Core Viewpoint - The discussion around VLA (Vision-Language-Action model) is intensifying, with contrasting opinions on its short-term feasibility and potential impact on the automotive industry [2][12]. Group 1: VLA Technology and Development - The Li Auto i8 is the first vehicle to feature the VLA driver model, positioning it as a key selling point [2]. - Bosch's president for intelligent driving in China, Wu Yongqiao, expressed skepticism about the short-term implementation of VLA, citing challenges in multi-modal data acquisition and training [2][12]. - VLA is seen as an "intelligent enhanced version" of end-to-end systems, aiming for a more human-like driving experience [2][5]. Group 2: Comparison of Driving Technologies - There are two main types of end-to-end technology: modular end-to-end and one-stage end-to-end, with the latter being more advanced and efficient [3][4]. - The one-stage end-to-end model simplifies the process by directly mapping sensor data to control commands, reducing information loss between modules [3][4]. - VLA is expected to outperform traditional end-to-end models by integrating multi-modal capabilities and enhancing decision-making in complex scenarios [5][6]. Group 3: Challenges and Requirements for VLA - The successful implementation of VLA relies on breakthroughs in three key areas: cross-modal feature alignment, world model construction, and dynamic knowledge base integration [7][8]. - Current automotive chips are not designed for AI large models, leading to performance limitations in real-time decision-making [9][11]. - The industry is experiencing a "chip power battle," with companies like Tesla and Li Auto developing their own high-performance AI chips to meet VLA's requirements [11][12]. Group 4: Future Outlook and Timeline - Some industry experts believe 2025 could be a pivotal year for VLA technology, while others suggest it may take 3-5 years for widespread adoption [12][13]. - Initial applications of VLA are expected to be in controlled environments, with broader capabilities emerging as chip technology advances [14]. - Long-term projections indicate that advancements in AI chip technology and multi-modal alignment could lead to significant breakthroughs in VLA deployment by 2030 [14][15].

Core Insights - Xiaopeng has developed its own Turing AI chip, which has passed the most stringent international automotive-grade certification systems [1] Group 1: Certification Achievements - The chip has received ASIL-B functional safety certification [1] - It has undergone AEC-Q100 reliability testing [1] - Functional safety complies with ISO26262 ASIL-D highest standard [1] - The production process meets IATF16949 automotive quality system standards [1]

Core Insights - Nvidia's introduction of the H20 chip and its tailored RTX Pro GPU for the Chinese market signifies a shift in the trade dynamics between the US and China, as both Nvidia and AMD resume AI chip exports to China [2][5]. Group 1: H20 Chip Overview - The H20 chip is designed specifically for the Chinese market to comply with US export restrictions, based on Nvidia's Hopper architecture, and is suitable for vertical model training and inference, but not for trillion-scale model training [3]. - Nvidia's exhibition at the event did not feature the H20 chip or any specific GPU products, instead showcasing integrated hardware and software solutions, including humanoid robots powered by Nvidia technology [3]. Group 2: Market Implications - Nvidia's CEO Jensen Huang highlighted the potential loss of $15 billion in sales due to previous export restrictions and emphasized that the Chinese AI market could reach $50 billion in the next two to three years [5]. - Huang criticized the US export control policies, arguing that they are based on the flawed assumption that China cannot develop its own AI chips, which he believes is incorrect [5]. Group 3: Robotics and AI Integration - Huang predicts that AI will drive the future of manufacturing, with robots collaborating with humans, creating new opportunities within China's supply chain ecosystem [6]. - The demand for specialized chips in robotics is expected to grow as humanoid robots and embodied intelligence become more integrated into daily life [8]. Group 4: Alternative Chip Solutions - Due to previous trade restrictions, companies have begun to adopt domestic alternatives for high-performance AI chips, leading to the emergence of System on Chip (SoC) solutions in the humanoid robotics sector [8]. - For instance, XPeng's AI humanoid robot is equipped with the XPeng Turing AI chip, which claims to offer performance equivalent to multiple Nvidia chips [9]. Group 5: Chip Architecture and Development - The architecture of autonomous driving chips and humanoid robot chips shows significant similarities, suggesting that the same core technologies can be applied across different applications [11][14]. - Nvidia's Orin SoC and AI GPU chips share common architectural elements, indicating a convergence in design for various AI applications [15][16]. Group 6: Market Position and Future Outlook - Nvidia achieved a remarkable 3.76 million AI GPU shipments in 2023, capturing 98% of the global data center GPU market, with expectations of exceeding 4 million shipments in 2024 [16]. - The collaboration and shared ecosystem among developers are seen as essential for the continued growth of AI and robotics, suggesting that partnerships may be more beneficial than competition in this evolving landscape [16].

