The blog post critiques the commentary generated by large language models (LLMs), arguing that their verbose and contextually poor comments stem from the reinforcement learning (RL) and training methods employed. The author suggests that these LLM-generated comments, while improving software agents' performance, may not serve human developers well. The complexities of LLMs' training and their potential to lead to emergent behaviors are discussed, along with the implications for software engineering practices.