https://aimc2023.pubpub.org/pub/q8c63z7t It may be surprising to some that sometimes a technique, more than a creative impetus, can yield the aesthetic of an artwork or a musical composition. We can be reminded of the controversy in early modern music between the structural and phenomenological aspects of music creation. We argue for a phenomenological analysis of a particular use of Artificial Intelligence (AI) and General Adversarial Networks (GAN) (Goodfellow et al. 2020) in musical composition that we have described in recent publications: more specifically, a double process of deconstruction (i.e. feature extraction, learning transformation matrices) and reconstruction (i.e. generating transformation matrices using deep learning and musical scores). In Heidegger’s premonitory phenomenological destruktion of technique (tekhnè) (Heidegger 1958), technique becomes a logic of its own that may not easily be accessed by human reason (Husserl had previously spoken of deconstruction in the context of normativity (Husserl 2020)). This announces the age of machine learning in the arts (recently advocated by critics such as Sofian Audry (Audry 2021)) where deep neural networks are often created as black boxes, beyond the usual grasp of human intelligence. We believe that our particular use of AI in music composition (i.e. the process of deconstruction and reconstruction) yields a deconstructivist aesthetic (in the sense of the deconstructivist aesthetic that can be seen in architecture since the 1980’s). We provide a description of the sensations (in the sense of Deleuze’s sensation block description of music) that emerge from the experience of our AI music and we attempt to give a context to our deconstructivist aesthetic of music through the philosophies of Martin Heidegger or Gilles Deleuze for example, the works of writers such as William Burroughs or Ronald Johnson, architects such as Daniel Libeskind or Peter Eisenman and composers such as John Cage or Helmuth Lachenmann.