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

Peter John Lambert and Yannick Schindler
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Peter John Lambert: University of Warwick
Yannick Schindler: Ellison Institute of Technology, Oxford

CAGE Online Working Paper Series from Competitive Advantage in the Global Economy (CAGE)

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.

Keywords: Remote Work; Generative AI; Junior Hiring; Technology Exposure; Omitted Variable Bias JEL Classification: J23; J24; J31; O33 (search for similar items in EconPapers)
Date: 2026
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