It's a valuable pattern because a lot of communication is about drawing distinctions and making delineations. Saying what a thing isn't (despite being close to it) gives you a more clear understanding of what it is.

I wouldn't put too much emphasis on pre-training data, there are so many steps afterwards in modern LLM development that completely change the output. Generally, if you're looking at LLM output in 2026 and thinking something along the lines of "this must be what humans were doing with their language in the training data", you need to pause and reconsider, because modern LLMs are much more than machines which repeat what was in the training data.