Core Insights - The automotive industry is facing unprecedented challenges in software engineering, with the proportion of software developers at Geely increasing from less than 10% to 40% in recent years, highlighting the exponential growth in complexity as the codebase for smart vehicles surpasses 100 million lines [3][5] - Geely is leveraging AI technology, specifically through collaboration with Alibaba Cloud's Tongyi Lingma, to enhance development efficiency, achieving a 20% increase in coding efficiency and over 30% of code generation being AI-driven [5][6] - The shift from hardware-dominated to software-centric automotive products necessitates a transformation in development models, moving towards agile and DevOps methodologies to support rapid iterations [8][19] Development Challenges - The automotive industry is transitioning from distributed ECU architectures to centralized computing and service-oriented architectures (SOA), which significantly increases system integration complexity [8] - Compliance with stringent international safety standards such as ISO 26262 and ASPICE poses additional challenges, creating tension between rapid agile development and necessary safety protocols [8] AI Integration - Geely's R&D system encompasses application software development, embedded development, and algorithm research, with AI tools like Tongyi Lingma being integrated across all areas [10][11] - AI is being utilized to automate repetitive tasks, allowing engineers to focus on system architecture and core business logic, leading to a 30% efficiency improvement in coding phases [16][18] Knowledge Management - AI's ability to quickly read and interpret legacy code helps mitigate the challenges of "technical debt," allowing new engineers to understand complex systems more rapidly [17][18] - The collaboration between Geely and Alibaba Cloud aims to create a proprietary knowledge base that enhances AI's contextual understanding of Geely's specific technical stack and business logic [14][15] Role Transformation - The role of engineers is evolving from executors to "AI commanders," where they define problems and oversee AI execution, shifting the focus from implementation to strategic oversight [20][21] - The ultimate goal is to achieve a highly automated R&D environment, where AI and human engineers collaborate throughout the entire development process [22][23] Industry Implications - The demand for cross-disciplinary talent that understands both mechanical hardware and software systems is increasing, highlighting a significant skills gap in the automotive industry [23] - The integration of AI in software development may lower technical barriers, enabling engineers with mechanical backgrounds to participate more actively in software engineering [23]

Group 1 - Nanjing University aims to provide the best undergraduate education in China, launching initiatives to cultivate top innovative talents [1] - The university has established a new Robotics and Automation College at its Suzhou campus, introducing a major in Automation (Robotics Direction) focused on smart manufacturing and robotics technology [1] - The "Zhicheng Class" is introduced to strengthen practical training and industry-education integration, featuring a talent cultivation system driven by industry needs and deep involvement from central enterprises [1] Group 2 - In 2025, Nanjing University will add "Intelligent Science" and "Electronic Science" directions to its Mathematics and Physics programs, respectively [2] - The Kuang Yaming College will continue to reform its outstanding talent cultivation model, allowing students to freely choose their academic paths and mentors, with over 80% of graduates pursuing further studies at prestigious institutions [2] - New dual-degree programs in Software Engineering combined with Business Management and Economics will be introduced to foster interdisciplinary talents [3] Group 3 - The university will continue to offer various dual-degree programs, including Computer Finance, German Law, Intelligent System Integration, and Big Data Communication, to provide students with ample choices and development opportunities [3]

Core Viewpoint - The article emphasizes that human programmers possess superior capabilities compared to large language models (LLMs), despite the usefulness of AI tools in assisting with programming tasks [3][10]. Group 1: Human vs. AI Capabilities - The article discusses a scenario where a complex bug in Redis was addressed, highlighting the limitations of LLMs in generating innovative solutions compared to human creativity [5][10]. - It is noted that while LLMs can assist in problem-solving, they often lack the ability to think outside conventional frameworks, which is a significant advantage of human programmers [10]. Group 2: Practical Applications of LLMs - The author shares experiences of using LLMs for code review and idea validation, indicating that these tools can enhance productivity but cannot fully replace the nuanced understanding required in software engineering [3][10]. - The article mentions that LLMs can serve as a sounding board for ideas, providing feedback that can help refine thought processes [13]. Group 3: Software Engineering Complexity - The article points out that software engineering encompasses much more than just coding, including understanding client needs and requirements, which LLMs are currently ill-equipped to handle [14]. - It emphasizes the social attributes of software engineering, where human interaction and comprehension of client demands play a crucial role [14].