1. 低开。理想汽车在做产品方面，都很"Think Different"，我们对用户需求的理解和对应的解决方案，确实和其他人想的不太一样。所以理想每一次发布 全新车系，基本都是在质疑中前行，理想ONE、理想L9、理想MEGA、理想i8，是不是都是这样？ 2. 高走。产品踏踏实实地解决好用户需求与痛点，伴随着体验过的人越来越多，口碑和NPS越来越好，自然就卖得好了！理想ONE、理想L9、理想 MEGA，哪个不是靠着用户口碑和NPS一路走来的？至于理想i8，我相信随着交付后大家体验越来越多，口碑和NPS也一样会越来越好，现在大家都还没 真的深度体验过，期待大家的深度体验。 请大家不要嘲笑我们"Think Different"的行为，欢迎大家都来试试我们"Think Different"的产品，理想i8。" TOP2个人记忆里李想此前在公开场合没有自称过think different. TOP2此前很多地方文章提到了think different 这点。 李想25年8月15日微博： "不知道大家发现没有，理想的全新车系大多是"低开高走"，为什么？我觉得是因为它们都很"Think Different"。 在理想阶段性 ...

Core Viewpoint - The article discusses the restructuring of Li Auto's sales and service system, emphasizing a shift towards a store-centric user operation capability, with a focus on empowering store managers to enhance value delivery and order cycles [1][5]. Group 1: Organizational Changes - Li Auto has abolished the previous five regional divisions, opting for direct management from headquarters over 23 districts nationwide [1]. - The new sales business head, Han Xi, will report directly to Li Auto's president, Ma Donghui, indicating a streamlined leadership structure [1]. Group 2: Sales Strategy and Challenges - The recent reforms aim to allow sales teams to focus on value delivery, but the current pressure for orders has led to a culture of high-frequency order pushing, undermining the intended value transmission [2][3]. - Store managers are seen as crucial in managing sales teams effectively, as they typically oversee a limited number of sales personnel, making their role pivotal in the sales process [2]. Group 3: Key Insights on Sales Management - The article identifies three critical points for store managers to successfully drive sales: genuine recognition of value delivery, avoidance of excessive order pressure, and intrinsic motivation to act independently [2][5]. - The current environment, characterized by high-pressure tactics from various levels of management, has resulted in store managers becoming mere policy transmitters rather than value communicators [3][4]. Group 4: Conclusion and Future Outlook - For Li Auto to achieve its objectives, it is essential to center operations around store managers who genuinely embrace the outlined principles, which aligns with the company's goal of enhancing user operations [5][7].

Group 1 - The core viewpoint of the article highlights the progress of the company's supercharging station construction, with a current total of 3058 stations and a target of over 4000 by the end of 2025, indicating a remaining need for 942 stations to meet this goal [1] - The article notes that the current progress rate for new stations this year is 58.56%, with 140 days left in the year, requiring an average of 6.73 new stations to be built daily to achieve the year-end target [1] - Two new supercharging stations have been completed in Guangdong and Hubei provinces, while one station in Gansu province has been removed from the list of completed stations [1]

Core Viewpoint - The recent discussions surrounding "Li Auto owners" have led to misleading and attacking comments online, prompting the platform to take action against such content to protect the company's reputation and ensure a safe communication environment [1][2]. Group 1: Platform Response - Douyin has actively verified and addressed complaints from Li Auto regarding misleading content, taking measures against violations of community rules [1]. - The platform has implemented rules to manage inappropriate content, including marketing exploitation and personal attacks against Li Auto owners [1]. - Douyin emphasizes its commitment to protecting corporate rights and maintaining a trustworthy online space for users and businesses [1]. Group 2: Li Auto's Position - Li Auto's legal department has expressed support for public scrutiny and user feedback, while warning that any unlawful actions will face legal consequences [2]. - The company is open to dialogue and encourages constructive communication regarding its operations and long-term fundamentals [3].

