This exposition documents artistic research that engages with generative neural networks and artificial intelligence-driven visual synthesis, the goal being to challenge the limits of the research and question the value of the generated visual outcomes. We present here our experiment with a customised StyleGAN model. In contrast to its utilisation by computer scientists, it has been trained on a heterogeneous dataset, voluntarily exposing the generative neural network to failure while focusing on the unexpected moments of surprise that arise from such a process.