Apart from technical differences, even a more significant, ana-material difference lurks in the concept. GANs are based on the mimesis model, in which they generate a resemblance, not to a single instance of dataset or, in general, an original (landscape, portrait). However, there is a statistical resemblance to the data set itself. It operates as passive matter, on which the model of an external data set is pressed like a mould on wet clay. With incredible accuracy, the generator generates a copy of the statistical original. It presents it, first to the discriminator, a digital version of Plato, that replies good copy, bad copy, bad copy, bad copy, good copy, and when sufficiently trained, to the artist who replies: printable, unprintable, unprintable, unprintable, printable. As a machinic surrogate, it learns with time through the feedback to produce the results that the artist desires, hence its emergent identity, bound to the artist.