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# Cook, An Update on AI and the Economy
- URL: https://www.advansinvestments.com/actualidad/fuente-832544fe7edda556/
- Published: 2026-09-28T17:25:00.000Z
- Updated: 2026-09-28T17:25:00.000Z
- Description: Speech At the Oakland Tech Week Opening Keynote, cohosted by the Kapor Center, Oakland, California
- Author: Diego Gustavo Vasquez
- Tags: actualidad, #es, #Import 2026-09-28 18:32

Documento oficial en su idioma original. Publicación de la fuente: 2026-09-28T17:25:00+00:00

Thank you, Freada and Mitch, for that kind introduction. I appreciate the invitation to speak here at Oakland Tech Week and for the opportunity to return to the East Bay, where I lived and spent several formative years when I attended graduate school at Berkeley.1 Oakland was a vibrant, exciting place then and is an even more vibrant and exciting place now, and I am happy to have the opportunity to engage with you on a critical topic: artificial intelligence (AI) and its effects on our economy.

In fact, my interest in the economics of innovation and artificial intelligence began at Cal. One of the most intriguing, impactful courses I took was on growth theory taught by Paul Romer who posited that investing in science and ideas could produce continuous, unbounded growth and later received the Nobel Prize for his seminal contributions advancing our thinking related to economic growth. I believe we are living in an era that, coupled with the historic, post-World War II investment in basic science, is seeing these and similar ideas coming to fruition and being tested in the global economy today.

AI is poised to become the most significant technological shift of our lifetime. I view it as a general-purpose technology, on par with or exceeding breakthroughs, such as the steam engine, electricity, and the internet. Those innovations spread throughout the economy, sparked downstream innovation, and improved over time.2 AI is doing the same. As a long-time researcher of the economics of innovation and current monetary policymaker, I have observed these developments with keen interest. AI affects nearly every aspect of my role at the Federal Reserve, including monetary policy, financial stability, bank supervision, financial market infrastructure, and our own operational preparedness.3

While AI introduces an infinite set of exciting possibilities, it also offers us much to contemplate in a sober way. From the data on which AI models are trained to AI safety, I have long been an advocate of responsible AI development.4 Whether all the beneficial possibilities of AI are realized—and how—will depend on how researchers, consumers, businesses, and policymakers across the country rise to meet its opportunities and its challenges. To fully recognize the benefits of AI, we must pair optimism with caution and be cognizant of valid concerns AI may pose for privacy, bias, workers, fraud, cybersecurity, and intellectual property rights. For our part at the Fed, as a supervisor of banking organizations, we have encouraged, and continue to encourage, responsible use of AI within the financial sector in a manner that is consistent with safe and sound practices and in compliance with applicable law. Beyond that, any specific role for the government in the path of AI's trajectory is for elected officials to decide. I will continue to monitor closely the speed, direction, and magnitude of AI development as I assess the economic outlook, the appropriate path of monetary policy, and financial stability.

What are AI's implications for monetary policy? Today, I will offer an approach for thinking about AI's economic implications related to our dual mandate of promoting price stability and maximum employment. I am studying these effects closely, because it is my job to set monetary policy in a way that will navigate the multiple sources of near-term pressure while fostering conditions for long-run gains that benefit all Americans.

**AI and Inflation**  
First, I would like to consider inflation implications in the short run and longer run. A broad range of factors have caused inflation to remain above the Fed's 2 percent target over the past five years. Over the past year, one of these factors has been AI-driven investment. Prices for AI-related goods—such as chips, computers, and software—have surged.

I believe that some of these steep price increases reflect a *shift* in demand toward AI-related sectors rather than an *increase* in economy-wide demand. When a surge in demand is concentrated in one sector, goods and services in that sector can get pushed onto a steep part of its supply curve. Had that demand been spread evenly across the economy, the overall price index would not climb as much. As supply chains adjust and efficiency gains accrue, this kind of pressure should resolve on its own without policy intervention. In fact, attempting to fight sector-specific inflation with monetary policy could be a mistake. Our tools are too blunt to target narrow sectors, and addressing relative price shifts is not our role.

