This paper advances a theoretical argument that generative AI is accelerating the democratization of computational thinking and, in turn, reshaping education, professional practice, and the nature of computing itself. Traditionally, computational thinking has been closely tied to learning to program, thereby limiting who could effectively employ it. The emergence of large language models (LLMs) challenges this linkage by decoupling many forms of computational problem solving from direct programming. In response to this shift, the paper explores the implications for curriculum design and workforce roles through a theoretical and interpretive lens. Drawing on prior literature, historical context, and illustrative examples from recent scholarship and practice, we develop a conceptual account of AI-augmented computing. We argue that LLMs lower barriers to entry by abstracting away much of manual coding and reallocating effort toward problem framing, prompt engineering, oversight, and validation. We further argue that this transition is redistributing computational skills across disciplines, positioning prompt engineering as an emerging engineering practice, and increasing pressure on universities to redesign curricula around AI literacy, fluency, and mastery.
Schmidt et al. (Wed,) studied this question.