This is what LLM (and AI in general) naysayers are missing. LLMs will likely never get us to 100% solutions on a large class of problems. But! A lot of proble

This is what LLM (and AI in general) naysayers are missing. LLMs will likely never get us to 100% solutions on a large class of problems. But! A lot of problems can be converted into versions with a subcomponent that LLMs can solve 100% of. And the fusion of LLMs doing 100% of that subportion + humans doing the remainder = increased productivity. Re-engineering problems to be LLM-tolerant, then using LLMs to automate that portion of the problem, is the winning approach.