Early image generators frequently defaulted to generic outputs, a phenomenon known as mode collapse. While critics once argued that training on synthetic data would inevitably destroy model quality, practitioners found ways to mitigate this decay. The risk persists, but it is a technical hurdle rather than a fundamental ceiling for AI development.