The article discusses the evolution of large language models (LLMs), highlighting the shift in perception among researchers regarding their capabilities. It emphasizes the role of chain of thought (CoT) in enhancing LLM outputs and the potential of reinforcement learning to drive further improvements. The piece also touches on the changing attitudes of programmers toward AI-assisted coding and the ongoing exploration of new model architectures.