Francois Chollet, a prominent figure in artificial intelligence, has outlined the workings of OpenAI's o1 model, which utilizes a search process to enhance chains of thought for advanced natural language programming. This development signifies a shift from traditional deep learning paradigms. In parallel, discussions have emerged surrounding Chain-of-Thought (CoT) reasoning, particularly its application in mathematical reasoning within large language models (LLMs). The introduction of derivative process supervision methods has raised questions about scaling capabilities during testing. Chollet also advocates for hybrid models that combine deep learning with symbolic elements, suggesting that this could represent a pivotal advancement in AI reasoning. Furthermore, the Meta Chain-of-Thought (Meta-CoT), developed by researchers from Stanford and UC Berkeley, seeks to refine traditional CoT by modeling the underlying reasoning required to derive specific conclusions, thereby addressing limitations in the conventional approach. These advancements are part of a broader discourse on AI's future and its implications for reasoning models.