At the Beijing Auto Show in late April 2024, numerous automakers showcased new models equipped with AI agents and announced deep collaborations with large-model technology companies. Volcano Engine unveiled an end-to-end AI cockpit architecture that enables a unified “AI brain” to integrate vehicle control, intelligent driving, navigation, and cabin functions—shifting the user experience from passive response to proactive service. Dongfeng Motor announced a joint initiative with Volcano Engine to co-define the next generation of “AI cars.” Chery integrated the D-Bag large model into its self-developed AI agent, covering both current and future full-line models. The first model of SAIC Roewe’s “GenAI” (native AI) car series, “Jiayue,” co-developed with Volcano Engine, is scheduled for mass production in the second half of 2024. Beyond Volcano Engine, Alibaba’s Qwen large language model is also accelerating its entry into in-vehicle interaction. Automakers including Changan, Dongfeng, BAIC, BYD, Geely, Great Wall, and Li Auto have announced integration with Qwen, enabling certain models to support complex scenarios such as multi-step route planning, hotel booking, and food delivery. Meanwhile, automakers with in-house R&D capabilities are advancing their own AI agents: Geely, together with Jieyue Xingchen and Qianli Tech, launched a “Super Agent” debuting on the Zeekr 8X; Li Auto defined its newly released L9 Livis as an “automotive robot” with proactive service capabilities; and Volkswagen plans to embed AI agents into new models based on its CEA architecture starting in 2026. This year’s auto show reflects a strategic shift in automotive competition—from traditional specifications toward AI-driven experiences. Multiple new models—including the Mercedes-Benz all-electric GLC, SAIC Audi E7X, SAIC Volkswagen ID. ERA 9X, Chery Exeed EX7, FAW Hongqi HS6 PHEV, and Buick Zhi Jing E7—are all powered by the D-Bag large model, highlighting the interactive upgrades enabled by AI agents. Industry consensus holds that cabin innovation has hit a bottleneck, with standardized configurations failing to create differentiation. In contrast, AI agents can redefine human-car relationships by understanding ambiguous commands and proactively executing tasks—such as adjusting climate settings or suggesting rest stops. However, merely invoking external large-model APIs falls short of delivering true agent capabilities. Previous collaborations between some brands and DeepSeek largely remained at the API integration level, yielding subpar real-world experiences. Current trends indicate automakers are now embedding large models into the earliest stages of product definition, driving co-development of underlying architectures. For example, the “Jiayue” series emphasizes integrating AI agent logic from the very beginning of vehicle design—spanning sensor selection, microphone placement, chip compute allocation, and UI/UX design. Technical implementation faces multiple challenges. Granting AI access to vehicle functions requires a tiered permission framework: “green zones” (e.g., climate control, lighting) can be directly controlled by AI; “gray zones” (e.g., seat adjustment while driving) require rule-based judgment; and “black zones” (e.g., braking, steering) must remain off-limits to AI. This demands deep involvement of large-model companies throughout the vehicle development process, evolving partnerships beyond traditional procurement toward an “AI Inside” model. Moreover, native AI car development cycles are expected to extend to 30–36 months, due to the need to align the automotive industry’s functional safety validation timelines with the rapid iteration pace of large-model companies—and to resolve issues around data ownership, liability boundaries, and cloud-edge coordination. In the long run, vehicle-level AI capability will become a key competitive differentiator. Yet technology alone cannot sustain advantage. Industry experts stress that user experience, ecosystem building, and brand emotional value remain central—with AI serving primarily as an enabler to enhance product perception.