This paper examines the emerging application of Julian Jaynes’ bicameral mind theory to modern artificial intelligence systems, particularly in reinforcement learning (RL) and large language models (LLMs). The dual-process structure proposed by Jaynes—consisting of "speaking" and "listening" components—has shown remarkable parallels with observation-action cycles in RL and thinkingwriting processes in contemporary language models. Through a systematic review of recent research and analysis of prominent AI systems including OpenAI's CoinRun, RainMazes models, and advanced LLMs (Claude, Gemini, ChatGPT), this study evaluates the potential of bicameral principles in enhancing AI system efficiency and adaptability. The evidence suggests that dual-component architectures may represent a universal organizational principle for AI systems, offering new pathways for developing more robust and adaptive artificial intelligence. This review contributes to the growing interdisciplinary dialogue between cognitive science and AI development, proposing a conceptual framework for future research directions.