Artificial intelligence models are facing significant degradation due to excessive use of synthetic training data, according to Rick Song, CEO of Persona, writing in VentureBeat. This phenomenon, known as “model collapse” or “model autophagy disorder,” occurs when AI systems are repeatedly trained on artificially generated content rather than human-created data. The practice can lead to reduced accuracy, loss of nuance, and potentially dangerous errors in critical applications. A study published in Nature found that language models trained on AI-generated text showed complete deterioration by the ninth iteration. To address these challenges, Song recommends that enterprises invest in data provenance tools, implement AI-detection filters, and partner with trusted data providers. Companies should also focus on digital literacy to help users recognize synthetic content and understand its risks.