron Acemoglu and Pascual Restrepo (2020), "Robots and Jobs: Evidence from U.S. Labor Markets," Journal of Political Economy, vol. 128, pp. 2188–2244; Daron Acemoglu, Claire Lelarge, and Pascual Restrepo (2020), "Competing with Robots: Firm-Level Evidence from France," AEA Papers and Proceedings, vol. 110, pp. 383–88
• Roc Armenter (2015), "A Bit of a Miracle No More: The Decline of the Labor Share," Federal Reserve Bank of Philadelphia, Business Review — philadelphiafed.org; Gustavo Grullon, Yelena Larkin, and Roni Michaely (2019), "Are U.S. Industries Becoming More Concentrated?" Review of Finance, vol. 23, pp. 697–743
• Emin Dinlersoz and Zoltan Wolf (2024), "Automation, Labor Share, and Productivity: Plant-Level Evidence from U.S. Manufacturing," Economics of Innovation and New Technology, vol. 33, pp. 604–26
• David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John Van Reenen (2020), "The Fall of the Labor Share and the Rise of Superstar Firms," Quarterly Journal of Economics, vol. 135, pp. 645–709
• Gene M. Grossman and Ezra Oberfield (2022), "The Elusive Explanation for the Declining Labor Share," Annual Review of Economics, vol. 14, pp. 93–124; Anna Stansbury and Lawrence Summers, "The Declining Worker Power Hypothesis"
• David H. Autor (2025), "Polanyi's Paradox and the Shape of Employment Growth," in Re-Evaluating Labor Market Dynamics: A Symposium Sponsored by the Federal Reserve Bank of Kansas City, pp. 129–77
• Anton Korinek and Jai Vipra (2025), "Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence," Economic Policy, vol. 40, pp. 225–56
• Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen (2026), "No Widespread Displacement, but the AI Employment Gap for Young Workers Has Widened to 19%," Stanford Digital Economy Lab, August 12 — stanford.edu; David Autor, Caroline Chin, Anna M. Salomons, and Bryan Seegmiller (2026), "What Makes New Work Different from More Work?" NBER Working Paper 34986 — nber.org; Isabella Loaiza and Roberto Rigobon (2024), "The EPOCH of AI: Human-Machine Complementarities at Work," MIT Sloan Research Paper No. 7236-24; Wilbur Xinyuan Chen, Suraj Srinivasan, and Saleh Zakerinia (2024), "Displacement or Complementarity? The Labor Market Impact of Generative AI," Harvard Business School Working Paper 25-039; Kathryn Bonney, Cory L. Breaux, Emin Dinlersoz, Lucia S. Foster, John C. Haltiwanger, and Aditya A. Pande (2026), "The Microstructure of AI Diffusion: Evidence from Firms, Business Functions, and Worker Tasks," NBER Working Paper 35141 — nber.org

Source: American Inequality and the Future of AI — remarks by Governor Michael S. Barr, Federal Reserve