If you've played around with LLMs in any serious capacity, you'd figure out that they are pretty good at doing the most sensible thing. So they're pretty good

If you've played around with LLMs in any serious capacity, you'd figure out that they are pretty good at doing the most sensible thing. So they're pretty good in applications where you have to produce the most sensible thing. For example, in any kind of triaging (Tier 1 customer support, copywriting, qualifying leads, setting up an npm project) the best thing to do most likely falls right in the smack of the distribution curve. It's not good for things that you'd want the most optimal outcome for. Now there will be abstractions that close the feedback loop to tell the LLM "this is not optimal, refine it". But someone still has to build that feedback loop. Right now RLHF is how most companies get around to it. But that capital G - generalizability is what makes AI → AGI. LLMs are really good exoskeletons for human thought and autonomous machines for specialized tasks. Not saying that this will be the case always, but everyone who's saying AGI is around the corner has a funding round coming up ¯_(ツ)_/¯