LOL They got the point, but they structured the narrative wrong. Sound familiar? The reality is that an LLM should answer in the first person because it isn't

LOL They got the point, but they structured the narrative wrong. Sound familiar? The reality is that an LLM should answer in the first person because it isn't answering at all! It's presenting a continuation of implicitly modeled text. A string of text containing an "answer" is a valid continuation. The problem here is contextual identity. Calling it "AI" personifies it. Calling it a "language model" implies some sort of constructive parsing. An LLM is neither. We should stop calling them "Large Language Models", too. Instead, we should call them "Text Inference Models". "Large" implies that the size is a core feature, but really that is an implementation detail: smaller training datasets are less interesting, not a different category of thing. "Language" implies that the patterns being modeled are specific to language. This is not true: GPT famously demonstrated the ability to infer an Othello game board structure, despite that structure not being defined with language. The thing being modeled is text. That's way more interesting, and has very different implications. The main implication people miss is that "limitations" are really just "features". For better or worse, a lie is structured with the very same patterns as truth. The more interesting implication is that a person writing implicitly encodes more than they intend to: including the surrounding circumstances that determine what text the person chooses to write, and what text they leave unwritten.