That is true, but if you don't look at them as a machine learning method, but rather as a computer graphics method, then it is quite impressive. It has the added benefit of being allowed to overfit as long as the average human does not find out. If you optimize for psychovisual metrics, GANs are fine.
Actually, GANs reach state of the art in anomaly/outlier detection and drug/molecule prediction, so there is certainly more to it than just artistic applications:
But if you don't see it as a machine learning method, and don't care if the things the GAN spits out are just memorized photos, that means you don't actually care about the synthesis parts of the GAN? Thus the only reason to get excited about it is the interploration stuff; which significantly reduces how interesting it is in my eyes.
