Contemporary debates about large language models (LLMs) remain shaped by the logical structure of Searle’s Chinese Room Argument (1980). Sceptics routinely argue that since LLMs merely manipulate statistical patterns without understanding the content they process, they possess no genuine understanding or mentality. This inferential pattern recapitulates Searle’s move from “the person in the room does not understand Chinese” to “running a program is insufficient for mentality”.
This paper argues that this inference is illegitimate. It accepts Searle’s premise that the person in the Chinese Room does not understand Chinese. However, it shows that the person does understand something else: the rule system itself.
This understanding is genuine and intrinsic by Searle’s own criteria, and it is acquired during the process of running the program. Searle’s argument therefore contains an unacknowledged inferential gap: from “no understanding of the processed content” to “no understanding whatsoever”. Furthermore, Searle’s structural equivalence premise, that the person and the computer are doing the same thing, is shown to constitute a double-edged sword: if consistently applied, it prevents Searle from denying that the machine may possess an analogue of the person’s understanding.
The Chinese Room Argument cannot establish its intended conclusion. The author declares no competing interests. Ethical approval was not required as this research did not involve human participants or their data.
Informed consent was not required as this research did not involve human participants. OpenAI’s ChatGPT was used solely for language editing improve the readability, grammar, and style of the manuscript. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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