Core Viewpoint - The article discusses the advancements and challenges in the development of the VLA (Visual-Language-Action) model for autonomous driving, emphasizing the importance of reinforcement learning and the integration of 3D spatial understanding with global semantic comprehension. Group 1: VLA Model Development - The VLA model incorporates reinforcement learning, which is crucial for its development and performance [1] - The integration of 3D spatial understanding and global semantic comprehension enhances the model's capabilities compared to previous versions [7] - The transition from VLM (Visual-Language Model) to VLA involves a shift from parallel to a more integrated architecture, allowing for deeper cognitive processing [3][4] Group 2: Technical Challenges - The deployment of the VLA model faces challenges such as multi-modal alignment, data training difficulties, and the complexity of deploying on a single chip [8][9] - The model's performance is expected to improve significantly with advancements in chip technology and optimization techniques [9][10] - The need for extensive data labeling and the potential for overfitting in simulation data are highlighted as ongoing concerns [23][32] Group 3: Industry Comparisons - The article compares the gradual approach of the company in advancing from L2 to L4 autonomous driving with the rapid expansion strategies of competitors like Tesla [11] - The company aims to provide a more comprehensive driving experience by focusing on user needs and safety, rather than solely on technological capabilities [11][22] Group 4: Future Directions - The company plans to enhance the VLA model's capabilities through continuous iteration and integration of user feedback, aiming for a more personalized driving experience [35] - The importance of regulatory compliance and collaboration with government bodies in advancing autonomous driving technology is emphasized [17][18]

Core Insights - The article discusses the performance and user experience of the Li Auto's VLA (Vehicle Lane Assist) system compared to Tesla's FSD (Full Self-Driving) system, highlighting that while VLA shows promise, it still falls short of the seamless experience provided by FSD in certain scenarios [1][2][3]. Experience Evaluation - The experience is divided into three parts: driving in a controlled environment with no driver present, a one-hour public road test, and a two-hour self-selected route test [1]. - Feedback from users indicates that the VLA system provides a comfortable and efficient experience, particularly in controlled environments, but its performance in more complex road scenarios remains to be fully evaluated [2][3]. User Feedback - Users noted a significant difference in the braking experience of VLA, describing it as smooth and seamless compared to traditional driving, which enhances the perception of safety and comfort [3][4]. - The article emphasizes that the initial goal for autonomous driving systems should be to outperform 80% of average drivers before aiming for higher benchmarks [4][5]. Iteration Potential - The VLA system is believed to have substantial room for improvement compared to its predecessor, VLM, with potential advancements in four key areas: simulation data efficiency, maximizing existing hardware capabilities, enhancing model performance through reinforcement learning, and improving user voice control experiences [6][7]. - The article suggests that the shift to reinforcement learning for VLA allows for targeted optimizations in response to specific driving challenges, which was a limitation in previous models [8][9]. User Experience and Product Development - The importance of user experience is highlighted, with the assertion that in the AI era, product experience can be as crucial as technical capabilities [10]. - The voice control feature of VLA is seen as a significant enhancement, allowing for personalized driving experiences based on user preferences, which could improve overall satisfaction [10].

Core Viewpoint - The company is making progress towards its goal of establishing over 4000 charging stations by the end of 2025, with a current total of 3057 stations built as of August 12, 2025 [1]. Group 1: Charging Station Development - The total number of charging stations increased from 3050 to 3053 on August 11, 2025, and then to 3057 on August 12, 2025 [1]. - The company has a remaining target of 943 stations to reach the goal of over 4000 by the end of 2025 [1]. - The progress rate for new stations this year has improved from 58.34% to 58.51% [1]. Group 2: Daily Target and Timeline - There are 141 days left in the year, with a time progress value of 61.37% [1]. - To meet the year-end target, the company needs to build an average of 6.69 stations per day [1]. Group 3: New Charging Stations Details - Four new charging stations were added in various locations, including: - Guangdong Province: Guangzhou, Tanbu Service Area (5C × 4) [1]. - Hubei Province: Wuhan, Hilton Garden Inn on Baisha Avenue (4C × 4) [1]. - Jiangxi Province: Ganzhou, Xinfeng West Service Area (2C × 7, 5C × 1) [1]. - Chongqing City: Wushan County, Wushan Service Area (2C × 6, 5C × 2) [1]. - Hunan Province: Hengyang, Hengyang Dahuang City (4C × 4) [1]. - Zhejiang Province: Jiaxing, Jiaxing Huayan Plaza (4C × 4) [1]. - Zhejiang Province: Ningbo, Ningbo Yaofeng Yujiji (4C × 6) [1].

