Core Viewpoint - The article discusses the evolution of Li Auto's autonomous driving technology from following Tesla to referencing Tesla, highlighting original innovations made by Li Auto that Tesla has not publicly addressed [2][3]. Group 1: Development Line of Li Auto's Autonomous Driving - Initially, Li Auto's autonomous driving was considered to be following Tesla, but after the introduction of VLM, it transitioned to a reference model, showcasing original innovations not mentioned by Tesla [2]. - The core innovation of Li Auto's VLA is at the DeepSeek MoE level, which is lower than the DeepSeek MLA innovation level [2]. - During the V10-11 period, it was acceptable to say Li Auto was following Tesla, but from V12 onwards, the extent of following has significantly decreased [2]. Group 2: Ashok's Presentation at ICCV 2025 - Ashok Elluswamy discussed Tesla's shift to a single, large end-to-end neural network that directly generates control actions from sensor data, eliminating explicit perception modules [4]. - The reasons for this shift include the difficulty of encoding human values into code, poor interface definitions between traditional perception, prediction, and planning, and the need for scalability to handle real-world complexities [5]. - Key challenges in learning from pixels to control include the curse of dimensionality, interpretability and safety guarantees, and evaluation [6]. Group 3: Solutions to Challenges - To address the curse of dimensionality, Tesla utilizes extensive data from its fleet and employs complex data collection methods to extract valuable corner case data [7]. - For interpretability, end-to-end models can be prompted to predict auxiliary outputs for debugging and safety assurance, with the main focus being on control actions [8]. - The evaluation challenge is addressed through a neural network closed-loop simulator that allows for comprehensive testing and performance assessment [10]. Group 4: Comparison with Li Auto - The article argues that Li Auto's prior announcements on natural language processing and 3D Gaussian representation predate Ashok's presentation, indicating that Li Auto is not merely following Tesla [13]. - The discussion highlights that Ashok's concepts lack groundbreaking ideas, suggesting that Li Auto's innovations are leading rather than following [13]. - The article also notes that Tesla's potential adoption of a VLA-based solution aligns with Li Auto's previously published architecture [16].

Core Viewpoints - The role of Li Xiang in Li Auto's autonomous driving can be highly compared to Elon Musk's role in Tesla's autonomous driving, focusing on resource expansion, ensuring continuous investment, and possessing the ability to understand AI fundamentals and participate in technical discussions [1][2][3] - The main contradiction in Li Auto's autonomous driving development lies in the global AI industry's development stage, the matching of various production factors, and the capabilities of Li Xiang [1][5] Group 1: Resource Management - Li Xiang's core functions include expanding resources, ensuring sustained investment, and having the ability to make critical judgments regarding the company's long-term direction and technology roadmap [3][4] - The adjustment of Li Auto's secondary departments from 3 to 11 indicates a minor contradiction under the broader context of resource matching [2] Group 2: Iteration and Development - Li Auto is expected to have multiple high-quality rapid iterations in the next 1-12 months due to a clear iterative direction [2][6] - The focus on enhancing simulation data quality and leveraging existing vehicle computing power is crucial for the development of autonomous driving capabilities [6][7] Group 3: AI and Organizational Structure - Successful implementation of physical AI is essential for Li Auto to excel in autonomous driving, requiring a leader who can make key judgments and adapt the organizational structure accordingly [6][8] - The importance of having the right talent aligned with future needs rather than relying solely on past achievements is emphasized, suggesting that the right fit is more critical than resumes [11]