The Imminent Risk of AI Data Dead Loops: Model Collapse and Content

Kalyanasundharam Ramachandran

Abstract: With the proliferation of generative artificial intelligence (AI), especially large language models (LLMs), a new systemic risk emerges training models on data they or their predecessors have generated. This recursive learning loop, commonly known as "Model Collapse" or "Data Feedback Poisoning," could result in irreversible degradation in model quality, creativity, and factual correctness. This paper introduces the concept of the "AI Data Dead Loop," quantifies when such phenomena could manifest under current growth rates, and proposes robust strategies to mitigate it. Through a combination of theoretical modeling, empirical observation, and future projection, this study aims to provide a roadmap for sustainable AI development.

Keywords: Model Collapse, Data Poisoning, Feedback Loop, Generative AI, Synthetic Data, AI Alignment, Data Quality, Reinforcement Learning, Content Integrity

How to Cite?: Kalyanasundharam Ramachandran, "The Imminent Risk of AI Data Dead Loops: Model Collapse and Content", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 41-45, https://www.ijsr.net/getabstract.php?paperid=SR25629232330, DOI: https://dx.doi.org/10.21275/SR25629232330