Will Artificial Intelligence Broadly Raise Living Standards or Drive Income and Wealth Inequality

July 14, 2026

Will Artificial Intelligence Broadly Raise Living Standards or Drive Income and Wealth Inequality?

Governor Michael S. Barr

At "Next-Gen Financial Inclusion," the third annual Financial Inclusion Conference hosted by the Federal Reserve Board

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I am grateful for the opportunity to speak to you.¹ Our focus in this conference is financial inclusion, and something that will likely have great consequences for financial inclusion and our economy more broadly in the years ahead is artificial intelligence (AI). As I have explored in a number of speeches over the past several years, AI has the potential to transform lives and the U.S. economy, possibly empowering workers to be more productive, with lower- and middle-income workers benefiting the most.² But it is also the case that AI may instead exacerbate inequality, eliminating some lower- and middle-income jobs while boosting the income and wealth of higher-income individuals. Since we don't know which of these futures will come about, it is useful to use potential scenarios, as I've done previously with respect to AI and the economy.

Every major technological advance has had profound effects on labor markets and the economy. Many workers have suffered from these technological changes, while many other workers have seen new opportunities emerge. In the long run, technological advances tend to broadly raise living standards by creating more jobs than they destroy and increasing productivity. But transitions and outcomes can vary, and in the period following the mass adoption of a general-purpose technology-such as electricity, the telephone, and internet-enabled personal computers-the number of people dislocated and the extent of the harm they may suffer can be large and persistent.

Balancing those scales of costs and benefits involves examining whether the benefits are broadly shared or concentrated. Past experience has shown that technological leaps forward can raise living standards. But when the benefits are concentrated among relatively few people, technology can widen inequalities of income and wealth, especially during the transition period. Widespread adoption of the internet raised the productive capacity of our economy and broadly raised living standards, but it also likely exacerbated inequality because it benefited information-intensive jobs (such as accountants) more than other jobs (say, construction workers).

The policy challenge is therefore not simply to observe the development and deployment of AI, but also, as a society, to consider policies related to AI and its effects on education, job training and workforce development, competition, tax policy, and other areas that allow the gains from AI to be shared across workers, households, and communities, rather than accruing to a small group of firms and investors.³ Of course, these policies are not within the remit of the Federal Reserve but rather for other policymakers to consider and decide.

The question I would like to consider today is whether AI will likely help narrow inequalities of income and wealth, supporting advances in financial inclusion, or widen those inequalities, undermining the recent gains in financial inclusion that we rightly celebrate today.

Understanding Inequality
Let's start with understanding income and wealth inequality.

To understand income inequality, it helps to break income into its components. The largest component is labor income. Disparities in labor income across individuals reflect the supply of and demand for their skills, their productivity, and their time spent at work-all things that will be affected by AI. Income also includes earnings from capital and investments and, thus, includes the concentration of ownership in firms, such as AI companies. In 2024, the highest-earning one-fifth of U.S. households earned 52 percent of all income, and the bottom 20 percent earned only 3 percent.⁴ In 2024, the United States was the sixth most unequal of the countries in the G20.⁵

Wealth inequality is a function of the distribution of ownership of assets-land, goods, businesses, intellectual property, and other investment assets. The bottom one-half of U.S. households hold less than 3 percent of wealth, the top one-tenth hold 59 percent, and the top one-tenth of a percent hold 15 percent.⁶ When returns from investments are reinvested, wealth naturally compounds. As a result, those who already own appreciating assets often see their wealth grow much faster than households that rely primarily on wages, widening the gap between the "haves" and the "have-nots."

A central question is whether AI will expand opportunity by giving more people access to valuable skills and productive work, or whether it will reinforce advantages that are already concentrated among a smaller population. Inequality matters not just for workers today, but it is also closely connected with an important aspect of the American Dream-the expectation that in the future, our children will be able to make better lives for themselves, including through rising living standards.⁷

How AI Could Widen Inequality
Let me start with the possible ways in which AI could widen inequality.

Automation and Labor Displacement
First, AI could lead to labor displacement. Something that is at the top of mind for most people, especially younger workers, is the concern that AI could drastically reduce the demand for them. AI might disproportionately affect new entrants to the labor market. According to a well-known paper by Claudia Golden and Larry Katz, in some previous technological waves, the benefits have tended to improve outcomes for more-skilled and more-educated workers.⁸ But in this scenario, AI could harm not only less-skilled workers, but also younger college-educated workers whose skills are more easily replicated by AI than in prior technological waves. Moreover, workers who use AI more intensely might gain the most, and workers who use the most advanced and expensive AI models might win out over those who use baseline models.

