> No, they use "the same cadence and cliches" because they inflate a short and ambiguous prompt into long and specific prose by making statistical assumptions

Even if LLM output has to largely follow some statistical rules, yet, first of all, some amount of randomness is normally injected during token generation, and, secondly same true for human speech.

> about what best fills in the gaps. It's not a training problem, it's an information theory problem, and it's not really surmountable.

This is not true, as LLM has internal knowledge storet in its weight. Unless you force it to produce 2000 words doc out of 3 word prompt, you would end up adding some sense information.

>Users can use more elaborate prompts that shift the voice away from the most normative and towards some other nodes, but they need to put in special effort for that, and what people-at-scale specifically want from these tools is to put in very little effort, so we can expect that overwhelming number of casual and naive users will always be generating cliche slop with them.

True, here I agree with you. But using finetuned or simply less popular models like Kimi, Hy etc. should take care of that.