Core Insights - The "advertising incident" involving Deep Blue Automotive has sparked significant discussion within the automotive industry regarding the potential of software services as a new revenue source amid declining hardware profits [2] - The event highlights the anxiety faced by automakers during their business model transformation, particularly as the market penetration rate of smart connected vehicles in China is expected to exceed 80% next year [2] - The challenge lies in effectively monetizing in-car systems while ensuring a positive user experience [2] Advertising Monetization - In-car advertising is supported by a robust commercial logic, as the central control screen of smart vehicles serves as a focal point for user attention, similar to smartphones [3] - Chinese car owners spend an average of 87 minutes in their vehicles daily, creating significant commercial value through attention economics [3] - Deep Blue Automotive's CEO stated that targeted advertising based on user behavior can enhance marketing efficiency, as internal statistics show that about 50% of target users missed promotional offers [3] User Experience Challenges - There is a delicate balance between advertising methods and user experience, as intrusive ads can provoke negative reactions from users [4] - A survey indicated that 68% of respondents accept passive advertising formats that do not interfere with driving, while 92% oppose ads during navigation [4] - The industry faces a common challenge in balancing data utilization for targeted advertising with privacy protection [4] Software Commercialization Disparities - The automotive industry's software commercialization shows a stark divide, with pioneers like Tesla achieving significant results, while most domestic automakers see software revenue at only 1% to 3% [5] - Subscription models are becoming mainstream among high-end brands, but they require strong brand premium capabilities [5] - Advertising revenue sharing is another explored avenue, with companies like Li Auto implementing light ad placements that yield limited revenue [5] User Demographics and Preferences - Different user demographics exhibit varying tolerances for in-car advertising, with younger users (ages 25-35) showing higher acceptance but demanding higher quality ads [6] - This shift in user mindset indicates that in-car advertising must evolve to be more refined and intelligent [6] Creating a Sustainable Ecosystem - The exploration of in-car profitability models must focus on creating user-recognized value, as highlighted by Mercedes-Benz's CTO [7] - Intelligent service scenarios, such as offering car wash appointments during bad weather, can blur the lines between advertising and service, increasing user acceptance [7] - Building an open ecosystem is crucial for expanding revenue bases, as individual automakers have limited user scales [8] Regulatory and Standardization Needs - The establishment of industry standards for in-car advertising is becoming urgent, especially as more vehicles become connected [8] - The EU is already working on regulations requiring explicit user consent for in-car ads and providing easy opt-out options [8] Future Outlook - The transition from "functional vehicles" to "smart vehicles" necessitates trial and error in business model exploration, similar to the internet industry's evolution from free to paid services [9] - Successful business models must be built on user value, with the potential for suitable profitability models emerging in the next 3-5 years as technology matures and user habits evolve [9] - The Deep Blue Automotive incident should be viewed as a starting point for industry reflection and progress, emphasizing the importance of maintaining the core value of providing safe and comfortable mobility experiences [9]

Core Insights - Huawei has launched the "Genius Challenge" to attract global talent in five key areas: intelligent connectivity & computing, fundamental research and innovation, intelligent terminals, cloud computing, and intelligent vehicles [3][4][5][6] Group 1: Intelligent Connectivity & Computing - The challenge includes research on autonomous intelligent wireless communication architecture and key technologies to meet future communication demands [3] - It also focuses on the key technologies of the Ascend reinforcement learning system to enhance performance [3] - Research on AI cluster all-optical switching networks aims to improve data transmission speed and efficiency for large-scale AI computing [3] Group 2: Fundamental Research & Innovation - Key technologies for large model security are being explored to address safety risks in current applications [4] - Research on intelligent imaging/editing technology aims to achieve breakthroughs for enhanced user visual experiences [4] - The design and optimization of training cluster architecture will improve the efficiency and quality of model training [4] Group 3: Intelligent Terminals - The challenge includes research on world models to help intelligent terminals better understand and simulate physical laws [5] - It aims to enhance personalization and memory capabilities for intelligent terminals [5] - Research on multimedia algorithms based on computer vision and multimodal understanding is also included [5] Group 4: Cloud Computing - Research on generalizable embodied intelligent operation technology seeks to enable cloud AI to control physical devices [6] - The challenge includes exploring core technologies for the digital-native era [6] - AI-based next-generation cloud network infrastructure research aims to build advanced cloud network systems [6] Group 5: Intelligent Vehicles - The challenge focuses on training and optimizing large models for intelligent vehicles [6] - Research on advanced autonomous driving models is part of the initiative [6] - The development of collaborative control technologies for vehicle chassis aims to enhance safety and comfort [6] Group 6: R&D Investment and Talent Development - Huawei's R&D expenditure for 2024 is projected to reach 179.7 billion yuan, accounting for approximately 20.8% of total revenue [7] - Over the past decade, Huawei has invested more than 1.249 trillion yuan in R&D [7] - The "Genius Challenge" reflects Huawei's commitment to fundamental research and innovation, emphasizing the importance of active participation in basic research [7]