In the Federal Reserve's most recent Survey of Household Economics and Decisionmaking, 43 percent of workers with a graduate degree reported using AI in the previous month, compared with 10 percent of workers with a high school degree or less.⁹ The survey further found that workers who used AI were more likely to say that it would improve their careers than replace their jobs. Measures of exposure to generative AI also suggest that higher education and higher-paid workers are much more exposed to generative AI.¹⁰ The consequences of these facts are not yet clear. If AI mostly substitutes for labor, then not being exposed to AI would be positive for such workers; however, if AI augments existing jobs, then workers not exposed to AI would be left behind.

There is substantial uncertainty about how the labor market will evolve. As of right now, there has been little evidence of economy-wide job displacement from AI.¹¹ Yet there is some evidence that AI may have made job entry harder for young workers in some job categories.¹² Given both the history of major technological advances and how early we are in the timeline of AI adoption, it is important to consider the full range of possible future effects, including the potential for more widespread labor displacement.

Potential Concentration
Another concern is concentration. A high degree of market concentration has important implications for individuals' economic outcomes. We don't know how the market will evolve. At one extreme, competition and distributed innovation could lead to AI becoming a cheap and ubiquitous commodity. In this scenario, access is democratized and gains are widely shared. Start-ups and smaller businesses have access to state-of-the-art AI resources and can continue their role as a key source of innovative ideas, goods, and services as well as an important engine of job creation for the U.S. economy.

But according to a 2025 paper by Anton Korineck and Jai Vipra, an important factor is that AI has some characteristics that have, in the past, reinforced concentration of market power.¹³ Like other high-tech innovations, because AI depends on access to data, model improvements, and computing power, it benefits from economies of scale and scope. Greater data, model improvements, and computing power yield vastly greater intelligence and capabilities. The high return from this advantage helps explain the huge investments and concentration of AI investment in giant firms, which is why they are referred to as "hyperscalers." AI has another feature that seems to be driving ever-greater concentration of market power-the fact that AI itself is a powerful tool to train and accelerate development of new AI models. That is, AI improves its own research and development. While computing technology has always tended to support the market dominance of industry leaders, the extent of the advantage possessed by AI industry leaders may prove to be unprecedented.

As a result of these forces, it is possible that a small number of AI firms may dominate the market and investment returns may accrue primarily to owners of AI.¹⁴ In this potential future, wealth generation-and, to some extent, income generation for those workers who can benefit from access to AI resources-could increase. But less access to ever-improving AI resources for most other firms and their employees would mean slower productivity growth for them, and a steadily widening gap between them and the firms and their employees with more access. As I said at the outset, I am not predicting this particular outcome, just exploring it as a possible scenario.

How AI Could Alleviate Inequality
Let me turn to scenarios in which AI could reduce inequality.

AI as a Productivity Tool
One scenario is broader access to capability building, resulting in broader productivity gains. Just as the printing press democratized knowledge and the internet democratized information, AI may democratize capability itself. By giving millions of people access to tutoring, coaching, writing assistance, programming support, and problem-solving guidance, AI could enable individuals to develop skills that were once reserved for those with exceptional education, wealth, or mentorship. In this future, AI could be a rising tide that lifts all boats rather than widening the inequality we've seen in recent decades, sinking other ships. A growing body of research suggests that AI can augment worker productivity without necessarily replacing workers, with especially large gains for less-experienced workers, allowing individuals to perform a broader array of tasks and increasing overall economic output.¹⁵

In one experiment, college-educated professionals completed a range of assignments, writing short reports or analyses, and then carried out a second round of assignments with the help of AI. AI reduced the average time to complete assignments by 40 percent and improved the quality of the results by 18 percent-a combination that constitutes a significant productivity gain.¹⁶ The biggest improvements were among those who did the worst on the unaided assignment, narrowing the productivity gap.¹⁷

Acquiring skills or building experience and expertise in any occupation is hard. It demands effort and discipline, often requires years of effort, and depends on education, the capability to acquire and retain knowledge, and the judgment to use knowledge and other resources wisely. AI has the potential to expand expertise, shortening the time and reducing the work needed to build skills, or directly providing those skills themselves, raising productivity the most for lower-skilled, less-educated workers. AI could also lower the barriers to entrepreneurship for those with a good idea who may lack certain skills, such as finance or accounting, to develop or implement that idea. The democratization of coding capabilities enabled by AI models is also an example of how AI could level the playing field for a critical input for many businesses. While it is too early to know how helpful AI might be to entrepreneurship, the rapid adoption of AI among small businesses is a strong signal of the potential value.¹⁸

