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PreprintCited by 2Labor market dynamics and wage inequalityDigital Economy and Work TransformationEthics and Social Impacts of AI

The Broken Ladder: AI, Remote Work, and Early-Career Hiring

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Lambert, Peter John, Schindler, Yannick·2026·SSRN Electronic Journal
DOI 10.2139/ssrn.6787638
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Abstract

Is generative AI replacing junior workers? A growing literature answers yes, citing large declines in early-career hiring concentrated in GenAI-exposed occupations. We argue that this verdict is premature because GenAI exposure is strongly correlated with another post-pandemic shock, working from home (WFH). Using two data sources spanning 243 million new hires and 407 million online job postings, collected across the US, UK, Canada, and Australia during 2017-2025, we estimate difference-in-difference designs at the occupation, region, and firm level. When estimated separately, a two-standard-deviation increase in GenAI and WFH exposure each predicts, by 2025, a fall of around 5pp in the junior-share of new hires and around 3pp in the share of job ads requiring limited experience. Estimated jointly, the WFH effect remains, while the GenAI coefficient attenuates sharply and is often statistically indistinguishable from zero. Alternative exposure measures, residualization designs, flexible non-parametric co-treatment controls, and replacing exposure-measures with actual WFH adoption as the treatment all support our finding that WFH is a robust predictor of the decline in early-career hiring.

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  • An occupation's remote-work exposure correlates strongly with its GenAI exposure.

    “Lambert and Schindler (2026) show that occupational exposure to working from home is strongly correlated with GenAI exposure”

    From Labor Market Consequences of Generative AI: Early Evidence from Norway (2026) · cites this paper as Lambert and Schindler (2026)

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  71. Note: Figure depicts the 'junior-share' of new hires for the US, UK, Canada, and Australia, expressed as the percentage-point change from each country's 2019 mean. Data comes from Revelio Labs (see Section 4 for detailed description). 'Junior' is defined using the bottom-two groups of a six-level scale which accounts for the job description and each worker's past experience at the beginning of their tenure in a new position. All series are constructed as a weighted arithmetic mean of 3-digit-occupation junior shares, using the US 2019 3-digit O*NET share of all hires as the (fixed) weight, to net out temporal and cross-country compositional differences. The 2019 (reweighted) junior share of new hires is 66.9% in the US, 64.9% in the UK, 69.3% in Canada, and 65.8% in Australia. Series are at quarterly frequency and seasonally adjusted using the U.S. Census Bureau's X-13ARIMA-SEATS procedure applied separately to each country's series. Dashed vertical lines mark the COVID-19 pandemic and the public release of ChatGPT.

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Authors
Lambert, P. J., & Schindler, Y.
Affiliations
  • University of Warwick (GB)
  • London School of Economics and Political Science (GB)
Corresponding author
Lambert, Peter John
ORCID
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2026
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.6787638
OpenAlex
W7161775322
Cited by
0
References
0
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • Warwick Research Archive Portal (University of Warwick) (submittedVersion, other-oa)
  • University of Warwick (cc-by)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
generative AI, remote work, working from home, new employees, job advertisements, experience requirements, post-pandemic labor market

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