Since the capacity to form coherent sentences is deeply associated with consciousness and intelligence, users keep attributing such human traits to LLMs. Silicon Valley managed to produce a tool that masters language using nothing more than mathematics, probability, and massive amounts of data. LLMs simply generate the most probable string of words based on their training data and the input — nothing more. They don’t “think,” “agree,” or “argue.”
If you ask an LLM to define, describe, or recognize a chair, it will do so — even though it has no real-world understanding or conception of what a chair is. It only “knows” that, based on its training data, the string of five characters “c h a i r” is usually described in a certain way or looks a certain way in images tagged “chair.” It’s a statistical match, not an act of recognition. When you train a monkey to perform tricks, it may appear to carry out human tasks, but it’s still a monkey and won’t go beyond what it was trained to do. It’s the same with LLMs.
How did AI firms achieve that? They brute-forced language. We expected computers to get good at chess — after all, there are only 64 squares and 16 pieces per side. Despite the massive number of possible moves, the finite scope makes it manageable for machines. Language works similarly: a few thousand commonly used words, dozens of grammatical rules, and a few hundred exceptions. LLMs are trained on billions of conversations, articles, and books to figure out what to respond in a given context. It’s the linguistic equivalent of “what piece to move where” on a chessboard.
One of the strongest illusions occurs when you start arguing with an LLM about a controversial topic and seem to win the argument and “convince” the tool. By design, LLMs initially provide the most probable answer, which often corresponds to the majority consensus: “no, it’s not like that.” If you persist, the model may begin answering, “you’re making some good points, but…” and eventually concede with “ok, you’re right.” This doesn’t reflect agreement. It’s just that the weight of your repeated input grows stronger relative to the training data, and the system mirrors the statistical progression of a typical conversation. The LLM never actually agreed or changed its position — it only generated what seemed like the most plausible next response.
When you think you’re having a conversation with an LLM, you’re not — at least not in the human sense. You are exchanging strings of characters carrying meaning, back and forth, and it looks like a conversation. But it’s just a series of probabilistic, robotic input-output interactions.
LLMs are still useful for countless tasks. And let’s be honest: many humans don’t reason deeply either — much of what passes for “thinking” is just recycling what people see on TV or read in the news or on social media. So this isn’t a value judgment. Just don’t be fooled: statistical mimicry may look like symbolic reasoning, but it isn’t.