AI Competency Erosion: Understanding Expertise Decay

Prashant Singh Yadav

Chapter 5 in The AI Competency Paradox, 2026, pp 89-111 from Springer

Abstract: Abstract This chapter examines the mechanisms through which the existing human competencies deteriorate under AI dependency, presenting empirical evidence for systematic disruption of professional expertise across multiple interconnected dimensions. Building on established theories of skill acquisition and tacit knowledge transmission, we demonstrate how AI adoption creates measurable competency decay through four pathways: individual skill atrophy, structural erosion of expertise development systems, systemic organizational vulnerability, and fundamental redefinition of cognitive requirements. The analysis reveals how AI disrupts both explicit knowledge mastery and tacit knowledge development, creating “false expertise transitions” where apparent competence masks underlying knowledge gaps. Contemporary evidence from medical practice, legal profession, and broader cognitive research validates these theoretical predictions, showing measurable competency decline within months of AI adoption. We develop a climate-cognition parallel that positions competency erosion as a systemic risk requiring coordinated intervention, while presenting a revised Dreyfus model of skill acquisition that accounts for AI’s transformative effects on traditional expertise development pathways.

Date: 2026
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DOI: 10.1007/978-3-032-11748-9_5

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