MEMORANDUM
TO: Investment Committee
FROM: Senior Economist
DATE: May 16, 2026
SUBJECT: Analysis of Recent Federal Reserve District Research
I have reviewed the latest research output from the Fed districts. The current intellectual focus is heavily skewed toward the productivity-labor nexus, specifically regarding the integration of Generative AI.
Below are the most analytically significant publications for our current positioning:
1. [NY] Do Job Postings Show Early Labor-Market Effects of AI?
This research seeks empirical evidence of AI-driven labor displacement or augmentation within real-time job posting data. For our models, this is critical for determining whether we are seeing a "jobless productivity" surge or a structural shift in skill premiums.
2. [RIC] Productivity Growth and Monetary Policy in the 1990s
By revisiting the 1990s, the Richmond Fed is drawing a parallel between the PC revolution and the current AI wave to assess how the FOMC should handle "productivity shocks." This suggests the Fed is preparing for a scenario where growth accelerates without triggering inflation, potentially allowing for a "higher-for-longer" growth regime with lower terminal rates.
3. [STL] Mind the Gap: AI Adoption in Europe and the US
This paper analyzes the divergence in AI integration speeds between the US and EU. This is a key signal for our FX and equity desks, as a widening "adoption gap" could lead to structural US outperformance in productivity and GDP growth relative to the Eurozone.
4. [STL] Firm-Worker Matches: Experience or Inspection Goods?
This study examines the friction in how firms value worker experience versus observable skills. In an AI-disrupted market, if "experience" is deprecated in favor of "AI-fluency," we expect a period of high frictional unemployment even amidst strong headline growth.
Synthesis: The Federal Reserve is actively benchmarking the current AI transition against the 1990s to determine if productivity gains will neutralize inflationary pressures. We should anticipate a policy framework that is increasingly tolerant of growth acceleration, provided it is driven by technological efficiency rather than demand overheating.
The paper investigates whether generative AI tools are influencing labor demand by analyzing U.S. job postings. It utilizes occupational AI exposure measures to identify early shifts in hiring patterns.
As generative AI tools become more widely used, a key issue is the technology’s impact on labor demand. Where might we find evidence of that impact? In this post, we examine whether early evidence of AI’s effect on the labor market appears in firms’ job postings. We combine an occupational measure of AI exposure with detailed U.S. job-posting data from Lightcast, which aggregates listings from company career pages, national and local job boards, and job-listing aggregators. Using this data, we test whether postings for AI-exposed occupations declined disproportionately since the release of Cha
The paper examines the relationship between productivity growth and monetary policy decisions during the 1990s. It specifically analyzes how productivity gains influence FOMC deliberations and draws parallels to the potential impact of artificial intelligence on trend growth.
With the potential of artificial intelligence to raise trend productivity growth, it is illuminating to revisit Al Broaddus' contributions to late 1990s FOMC discussions.
The research examines the nature of firm-worker matching, analyzing whether worker quality is determined by accumulated experience or through a screening process. It explores the implications for labor market efficiency and hiring frictions.
This study compares the rate and depth of AI adoption between European and United States economies. It analyzes the structural and regulatory drivers that create a gap in technological integration.