Yes, of course we'd need a way to evaluate it. I don't right now have a fully conceived answer to what that will look like. But I'm confident at least in saying we would not evaluate it, like Tao is suggesting, by only accepting something once a human can easily teach it unassisted to another human. That sets the bar dramatically too low and would quickly become an extraordinary impediment to progress. You'd have to think of yourself less like a researcher and more like the director of the world's largest research institute. It's highly unlikely you'll understand or even care about every single paper every one of your researchers is producing, but you'll care about the overall research direction and whether the intermediate results are accumulating into outcomes you consider meaningful. How to do this where the institute is based on superhuman AI mathematicians is an unsolved problem, but I see no reason to imagine it's unsolvable.

Let me make up an example of where I could imagine this going. Something we essentially cannot do right now is predict coarse-grained phenomena from systems that involve millions or trillions or more of interacting components. Over hundreds/thousands of years of experiment and theory we've derived laws that essentially do this in a few special cases, but we have no systematic theoretical way of doing it in general, and frankly I think it's beyond human ability. Whatever deep patterns or structures exist for doing this in a general way I think are simply out of reach for us.