Yes. Sometimes people treat intelligence as a single line, or as nested sets, where a greater intelligence can solve all the problems a lesser one can, plus more.

While in some contexts these are useful approximations, they break down when you try to apply them to large differences not just between humans, but between species (for a humorous take, see https://wumo.com/wumo/2013/02/25), or between humans and machines.

Intelligence is about adaptability, and every kind of adaptability is a trade-off. If you want to formalize this, look at the "no free lunch" theorems.

It's an important point to make.

LLMs of today copy a lot of human behavior, but not all of their behavior is copied from humans. There are already things in them that come from elsewhere - like the "shape shifter" consistency drive from the pre-training objective of pure next token prediction across a vast dataset. And there are things that were too hard to glimpse from human text - like long term goal-oriented behavior, spatial reasoning, applied embodiment or tacit knowledge - that LLMs usually don't get much of.

LLMs don't have to stick close to human behavior. The dataset is very impactful, but it's not impactful enough that parts of it can't be overpowered by further training. There is little reason for an LLM to value non-instrumental self-preservation, for one. LLMs are already weird - and as we develop more advanced training methods, LLMs might become much weirder, and quickly.

Sydney and GPT-4o were the first "weird AIs" we've deployed, but at this rate, they sure wouldn't be the last.

See also a paper from before the ice age (2023): Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321

This strongly reminds me of the Orthogonality Thesis.

https://www.lesswrong.com/w/orthogonality-thesis