New research by researchers in the United kingdom and Canada has highlighted the possibility effects of utilizing AI-generated content because the primary training data for language models. The research reveals this process results in irreversible defects along with a phenomenon referred to as “model collapse,” which progressively erodes ale AI models to support the true distribution and essence from the original data these were trained on. This lack of diversity in AI-generated content raises concerns about discrimination and biased outcomes, posing a substantial chance of perpetuating existing biases. The research shows that preserving a pristine copy of solely or predominantly human-generated datasets and periodically retraining the AI model by using this supply of high-quality data could combat model collapse. The study community have to get innovative methods to keep up with the fidelity of coaching data and preserve the integrity of generative AI to guarantee the ongoing improvement of AI while mitigating potential risks.