And with what we know of LLMs? Autoregressive transformers are Turing complete in theory, and we are yet to find anything that LLMs are "fundamentally incapable" of in practice. Even continuous learning is already approximated with in-context learning - both allow a system to learn from prior experience, both have practical limits on how far they go. That's what powers "trial and error" in today's agentic LLMs.
"LLMs can only predict next tokens based on training data" is comforting but misleading. It just isn't the saving grace you want it to be. It describes an interface, not a ceiling. And if there is some sort of fundamental "capability ceiling" that LLMs are heading towards, we are yet to see it. We know plenty of things LLMs are bad at, but they keep getting less bad at them release to release.
If there is none, then, simply improving over the current recipes iteratively might yield systems that only "need a human" in the same way you "need" to have a boss. Maybe less so.
How do they decide between using 'a' or 'an'?
"There is an animal very similar to a crocodile but I cannot remember it's name"
and the model responds with
"I believe the animal you are thinking of might be " ("a" / "an")
Are you saying that it would pick the result fairly randomly and then based on it's choice pick an animal that starts with a consonant or a vowel?