The paper discusses the limitations of large language models (LLMs) in effectively predicting and optimizing user behavior, particularly in terms of communication effectiveness. The authors propose a new approach called Large Content Behavior Models (LCBMs), which incorporates behavioral tokens into LLM training corpora to improve performance. They draw parallels between LCBMs and information theory, specifically Claude Shannon's seminal work on communication. The authors demonstrate the effectiveness of LCBMs in better simulating content understanding and behavior understanding compared to traditional LLMs. However, they also acknowledge potential issues with data quality, noise, and ethics in using behavioral tokens for prediction purposes. The discussion highlights the need for more research on the intersection of AI, communication, and information theory, as well as the importance of considering human behavior and ethics in AI development.