An analogy that works without having to explain anything at all about how LLMs actually work (or maybe does explain a lot, depending on how you look at it) could be:

* LLMs are lossy compression functions on their training data.

* The size of the model dictates how lossy the compression is.

* You can't spend compute to get more detail out of a model once it's been compressed/trained, anymore than you can spend compute to get an incredibly lossily-compressed movie to go from 240p back to the original 1080p source.