One of the strangest things about large language models is not what they get wrong, but what they assume to be correct. LLMs behave as if every question already has an answer. It’s as if reality itself is always a kind of crossword puzzle. The clues may be hard, the grid may be vast and complex, but the solution is presumed to exist. Somewhere, just waiting to be filled in. When you ask a large language model something, it doesn’t encounter an open unknown. It encounters an incomplete pattern. Its job isn’t to ponder this uncertainty, but to complete the shape. It moves forward because that’s the only thing it knows how to do. Completion Is Not the Same as Knowing Humans experience not-knowing very differently. We experience, if not wallow, in hesitation and doubt. We feel the gap between what we understand and what we don’t. Sometimes we live inside questions that may never close or, in a Zen koan sort of way, may never have completion as their point at all. That’s not …