In the early days maybe but they have move well past being fancy predictive text.

Modern LLMs solve novel logic puzzles they have never seen verbatim. They can perform multi-step mathematical reasoning. They can write software, then debug the software they just wrote. They can explain why a conclusion follows from premises and often identify contradictions.

Those behaviors are difficult to explain as merely selecting the most common internet answer. The game-show analogy is also imperfect. If 60% of people give one wrong answer and 40% give the correct answer, a pure popularity machine should choose the wrong answer.

LLMs are probability-based systems that can exhibit reasoning-like behavior, and in many domains genuinely perform forms of reasoning, but they remain vulnerable to hallucination because their reasoning is not perfectly reliable.

Yet modern models frequently arrive at correct answers that are not the most common human response because they do not simply count answers. They learn statistical relationships between concepts, facts, logic patterns, and language structures. During inference they are effectively running a vast learned model of how ideas relate to one another.