AI could eventually train itself to death due to one simple problem generative AI models require massive amounts of data, and since the world is running out of data to train AI models, AI is now generating its own synthetic data and using it to train itself. Researchers highlight the dangers of over reliance on this approach, leading to a phenomenon called model autophagy disorder or MAD. Like mad cow disease, where cows were fed the remains of their own kind. A I models trained repeatedly on synthetic data generated by previous models enter a self consuming loop. This results in a degradation of model quality over time, producing increasingly distorted and less diverse outputs. A new study explored different training scenarios, including fully synthetic loops, synthetic augmentation loops with fixed real data, and fresh data loops with new real data added to each generation. All scenarios demonstrated that without sufficient fresh real data, AI models succumb to MAD, generating outputs marred by artifacts and lacking quality or diversity. The consequences of unchecked MAD could be severe, potentially poisoning the quality and diversity of data on the internet. Even in the short term, unintended consequences are likely to arise. While cherry picking or favouring high quality synthetic data may temporarily preserve quality, it further accelerates the decline in diversity. The research reveals the critical need for fresh real world data to maintain the health and effectiveness of AI models, preventing them from falling victim to self destructive cycles.