Hundreds of images. One subject. One algorithm.

The Averages series begins with a question: if you gather every photograph of a forest - every angle, every season, every quality of light - and average them together, what remains? What is the essential forest?

The process starts with a single word. That word generates a search. Hundreds of images are collected, clustered by perceptual similarity, then mathematically averaged within each cluster. What emerges is something between memory and hallucination - not a photograph, not a painting, but the distillation of how a subject appears across thousands of human interpretations of it.

The result is both precise and dreamlike. The sharp particulars dissolve. What persists is essence: the characteristic light of a lavender field, the vertical rhythm of sequoias, the particular way a window frame divides inside from outside.

These are not AI-generated images. No model learned to imagine them. Each is the direct mathematical output of pixel arithmetic applied to real photographs - a mean, taken carefully. The code that runs this pipeline was written by the artist.

And yet the mean is where machine vision begins. A classifier learns a label the same crude way: shown enough examples, it keeps what they have in common and lets go of what only one of them happened to contain, until the class exists for it as a prototype rather than as any particular picture. Recognition is a distance, not an understanding. Is this near enough to what I hold as forest?

The blur is the concept. What survives the averaging is what a thing must have in order to be that thing at all, and what dissolves is everything a single photograph merely happened to catch.