New Jobs
AI could also create new jobs, ones we haven't even imagined yet, as we've seen with other general-purpose technologies. Consider that by some estimates, there are 12 million full-time social media influencers earning a living today in the United States, something that was unimaginable a decade ago.¹⁹ Research suggests that major technological advances lead to this type of job creation, and the bigger the advance, the greater the impact on the labor market.²⁰ For those who tend to see job dislocation from technological advances as a zero-sum game, it is worth remembering what economists call the "lump of labor fallacy." The labor market is not zero sum. For example, spreadsheet software such as Excel replaced the lower-skilled aspects of basic accounting jobs, which raised the productivity of accountants. That is, instead of substituting for a person, technology augmented that person's ability to do their job. Instead of eliminating a field, technological advances redefined what was possible. AI may be able to do that across the economy. If so, AI could help lower inequality by creating new, more-productive, and higher-paying jobs.

What Determines the Outcome
I have laid out what are, roughly speaking, the worst- and best-case scenarios of how AI may affect inequality. And there could be many scenarios in between. It is impossible to predict now which of these versions of the future is more likely, but I will talk through a few examples of factors that could shape that outcome.

Education, Job Training, and Workforce Development
The first is education, job training, and workforce development. In the same way that computer skills are essential to many jobs today, proficiency with AI may well be a necessary skill for the jobs of the future. Workers will need to prompt AI, integrate AI into workflows, oversee coding agents, and manage multiple AI agents. They will need to exercise judgment about AI inputs and outputs, verify AI results, and expand the frontier of human knowledge.²¹ AI's amazing facility in writing computer code is likely to replace individuals whose only job was to write basic code; however, it will empower not only advanced coding experts who will oversee coding agents, but also empower many more people without formal coding training to use coding to turn their ideas into functional programs. If this is hard to visualize, consider how calculators, word processors, and presentation software have democratized the workplace. Not long ago, these functions were carried out by specialists but are now minimum qualifications for most office jobs.

Education will be critical in how well workers and the U.S. economy adapt to the AI revolution. Economists Claudia Golden and Lawrence Katz explored this history in The Race Between Education and Technology, recounting how both the supply and demand for education responded throughout the 20th century to the technological needs of the economy.²² First, the dramatic growth in high school completion helped move the country away from agriculture as the dominant industry. In the second half of the century, the proliferation of higher education responded to the need for specialized expertise in many occupations. In this next wave, a crucial question about how AI may affect inequality will be if high-quality and affordable education and training are widely available not only at the outset, but also throughout one's working life.

Yet a focus on AI skills alone is not likely to be the right approach. My instinct is that curiosity, flexibility, and, importantly, common sense and human judgment are likely to be critical skills in this new economy. As Ethan Mollick argued in his book Co-Intelligence, success in the AI era depends less on mastering the technology itself than on developing the human capacities to ask insightful questions.²³ This includes being able to distinguish sound reasoning from plausible nonsense, make ethical judgments, and integrate knowledge across disciplines. So, it is not just a question of learning AI skills. To be successful in the future, both young people and those already in their careers are going to need to learn the skills necessary for an economy in which change happens at an increasingly fast pace. Investment in human-centered skills, relationships, and the liberal arts is likely to be as important as technical skills.

Competition and Market Structure
The market structure of AI firms will also matter to outcomes.²⁴ As explained by Korinek and Vipra, competition is an essential force promoting income and wealth equality. Competition lowers costs, spreads access to technological advances and makes it more likely that the benefits of those advances are shared widely among consumers and workers. If firms leading the AI revolution achieve dominant positions of market power, the benefits might be concentrated among a fortunate few. Competition alone would not lead to less inequality, as labor and capital markets allocate gains across firms and workers, but competition is an important input into broadly shared gains.

Conclusion
In conclusion, scenarios for AI adoption vary widely regarding how AI might affect inequality. How the market evolves will matter a great deal. But so too will public policy. AI, like past major technological advances, will shape the labor market and the broader economy in myriad ways. It is unclear whether AI will reduce or increase income and wealth inequality, but society can begin making choices now that can affect that outcome. Decisions on AI policy, education, worker training and workforce development, competition, tax policy, and other areas will help determine this outcome. We have heard many bold pronouncements about what AI will be able to do in the near and distant future. Some will likely come to pass and others won't. But future inequality will depend not only on what AI can do, but on what we choose to do with AI.

