Core Viewpoint - The article discusses the five stages of artificial intelligence (AI) as defined by OpenAI, emphasizing the importance of each stage in the development and application of AI technologies [10][11]. Group 1: Stages of AI - The first stage is Chatbots, which serve as a foundational model that compresses human knowledge, akin to a person completing their education [2][14]. - The second stage is Reasoners, which utilize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to perform continuous reasoning tasks, similar to advanced academic training [3][16]. - The third stage is Agents, where AI begins to perform tasks autonomously, requiring a high level of reliability and professionalism, comparable to a person in a specialized job [4][17]. - The fourth stage is Innovators, focusing on generating and solving problems through reinforcement training, necessitating a world model for effective training [5][19]. - The fifth stage is Organizations, which manage multiple agents and innovations to prevent chaos, similar to corporate management [4][21]. Group 2: Computational Needs - The demand for reasoning computational power is expected to increase by 100 times, while training computational needs may expand by 10 times over the next five years [7][23]. - The article highlights the necessity for both edge and cloud computing to support the various stages of AI development, particularly in the Agent and Innovator phases [6][22]. Group 3: Ideal Self-Developed Technologies - The company is developing its own reasoning models (MindVLA/MindGPT), agents (Driver Agent/Ideal Classmate Agent), and world models to enhance its AI capabilities [8][24]. - By 2026, the company plans to equip its autonomous driving technology with self-developed advanced edge chips for deeper integration with AI [9][26]. Group 4: Training and Skill Development - The article emphasizes the importance of training in three key areas: information processing ability, problem formulation and solving ability, and resource allocation ability [33][36]. - It suggests that effective training requires real-world experience and feedback, akin to the 10,000-hour rule for mastering a profession [29][30].

Core Viewpoint - The automotive industry is evolving with the integration of software, which is expected to create significant user value and lead to a high concentration of successful companies in the sector. The future of intelligent vehicles is promising, with expectations of substantial free cash flow for leading firms [1]. Group 1: Industry Insights - Hardware has high marginal costs, low concentration, and slower iteration cycles, making it easier to imitate compared to software, which has low marginal costs, high concentration, and faster iteration cycles [1]. - The perception that the automotive industry is not a good sector is challenged by the belief that software will play a decisive role in creating user value in vehicles [1]. - Intelligent vehicles are anticipated to generate high incremental user value through AI, leading to a concentration of market power among top companies [1]. Group 2: Technological Developments - The new Li Auto model will utilize Face ID and voiceprint recognition for an account system, aiming for a level of sophistication comparable to Apple ID in the long term [2]. - The integration of payment systems with platforms like Alipay allows for seamless transactions in vehicles, enhancing convenience for users [6]. Group 3: User Experience and Adoption - The initial use of the vehicle's agent system may be cumbersome, but subsequent interactions will become more efficient as users develop habits [8]. - The vehicle's agent is designed to facilitate interactions while driving, addressing the limitations of mobile agents that lack practical scenarios [7]. - The connection between the agent and intelligent driving systems allows for a more integrated user experience, enhancing the overall utility of the agent [9].