Richard discusses the surprising resilience of AI-generated images against blurring and compression, revealing that meaningful cues exist at various scales, which help maintain performance. This challenges the notion that data-driven methods always lag behind, suggesting that inherent signals in AI images can generalize across different methods, even those not yet developed at the time of training.
