Investment Logic in the Integration of AI Chips and Automotive Electronics

With the rapid breakthroughs in artificial intelligence technology, the automotive industry is undergoing a profound structural transformation. The product form, once dominated by traditional mechanical engineering, is gradually evolving into a 'mobile intelligent terminal' centered on software and computing power. In this process, the integration of AI chips and automotive electronics not only reshapes the industry value chain but also provides a clear and imaginative investment theme for the capital market: whoever can master the 'integration of hardware and software' may establish an insurmountable competitive moat.

In the past, competition in the automotive industry was more focused on manufacturing efficiency, supply chain management, and brand influence. However, against the backdrop of increasing complexity in autonomous driving and in-vehicle systems, the core value of vehicles is gradually shifting towards 'computing power' and 'data loops.' As the foundation of computing power, AI chips are deeply integrated with vehicle sensors, domain controllers, and operating systems, transforming cars from single products into continuously upgradable technology platforms. This shift means that companies relying solely on hardware or software will struggle to gain long-term advantages; only those who synergize both can create high barriers to entry.

Take Tesla, for example. Its competitive advantage lies not only in electric vehicles themselves but also in the deep integration of its self-developed FSD (Full Self-Driving) chip and software algorithms. Tesla has not completely relied on traditional semiconductor suppliers but instead opted to design its own AI chips, tightly coupling them with its autonomous driving system. This allows Tesla to achieve optimal balance between performance, energy consumption, and algorithm optimization while continuously training models through vast amounts of real-world driving data, forming a powerful data flywheel effect. From an investment perspective, this vertical integration capability significantly raises the difficulty for competitors to replicate, constituting a typical 'technology + data' dual moat.

Another noteworthy case is BYD. Unlike Tesla, BYD’s advantage comes from its more comprehensive industrial chain integration capabilities. It has long had deep expertise in core hardware fields such as batteries, motors, and electronic controls. In recent years, it has actively strengthened its intelligent capabilities, advancing the upgrade of in-vehicle chips and electronic architecture. BYD's strategy does not involve fully self-developing high-end AI chips but rather collaborating with the supply chain while gradually enhancing its own capabilities to strike a balance between cost and performance. In the context of increasingly fierce competition in the Chinese market, this 'hardware foundation + intelligent upgrade' approach gives it clear advantages in pricing and scalability, providing investors with a growth logic distinct from Tesla.

In comparison, Huawei’s layout in the autonomous driving field is more platform-oriented. Huawei does not directly manufacture cars on a large scale but instead enters as an 'enabler,' providing AI computing power infrastructure, including the Ascend and Kirin series chips, combined with its self-developed autonomous driving system and in-vehicle operating platform to create complete solutions. Its core advantage lies in the long-term accumulation of ICT (Information and Communication Technology), allowing it to create synergies in communications, computing power, and software architecture. This model lowers the threshold for automakers to enter the intelligent field while positioning Huawei in a key role within the supply chain. For investors, this means that in addition to whole vehicle enterprises, platform-based tech companies also have the ability to benefit from industry growth.

The key to the integration of AI chips and automotive electronics, as seen from the three cases above, lies in whether a closed loop can be formed: chips provide the computational power foundation, software enables functional implementation, data feedback continuously optimizes, and this optimization in turn drives chip and algorithm upgrades. Once this closed loop is established, it generates strong network effects and scale advantages, making it difficult for latecomers to catch up. Especially in fields like autonomous driving, which require large amounts of real-world scenario data for training, the first-mover advantage will continue to grow over time.

(Chip and Computing Power Series, Part 52)

$BYD COMPANY (01211.HK)$ $Tesla (TSLA.US)$ $XPeng (XPEV.US)$ $XPENG-W (09868.HK)$ $LI AUTO-W (02015.HK)$ $NIO Inc (NIO.US)$ $Li Auto (LI.US)$ $NIO-SW (09866.HK)$

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