I'm going to bet that if you do some searching, you can turn up some text on the internet where someone was asking "how many of x animal would it take to y human task", and answered the question using a neuron comparison. It's not inventing this approach from scratch (because it can't). Every word in that answer is there because statistically, based on the text in its database, it was the most likely word to occur in that context. Hell look at the way it chose to describe flight attendants, you can see it grabbed that directly from some snippet somewhere.

I think a lot of the misconceptions about these LLMs (which I actually think are pretty cool and useful) stem from not really understanding what they are doing and how they arrive at their answers. This is pretty well illustrated by the fact that ChatpGTP only does well on tests where the content is well-represented in its training dataset, and terrible on tests on otherwise. But you have to keep in mind that the training dataset is enormous, and likely includes every random forum or reddit post ever made, so you shouldn't be surprised that it can formulate an answer like this.