Core Viewpoint - The article aims to provide insights into the current state of Li Auto, emphasizing that the analysis is neutral and not overly promotional, indicating that the company's positioning as a top player is based on objective assessment rather than hype [1] Group 1: Events and Reactions - On August 3, a Li Auto owner expressed concerns over negative comments targeting the Li Auto community and initiated a series of complaints to the company's management, highlighting a perceived lack of action from the relevant departments [2] - The owner engaged in 27 phone communications with Li Auto's customer service from 9:30 PM on August 3 to 2:19 AM on August 4, totaling approximately 4.5 hours, seeking a response from higher-level management regarding the situation [2] Group 2: Outcomes and Customer Service Issues - Approximately 20 frontline employees apologized but were unable to escalate the issue effectively, indicating a potential flaw in the company's communication system, particularly within customer service [3][4] - A technical expert contacted the owner but refused to disclose their position, stating that management was aware of the public sentiment and was addressing it, yet could not provide satisfactory answers regarding the lack of action [3] Group 3: Analysis of Customer Feedback - The article suggests that a complaining customer is a valuable asset for companies, as it provides genuine feedback about user experiences, contrasting with the leadership's perception of customer dissatisfaction [5] - It highlights a discrepancy between Li Auto's public image and the actual sentiments expressed by users, indicating a potential disconnect in the company's communication strategy [5]

Core Viewpoints - The article presents four logical chains regarding the understanding of "predict the next token," which reflects different perceptions of the potential and essence of LLMs or AI [1] - Those who believe that predicting the next token is more than just probability distributions are more likely to recognize the significant potential of LLMs and AI [1] - A deeper consideration of AI and ideals can lead to an underestimation of the value of what ideals accomplish [1] - The ideal VLA essentially focuses on reinforcement learning dominating the continuous prediction of the next action token, similar to OpenAI's O1O3, with auxiliary driving being more suitable for reinforcement learning than chatbots [1] Summary by Sections Introduction - The article emphasizes the importance of Ilya's viewpoints, highlighting his significant contributions to the AI field over the past decade [2][3] - Ilya's background includes pivotal roles in major AI advancements, such as the development of AlexNet, AlphaGo, and TensorFlow [3] Q&A Insights - Ilya challenges the notion that next token prediction cannot surpass human performance, suggesting that a sufficiently advanced neural network could extrapolate behaviors of an idealized person [4][5] - He argues that predicting the next token well involves understanding the underlying reality that leads to the creation of that token, which goes beyond mere statistics [6][7] Ideal VLA and Reinforcement Learning - The ideal VLA operates by continuously predicting the next action token based on sensor information, indicating a real understanding of the physical world rather than just statistical probabilities [10] - Ilya posits that the reasoning process in the ideal VLA can be seen as a form of consciousness, differing from human consciousness in significant ways [11] Comparisons and Controversial Points - The article asserts that auxiliary driving is more suited for reinforcement learning compared to chatbots due to clearer reward functions [12][13] - It highlights the fundamental differences in the skills required for developing AI software versus hardware, emphasizing the unique challenges and innovations in AI software development [13]

Core Insights - The article discusses the progress of the company's supercharging station construction, highlighting the recent additions and the target for the end of 2025 [1]. Group 1: Supercharging Station Progress - The total number of supercharging stations has increased from 3043 to 3050, with a goal of exceeding 4000 stations by the end of 2025 [1]. - The current progress towards the annual addition target is 58.21%, with 143 days remaining in the year [1]. - To meet the year-end target, an average of 6.64 new stations must be constructed daily [1]. Group 2: New Stations Details - New supercharging stations have been established in various locations, including: - Jinan, Shandong: 5C × 4 configuration - Ningbo, Zhejiang: 4C × 6 configuration - Fuzhou, Fujian: 4C × 4 configuration - Guangzhou, Guangdong: 4C × 6 configuration - Suzhou, Jiangsu: 4C × 6 configuration - Yancheng, Jiangsu: 4C × 6 configuration - Ulanhot, Inner Mongolia: 4C × 6 configuration [1].