[kat_rebelo]

LLM's only work on the data they have been trained on so all outputs are merely based on information that has already been written about by a human. Furthermore, LLM's do not truly "understand" even first order causal relationships, meaning whatever it plans will have no foresight to evaluate how a plan it generates will impact downstream components of a complex system.

LLM's live in "the world that has been written about", not the real world, and thus cannot formulate new ideas or hypothesis other than by accident. This, coupled with the lack of an ontological system for evaluating the validity of the statements it makes about a complex system, and, its lack of causal reasoning, means they cannot effectively plan.

I've worked on research related to causality that used LLM's (admittedly, pre ChatGPT and using much smaller models) and it was not uncommon to see extremely bogus causal relationships inferred such as "rising cost of living in NYC caused a flood in Argentina".

[theonlybutlet]

Neither can a human. Unless the human subconsciously re-evaluates their output and refines it before speaking, much like running the LLM output through the model again. Or if they've learnt it through past experience reinforcing that pathway much like LLM learning and adjusting weights to factor that in so it would impact its output in future.