Core Viewpoint - The article emphasizes the significance of high computing power, large models, and extensive data in achieving Level 3 (L3) autonomous driving, highlighting the advancements made by XPeng with its G7 model and its proprietary AI chips [3][18][19]. Group 1: Technological Advancements - XPeng's G7 is the world's first L3 level AI car, featuring three self-developed Turing AI chips with over 2200 TOPS of effective computing power [3][18]. - The G7 introduces the VLA-OL model, which incorporates a "motion brain" for decision-making in intelligent assisted driving [4]. - The VLM (Vision Large Model) serves as the AI brain for vehicle perception, enabling new interaction capabilities and future functionalities like local chat and multi-language support [5][19]. Group 2: Industry Positioning - XPeng was the only invited Chinese car company to present at the global computer vision conference CVPR 2025, showcasing its advancements in autonomous driving models [6][13]. - The company has established a comprehensive system from computing power to algorithms and data, positioning itself as a leader in the autonomous driving sector [8][18]. Group 3: Model Development and Training - The next-generation autonomous driving base model developed by XPeng has a parameter scale of 72 billion and has been trained on over 20 million video clips [20]. - The model utilizes a large language model backbone and extensive multimodal driving data, enhancing its capabilities in visual understanding and reasoning [20][21]. - XPeng employs a distillation approach to adapt large models for vehicle-side deployment, ensuring core capabilities are retained while optimizing performance [27][28]. Group 4: Future Directions - The development of a world model is underway, which will simulate real-world conditions and enhance the feedback loop for continuous learning [36][41]. - XPeng aims to leverage its AI advancements not only for autonomous driving but also for AI robots and flying cars in the future [43][64]. - The transition to an AI company involves building a robust AI infrastructure, with a focus on optimizing the entire production process from cloud to vehicle [50][62].

Core Insights - Xiaopeng Motors has officially launched its new SUV model G7, which is positioned as the "world's first L3-level AI car with high computing power" [1][2] - The G7 will be available in two versions, Max and Ultra, with the Ultra version featuring three self-developed Turing AI chips, achieving a total computing power of 2200 Tops, which is three times higher than the current industry flagship standards [1][2] - The G7 introduces the industry's first AR-HUD technology that projects navigation information directly onto the road, enhancing driving safety and experience [2] Product Features - The G7 Max version is equipped with two Orin-X chips, while the Ultra version includes three Turing AI chips, with a single Turing chip's performance equivalent to three Orin-X chips [1] - The vehicle's design maximizes interior space utilization, achieving a roominess rate of 88%, with a trunk capacity of 819L that can be expanded to 2277L [1] - The G7 features the VLA-OL model for intelligent driving assistance, marking a technological leap from traditional reflective driving assistance to cognitive intelligent driving [1][2] Industry Impact - The launch of the G7 signifies the entry of the smart automotive industry into the "Ultra era," laying a hardware foundation for the mass production application of L3/L4 level autonomous driving technology [2] - The significant increase in computing power standards is expected to lead to greater breakthroughs in intelligent driving technology in the coming years [3]

Core Viewpoint - AAC Technologies has officially entered the automotive acoustic industry through the acquisition of a controlling stake in Hebei Chuguang Automotive Parts Co., signaling a shift in smart cockpit perception from "visible" to "audible" [1] Group 1: Acquisition Details - AAC Technologies acquired 53.74% of Hebei Chuguang for 288 million RMB, making it an indirect subsidiary, ensuring business continuity for the founding team [1] - The acquisition is seen as a strategic move to enhance AAC's capabilities in the automotive sector, particularly in the context of evolving smart cockpit technologies [2] Group 2: Industry Implications - The automotive acoustic sector, previously undervalued, is becoming a critical component in human-machine interaction as smart cockpits evolve beyond simple screen-based interfaces [1][3] - The acquisition allows AAC Technologies to leverage its extensive experience in consumer electronics to fill a gap in the automotive supply chain, particularly in digital microphones where Hebei Chuguang holds over 50% market share in China [2] Group 3: Future Developments - Post-acquisition, AAC Technologies plans to collaborate with Hebei Chuguang on acoustic system development, vehicle adaptation, and international market expansion [3] - The integration of AAC's algorithm optimization capabilities into Hebei Chuguang's existing platform aims to create new automotive-grade products and comprehensive acoustic solutions for OEMs [3][4] Group 4: Market Context - As the technology benefits in consumer electronics diminish, the smart automotive sector is emerging as a new growth axis, prompting many tech companies to explore cross-industry opportunities [4] - The partnership between AAC Technologies and Hebei Chuguang may serve as a replicable model for building acoustic system capabilities within the domestic smart automotive supply chain [4]

