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Notes -
There's definitely no "general" AI these days that doesn't need careful double-checking.
But this doesn't follow. I don't use AI for my job yet, but at least for independent research it often makes a much better search engine than a search engine. The results are full of as much nonsense as reality, but that's often true of search engine results too. Weeding out the nonsense is generally much faster than fighting to find exactly the right search terms, especially when the problem is related to a field of math where the search terms include words like "normal" that have been overloaded ten different ways.
It's kind of like having an intern, but instead of handing them a tedious task and expecting to have to double-check the results with a fine-toothed comb a day later, you get the results and have to get out of the comb a moment later. With an intern there's an investment aspect (they're learning fast from us and that'll make some of them better permanent hires in the future) that conversing with an AI lacks, but despite that AI is currently improving faster than a typical intern learns IMHO. Over the last year or two the top commercial LLM performance on my favorite "benchmark" grad-school-level applied math question has gone from "making basic sign errors on the easiest part of their answer and then arguing about them or making other errors when they're pointed out" to "missing a subtle inconsistency in the hardest part of their answer and then correcting it when it's pointed out".
Indeed, one of the fundamental conjectures in CS, "P != NP", can be somewhat rephrased as "it's easier to check an answer than to produce it". I think it's actually something of an optimistic view of the future that most things will end up produced with generative AI, but humans will still have a useful role in checking its work.
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