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> by and large, the processes people are scrambling to place LLMs in are ones that typical machines struggle or fail

I'm pretty sure they are scrambling to put them absolutely anywhere it might save or make a buck (or convince an investor that it could)

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100%, and a lot of them are truly terrible use cases for LLMs.

For example, using a LLM to transform structured data into JSON, and doing it with two LLMs in parallel to try to catch the inevitable failures, instead of just writing code that outputs JSON.

Your example does not make much sense (in response to OP). That's not saving anybody any money.

If your task was being solved well by a deterministic script/algorithm, you are not going to save money porting to LLMs even if you use Open Source models.

'could' is doing a whole lot of work in that sentence, I'm being charitable. Reality is LLMs are being crammed in places where it isn't very sensible under thin justifications, just like the last few big ideas were (c.f. blockchain)

If it can't be solved by a script then what's problem with seeing if you can use LLMs ?

I guess I just don't see your point. So a few purported applications are not very sensible. So what ? This is every breakthrough ever.

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