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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

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

Journal Article
Machine Learning a Ramsey Plan
Sargent, Thomas J.; Yang, Ziyue (2025-05-12)
We use a Python program to calculate a pair of infinite sequences of money creation and price level inflation rates that maximize a benevolent time 0 government’s quadratic objective function for a linear-quadratic version of Calvo (1978). The program computes an open-loop representation of the optimal plan and an associated monotonically declining, bounded from below sequence of continuation values whose limit is a worst continuation value that is associated with a “timeless perspective”. We run some least squares regressions on fake data to try to learn about the structure of the ...
Quarterly Review , Volume 45 , Issue 1

Discussion Paper
By Degree(s): Measuring Employer Demand for AI Skills by Educational Requirements
Galeano, Sergio; Hodge, Nye; Ruder, Alexander (2025-05-21)
The rapid advancement of artificial intelligence (AI) has prompted widespread interest and discussion about its potential to transform the labor market. For workforce development practitioners, a key issue is how AI is changing the nature of work, mainly through changes in the skills workers need to be competitive for the jobs of today and of the future. In this Workforce Currents, we explore the growth of employer demand for AI skills in online job postings data between 2010 and 2024. Lightcast, a labor analytics firm, provides job postings data that includes several useful features of ...
Workforce Currents , Paper 2025-01

Working Paper
Firm Data on AI
Yotzov, Ivan; Barrero, Jose Maria; Bloom, Nicholas; Bunn, Philip; Davis, Steven J.; Foster, Kevin; Jalca, Aaron; Meyer, Brent; Mizen, Paul; Navarrete, Michael; Smietanka, Pawel; Thwaites, Gregory; Wang, Ben (2026-03-24)
We present the first representative international data on firm-level AI use. We survey almost 6,000 CFOs, CEOs, and executives from stratified firm samples across the US, UK, Germany, and Australia. We find four key facts. First, around 70 percent of firms actively use AI, particularly younger, more productive firms. Second, while over two-thirds of top executives regularly use AI, their average use is only 1.5 hours a week, with one quarter reporting no AI use. Third, firms report little impact of AI over the last three years, with more than 80 percent of firms reporting no impact on either ...
FRB Atlanta Working Paper , Paper 2026-3

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