Nonetheless, I see some economy-wide pressure from AI-fueled demand. Data-center investment relies on inputs, like construction labor and energy, that are broadly used in many sectors in the economy. As a result, increased AI investment could introduce price pressure to those other sectors. And even more investment is in the pipeline, and companies have only spent a small fraction of the $2 trillion in announced plans.5 Further, a large portion of the rise in equity prices over the past few years can be attributed to enthusiasm about AI, and that added wealth appears to be feeding through to household spending. You can see signs in the inflation data that the pressure may be broadening: Electricity and water costs are each up around 5 percent over the past year, potentially attributable in part to AI, and core goods prices, which were drifting down before the pandemic, are running over a 3 percent annual pace so far this year. This introduces the risk that, even as inflation in the narrow AI sector moderates, new and more broadly based price pressures may take its place.

A well-timed productivity boom could counter broadening price pressure, if it were to increase the supply capacity of the economy more than it increases demand. To understand this mechanism, consider an economy where, because of lower input costs, it becomes possible to produce more goods and services at the same cost. If demand does not expand to meet additional supply, you would expect prices to fall. This reduction in price pressure can be described in a classic aggregate supply—aggregate demand framework—a first and major toolkit of macroeconomists.

Currently, I anticipate that productivity gains will provide modest disinflation within the next few years. However, I do not expect those effects to arrive in time to offset the broadening inflationary pressure later this year. Moreover, uncertainty surrounds any estimates related to how and when this mechanism may operate, and it warrants further research and discussion.

The key is determining the conditions under which a productivity boom would provide inflation relief relative to today by increasing the economy's supply potential more than demand. In a scenario where the productivity gains are spread evenly across the economy, I expect the relief to be limited but real. It is limited, because higher productivity not only raises the economy's potential supply but also raises demand through the expectation of higher future wages, better returns on investment, and the accompanying gains in wealth. In fact, some of that extra demand is likely already baked into today's economy through AI data center investment and wealth effects. Therefore, going forward, the new supply generated by rising productivity may outweigh the new demand, bringing supply and demand into better alignment and reducing the upward pressure on prices felt today. On balance, a broad-based increase in productivity, when and if it comes, could lead to a modest easing in price pressure.

To be clear, I am also attuned to other scenarios, including those in which a productivity boom generates less disinflation than in my baseline, as well as those in which it leads to even more downward pressure on inflation. The scenarios where we get more inflation relief imply that demand is adversely affected and falls well short of the economy's capacity. This could happen. if the productivity gains were highly concentrated among higher-income consumers who tend to spend less of each additional dollar of income or wealth; if productivity gains are not passed on to wages because of low worker bargaining power; or if a painful stretch of job reallocation leaves unemployment higher and confidence lower, increasing households' precautionary savings.

Scenarios where we might get less inflation relief are ones in which productivity gains are not passed on to prices—say, if market concentration leads to less competition and higher markups.

Ultimately, the timing of any disinflationary payoff will depend on how quickly and broadly businesses adopt AI tools, how changes in business practices translate to higher productivity, and how fast any productivity gains pass through to the labor market. I am very uncertain as to the breadth and timing of these channels and will adjust my view depending on what I see in the data.

**AI and the Labor Market**  
While AI's implications for inflation are increasingly notable, the technology's potential effects on the labor market have long captured the public's attention and deserve policymakers' close study. Looking at the history of technologies that changed the way humans work—such as the mechanical loom and the PC—we see widespread anxiety about those advancements at the time of their introduction. In the past, the benefits and costs of technological change did not necessarily arrive simultaneously, and that could well be the case with AI. This technology could bring the most significant reorganization of work in generations.6

AI technology and its adoption are still in their early stages. At the moment, there is limited evidence that AI is yielding significant changes to the structure of the labor market. The data underscore this: Both the unemployment rate and layoffs remain low. Those readings have been relatively flat over the last two years, even as AI adoption has picked up.

So far, I have been heartened to see that the labor market has remained remarkably resilient through the early stages of AI adoption.

Nevertheless, some workers have already been affected, and I understand the hardship that joblessness creates for them and their families. There is evidence in some sectors, including coding jobs in the software industry and in simultaneous translation, that AI might be decreasing labor demand. In addition, many recent college graduates are facing more difficulty finding their first jobs. This is possibly partly related to AI's ability to take on entry-level tasks. These types of shifts could broaden as AI adoption deepens and AI technology improves. At the same time, AI is likely to lead to great innovation, with new tasks and occupations cropping up that we cannot imagine today.

