I think there’s a lot of truth to this—modern LLMs are kind of competence multiplier, where some competence values are negative (perhaps a competence exponentiator?).
I find that I can extract value from LLMs only if I’m asking about something that I almost already know. That way I can judge whether an answer is getting at the wrong thing, assess the relevance of citations, and verify a correct answer rapidly and highly robustly if it is offered (which is important because typically a series of convincing non-answers or wrong answers comes first).
Though LLMs seem to be getting more useful in the best case, they also seem to be getting more dangerous in the worst case, so I am not sure whether this dynamic will soften or sharpen over time.
I think there’s a lot of truth to this—modern LLMs are kind of competence multiplier, where some competence values are negative (perhaps a competence exponentiator?).
I find that I can extract value from LLMs only if I’m asking about something that I almost already know. That way I can judge whether an answer is getting at the wrong thing, assess the relevance of citations, and verify a correct answer rapidly and highly robustly if it is offered (which is important because typically a series of convincing non-answers or wrong answers comes first).
Though LLMs seem to be getting more useful in the best case, they also seem to be getting more dangerous in the worst case, so I am not sure whether this dynamic will soften or sharpen over time.