Core Insights - The Guangdong International Supply Chain Cooperation Conference aims to enhance global industrial and supply chain collaboration, showcasing Guangdong's commitment to becoming a global industrial hub [1][2]. Group 1: Event Overview - The conference attracted nearly 300 participants, including government and business representatives from China and abroad [1]. - 64 Guangdong enterprises, including notable companies like Xpeng Motors and DJI, showcased innovative products at the event [1]. - The event featured policy discussions and business environment promotions to highlight Guangdong's industrial capabilities [1][2]. Group 2: Government and Business Perspectives - Guangdong is positioned as a crucial node in the global supply chain, with a robust industrial ecosystem and a commitment to innovation [2]. - The Thai Investment Promotion Committee expressed interest in further collaboration with Guangdong, highlighting existing investments from Guangdong companies in key sectors like electronics and biotechnology [2]. Group 3: Industry Highlights - Guangdong's policies, such as "Yue Chain Yue Strong," focus on fostering modern and future industries, including AI and biotechnology [3]. - The Huangpu District in Guangzhou is promoted as a prime investment opportunity, particularly in the biopharmaceutical sector, supported by strong research institutions [3]. Group 4: Company Achievements - Xpeng Motors reported significant growth, with over 190,000 vehicles delivered in the first half of the year, surpassing the total for the previous year [4][5]. - The company has invested over 23.9 billion yuan in R&D, holding 5,675 core patents, and is focusing on AI and autonomous driving technologies [5]. Group 5: Supply Chain Innovations - The conference highlighted the shift towards service trade in response to changing international trade dynamics, with companies like Zhenzhong Supply Chain emphasizing AI applications in supply chain management [6]. - Several cooperation agreements were signed to enhance trade and investment between Guangdong and Thailand, showcasing a commitment to deepening international collaboration [6]. Group 6: Future Directions - The Guangdong Trade Promotion Council aims to integrate industrial, innovation, financial, and talent chains to enhance market access for local enterprises [7]. - The conference is positioned as a key platform for global supply chain cooperation, with over 650 participating companies from 75 countries, including a significant presence from Fortune 500 companies [7].

Core Viewpoint - The article emphasizes that the demand for advanced process technology driven by autonomous driving (AD) and embodied intelligence will significantly surpass that of AI GPUs, despite the current hype surrounding AI models like ChatGPT and the performance of companies like NVIDIA [1][2]. Group 1: Wafer Capacity Perspective - The die size of autonomous driving chips is comparable to that of AI GPUs, but the terminal quantity for autonomous driving is several times greater, leading to a much higher demand for advanced process wafer capacity [2][8]. - The value contribution of wafer manufacturing to AI GPU is only 2.25%, indicating that the demand for AI GPUs does not significantly drive wafer capacity needs [10][11]. - The global demand for advanced process capacity for autonomous driving is estimated at 136,200 wafers per month, compared to only 39,700 wafers for AI GPUs [5][6]. Group 2: Application Scenario Perspective - Autonomous driving chips can be viewed as the brain of robots, sharing significant similarities in architecture and application scenarios with robotic intelligence [3][4]. - Companies like Tesla and XPeng are utilizing similar AI chips for both autonomous driving and robotics, indicating a convergence in chip technology across these applications [3][4]. Group 3: Structural Changes in Advanced Process Demand - The anticipated production of robots could reach 1 billion units annually, which, combined with autonomous driving, will disrupt the downstream structure of advanced process applications [4][5]. - The combined demand for advanced process capacity from autonomous driving and embodied intelligence is projected to be approximately 1.65 million wafers per month, significantly exceeding the current capacity of major manufacturers like TSMC [5][6]. Group 4: Die Size and Yield Considerations - The die sizes of autonomous driving chips are generally in the range of 400-600 mm², which is close to that of AI GPUs, but the terminal market for autonomous driving is vastly larger, leading to higher wafer consumption [28][31]. - The yield of larger die sizes is lower, which impacts the overall efficiency of wafer production, making the demand for advanced process capacity even more critical as the industry evolves [39][40]. Group 5: Future Outlook - As the demand for autonomous driving and embodied intelligence grows, the advanced process wafer manufacturing sector is expected to experience a significant expansion, driven by the need for higher performance and more complex chips [6][8]. - The slowdown of Moore's Law suggests that the growth in chip performance will increasingly rely on the volume of chips produced rather than on technological advancements alone [6].

