On February 24, 2026, the Federal Reserve Bank of Dallas published research showing that industries most exposed to artificial intelligence are shedding jobs while paying the workers who remain substantially more. The finding captures a labor market in which AI is not simply replacing or complementing human work but doing both at once, divided along lines of experience.
J. Scott Davis, an assistant vice president in the Dallas Fed’s research department, analyzed wage and employment data across industries ranked by AI exposure. Since the fall of 2022, when generative AI tools entered wide commercial use, total U.S. employment has grown roughly 2.5% Computer systems design, the most AI-exposed industry, lost 5% of its workforce over the same period. The top ten percent of AI-exposed industries collectively saw employment decline by 1%.
Wages moved in the opposite direction. Average weekly pay in computer systems design rose 16.7%, more than double the national average of 7.5%. Across the top decile of AI-exposed industries, wages climbed 8.5%.
The Dallas Fed attributed the split to a distinction between codified and tacit knowledge. Codified knowledge covers rules, procedures, and textbook processes that AI can replicate. Tacit knowledge involves the judgment, intuition, and contextual awareness built through years of direct professional experience. Workers whose value rests on the first type face displacement; those whose value rests on the second see their skills amplified.
A separate Dallas Fed study, published January 6, 2026, by economists Tyler Atkinson and Shane Yamco, reinforced the pattern among young workers specifically. The share of employment held by workers aged 20 to 24 in high-AI-exposure occupations fell from 16.4% in November 2022 to 15.5 percent by September 2025. The decline was driven not by mass layoffs but by a drop in the job-finding rate, which fell more than three percentage points for that age group since November 2023.
The experience premium is widening.
In occupations such as law, insurance underwriting, credit analysis, and marketing, experienced workers now command wages more than double those of their junior counterparts. The median experience premium across AI-exposed occupations sits at 40%, with some fields exceeding 100%.
Federal Reserve Governor Michael Barr outlined three scenarios for AI’s labor market trajectory in a February 17, 2026 speech. In the first, adoption proceeds gradually and the economy absorbs displaced workers through retraining and new job categories. In the second, agentic AI scales rapidly enough to render professional and service workers functionally unemployable. In the third, energy and data constraints crash the AI expansion, producing financial stress comparable to the early 2000s.
The policy response is still forming. Senators Mark Warner and Josh Hawley introduced the AI-Related Job Impacts Clarity Act, requiring quarterly reports on AI-driven job cuts to the Department of Labor, while a companion bill, the AI Workforce PREPARE Act, would establish a dedicated research hub within the department. Brookings estimated in January 2026 that 6.1 million of the 37.1 million workers in highly AI-exposed occupations lack the adaptive capacity to transition, with 86% of that vulnerable group being women.
The Dallas Fed’s own warning was narrower but pointed. If firms find it cheaper to use AI than to train junior employees, the pipeline that builds experienced workers disappears — the institution that produces tacit knowledge erodes precisely as tacit knowledge becomes the asset that AI cannot replicate.
