Good morning. Thank you to the National Association for Business Economics for inviting me to speak with you today, and thank you to Jared for serving as our moderator.1 I am honored to share the stage with my former colleague Michael and my former student and research assistant Ging Cee, and I look forward to our discussion.
In economics, artificial intelligence (AI) and productivity are among my favorite areas of inquiry. In the two decades before I became a Federal Reserve governor, I studied and contributed research to the economics of innovation and used machine learning in my research. In my current role, I have continued to pursue this line of inquiry and have frequently spoken about the advances in AI, the implications of that progress for the economy, and the Fed’s dual-mandate goals of maximum employment and price stability.2 Today, I appreciate the opportunity to briefly update you on my thinking. While I am appropriately cautious, in the long run, I am optimistic about AI’s ability to contribute to new products and processes—innovation—that will make our lives better.
People are using AI in varied and interesting ways, including writing romance novels more quickly, dreaming up new recipes, and even deciding marital disputes.3 I am thrilled to see firms experimenting with AI in novel, creative ways, which I look forward to learning about from my fellow panelists. At the Federal Reserve, we hold ourselves to the highest standard of safety and exercise caution. Simultaneously, like many organizations, the Fed sees value in using AI to summarize research and other documents, generate code, and plan travel, among other uses.4 As you might imagine, there is an extensive research effort to examine AI and its effects on the economy at the Board and throughout the Federal Reserve System.5
AI can boost productivity growth by speeding up idea creation—a key element in Paul Romer’s endogenous growth theory—through its ability to process and combine knowledge rapidly.6 By making powerful analytical tools available to everyone, not just experts, AI democratizes innovation: it allows more people to become inventors, innovators, and entrepreneurs, creating a cycle of self-generating ideas where ideas generate more ideas. This democratization supports Romer’s concept that ideas can be shared repeatedly without being “used up,” potentially transforming innovation by allowing more people to contribute to economic growth. Undoubtedly, AI will usher in new tasks and occupations, many of which are impossible for us to conceive of today. That is not surprising, considering that 60 percent of today’s occupations did not exist in 1940.7 Airplane designer, computer application engineer, and cybersecurity analyst have become Census-recognized occupations since World War II.
AI has tremendous promise. Nonetheless, I view its general adoption with caution. AI’s emergence is poised to be the latest example of the creative destruction economist Joseph Schumpeter described almost a century ago. We appear to be approaching the most significant reorganization of work in generations. This transition could create new opportunities, but it could also come with some costs. In a recent speech, I discussed the possibility that job displacement may precede job creation such that the unemployment rate may rise and participation in the labor force may decline as the economy transitions.8 This outcome could cause hardship for many workers and their families.
Evidence that the transition has commenced has emerged, even if it is too soon to see the effects in the aggregate. Demand for labor in certain occupations has declined—most notably for coders, a field where AI has made significant strides. Similarly, the unemployment rate for recent college grads 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. Nevertheless, the overall unemployment rate is still at a low 4.3 percent, and recent measures of layoffs remain subdued. Therefore, we do not yet know the exact evolution of this labor-market transition nor its intensity.
To be sure, the AI transition I am contemplating could have profound implications for monetary policy. It is too early to observe the exact contours, but I am carefully studying several aspects of this transition. Allow me to briefly raise two issues for consideration.
First, if AI continues to raise productivity, economic growth could remain strong, even as churn in the labor market leads to an increase in unemployment. 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. While there is a role for monetary policy, education, workforce, and other policy that is nonmonetary may be better suited to address these challenges in a more targeted way.
Second, I am thinking about how AI might affect the neutral rate of interest in the short run and over time. To recall, the neutral rate is a long-run concept that articulates the equilibrium level of interest rates that is noninflationary and consistent with maximum employment. The AI investment context compels us to understand what is happening in the short run. In anticipation of future productivity gains, we already see soaring AI-related business investment in data centers and chips, despite interest rates broadly being elevated relative to levels over the past 20 years. With investment contributing to strong aggregate demand, it is possible that the current neutral rate is higher than before the pandemic. This could reverse when the AI productivity gains are more fully realized or if the labor market transition leads to a rise in income inequality, such that well-off consumers receive a larger share of income, which could lower the neutral rate, all else equal.
AI is poised to profoundly change the economy and our lives—I believe ultimately for the better. I briefly discussed how AI could affect the labor market and the neutral rate of interest, but there are many other factors to examine. And it is too early to observe the exact contours of any changes. I will conclude by reminding those of you in this room that you will play a crucial role in helping employers—and policymakers—understand these rapidly changing dynamics in real time through your careful observations and thoughtful analysis. Thank you for your work. I look forward to the conversation.
1. The views expressed here are my own and not necessarily those of my colleagues on the Federal Open Market Committee. Return to text
2. See Lisa D. Cook (2024), “Artificial Intelligence, Big Data, and the Path Ahead for Productivity,” speech delivered at the 2024 Technology-Enabled Disruption Conference, organized by the Federal Reserve Banks of Atlanta, Boston, and Richmond, held in Atlanta, Georgia, October 1. Return to text
3. See Alexandra Alta (2026), “The New Fabio Is Claude,” New York Times, February 8; see also Rachel Rood (2026), “AI Played Marriage Ref for My Husband and Me. What Does It Do for You?” Ideastream Public Media, February 12. Return to text
4. See “AI Use Case Inventory 2025,” which is available on the Federal Reserve’s website at Return to text
5. See Lisa D. Cook (2025), “AI: A Fed Policymaker’s View,” a speech delivered at the 2025 National Bureau of Economic Research Summer Institute, Cambridge, July 17. Return to text
6. See Paul M. Romer (1990), “Endogenous Technological Change,” Journal of Political Economy, vol. 98 (October), pp. S71–S102. Return to text
7. See David Autor, Caroline Chin, Anna Salomons, and Bryan Seegmiller (2024), “New Frontiers: The Origins and Content of New Work, 1940–2018,” Quarterly Journal of Economics, vol. 139 (August), pp. 1399–1465. Return to text
8. See Lisa D. Cook (2026), “Economic Outlook,” speech delivered at the Economic Club of Miami, Miami, February 4. Return to text
