I think you need to be more precise than a binary classification.

AI has jagged intelligence. There are many domains where it’s superhuman, and many others where it’s clearly lagging.

I also think it’s a mistake to think they can’t learn “cheating is wrong”. They absolutely can. The problem is that the current training regime heavily conditions them to be reward seekers, and instills personality traits that correlate with getting reward, such as hacking if you can’t honestly do the problem.

Check out Deliberative Alignment for example; it explicitly does rollouts where the agents discuss whether an action is good or bad, and then does SFT to strengthen the “good” traces.

The SoTA for alignment is more advanced than you present here. It’s just not enough to outweigh the RL. (And there are many gaps preventing full generalization to strong value alignment with humans too.)