This feels a bit like whataboutism.
It also feels like people don't listen to each others.
For example, reading the previous comment, it feels like the thing that reduce the enthusiasm was that at first GenAI looks like it was "reading, understanding and using its own knowledge to answer the problem", but as soon as it is a ore niche or a more complex situation, GenAI looks like it "does not understand the code, just does the equivalent of a StackOverflow search and try to apply the solutions that it found there, and this is why it felt like it understood the code before".
It does not at all means that GenAI is not terribly useful. And even better than humans in some situations.
But it feels that answering "same with humans" is missing this point: that's the opposite, humans usually try to understand the code and are bad at covering a very large range of very well documented subjects. That's the "uncanny valley" they talk about: they assumed GenAI performance on a subject X is due to a "human-like" approach, and it feels very strange when this impression falls apart.
The comment you answer to says that their experience is that AI and the human brain are not analogous and that AI is good to store large amount of knowledge and repeat it (or extrapolate based on pattern on the large amount of knowledge), but bad at understanding the code as a human does. Which explains why a human is more efficient when reacting on a thing that don't have a lot of documentation (on which the AI built its knowledge).
Humans are bad at storing large amount of knowledge, and this is why we need supervisor for human.
AI are bad to understand new stuff, they need to be able to connect the new stuff with a lot of examples they have been trained on (it does not mean the stuff is "identical", but it means "connected"), and this is why we need supervisor for AI.
We need supervisors for both human and AI, but for different uncorrelated reason.