Core Insights - The automotive industry is increasingly focused on the computational power required for intelligent assisted driving, with Tesla leading in this area due to its early investments in self-developed chips and high-performance computing systems [2][3][4] Group 1: Tesla's Technological Leadership - Tesla's Dojo system, launched in July 2023, is designed to handle vast amounts of video data collected from its global fleet, processing approximately 160 billion frames daily to enhance its Full Self-Driving (FSD) capabilities [3][4] - The first-generation D1 chip provides 10 PFLOPS of computing power, with the second-generation chip expected to be ten times more powerful, indicating a significant leap in training efficiency for FSD [4] - Tesla's strategy includes not only FSD but also ambitions for Robotaxi and fully autonomous driving, necessitating high computational capabilities [3][4] Group 2: Domestic Competitors' Response - Chinese automakers such as Great Wall, Geely, Xpeng, and Li Auto are establishing their own supercomputing centers to compete with Tesla's AI capabilities, with Xpeng's center reportedly achieving 600 PFLOPS [5][6] - Li Auto has invested heavily in its computing infrastructure, aiming for a training capacity of over 8 EFLOPS by the end of the year, with long-term goals of reaching 100 EFLOPS [6] - The rise of supercomputing centers in China reflects a shift in strategy from self-built centers to hybrid cloud solutions and specialized services due to cost pressures and evolving technology [7] Group 3: Challenges and Strategic Shifts - Despite the rapid development of computational infrastructure, there remains a significant gap in computational power between domestic automakers and Tesla, as highlighted by industry experts [7][8] - The automotive industry's focus is shifting from merely building computational power to integrating advanced algorithms and data strategies, emphasizing the importance of data quality and training [10][12] - The competition in intelligent assisted driving is not solely about computational power but also involves optimizing algorithms and leveraging local data to enhance driving experiences [11][12] Group 4: Future Directions - The automotive industry's future will require a balanced approach to computational infrastructure, data algorithms, and innovative thinking to close the existing gaps with Tesla [14] - The Chinese government's initiatives, such as the "East Data West Computing" project, aim to enhance the national computational network, which will support the automotive industry's growth [13][14] - As the demand for computational power grows with the increase in smart vehicle sales, automakers must prioritize building robust computational infrastructures to handle the exponential data generated [12][13]