Investment Rating - The industry investment rating is "Outperform the Market," indicating that the industry index is expected to perform better than the benchmark index over the next 6-12 months [32]. Core Insights - The collaboration between NVIDIA and MediaTek to develop high-performance APU is expected to innovate the gaming laptop market and capture the enterprise-level AI PC market [4][11]. - SoftBank and Intel's partnership aims to create AI storage chips that could reduce power consumption by 50%, potentially revolutionizing AI infrastructure [14][15]. - The launch of the Huawei and JAC Motors' ZunJie S800 showcases China's strength in smart automotive technology, enhancing its global competitiveness [16]. Summary by Sections Company Developments - Kingsoft Office plans to acquire a 31.9769% stake in Digital Network Technology for approximately 25.37 million yuan, achieving full ownership [3]. - Chuangye Heima announced its participation in acquiring a 36.6015% stake in Beijing Banxintong Technology for 10.25 million yuan, with a payment plan involving a 30% upfront payment [3][23]. Investment Recommendations - Attention is recommended for companies related to AI PCs and gaming PCs, including Zhongke Chuangda, Softcom Power, Zhiwei Intelligent, Raytheon Technology, and Yidao Information, due to the anticipated impact of the new APU [4]. Industry News - The global gaming laptop shipment is projected to grow by 9% year-on-year in 2024, with an expected shipment of 9.2 million units in China by 2028, reflecting a compound annual growth rate of 4.2% [12]. - The collaboration between SoftBank and Intel is expected to address the energy consumption challenges in AI computing, with a total investment of approximately 1.5 billion yuan [14][15].

编者荐语： 日经中文网与36氪展开内容交换合作，精选36氪的精彩独家财经、科技、企业资讯，与读者分享。 以下文章来源于36氪Pro ，作者李安琪 李勤 36氪Pro . 36氪旗下官方账号。深度、前瞻，为1%的人捕捉商业先机。 车企的AI辅助驾驶人才饥渴症。 文 ｜ 李安琪 编辑 ｜ 李勤 封面来源 ｜ 日经中文网 入职新公司第一天，张杨（化名）被要求"吐露"上家公司的辅助驾驶算法与代码。因没有积极配合，张杨没在新公司待多久就离 开了。 张杨的前东家是理想汽车，近年因迅速落地辅助驾驶而被行业关注，成为同行重点"探秘"的对象。 辅助驾驶的技术演化在持续喷发。从传统的基于规则的方案转向"端到端"模型路线后，车企的人才画像需求发生了极大变化，中 国车企像互联网大厂与AI公司一样渴求AI人才。 行业竞争激烈而持续。车企内部，团队赛马、立军令状、集体封闭式开发、"做不出来就换人"等，已经成为辅助驾驶部门的常 态。在高压的交付压力下，挖角高端人才、解密头部公司的技术，成为企业的一些"水下动作"。 尤其今年以来，辅助驾驶第一梯队公司的人才遭到了哄抢。有猎头人士告诉36氪，在端到端、AI大模型这波浪潮中，华为、理 想、Mom ...

Group 1 - The core focus of the collaboration between Xiaopeng Motors and Huawei is on AR-HUD technology, which integrates augmented reality head-up display with advanced driver assistance systems (ADAS) [4][8] - The newly launched "Chasing Light Panorama" AR-HUD features an 87-inch display, supports cross-lane information, and covers eight driving scenarios, marking the industry's first instance of displaying navigation routes on real roads [7][11] - The partnership is expected to enhance the scale and implementation of AR-HUD technology, with the "Chasing Light Panorama" AR-HUD debuting on the Xiaopeng G7 and potentially being used in more new vehicles in the future [7][11] Group 2 - The collaboration comes after a period of tension between Xiaopeng and Huawei, particularly regarding AEB technology, which has now transitioned into a cooperative relationship [9][11] - Both companies have made strategic adjustments, with Huawei positioning itself as a supplier of smart automotive components and Xiaopeng focusing on enhancing its intelligent features amid increasing competition [9][11] - The market response has been positive, with Xiaopeng's stock price rising following the announcement of the partnership, indicating investor confidence in the technological synergies between the two companies [11]

Group 1 - The core viewpoint is that the commercialization of Robotaxi is expected to accelerate as domestic and international companies increase their investments in this sector [1] - Xiaoma Zhixing reported a 200% year-on-year growth in Robotaxi business revenue in Q1 2025, with plans to expand its fleet to 1,000 vehicles by the end of the year [1] - Tesla plans to launch its autonomous taxi service in June and gradually expand its scale [1] Group 2 - WeRide's Robotaxi revenue accounted for 22.3% of total revenue, showing a year-on-year increase of 10.4 percentage points, and it is deepening cooperation with Uber [1] - NVIDIA is applying AI models to autonomous vehicles and has partnered with Mercedes to launch a fleet [1] - Baidu's Apollo Go service recorded over 1.4 million autonomous driving orders in Q1, representing a 75% year-on-year growth [1] Group 3 - The high-level autonomous driving sector requires support from regulatory and testing systems, indicating a new phase for automotive technology services [1] - The value and penetration rate of the autonomous driving segment are expected to increase, presenting significant benefits [1] - The Smart Car ETF (code: 159889) tracks the CS Smart Car Index (code: 930721), which includes listed companies involved in smart driving, vehicle networking, and new energy vehicles [1]

Group 1 - The Nasdaq Golden Dragon China Index increased by nearly 2% [1] - Zai Lab saw a rise of over 13% [1] - Yika Tong experienced a growth of over 10% [1] - NIO's stock rose by over 5% [1] - Tuya Smart increased by over 4% [1]