GEELY AUTO(HK:00175) 自动驾驶之心·2025-11-13 00:04Core 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]