Automation as an Equalizer: How Easy‐to‐Use Technologies Narrow Skill Gaps Between Low‐ and High‐Skilled Workers

ABSTRACT This paper investigates the labor market effects of the two most disruptive technologies of the past decade–industrial robots and artificial intelligence (AI). By reviewing the empirical literature and discussing existing models, we explore how these technologies affect workers based on their level of skills. The reviewed studies indicate that, contrary to popular belief, AI use does not hurt workers, including the low‐skilled. On the contrary, the ease of using these technologies, particularly the more recent iterations of AI and large language models (LLMs), makes them complimentary to the low‐skilled workforce, enabling them to reach productivity levels closer to those of high skilled workers. This implies that the concerns about systemic AI‐driven job displacements are not strongly supported by the recently emergent empirical evidence.

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Publication Details

Journal
Journal of Economic Surveys
Published
2026-02-04
DOI
https://doi.org/10.1111/joes.70072
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
0.00
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article

Automation as an Equalizer: How Easy‐to‐Use Technologies Narrow Skill Gaps Between Low‐ and High‐Skilled Workers

Nopadol Rompho, Shahab Sharfaei
Journal of Economic Surveys
Digital Economy and Work Transformation
article

Automation as an Equalizer: How Easy‐to‐Use Technologies Narrow Skill Gaps Between Low‐ and High‐Skilled Workers

Nopadol Rompho, Shahab Sharfaei
article en

Abstract

ABSTRACT This paper investigates the labor market effects of the two most disruptive technologies of the past decade–industrial robots and artificial intelligence (AI). By reviewing the empirical literature and discussing existing models, we explore how these technologies affect workers based on their level of skills. The reviewed studies indicate that, contrary to popular belief, AI use does not hurt workers, including the low‐skilled. On the contrary, the ease of using these technologies, particularly the more recent iterations of AI and large language models (LLMs), makes them complimentary to the low‐skilled workforce, enabling them to reach productivity levels closer to those of high skilled workers. This implies that the concerns about systemic AI‐driven job displacements are not strongly supported by the recently emergent empirical evidence.

Journal of Economic Surveys
Thammasat University (TH), Prague University of Economics and Business (CZ)
Decent work and economic growth
Openalex Percentile: Top 70%
Digital Economy and Work Transformation
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