Search Results
Discussion Paper
Firms and Artificial Intelligence: A Regional Update
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 ...
Report
How Retrainable Are AI-Exposed Workers?
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 ...
Working Paper
Artificial Intelligence and Inflation Forecasts
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 Paper
Artificial Intelligence and Inflation Forecasts
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 Paper
The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance
We document key stylized facts about the time-series trends and cross-sectional distributions AI pricing and study its implications for firm performance, both on average and in response to monetary policy shocks. We use the online job postings data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI pricing jobs in all pricing jobs has increased more than tenfold since 2010. The rise of AI pricing jobs has been ...
Journal Article
On-the-Job Exposure to AI Among Lower-Income Workers
To better understand the potential impacts of generative AI (gen AI) on the economy, this analysis uses quantitative methods to assess the extent to which workers are likely to be exposed to AI on the job, paying particular attention to workers in lower-income households, the occupations and industries in which they work, and how exposure varies across different parts of the country. It also draws on qualitative insights to understand how the impacts of AI integration are showing up in real time and how workforce and training organizations, nonprofits, and employers are adapting.
Journal Article
Federal Reserve: Artificial Intelligence and Bank Supervision
Artificial intelligence has come a long way since English mathematician, logician, and cryptographer Alan Turing's seminal 1950 essay, "Computing Machinery and Intelligence," which explored the idea of building computers capable of imitating human thought. In 1997, almost 50 years after Turing's essay, AI posted a historic breakthrough when the IBM supercomputer Deep Blue won a chess match against reigning world champion Garry Kasparov. Since then, AI's capabilities have improved rapidly, largely through advances in machine learning (ML), especially in ML models that use digital neural ...
Working Paper
Artificial Intelligence and Inflation Forecasts
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 ...
Journal Article
Interview: Daron Acemoglu
Daron Acemoglu is one of MIT's nine university- wide Institute Professors, the university's highest faculty rank. One of his predecessors, Robert Solow, developed a pathbreaking mathematical model of economic growth in the 1950s. Today, Acemoglu says hurray for economic growth — but is also concerned that choices made by policymakers and companies are channeling the gains from that growth away from workers. And as he sees things, the powerful AI technologies that have come to the fore in the past several years, embedded in products such as ChatGPT, should be regulated with the economic ...
Report
Shaping the Future of Work: Workers’ Optimism and Pessimism about AI
The artificial intelligence (AI) revolution is here and is expected to transform the labor market, creating new opportunities for some workers while eliminating the jobs of others. At the end of 2024, the authors of this brief surveyed a sample of US household heads working in different industries and with different educational backgrounds to better understand how they view AI and its potential effects on their job prospects and financial well-being.