蔚小理自研智驾芯片：谁在掉队、谁在摇摆、谁在大步向前？

Core Viewpoint - The delay of Nvidia's Thor chip has made the external chip supply chain uncertain and expensive, highlighting the importance of self-developed chips to reduce costs and enhance the technological narrative of companies [1][35]. Group 1: Industry Overview - The arms race for computing power in smart driving began in 2021 with the launch of Nvidia's Orin-X chip, which boasts 254 TOPS, outperforming Mobileye's Q5H and Tesla's HW3.0 [2]. - The narrative logic among car manufacturers emphasizes the importance of self-developed chips as a crucial aspect of their strategy [3]. Group 2: NIO's Chip Development - NIO was the first to propose self-developed chips, with founder Li Bin initiating the "chip-making" project in 2020 despite skepticism [6][7]. - NIO's chip team, led by architect Zhang Danyu, has grown to around 400 members, with significant investment in R&D, totaling approximately 41.9 billion RMB from 2021 to 2024 [10][11]. - NIO aims to control the R&D process and reduce supply chain risks by fully self-developing core technologies [11][12]. Group 3: XPeng's Chip Strategy - XPeng has been aggressive in its chip development, launching the self-developed Turing AI chip for its P7 model, but faced challenges with internal collaboration between the chip and algorithm teams [19][20]. - The company initially outsourced chip design but shifted to full self-development due to delays from partners [20][24]. - XPeng's second-generation chip is under development, targeting a 5nm process, with ambitions to integrate advanced AI models into its vehicles [27][28]. Group 4: Li Auto's Approach - Li Auto started its chip development later than its competitors but faces fewer internal obstacles due to a unified leadership vision [29][30]. - The company has begun developing its second-generation chip, focusing on integrating its operating system and chip development under a single department [31][34]. - Li Auto aims to leverage its organizational structure to enhance collaboration between its algorithm and chip teams, which could lead to improved efficiency [35].