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Discussion Paper
Automation and AI: What Does Adoption Look Like for Fifth District Businesses?
Corcoran, Emily Waverling; Waddell, Sonya Ravindranath (2024-06-27)
Technological developments shift the kinds of skills needed in the labor force. From innovations in agriculture to electricity to the personal computer to the internet, technology has shaped the way we work and the types of workers we need to produce the goods and provide the services that consumers demand. The tight labor market of the last few years has provided employers with further incentive to find ways to use automation to increase the productivity of existing workers and even reduce the need to hire more. The opportunities of artificial intelligence (AI), particularly generative AI, ...
Regional Matters

Journal Article
Features: Will AI Investments Pay Off?
Wells, Matthew (2026-04-15)
Artificial intelligence (AI) is having a moment. In its Real-Time Population Survey, the St. Louis Fed found that 55 percent of people in the United States reported using AI as of August 2025. Stanford University reported that businesses adopted AI at a 78 percent clip in 2024, up from 55 percent the year before. This adoption rate exceeds those of personal computers and the internet at comparable stages.In response to this interest, companies are spending billions on the equipment, research and development, and infrastructure required to accommodate the demand from businesses seeking to ...
Econ Focus , Volume 26 , Issue Q1/Q2 , Pages 10-13

Working Paper
Artificial Intelligence and Inflation Forecasts
Leibovici, Fernando; Faria-e-Castro, Miguel (2024-02-26)
We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor.
Working Papers , Paper 2023-015

AI and Productivity Growth: Evidence from Historical Developments in Other Technologies
Kalyani, Aakash; Hogan, Marie (2024-04-04)
An analysis of the diffusion of PCs, smart devices, cloud computing and 3D printing suggests that AI may spread in a pattern similar to those of PCs and cloud computing.
On the Economy

Working Paper
Will AI Intensify or Weaken Market Competition?
Firooz, Hamid; Leduc, Sylvain; Liu, Zheng (2026-08-10)
We study how AI affects market competition based on a general equilibrium framework with heterogeneous firms facing idiosyncratic productivity and variable markups. Firms choose the AI technology subject to fixed costs, where AI production requires data and energy inputs. Our model predicts a non-monotonic relation of AI diffusion with industry concentration. As AI usage rises from an initially low level, large incumbent users gain market share. When AI usage is sufficiently diffused, entry of new and smaller adopters erodes the market share of incumbents, reducing industry concentration. The ...
Working Paper Series , Paper 2026-15

Journal Article
Is Optimism for Artificial Intelligence Boosting Investment?
Li, Huiyu; Kalyani, Aakash (2026-05-18)
U.S. business spending related to artificial intelligence (AI) grew substantially in 2025 among publicly traded firms, which account for the bulk of overall investment. Analyzing sentiment data from quarterly company earnings calls can help infer current and evolving optimism towards AI. Public firm data show growth in capital spending and research and development funding has come entirely from the largest companies that are positive about AI. While market concentration among large firms raises some challenges, optimism measures suggest that AI investment will continue to contribute to future ...
FRBSF Economic Letter , Volume 2026 , Issue 13 , Pages 6

Journal Article
AI-Powered Algorithmic Pricing and Monetary Policy
Avaradi, Greeshma; Liu, Zheng; Zhao, Steven (2026-05-11)
The business practice of adjusting prices using algorithms powered by artificial intelligence—known as AI pricing—has grown rapidly and spread across many sectors in the economy. Unlike traditional price setting, AI pricing uses predictive analysis of large data sets to incorporate real-time changes in supply and demand conditions into pricing decisions. This enables businesses to adjust prices more quickly in response to unexpected changes in market conditions and monetary policy. Industry-level evidence suggests that price adjustments are more sensitive to monetary policy in sectors ...
FRBSF Economic Letter , Volume 2026 , Issue 12 , Pages 5

Working Paper
Is the AI Boom Volatility-Biased Technological Change?
Munoz Henao, Juan David; Sly, Nicholas (2026-08-13)
We show that AI technologies are oriented toward jobs and workers that typically exhibit greater volatility in labor market outcomes over the business cycle. Occupations currently most exposed to AI are those that have historically exhibited (i) greater volatility in employment levels over business cycles, (ii) higher job-switching rates by workers, (iii) higher job-finding rates, and (iv) a lower likelihood for workers to exit the labor force following a job loss. The sectors of the U.S. economy that produce AI technologies have also historically exhibited high volatility in productivity. We ...
Research Working Paper , Paper RWP 26-06

Working Paper
Processing Power: The Effect of Data Centers on Wholesale Electricity Markets
Reaser, Robert; Kay, Owen; Taylor, Reid (2026-03-20)
Artificial-intelligence-driven data centers are reversing two decades of flat U.S. electricity demand and have generated questions about how this growth will impact electricity prices. We quantify this effect using an hourly, unit-level least-cost dispatch model covering wholesale electricity markets in the continental United States. We find that existing data centers have already increased wholesale prices by 3 to 5% on average nationwide, with substantially larger effects in regions hosting major data center corridors. Extending the model through 2028, we show that if proposed construction ...
Working Papers , Paper 2606

Journal Article
New from the Richmond Fed’s Regional Matters blog
Mullen, Katrina (2024-08-13)
Econ Focus , Volume 24 , Issue 3Q , Pages 2

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