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Working Paper
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models
Crane, Leland D.; Karra, Akhil; Soto, Paul E. (2025-06-25)
We evaluate the ability of large language models (LLMs) to estimate historical macroeconomic variables and data release dates. We find that LLMs have precise knowledge of some recent statistics, but performance degrades as we go farther back in history. We highlight two particularly important kinds of recall errors: mixing together first print data with subsequent revisions (i.e., smoothing across vintages) and mixing data for past and future reference periods (i.e., smoothing within vintages). We also find that LLMs can often recall individual data release dates accurately, but aggregating ...
Finance and Economics Discussion Series , Paper 2025-044

Discussion Paper
Firms and Artificial Intelligence: A Regional Update
Kosakow, Jason; Waddell, Sonya Ravindranath (2020-60-20)
Similar to past technological developments, the productivity implications, labor market implications, and thus economic implications of Artificial Intelligence (AI) will evolve over time. A lot depends on who is using AI tools, when they are using them, and how they are using them.In the Richmond Fed's December business surveys — which were fielded between Dec. 1 and Dec. 17 — we asked firms if they have adopted AI and if so, how they were using it. Businesses reported that they were increasingly providing employees with access to AI tools to complete tasks but were less likely to have ...
Regional Matters

Report
How Retrainable Are AI-Exposed Workers?
Hyman, Benjamin; Lahey, Benjamin; Ni, Karen; Pilossoph, Laura (2025-08-01)
We document the extent to which workers in AI-exposed occupations can successfully retrain for AI-intensive work. We assemble a new workforce development dataset spanning over 1.6 million job training participation spells from all U.S. Workforce Investment and Opportunity Act programs from 2012-2023 linked with occupational measures of AI exposure. Using earnings records observed before and after training, we compare high AI exposure trainees to a matched sample of similar workers who only received job search assistance. We find that AI-exposed workers have high earnings returns from training ...
Staff Reports , Paper 1165

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

Journal Article
Have We Entered an Era of High Productivity Growth?
Abdelrahman, Hamza; Foerster, Andrew (2026-05-26)
Labor productivity gains over the past three years helped the U.S. economy expand steadily, even with near-zero employment growth. Combined with substantially increased business investment in artificial intelligence technology, these conditions have raised the question of whether the economy is entering a high-productivity growth period. Two well-known productivity measures do not yet provide strong evidence of this shift. However, recent patterns resemble the mixed signals during the early stages of the 1990s productivity surge before a sustained high-growth period materialized, giving ...
FRBSF Economic Letter , Volume 2026 , Issue 14 , Pages 5

Working Paper
Artificial Intelligence Innovation by Financial Innovators: Evidence from US Patents
Timmerman, Jean Xiao (2025-12-12)
This paper examines the evolution of artificial intelligence (AI) patent rates (i.e., the number of AI patents/number of firms of the same type) and concentration metrics (i.e., the Herfindahl-Hirschman Index (HHI) and Gini coefficient) among financial market participants from 2000 to 2020. It documents the historical trajectories of AI innovation for regulated banking entities and less-regulated firms, revealing that nonfinancial companies exhibit the highest baseline AI patent rate, while banks show the highest growth in AI patent rate over time. Banks have the highest HHI, and nonfinancial ...
Finance and Economics Discussion Series , Paper 2025-104

Working Paper
Artificial Intelligence and Inflation Forecasts
Leibovici, Fernando; Faria-e-Castro, Miguel (2024-01-11)
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

Working Paper
Artificial Intelligence and Inflation Forecasts
Leibovici, Fernando; Faria-e-Castro, Miguel (2023-07-14)
We explore the ability of Large Language Models (LLMs) to produce 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. We argue that this method of generating forecasts is inexpensive and can be ...
Working Papers , Paper 2023-015

Discussion Paper
AI and the Labor Market: Will Firms Hire, Fire, or Retrain?
Abel, Jaison R.; Deitz, Richard; Emanuel, Natalia; Hyman, Benjamin (2024-09-04)
The rapid rise in Artificial Intelligence (AI) has the potential to dramatically change the labor market, and indeed possibly even the nature of work itself. However, how firms are adjusting their workforces to accommodate this emerging technology is not yet clear. Our August regional business surveys asked manufacturing and service firms special topical questions about their use of AI, and how it is changing their workforces. Most firms that report expected AI use in the next six months plan to retrain their workforces, with far fewer reporting adjustments to planned headcounts.
Liberty Street Economics , Paper 20240904b

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