Even putting aside accuracy, if the plan is for the tools that verify the written content to be in any shape or form outside of the human loop, then this feels like it glosses over the issues of both passive bias in data & active bias (e.g data filtering etc. ) when training such models that you'd be poisoning this information sources with.

Even subtle bias can manifest in significant ways. Given that the predominant market leaders in producing such models are currently : A country that's attempting to squeeze the word "america" in any geographic formation possible + rewriting history to put itself first, and a country that has perfected censorship en-mass and already actively doctors its own history.

I'm not convinced giving LLMs carte blanche to write authoritative sources on topics that - often are ideologically sensitive/controversial isn't just needlessly reckless and short-sighted. It's not a far reach to imagine such models reflecting the current views of administrations in these countries and becoming reluctant to mention certain colonial ambitions, environmental damage, political corruption, etc. where it might be relevant to authoritative content.

Wikipedia already struggles with issues in idealogical bias from authors - human validation (somewhat)helps mitigate that. You could argue that human validation is still prone to same bias - but you can always draw on a more diverse pool of editors with little ideological overlap. With LLM validation you're pretty much at the mercy of a pretty incestuous pool of data & a handful of self-serving corporates & governments.