If the parent means the No Free Lunch Theorem, then the point is mistaken. That theorem says that you can't improve the performance of a classifier for some objective function, without making it worse on another -- in other words, all algorithms have an identical mean performance when averaged over all possible objective functions.

The reason this doesn't mean that human-level AI is impossible is that we too are designed (well, evolved by natural selection) to perform well for a particular objective function: one in which say, the standard laws of physics/optics apply. Optical illusions illustrate that our performance on this objective function is not perfect.

Moreover, you can see a human being's performance on a different objective function by, for example, trying to recognize objects in pictures which have been scrambled according to some predefined method (e.g. shuffle the pixels but use the same random seed each time). Each scene will still convey the same amount of information about the objects in it, but it'll be pretty tricky to recognize the objects.