Eh, this seems somewhat right, but somewhat not right either.

Language has to do with our Monkeysphere, that humans over long periods of time in the past were limited to a very small subset of people they interacted with socially and closely. A few institutions likely had a large effect on your language like the church depending on where you were. After that it was the people you interacted with to stay alive. Because almost everything was in person or person to person transfer of information a lot of socially encoded clues were involved which lessened the need for well defined words.

Books were the first stage of homogenizing language as they could be shared over long distances and to many people, but more sequentially than latter forms of communication. After that radio and TV had a huge effect, for example the 'General American' used in broadcasts that was based heavily on a midwestern accent.

As we encroached on the 70s and 80s the previous technological advances and things like high speed interstates and trucking shrank America to something you could drive across in less than a week, and you could reach anywhere by voice nearly instantly. Suddenly people in California, Texas and New York all could be in the same meetings and local colloquialisms would need explained, so people would trend to a shared vocabulary.

It's also odd to me to say an LLM isn't subjective. Each LLM has it's own behavior, it's that there are like 20 or 30 big LLMs in all, and people are using them millions to billions of time so we're getting that one LLMs language everywhere. And that's why I disagree and will say natural language is computable, but it's also lossy and probabilistic. And for the most part it's single prompt and being ran by the user for the cheapest price possible.

> It's also odd to me to say an LLM isn't subjective.

I was saying the opposite. The core feature of an LLM is that it is subjective. The implications of a written expression (prompt) are not well-defined objective truth, but instead a vague probability. We can compute the probability, but that doesn't ever intersect with logical reduction or arithmetic; so the implications we get are just vague guesses on the progression of the story. With enough examples and training, the LLM can guess correct arithmetic answers, but critically, it does not actually perform the arithmetic that verifies them.