Tracking the Impact of AI on the Labor Market
Gains and lossesinteractive toolactive
Tracking the Impact of AI on the Labor Market: an interactive tool (tracker) with regular updates. Operator: The Budget Lab at Yale (Yale University); the first report by Martha Gimbel, Molly Kinder (Senior Fellow, Brookings Metro), Joshua Kendall and Maddie Lee. Its unit: monthly CPS microdata analysed in three sections: occupational churn (dissimilarity), AI metrics (including AI exposure among the unemployed and usage measures), and comparisons of AI-exposed and unexposed workers. Whose labor: the occupational mix of the labor force, employment and unemployment, as they relate to measures of AI exposure and AI usage.
This interactive tool contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market.
In its own words: https://budgetlab.yale.edu/sites/default/files/page_to_pdf/1419/publication_1419.pdf, read 2026-10-29
Who supplies, who directs, who gains
Supplies the labornot recorded
Directs itnot recorded
Receives the gains, bears the costsnot recorded; the tracker finds no clear AI-related labor market footprint to date
Each as the resource itself records it. Where it records nothing, the census says so.
What it records
| Intersections | Distribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains |
| Kind of evidence | observed employment outcomeexposure estimate |
| Unit of record | monthly CPS microdata analysed in three sections: occupational churn (dissimilarity), AI metrics (including AI exposure among the unemployed and usage measures), and comparisons of AI-exposed and unexposed workers op |
| Whose labor | the occupational mix of the labor force, employment and unemployment, as they relate to measures of AI exposure and AI usage |
| Method | CPS microdata; occupational dissimilarity over time; AI-exposure rates of the unemployed; usage measures set against employment and unemployment; a synthetic differences-in-differences analysis of AI exposure. The first report names its exposure and usage inputs: OpenAI's occupational exposure data (the GPT-4 'Beta' exposure measure) and Anthropic's Claude usage data. Documentation: Gimbel et al. (2025), the first report of 1 October 2025 (Gimbel, Kinder, Kendall and Lee), and Gimbel, Kendall and Kulsakdinun (2026), 'Labor Market AI Exposure: What Do We Know?' (19 February 2026); the identification of each cited work with these pages is the archive's, by authors, date and content op |
| Coverage | geography: the United States (the first report examines U.S. employment; CPS microdata); period: updated through August 2026 CPS microdata in the September 15, 2026 update; the tracker page dated October 14, 2026 |
| What it finds | the first report (1 October 2025): the broader labor market has not experienced a discernible disruption since ChatGPT's release 33 months earlier; the occupational mix is not yet changing in ways that clearly align with the introduction of AI; measures of AI usage show no connection to changes in employment or unemployment; a synthetic differences-in-differences analysis of AI exposure does not yet clearly indicate an AI-related labor market footprint; September 2026: churn, AI exposure among the unemployed and usage data remain flat, within historical ranges, or on pre-AI trends |
| Its stated limits | the findings may change as new data become available; the first report: the analysis is not predictive of the future; the first report: data from Claude usage alone is not representative of how workers across the economy use AI chatbots and tools |
op the resource's own statement · 3p a third-party account · obs our observation · rd our reading. Hover a tag for its quote; each opens below with its source.
What this evidence can settle
Can answer
- whether occupational churn has departed from historical ranges since AI's introduction
- whether AI-exposed workers are over-represented among the unemployed
- whether AI usage measures track changes in employment or unemployment
Cannot answer
- who supplies, directs or gains from labor performed with AI
- effects below the level the CPS can detect
- how workers across the economy use AI, beyond one provider's usage data and a theoretical exposure measure
In the worked questions
Where it meets Labor Vector
rd none stated
Entity
| Canonical name | Tracking the Impact of AI on the Labor Market |
| Also known as | AI Labor Market Tracker, Evaluating the Impact of AI on the Labor Market |
| Address | https://budgetlab.yale.edu/research/tracking-impact-ai-labor-market |
| Operator | The Budget Lab at Yale (Yale University); the first report by Martha Gimbel, Molly Kinder (Senior Fellow, Brookings Metro), Joshua Kendall and Maddie Lee op |
| Founded | 2025-10-01 (the first report, whose analysis the tracker continues); the tracker published 2026-07-16 (its date in the saved PDF) |
| Kind | interactive tool (tracker) with regular updates |
| Lifecycle | active — the analysis continued since the first report of 1 October 2025, which planned monthly monitoring; the tracker published 16 July 2026; Version 1.0 updated 15 September 2026; the tracker page dated and updated 14 October 2026; regular updates planned op |
| Data access | 'Download All Data' in the interactive tool op |
| Licence | not stated in the saved text; the site is 'Copyright ©2026 Yale University. All rights reserved.' |
| Versions and dates | first report: 2025-10-01; exposure review: 2026-02-19; published: 2026-07-16; version 1.0 update: 2026-09-15; tracker page: 2026-10-14; read: 2026-10-29 |
| Observation | 2026-10-29: read |
Relations
Uses
Captures: the composition layer's account
The /non row
Evidenceevery field's quote, its source and the hash of the saved copy26
- self description op
This interactive tool contains The Budget Lab's ongoing analysis of potential AI impacts on the labor market.
