AI model collapse is a pressing issue in design tools, particularly in background removal applications. Tanya Donska highlights a personal experience where a background remover produced significantly worse results over a span of six months. This degradation stems from machine learning models training on datasets filled with AI-generated content. Each iteration introduces errors, leading to progressively poorer output quality. Research shows that quality declines within five training cycles, and by the thirtieth, the results can become nearly indistinguishable and often flawed. In practical terms, this model collapse affects various design workflows. Users now encounter inconsistent results from tools that once handled complex tasks well. For instance, background removal tools that previously managed hair edges with minimal effort now require extensive manual cleanup due to degraded training data. Image generators suffer similarly, producing distorted results and generic features. A specific example illustrates how a client rejected an AI-generated illustration because of subtle, unexplainable flaws that arose from this degradation. The challenges for tool makers are significant. They rely on vast datasets for training, but much of the internet is now filled with synthetic content. Efforts to filter out AI-generated material are complicated by unreliable detection methods and the sheer volume of content. By 2026, estimates suggest that over 90% of online content will be AI-generated or influenced, creating a feedback loop where models are trained on their own diminishing outputs. This situation raises serious concerns about the reliability and quality of AI tools in creative processes.