In the future, I continue to expect AI to fundamentally change business practices and transform the labor market in a commensurate way. It is my hope that AI adoption will continue to occur at a pace and in a way that allows job creation to match or exceed job destruction.

Nonetheless, I am highly attentive to a scenario where AI leads to at least a temporary increase in the unemployment rate. In this scenario, the rise in the unemployment rate might not necessarily reflect an increase in slack (or a shortage of demand) but rather a "supply-side" mismatch between workers' skills and available jobs. In that case, we at the Fed would have limited tools. We could lower the federal funds rate in an attempt to bring down high unemployment, but that could risk fueling inflation.

As a policymaker, I am watching the speed and acceleration of AI adoption across sectors and noting evidence of the changing mix of jobs within firms. I also pay attention to whether new job creation is at least offsetting displacement. I am constantly evaluating the broadest possible set of data and considering the evolving outlook and the balance of risks when making policy decisions. I will also note that fiscal policy could play a role in managing AI's effects on the labor force, but that is outside of my purview at the Fed.

**Diffusion of AI across Firms**  
One area I am studying is how AI is affecting firms of different sizes. Clearly, the leaders in AI technology are rapidly growing, high-profile companies. And this technology has also prompted considerable investment from some of the other largest and most established players in the tech industry. That has fueled the narrative that only large firms with substantial resources can deploy advanced AI. I would challenge that thinking. I see evidence that smaller firms are proving resourceful and adaptive when thinking about how to use AI to their advantage, even if they lag larger firms in overall adoption. Across the country, I have heard from both early-stage investors and founders that AI tools have made it easier and cheaper to start businesses, which could be a factor in the historic boom we observe in new business formation recently. I view this as a hopeful sign that AI could benefit smaller businesses and the workers they employ.

Take the findings in the Federal Reserve Small Business Credit Survey released earlier this year. That study found that nearly half of small employer firms are using AI and that 71 percent report increased productivity as a result.7 These data suggest that many small businesses are eager to find innovative tools that help them manage and grow their operations. This could help these firms compete more effectively, allowing them to grow and create more jobs. Evidence shows that AI adoption is happening faster than PC or internet adoption at comparable points.8 This is encouraging for small businesses because we know that some of the most significant breakthroughs this country has ever witnessed came from start-ups using frontier technologies in new and innovative ways.

This matters greatly to the overall economy and to achieving the Fed's dual-mandate goals because small businesses with fewer than 500 employees represent more than 99 percent of all U.S. businesses. Those firms have accounted for 61 percent of net new job creation since 1995\. When they become more productive, it strengthens the entire economy.

When powerful tools are available to everyone—not just large corporations—it unleashes more inventors, innovators, and entrepreneurs. I have long talked about Paul Romer's view that long-term economic growth is driven from within the economy by people generating new ideas and knowledge.9 AI can help us generate more ideas faster—suggesting we are at a Romer moment.

If adoption becomes as broad based as I believe is possible, and there is robust competition among producers of models, then productivity gains will not necessarily be concentrated among a few firms. If that is the case, AI could allow small companies to scale faster and create more jobs—not a guarantee, but a reason to be hopeful about this technology.

Here is what we do know: AI will transform the economy, perhaps in a bigger way than previous general-purpose technologies. However, we also know from those breakthroughs that their effects can have long and variable lags—a concept very familiar to me as a monetary policymaker. Ultimately the full benefits AI delivers will depend on countless decisions by firms, workers, and policymakers. Those decisions include investment decisions, and not just in chips and data centers. They also comprise complementary investments employers and others make in worker training, reorganization, and generating new processes and ideas.

As a policymaker, I consider the promise and challenge of AI over several time horizons. In the short term, AI appears to be adding inflationary pressures to the economy, postponing inflation's return to our 2 percent target. In the medium term, while I expect that productivity growth may modestly ease those inflationary pressures, the labor market will be at risk of entering a painful transition. In the long term, I am optimistic that AI-fueled productivity growth can raise living standards for all Americans. With appropriate vigilance and thoughtful navigation of these opportunities and challenges, the Federal Open Market Committee (FOMC) will be able to achieve its dual mandate of maximum employment and stable prices.