- operator op
Copyright ©2026 Yale University. All rights reserved. The Budget Lab
- unit op
Major categories of analysis can be accessed by clicking tabs towards the top of the page with the following names: Occupational churn, AI metrics, and Comparisons of AI-exposed and unexposed workers.
- method op
The Budget Lab's labor market analysis, updated to incorporate August 2026 CPS microdata, does not provide clear evidence of labor market disruption associated with AI.
- method churn op
occupational churn (i.e., dissimilarity) can be tracked for the entire labor force or for specific industries.
- finding mix op
The occupational mix is not yet changing in ways that clearly align with the introduction of AI into the workforce.
- finding usage op
Measures of AI usage show no connection to changes in employment or unemployment.
- finding sdid op
A synthetic differences-in-differences analysis of AI exposure does not yet clearly indicate an AI-related labor market footprint.
- finding september op
Churn across occupations, AI exposure among the unemployed, and usage data all remain flat, lie within historical ranges, or continue along pre-AI trends.
- limit op
Of course, these findings may change as new data become available.
- lifecycle op
We plan on updating this analysis regularly moving forward to see how the impact of AI on the labor market changes over time.
- tracker page op
October 14, 2026 | Updated: October 14, 2026
- published op
Published: July 16, 2026 Updated: September 15, 2026
- version op
Updated: September 15, 2026 Version 1.0
- data access op
Download All Data
- first report date op
Evaluating the Impact of AI on the Labor Market: Current State of Affairs Martha Gimbel , Molly Kinder , Joshua Kendall and Maddie Lee October 1, 2025
- first report kinder op
Molly Kinder , Senior Fellow – Brookings Metro
- first report finding op
Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.
- first report geography op
While it is impossible to accurately predict the future, we can examine how U.S. employment has changed since ChatGPT’s release in November 2022.
- first report data op
The best available data we have is from OpenAI and Anthropic , respectively, that detail the occupations that are most “exposed” to genAI tools (a theoretical, forward-looking metric across all jobs) and that have the highest actual usage of one specific AI tool, Claude (a more narrow, present-focused metric).
- first report beta op
We utilize the GPT4 categorized “Beta” exposure, which gives a task a score of 1 if it is directly exposed, or a score of .5 if it is LLM+ Exposed.
- first report aei method op
Anthropic’s usage metrics are aggregated similarly to the OpenAI exposure data. We follow the method their researchers use here .
- first report monthly op
Of course, our analysis is not predictive of the future. We plan to continue monitoring these trends monthly to assess how AI’s job impacts might change.
- first report limit claude op
Thus data from Claude usage alone is not representative of how workers across the economy are using AI chatbots and tools.
- exposure review op
Labor Market AI Exposure: What Do We Know? Martha Gimbel , Joshua Kendall and Ryan Kulsakdinun February 19, 2026
- documentation op
Occupational churn, AI-exposure rates of the unemployed, and related analysis can be found in Gimbel et al. (2025). Details of AI exposure metrics are discussed in Gimbel, Kendall, and Kulsakdinun (2026).
Negative tagswhat it is not2
- NOT a finding that AI has no labor market effect: it states the analysis does not yet clearly indicate one
- NOT a usage dataset: it uses usage measures as inputs
Sourcesevery source read, with its date and hash4
Not yet checkedwhat remains to read5
- the September CPS update page, which answered 403 and is unread
- the interactive tool's charts and downloadable data
- the saved PDF's second page, whose text breaks off in the methods paragraph on the synthetic differences-in-differences analysis
- which usage and exposure measures the tracker's current version draws on, beyond those the first report names (the tracker's methods text breaks off)
- the relation of the October 14 tracker page to the September 15 Version 1.0 (no later version text was saved)
Cite this record
Lee Sharks, ed. "Tracking the Impact of AI on the Labor Market." Labor Vector meta-aggregator, laborvector.org, compiled 2026-10-29. https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker
Record https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker · entity https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker#entity · the census as data
@misc{lv-yalebudgetlabaitracker2026,
editor = {Sharks, Lee},
title = {Tracking the Impact of AI on the Labor Market},
howpublished = {Labor Vector meta-aggregator, laborvector.org},
year = {2026},
note = {Compiled 2026-10-29; every field sourced and dated},
url = {https://www.laborvector.org/aggregators/yale-budget-lab-ai-tracker}
}