MIT researchers found that as AI diffusion models grow larger and are trained on more data, it becomes increasingly difficult to attribute their outputs to specific training inputs — a phenomenon they call "attribution decay." Even removing specific images entirely from training data doesn't prevent large models from reproducing similar content or styles. The findings complicate AI regulation and copyright litigation, while also raising questions about fair use and whether large-model outputs could be considered novel, creative works in their own right.
