[K0balt]

Coming up with new arrangements of bits is not a particularly hard problem on its own, but the current crop of ai is certainly able to do that in the extractive process, in fact randomness is a key part of training and inference. But making new things from old parts does not constitute innovation, insofar as the arrangements follow known paths.

That doesn’t make it non useful. It just makes it non innovative.

Trial and error within a defined problem space is an area where automation can definitely be useful. Once again though, the result is not innovation but rather automation of labor.

There is a -lot- of labor requiring mind numbing repetition or iteration. The vast majority of labor falls into this category, and exists in fundamentally solved problem spaces, but still is complex enough that the algorithms involved are opaque. This is where the current type of AI can work miracles when trained with enough oblique data.