You write code, fix bugs, polish emails. The result looks great. You feel smart and competent. But what if that good work was not yours?
A new academic study introduces a troubling concept: the LLM fallacy. The authors describe a cognitive error in which people mistakenly regard AI-generated outputs as evidence of their own abilities. The brain quietly takes credit for work that was largely done by a machine.
How the LLM fallacy arises
The researchers define the LLM fallacy as an error in attributing credit in which users of AI tools systematically overestimate themselves. This is not an isolated phenomenon. It is a structural problem built directly into the way language models work and the way people interact with them. Why does this happen? Three characteristics of AI tools combine to create fertile ground for this fallacy.
Output fluency plays a crucial role. Text, code, and analyses generated by language models are grammatically correct, stylistically consistent, and appear natural. The brain automatically interprets this fluency as a sign of competence, even though it was the machine that was competent, not the person at the keyboard.
Process opacity further complicates the situation. Unlike traditional tools, where you can see every intermediate step, a language model conceals its entire computational process. You do not know how it arrived at the result. This lack of visibility makes it impossible to distinguish precisely what you contributed to the work and what the model provided.
Low effort required for use completes the trio. The less cognitive effort a task requires, the fewer opportunities you have to verify whether you truly understand the subject. And it is precisely this ease that gradually convinces you that you are better than you really are.
Four areas where the fallacy is most pronounced
The study’s authors mapped where the LLM fallacy occurs most frequently. The results are not surprising, but they are uncomfortable.
Programming is the first area. A user has a language model generate a working script or an entire application. The code works. The user feels that they understand the architecture, dependencies, and logic. In reality, they would be unable to fix or extend the code on their own. Research cited in the article shows that the superficial correctness of code does not reliably indicate deeper understanding.
Writing and content creation form the second area. AI generates a draft, the user edits it, and then claims it as their own voice, style, and creativity. But where exactly is the line between authorship and editing someone else’s text? The study points out that this boundary is deliberately blurred in the context of language models.
Analytical tasks are the third area. A language model can produce a structured analysis or a detailed problem-solving procedure. The user accepts it, reproduces it, and internalizes it as their own analytical thinking. Yet the entire thought process was externalized rather than their own.
Language skills round out the list. Who among us has not written an email in a foreign language with the help of AI? The output was fluent and grammatically flawless. And the user came away feeling that they were actually fairly proficient in that language. The study’s authors describe this gap as the difference between surface form and genuine linguistic competence.
The brain takes credit
The cognitive mechanism behind the LLM fallacy is not accidental. The researchers describe it as a predictable result of how the human brain processes authorship and the outcomes of work.
Ambiguity in attributing credit arises because, when interacting with a language model, the user enters prompts, the model generates content, and the entire process unfolds in a continuous loop. The brain then retrospectively reconstructs a story about who did what, systematically overestimating its own contribution. Research on authorship and conscious agency shows that people infer authorship from outcomes, not from the processes that led to them.
The illusion of fluency then acts as a shortcut. If a text is easy to read and code compiles correctly, the brain interprets this as evidence of quality and competence. This heuristic mechanism is well documented in cognitive psychology, and language models activate it in almost every interaction.
Cognitive offloading weakens self-reflection. The more routine cognitive work the model takes over, the less the user exercises their own ability to assess what they know and what they do not. Opportunities for learning decrease while confidence grows. The authors describe this as the systematic inflation of perceived competence.
Impact on recruitment and education
The authors Kim, Yu, and Yi warn that the LLM fallacy does not operate solely within the individual. It affects entire evaluation systems that society uses to assess competence.
During recruitment, candidates may present outputs created with substantial AI assistance rather than through their own work. Evaluators who see only the result cannot easily distinguish AI-assisted performance from genuine expertise. More dangerously, the candidates themselves may sincerely believe that the outputs reflect their abilities.
A similar gap is emerging in education. Students who have AI generate explanations or solve tasks may achieve better short-term performance, but they engage less in genuine thinking. Studies show that such assistance weakens the relationship between completing a task and understanding the material. Certificates and credentials designed to signal verified expertise become less reliable. A person may meet entry criteria without actually possessing the corresponding expertise.
How to defend yourself against a fallacy you do not even realize you have
The researchers propose several ways to address the situation. None of them is simple, but they all stem from the same principle: process visibility.
AI tool interfaces could signal more explicitly what the model generated and what the user added. Educational approaches should foster metacognitive awareness—that is, the ability to assess accurately what we know and what we do not know. Evaluation should shift from outputs to processes, testing the ability to work without AI assistance rather than merely the ability to present results.
According to the authors, this is currently a conceptual framework. Empirical validation is yet to come. The study proposes testable theories and calls on the research community to measure the phenomenon systematically. One proposed method is longitudinal studies that would track how users’ self-perception changes over the course of long-term language model use.
The authors used AI themselves. And they admitted it.
One more aspect of the study is worth mentioning: in the final section of the paper, the authors openly state that they wrote it with the help of a language model. They used a structured prompting approach, while all conceptual decisions, interpretations, and ultimate responsibility remained with the human authors.
It is remarkable: the researchers studying an AI-related fallacy themselves acknowledge that they used AI in their writing. And they explicitly described how they tried to maintain the boundary between the tool and the author.
