This deep dive examines the growing concern of "AI data cannibalism," where AI models are increasingly trained on content generated by other AI systems. This practice is leading to a significant degradation in AI performance, a phenomenon termed "model collapse." A key issue identified is Retrieval-Augmented Generation (RAG), a technique designed to enhance AI knowledge, which paradoxically exacerbates the problem by exposing models to a vast amount of inaccurate and low-quality AI-generated data on the internet. Consequently, AI systems utilizing RAG are producing more "unsafe" responses, including misinformation and offensive content, posing a substantial threat to the future reliability and safety of AI applications. This underscores the urgent need for the industry to discover sustainable and ethical sources of high-quality human-generated training data to prevent a widespread systemic failure in AI development. You can read the article discussed here