That paper addresses your first question directly, and better than I can. But in brief, it's nonsense because how could it not be? If there is real, interesting information content to what it's saying, how was it generated? How would you expect your network to have an understanding of anything, use that understanding to synthesize new ideas, and then accurately convey those ideas to you? All it has been trained to do is probabilistically produce coherent text--the training process has no interaction with the information content of the training texts, much less anything that would allow it to generate novel meaning.

As for the rest of your reasoning, you could use the same argument for anything at all that causes you to think about things. In line with that paper, would you want to spend serious intellectual effort on deriving deeper meaning from a parrot's chatter? Maybe the network accidentally outputs something that sends you along a path to productive thoughts. Or maybe you waste all your time trying to turn lead into gold. Like, of course you're free to experiment with it, but it's irresponsible to pretend it's outputting anything profound if you're going to be sharing it with other people.