**Economic Outlook**  
Speaking of the dual mandate, I will conclude my remarks with a few words about my broader view of the economy and what that means for monetary policy. As you know, I voted along with the rest of the FOMC to raise rates 25 basis points at the recent September meeting.

This increase was to address inflation, which has been too high for too long. Total inflation rose an estimated 3.8 percent in the 12 months leading into August, almost double our target. Core inflation, which strips out energy and food prices, rose an estimated 3.4 percent. Furthermore, in coming months I expect to see continued pressure on inflation from the AI buildout, as discussed today, and from the pass-through of higher oil prices and supply chain disruptions associated with the conflict in the Middle East.

The labor market appears to be well positioned to handle an increase in rates. Over the course of this year, the unemployment rate has been trending down, with the most recent reading coming in at 4.1 percent in August. Other indicators also show a labor market that is roughly in balance and gradually improving. Payrolls have increased, job openings have ticked up, and initial unemployment claims have trended lower. The strength seen in the labor market is also present in the broader data on economic growth, which has remained remarkably resilient over the past year.

Looking ahead, I will consider what policy rate may be needed to continue to guide inflation down to our target. Of course, the number and magnitude of any future adjustments will be informed by observations of the economy's reaction to our policy actions thus far and the inflation and labor data over the coming months.

Thank you again for the invitation and the opportunity to speak to you today.

1\. The views expressed here are my own and are not necessarily those of my colleagues on the Federal Reserve Board or the Federal Open Market Committee. Return to text

2\. See Lisa D. Cook (2023), "Generative AI, Productivity, the Labor Market, and Choice Behavior," speech delivered at the National Bureau of Economic Research Economics of Artificial Intelligence Conference, Toronto, Canada, September 22\. Return to text

3\. See Lisa D. Cook (2026), "The Opportunities and Risks AI Presents for the Economy and Financial System," speech delivered at the Stanford Institute for Economic Policy Research, Stanford University, Standford, California, May 27\. Return to text

4\. See Lisa D. Cook (2024), "Artificial Intelligence, Big Data, and the Path Ahead for Productivity," speech delivered at "Technology-Enabled Disruption: Implications of AI, Big Data, and Remote Work," a conference organized by the Federal Reserve Banks of Atlanta, Boston, and Richmond, held in Atlanta, Georgia, October 1\. Return to text

5\. See Eirik Eylands Brandsaas, Daniel Garcia, Robert Kurtzman, Joseph Nichols, and Adelia Zytek (2025), "Estimating Aggregate Data Center Investment with Project-Level Data," Finance and Economics Discussion Series 2025-109 (Washington: Board of Governors of the Federal Reserve System, December). For updated data and publicly available results, see Eirik Brandsaas (2026), "Estimating Aggregate Data Center Investment with Project-Level Data," DataCenterPublic, GitHub repository, https://github.com/eirikbrandsaas/DataCenterPublic. Return to text

6\. See Lisa D. Cook (2026), "Opening Remarks for the 'AI and Productivity across the Economy' Panel," speech delivered at "The Great Realignment: Navigating AI, Demographic, and Geoeconomic Shifts," 42nd Annual NABE Economic Policy Conference, Washington, D.C., February 24\. Return to text

7\. See Lisa D. Cook (2026), "Welcome Remarks," speech delivered at the State of Small Business Symposium, Federal Reserve Bank of Cleveland (via pre-recorded video), June 24\. Return to text

8\. See Alexander Bick, Adam Blandin, and David J. Deming (2024), "The Rapid Adoption of Generative AI," NBER Working Paper Series 32966 (Cambridge, Mass.: National Bureau of Economic Research, September; revised February 2025). Return to text

9\. See Paul M. Romer (1990), "Endogenous Technological Change," *Journal of Political Economy,* vol. 98 (October), pp. S71–S102\. Return to text

## Fuente

Fed · discursos

[Cook, An Update on AI and the Economy](https://www.federalreserve.gov/newsevents/speech/cook20260928a.htm)

Documento consultado: 2026-09-28T18:14:45.507755+00:00