The argument that large language models (LLMs) merely predict the next token in a sequence without engaging in real reasoning is challenged by questioning if human cognition might operate in a similar manner. The focus should not be on the method of next-token prediction itself, but rather on the complexity and structure of the internal processes that drive it. If the system behind token selection is sophisticated enough, it could be considered a form of reasoning. The debate highlights the need to reconsider what constitutes intelligence and reasoning, suggesting that the internal processes are more crucial than the sequential output of tokens. This matters because it challenges our understanding of both artificial intelligence and human cognition, potentially reshaping how we define intelligence.
