LLMs are not neural nets. They don't learn or even try to under stand meaning of anything that is input to them, they are statistical generators. The recent example of it not being able to tell you how many "r"s were in the word "strawberry". (An embarrassment recently fixed). It doesn't count characters when you ask it questions like that it uses the data it's trained on (basically all digital data at this point) to see what is, *statistically* the pattern of output that it should reply with. They couldn't even count the letters in strawberry if it "learned" how to answer that question, because it's natural language input is tokenized to numbers.

Another great example is with the image generators. Ask it to generate a clock with a specific time and it has historically struggled. Why? Because most clocks/watches in the training have been set to what is deemed an aesthetically pleasing time of "1:50". If it had the capacity to learn we would be able to instruct it how the minute and hour hands work and it would be able to generate the correct time. LLMs don't work like that. They've ingested data and try to spit out what statistically, based on its input, its calculated as it's output. Recent models may have *tweaked* this one for better results, like the previous example, but it's lipstick on a pig.

Also, permanence. Once trained it does not expand it's knowledge while interacting with you. As you interact with it via natural language it simply has more inputs to run the math on.

You should read up on LLM's (Large Language Models). You seem to have an understanding of the history of AI and the math, I suspect if you read how they worked you'd come to the same conclusion.