The language of "generator that stochastically produces the next word" is just not very useful when you're talking about, e.g., an LLM that is answering complex world modeling questions or generating a creative story. It's at the wrong level of abstraction, just as if you were discussing an UI events API and you were talking about zeros and ones, or voltages in transistors. Technically fine but totally useless to reach any conclusion about the high-level system.
We need a higher abstraction level to talk about higher level phenomena in LLMs as well, and the problem is that we have no idea what happens internally at those higher abstraction levels. So, considering that LLMs somehow imitate humans (at least in terms of output), anthropomorphization is the best abstraction we have, hence people naturally resort to it when discussing what LLMs can do.
“My headphones think they’re connected, but the computer can’t see them”.
“The printer thinks it’s out of paper, but it’s not”.
“The optimisation function is trying to go down nabla f”.
“The parking sensor on the car keeps going off because it’s afraid it’s too close to the wall”.
“The client is blocked, because it still needs to get a final message from the server”.
…and one final one which I promise you is real because I overheard it “I’m trying to airdrop a photo, but our phones won’t have sex”.