Sadly it's not too counterintuitive; remember the old "how many r's are in the word strawberry"?

Also different tokens for the same named entity/concept if they almost entirely exclusively occur in non-overlapping contexts, and are themselves rare/uncommon in the first place, will result in behavior that's similar to the speech/phrasing/vocabulary registers humans exhibit, where the aspects of the named entity/concept get largely compartmentalized.

The most severe case along these lines were the old BERT models that ran over straight UTF-8 bytes (plus a handful special tokens).

But for the modern post-GPT2 LLMs such radical simplicity seems to mostly not be considered suitable. Note that CJK (the big one in particular, so Chinese semantic and Japanese Kanji) encodes each one into multiple UTF-8 bytes giving some automatic scaling for semantically dense languages; similar effects also apply to e.g. APL code.