But they are not the things themselves — they are incomplete representations; just words that approximately describe the application of that intution to the problem at hand.
Think about how you visualise complex code projects in your mind — how it's not quite diagrams or buildings or words but multiple things in a sort of multi-dimensional, itchy space that is satisfied when a really good implementation appears. Think about how often the code you have built retains that wordless shape long after you wrote it down. Think about how impossible it is to adequately describe it.
The downside: the model doesn't always have words to describe what it's feeling.
Those outcomes are more easily averaged and we are after all talking about artificial intelligence.
I just think it's important to note, every now and then, that words aren't feelings or thoughts, and that an author's next book cannot be calculated from only the words of their previous books, etc.
I'm sure the training data of LLMs contains a lot of those things, though.