It seems clear to me that the point of the post is that it's odd that the separate pieces of software are giving the same answer to this question, despite that this isn't some universal constant in the training data. The post is probably meant to provoke thought as to how the separate pieces of software are independently reaching this answer.

Your comment reaches directly for the most boring interpretation of the post while skipping over more charitable and intriguing alternatives.

That you might want to think twice before setting them lose in an agentic loop with your bank passwords, which another article that came out today shows that large numbers of people are actually doing.

It is sad when I realize people think less than LLMs.

Possibly used as a mirror for humans prevailing beliefs about themselves to some extent. Hard to say however given that the models have a heaping dose of rlhf on top of their training data.

As an aside, it would be cool to see a raw model training run before the current post-training cycles and see how it compares to earlier LLMs.

My first thought as well. I personally don't find these types of "studies" very interesting, but I assume I'm not the target audience. For someone not well exposed to AI, it might be interesting.

That they have the bog-average take of humans "with a controversial take".

I mean, "give me a controversial take" is more or less, "be anti-social, discard social norms, for a moment" and so when someone, human or simulated human, does that they naturally look critically at social norms.

The thing is that humans certainly let themselves be influenced by social norms (and LLMs certainly simulate that behavior "themselves") but there isn't an ethical or a what-to-do truth outside of society.