Definition

Young-Worker AI Exposure Effect = The trend where early-career workers (ages 22-25) in jobs heavily exposed to generative AI are seeing declines or stagnation in employment compared to older or less-exposed workers.

Why It’s Happened

• Generative AI has rapidly advanced and been widely adopted, affecting tasks in certain occupations.
• Entry-level roles rely more on codified knowledge that AI can replicate, making them more vulnerable.
• Jobs where AI substitutes human labor show declines; jobs where it augments human work show fewer losses.

Key Findings (“Six Facts”)

1. Declines for young workers in exposed jobs — Sharp employment drops for 22-25-year-olds in highly AI-exposed fields like software development and customer service.
2. Overall job growth, but not for entry-level — Total employment is rising, but young workers in exposed roles have stagnated or declined since late 2022.
3. Automation vs. augmentation matters — Declines are concentrated in occupations where AI replaces tasks, not where it supports workers.
4. Effects remain after controlling for firm shocks — The drop persists even when adjusting for broader firm-level changes.
5. Employment changes more than wages — Fewer jobs exist, but pay hasn’t shifted much yet, suggesting wage stickiness.
6. Robust across samples — The pattern holds across industries, education levels, and remote/non-remote roles.

Implications / Suggestions

• Closely track early-career workers in AI-exposed fields.
• Provide targeted training and reskilling for young workers.
• Improve data on firm-level AI adoption and its hiring impacts.