This paper defines the “LLM fallacy” as a bias where users credit their AI-assisted outputs to their own skill rather than the model’s contribution. It analyzes how fluent, opaque interactions with large language models blur human-machine boundaries, offers a framework for its mechanisms, and discusses impacts on education, hiring, and AI literacy.

• People using LLMs tend to internalize the AI's output as evidence of their own skill, not just trust the tool's answer (unlike classic automation bias).
• This happens because model reasoning is invisible, the prose feels fluently "authored," there's no clear handoff moment, and positive feedback loops reinforce the false belief.
• It shows up concretely: coding, academic writing, data analysis, and creative work all get passed off as personal competence when AI did much of the work.
• Real consequences include students graduating without real mastery, hiring managers misjudging AI-reliant candidates, and skewed performance reviews at work.