LLMs will predict the most likely output to the prompt input - does this mean that a hypothetical perfect LLM would produce the most generic and widely-acceptable response to any input, the "lowest common denominator" output?

The author seems to think so, and indeed, with today's GPTs, that is often what you might get back. But I think there's a lot more going on here - with natural language, a lot of the training effort goes into producing natural language output which conforms to the formal rules of language (it is intelligible, syntactically correct or roughly so, follows grammar, and semantically "about" what it is supposed to be about). But the interesting thing about natural language is that there are infinitely many correct responses which are valid formal responses to the prompt, both ones that introduce new or rare creative possibilities and ones that tread the same ground as a wikipedia article, for example (which is usually intended to be very neutral). So an LLM can produce both types of responses - formally correct and boring, formally correct and interesting - and you can see this today by offering "mix-ins" to the prompt like, "Tell me about Saudi Arabia's ruling family, in the style of Peter Griffin". Of course, it won't select to use the style of Peter Griffin without that prompt, but I think you can imagine a "weirdo" LLM that is trained with some positive response toward creative or interesting output too.