The AI-Competence Ceiling: Redefining Human Expertise in an AI-Augmented World

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.