---
id: "lib-2026-09-29-has-ai-impacted-the-labor-market-yet"
type: "library"
title: "Has AI impacted the labor market yet?"
lang: "EN"
org: "Alex Imas, Jacob Schaal (Substack)"
source_type: "Practitioner post"
date: "2026-09-29"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-09-29-has-ai-impacted-the-labor-market-yet/"
original_url: "https://aleximas.substack.com/p/has-ai-impacted-the-labor-market"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "A structured review of more than 20 recent labor-market studies by Chicago Booth economist Alex Imas and Jacob Schaal, reporting each study's data, effect size and identification problems."
summary_basis: "full-text"
original_minutes: "25"
tags: ["future of work", "entry level hiring", "evidence review"]
frames: ["verification-capacity"]
---

# Has AI impacted the labor market yet?

Tier A (Strong evidence): A structured review of more than 20 recent labor-market studies by Chicago Booth economist Alex Imas and Jacob Schaal, reporting each study's data, effect size and identification problems.

Summary based on the full text.

## In short

A September 2026 Substack review by Alex Imas (University of Chicago Booth) and Jacob Schaal. It asks whether AI has visibly changed the labor market yet. Their answer is narrow: there is contested evidence that AI is reducing hiring for junior white-collar roles most exposed to it, but no sign yet of aggregate displacement in unemployment or layoffs. Remote work is the main rival explanation for the junior decline.

## Key numbers

- 19%: employment of 22-25-year-olds in high-exposure US roles below trend by June 2026 (Canaries dashboard, descriptive)
- 8-10%: junior employment decline at GenAI-adopting firms vs non-adopters after 2 years (Hosseini and Lichtinger)
- 1%: laid-off US workers who attribute their dismissal to AI (Gallup)
- 88% / 21%: share of US employment vs share of tasks covered by AI use in Google's ATLAS data

## What it reports

- Brynjolfsson et al. (ADP payroll): 13% fall in junior employment in high-exposure roles by July 2025; dashboard shows 19% below trend by June 2026, roughly halving with education controls.
- Tucker (Census): hires of 22-24-year-olds in the most exposed fifth of industries fell about 9% after ChatGPT; 12.4% relative employment fall by mid-2025.
- Orr, Tucker and Warren: 6.7 million graduates; most exposed tenth of majors 5 pp less likely employed, initial earnings about 13% lower.
- Nordic data: Finland and Norway find no significant effect; Norwegian exposed occupations grew 0.1% vs 0.3% since October 2022.
- Lambert and Schindler: with remote work added, the AI-exposure coefficient on junior hiring drops to zero; Brynjolfsson et al. rerun it on payroll data and find the opposite.
- Humlum and Vestergaard (Denmark, 25,000 workers): null effects on earnings and hours, ruling out effects above 2%, despite 93% usage in firms with enterprise chatbots.
- Kharazian et al. (21,000 US firms): high-intensity AI spenders grew employment 10% and entry-level 12% vs later adopters.
- Fairlie and Wu: graduate unemployment 7.3% in summer 2026, within the 2022-25 range.
- Yotzov et al. (nearly 6,000 executives): 69% use AI; about 90% report no significant effect on employment or productivity so far.

## How they got it

A narrative literature review of working papers, administrative data studies, surveys and usage data available through October 2026, grouped by method: exposure-based designs, adoption-based designs, Nordic population data and remote-work tests. The authors summarise each study's sample and identification threats.

## Limits

- Not a systematic meta-analysis; study selection is the authors'.
- Imas contributed to Google's ATLAS, which the review cites.
- Most cited studies are unrefereed working papers.

## Why it matters for companies

The evidence says any AI effect so far shows up in fewer junior hires, not layoffs; in a UK survey subsample, firms expecting cuts put about two-thirds of them on reduced hiring. That makes graduate and entry-level intake the decision point. The review also flags a long-run cost: fewer juniors means fewer future seniors, with one model (Ide) estimating 0.05-0.35 pp lower long-run US GDP growth. Companies trimming junior roles should decide explicitly how they will train the next layer of experts.

Original: https://aleximas.substack.com/p/has-ai-impacted-the-labor-market