No one in this thread appears to disagree. The issue is that RLHF is prohibitively expensive and the number of disciplines you could target is massive, so for reasons of economics rather than fundamental theory, AIs do not perform well on tasks that aren't amenable to RLVR and even then off the shelf LLMs are really only well aligned for programming.
In the paper I linked they created a benchmark spanning 80 disciplines with tasks that could be checked automatically. So these are necessarily tasks that are tractable for RLVR, trivially you could use performance against the benchmark as a reward function. The performance was still mediocre in everything but programming. And as we're seeing in this article, there is still room for growth in programming.
In general you seem to be reading very literally in some places (taking the statement "AIs aren't good at X" as applying to all AI and perpetually) and very loosely in others (disregarding "easily" as unimportant) and misinterpreting statements you appear to agree with as being in disagreement. I don't think there's a real disagreement here, I think there's a misunderstanding.