Preprint. This manuscript has not been accepted as a JQPP journal article and is not a Version of Record.
Preprint · Applied Research Article

The Employment Transmission Gap: AI-Led Growth and the Weakening of Career Entry

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Abstract

Artificial intelligence may allow firms to expand output without expanding entry-level employment at the same rate. This possibility is usually discussed as a question of job displacement, but that framing misses a second mechanism: the weakening of the institutional pathway through which inexperienced workers acquire their first real responsibilities, accumulate judgment, and become experienced workers. This paper develops the Employment Transmission Gap framework by formalizing an argument introduced in Kao (2026): national and firm-level success can continue even as the connection between growth and career entry becomes less reliable.

The framework distinguishes productive growth from career-entry capacity. An Employment Transmission Gap arises when productive capacity, revenue, or value added grows while quality-adjusted opportunities for inexperienced workers grow materially more slowly, stagnate, or contract. Four mechanisms can generate the gap: incumbent productivity absorption, seniority-biased task reallocation, experienced-hire substitution, and the erosion of low-risk learning tasks. Korea is a useful motivating case because recent Bank of Korea evidence shows that youth employment weakness has been concentrated in AI-exposed industries while experienced-hire preferences and labor-market dualism predate the current AI wave. Comparable U.S. evidence points in a similar direction, although other recent studies show that the magnitude and persistence of entry-level effects remain unsettled.

The contribution is diagnostic rather than predictive. The paper does not claim that AI necessarily reduces aggregate employment or that entry-level tasks should be preserved unchanged. Instead, it proposes that the quality and quantity of career-entry opportunities should be measured separately from output and productivity. The relevant question is not only whether AI creates or destroys jobs, but whether an economy continues to produce the experience pathways required to create its next generation of skilled workers.

Suggested Citation:

Johnny Kao. (2026). The Employment Transmission Gap: AI-Led Growth and the Weakening of Career Entry. JQPP Preprint, 2026-015.

Declarations

Declaration of interest

The author declares no competing interests.

Funding

This research received no external funding.

Ethics statement

Not applicable. This study did not involve human participants, patients, animals, or identifiable personal data.

AI use

OpenAI's ChatGPT was used only for literature discovery and bibliographic verification. The Employment Transmission Gap framework and underlying concepts originate in the author's prior published work (Kao, 2026). The author independently reviewed all cited sources and remains solely responsible for the final manuscript, its arguments, source selection, and citations.

Data availability

No external dataset is associated with this article.

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