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As a counterpoint, I appreciated this recent post by Martin Kleppmann[1]:

I've worked out why I don't get much value out of LLMs. The hardest and most time-consuming parts of my job involve distinguishing between ideas that are correct, and ideas that are plausible-sounding but wrong. Current AI is great at the latter type of ideas, and I don't need more of those.

[1]: https://bsky.app/profile/martin.kleppmann.com/post/3kquvol6s...

3 comments

In the end an "idea" is by definition contrarian which is the opposite of the training objective of LLMs. The question is how far fine-tuning and tree-search can go to extrapolate from the data-manifold. And the answer is probably not that far, currently.

I’m personally not interested in using LLMs in my work, but I’d push back on this. A good idea can also synthesize a number of existing concepts into a new one, and LLMs appear to be well-suited to pattern recognition.

LLMs aren't a good fit to do the hardest part of our job. They're great at doing mental routine tasks, though. They take the easy, boring, menial yet necessary parts of our jobs off our hands.

I think it's valuable for brainstorming and refining my texts. As a non native, it helps me immensely in correcting errors and sometimes weird phrases that I accidentally translate literally without noticing. But it's only helpful when I know enough about the source material to judge the output. I wouldn't trust it e.g. in sieving through other people's ideas or applications

> As a non native, it helps me immensely in correcting errors and sometimes weird phrases that I accidentally translate literally without noticing.

Warning: every time I have seen somebody write that, and seen an example of their writing, it's been fine to start with but the LLM has completely trashed it. See: https://meta.stackexchange.com/a/396009/308065