I wouldn't say i'm more "emotionally invested" in this tech moreso than annoyed with people who expect it to be 100% perfect, as if they've accepted the snakeoil salesmen at face value and suddenly dismiss all useful applications of it at the first hurdle. Consider that your disdain for these sales people and their oft-exaggerated claims (which i absolutely despise) may cloud your judgement of the actual technology.

>it is silly to spend N compute on producing effectively the same summary on demand for the Mth chatbot user

Why? The compute is there, unused. Why is it silly to use it the way a user wants to? Is your argument more towards our effective use of electrical power across the globe or the quality of the summaries? What if the summaries are produced once and then loaded from some sort of cache - does that make it better in your eyes? I'm trying to understand exactly your point here... please accept my apologies for not being able to understand and please do not take my questions as "gotchas" or anything like that. I genuinely want to know the issue.

>A person in a room with a perfect dictionary and grammar reference can productively translate english texts (input) into Chinese texts (output) just by consulting the dictionary, but we wouldn't claim that this person knows Chinese.

Agreed, because you can't really know a language just from its words - you need grammar rules, historical/cultural context etc - precisely the kinds of things included in an LLM's training dataset. I'd argue the LLM knows the language better than the human in your example.

Again, i'm not sure how all of this is relevant to using LLMs to summarise long papers? I wouldn't have read them in the first place, because i didn't know they existed, and i don't have time to read them fully. So a summary of the latest papers every day is infinitely more better to me than just not knowing in the first place. Now if you want to talk about how LLMs can confidentally hallucinate facts or disregard things due to inherent bias in the training datasets then i'm interested because those are the things that are stopping me from actually trusting the outputs fully. (Note, i also don't trust human output on the internet either, due to inherent bias within all of us)

>human beings still move and exist in the world and some of us still appreciate that we need to help fashion those human beings into rational ones that are able to use reason to get along, and aren't codependent on the past N years of the internet to answer any and all questions

Do a simple experiment with the people around you. Ask them about something that happened a few years ago and see if they pull up Google or Wikipedia or whatever. I don't think you realise how far and few the humans you're talking about are left nowadays. Everyone, from teens to pensioners, have been affected by brain rot to some degree, whether it's plain disinformation on Facebook, or sweet nothings from their pastor/imam/rabbi, or innacurate Google search summaries (which is a valid point against LLMs - i'm also disappointed with how bad their implementation is).

And let's not assume most humans are even capable of being rational when the data in their own brains has been biased and manipulated by institutions and politicians in "democracies".