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The broken ladder: AI, remote work, and early-career hiring

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  • Peter John Lambert
  • Yannick Schindler

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 243million 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.

Suggested Citation

  • Peter John Lambert & Yannick Schindler, 2026. "The broken ladder: AI, remote work, and early-career hiring," CEP Discussion Papers dp2193, Centre for Economic Performance, LSE.
    • Handle: RePEc:cep:cepdps:dp2193
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