 The AI-Competence Ceiling: Redefining Human Expertise in an AI-Augmented WorldPrashant Singh Yadav Chapter 4 in The AI Competency Paradox, 2026, pp 65-87 from Springer Abstract: Abstract This chapter introduces the AI-Competence Ceiling hypothesis, which posits that artificial intelligence creates a threshold beyond which augmentation begins to impede rather than enhance the development of true human expertise. While AI dramatically accelerates skill acquisition through early stages of the Dreyfus model, it simultaneously creates barriers to developing the intuitive mastery characteristic of expert-level performance. Through analysis of established expertise development theory and empirical observations across professional domains, we demonstrate how AI’s selective amplification capabilities create what we term “performance-understanding gaps” where individuals can execute tasks at advanced levels without possessing the underlying cognitive foundations traditionally associated with such performance. The chapter presents a revised understanding of the Dreyfus skill acquisition model in AI-augmented environments and examines the strategic implications for individuals, organizations, and educational institutions seeking to preserve pathways to genuine expertise while leveraging AI’s benefits. Date: 2026 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-032-11748-9_4 Ordering information: This item can be ordered from DOI: 10.1007/978-3-032-11748-9_4 Access Statistics for this chapter More chapters in Future of Business and Finance from Springer |
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