The emergence of DeepSeek R1 marks a historic paradigm shift in artificial intelligence research. This groundbreaking achievement overturns the long-held dogma of traditional AI systems reliant on probabilistic generation and pattern matching. Through the deep integration of chain-of-thought and reinforcement learning, it pioneers industrial-scale AI reasoning capabilities for the first time. Its innovative reasoning architecture not only dramatically reduces the cost of training specialized AI models but also demonstrates human-like chain-of-thought characteristics in complex tasks such as mathematical proofs and logical reasoning, driving a profound transformation from probabilistic generation to causal reasoning and ushering in a new era of AI technological development. This paper traces the evolutionary trajectory of the DeepSeek model series, highlights key innovations in its chain-of thought framework and model architecture, and concludes with a discussion on the safety implications of its chain-of-thought.