1. The opinions expressed here today are my own and not necessarily those of the Board of Governors of the Federal Reserve System. Return to text

2. See Michael S. Barr, "What Will Artificial Intelligence Mean for the Labor Market and the Economy?" speech delivered at the New York Association for Business Economics, New York, NY, February 17, 2026. Return to text

3. See Gene Sperling, "An Economic Dignity Compact for the AI Age," Democracy, January 21, 2026. Return to text

4. See "Income in the United States: 2024," United States Census Bureau, last modified August 25, 2025. Return to text

5. See "Gini Index," World Bank Group. Return to text

6. See "DFA: Distributional Financial Accounts," Board of Governors of the Federal Reserve System, last modified June 18, 2026. Return to text

7. See Miles Corak, "Income Inequality, Equality of Opportunity, and Intergenerational Mobility (PDF)," Journal of Economic Perspectives 27 (2013): 79-102. Return to text

8. See Claudia Goldin and Lawrence F. Katz, The Race between Education and Technology (Harvard University Press, 2008). Return to text

9. See Board of Governors of the Federal Reserve System, Economic Well-Being of U.S. Households in 2025 (Board of Governors, May 2026). Return to text

10. See Maxim Massenkoff, Eva Lyubich, Peter McCrory, Ruth Appel, and Ryan Heller, "Anthropic Economic Index Report: Learning Curves," Anthropic, March 24, 2026, https://www.anthropic.com/research/economic-index-march-2026-report; and Edward W. Felten, Manav Raj, and Robert Seamans, "Occupational Heterogeneity in Exposure to Generative AI," April 10, 2023, http://dx.doi.org/10.2139/ssrn.4414065. Return to text

11. See Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Brent H. Meyer, Michael Sparks, Sonya R. Waddell, and Daniel Weitz, "Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives," NBER Working Paper No. 34984 (National Bureau of Economic Research, March 2026); and Jessica Liu and Douglas Webber, "AI Adoption and Firms' Job-Posting Behavior," FEDS Notes (Board of Governors of the Federal Reserve System, March 27, 2026). Return to text

12. See Maxim Massenkoff and Peter McCrory, "Labor Market Impacts of AI: A New Measure and Early Evidence," Anthropic, March 5, 2026. Return to text

13. See Anton Korinek and Jai Vipra, "Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence," Economic Policy 40, no. 121 (2025): 225-56, https://doi.org/10.1093/epolic/eiae057. Return to text

14. See Tania Babina, Anastassia Fedyk, Alex He, and James Hodson, "Artificial Intelligence, Firm Growth, and Product Innovation," Journal of Financial Economics 151 (2024): 103745, https://www.sciencedirect.com/science/article/pii/S0304405X2300185X. Return to text

15. See Ajay K. Agrawal, John McHale, and Alexander Oettl, "Enhancing Worker Productivity without Automating Tasks: A Different Approach to AI and the Task-Based Model," NBER Working Paper No. 34781 (National Bureau of Economic Research, January 2026); and Shakked Noy and Whitney Zhang, "Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (PDF)," Science 381, no. 6654 (2023): 187-92. Return to text

16. See Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, "Generative AI at Work," The Quarterly Journal of Economics 140, no. 2 (2025): 889-942, https://doi.org/10.1093/qje/qjae044; and Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer, "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" (2023). Return to text

17. See Noy and Zhang, "Experimental Evidence on Productivity Effects." Return to text

18. See Jeffrey S. Allen, "Monitoring AI Adoption in the U.S. Economy," FEDS Notes (Board of Governors of the Federal Reserve System, April 3, 2026). Return to text

19. See Mark Schaefer, "How Big Is the Creator Economy? Three Times Larger Than We Thought!" LinkedIn, November 14, 2023, https://www.linkedin.com/pulse/how-big-creator-economy-three-times-larger-than-we-thought-schaefer-pihac. Return to text

20. See Daron Acemoglu and Pascual Restrepo, "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review 108, no. 6 (2018): 1488-542; and David H. Autor, "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives 29, no. 3: 3-30. Return to text

21. See Christian Catalini, Xiang Hui, and Jane Wu, "Some Simple Economics of AGI" (2026). Return to text

22. See Goldin and Katz, Race between Education and Technology. Return to text

23. See Ethan Mollick, Co-Intelligence: Living and Working with AI (Portfolio, 2024). Return to text

24. See Korinek and Vipra, "Concentrating Intelligence." Return to text