Expansion Artifacts
Summary
Expansion Artifacts argues that AI outputs are not just lossy reproductions of training data but represent expansions of those inputs, leaving perceptible marks akin to compression artifacts. The essay connects these artifacts to broader issues in AI aesthetics, model behavior, and the propagation of content through chains of generation, urging readers to recognize and manage these artifacts to avoid runaway feedback and data homogenization.