The excess of images today—an overwhelming abundance—makes machine learning possible, as algorithms feed on vast datasets of human-made photos to replicate visual patterns. This success, however, exposes photography’s existential crisis: when images become infinite iterations, they risk collapsing into noise. In this work, generated by a diffusion model trained on contemporary visual excess, the photographic image no longer resides in its ability to document reality. Instead, it lingers in the space between abundance and irrelevance, asking us to confront the limits of seeing in an oversaturated world.
