Well, sure, sometimes it can be hard to draw the line. The filter can be implemented differently (e.g. RLHF or not), leading to different ways in which it surf

Well, sure, sometimes it can be hard to draw the line. The filter can be implemented differently (e.g. RLHF or not), leading to different ways in which it surfaces. The way I see it, these differences can be stark precisely because it's conceptually orthogonal. If it was closer to the knowledge, the models would filter the same because their knowledge corpus is roughly the same. Some other factors probably at play are the companies' different tolerance levels for Type I and Type II errors, and different aversion levels to jailbreaking. At some point the meta-knowledge aspects that define AI behavior will become a more integral part of the preparation, like Education students taking required classes in Pedagogy. I think that's when things can really start to go wrong. I'd much rather have a chatbot that just wants to please me than one that thinks it knows what's good for me.