Group 1 - The competition in the Robotaxi market is intense, with only three companies, Waymo, Pony.ai, and Baidu, meeting the standards for scale and automation, according to Pony.ai's CTO [4] - ByteDance's AI research team leader, Kong Tao, has left the company to start his own venture, indicating a shift in talent within the AI sector [14] - Xiaomi is set to launch its new SUV model, the YU7, with three versions and advanced features, highlighting its commitment to the automotive market [12][13] Group 2 - The semiconductor company, Chipone, has faced severe financial issues, including unpaid salaries and layoffs without compensation, raising concerns about its operational stability [8] - Intel is gradually shutting down its automotive business, focusing on its core client and data center products, which may impact its automotive clients [40] - The launch of Cao Cao Mobility on the Hong Kong Stock Exchange marks it as the largest tech mobility platform in Hong Kong, with a valuation of approximately 228 billion HKD [28][29] Group 3 - The ongoing layoffs at Microsoft’s Xbox division reflect a broader strategy to shift resources towards cloud services, indicating a significant restructuring within the company [41] - DJI is experiencing severe shortages of its drones in the U.S. market, leading to speculation about its future in that region, although the company remains committed to the market [22][23] - Ningde Times emphasizes overseas expansion as a priority amidst fierce competition in the domestic electric vehicle market, highlighting concerns over unhealthy competition [34][35]

Core Viewpoint - The automotive industry is entering a new era of chip self-research and high computing power, driven by the need for advanced autonomous driving capabilities, particularly L3 level automation, as exemplified by companies like Xiaopeng and NIO [6][39][60]. Group 1: Chip Development and Competition - The competition for automotive computing power began around 2021, initiated by NVIDIA's Orin-X chip, which boasts a computing power of 254 TOPS, significantly surpassing Mobileye's Q5H and Tesla's HW3.0 [1][6]. - Companies like NIO have adopted multiple Orin-X chips, achieving over 1000 TOPS in computing power [3]. - The automotive computing power has fluctuated between 80 to 1000 TOPS over the past four years, but a new phase has emerged in 2023 with the introduction of self-developed chips [5][34]. Group 2: Self-Developed Chips and Industry Trends - Xiaopeng's self-developed 5nm chip, NX9031, is expected to reach 2000 TOPS with two chips in the ET9 model, while the Xiaopeng G7 features three self-developed Turing AI chips, achieving 2200 TOPS [6][39]. - The trend of automakers developing their own chips is gaining momentum, similar to Tesla's earlier journey, as companies seek to overcome the limitations of the "black box" model previously used with suppliers like Mobileye [9][30]. - The emergence of domestic chip companies like Horizon and Black Sesame Intelligence is diversifying the market, with many automakers now developing their own chips that can compete with NVIDIA's flagship products [35][38]. Group 3: The Shift to L3 Autonomous Driving - The automotive industry is approaching the L3 autonomous driving era, with Xiaopeng defining its G7 as the "world's first L3 level AI car" [39]. - The effective computing power required for L3 autonomous driving has been clarified by Xiaopeng at 2200 TOPS, indicating a significant leap from L2 systems [43][55]. - The transition to L3 involves not only technological advancements but also a shift in liability, as vehicles may be held accountable for accidents, increasing pressure on automakers to refine their technologies [56][58]. Group 4: Challenges in Chip Development - The journey of self-developing chips is fraught with challenges, including architectural issues and the risk of costly failures during the chip manufacturing process [62][64]. - Companies must also ensure that their chips meet stringent automotive safety standards, which can extend the validation period significantly [69]. - The need for large-scale production to recoup development costs is critical, with estimates suggesting that around 1 million units may be necessary for profitability [71].

Core Viewpoint - The automotive industry is experiencing a shift towards a more collaborative relationship between manufacturers and suppliers, with a focus on reducing payment terms to enhance cash flow and stability in the supply chain [5][10]. Weekly Data - In the first week of June 2025, passenger car sales reached 362,000 units, representing a year-on-year increase of 12.4% but a month-on-month decrease of 22.3%. New energy vehicle sales were 202,000 units, up 18.9% year-on-year and down 18.4% month-on-month, with a penetration rate of 55.7%, an increase of 2.6 percentage points from the previous month [2][40]. Market Performance - The automotive sector underperformed the market in the week of June 9-13, 2025, with a decline of 0.37%, ranking 18th among sub-industries. Commercial vehicles, motorcycles, and other segments saw increases of 9.38%, 0.95%, and 0.56%, respectively, while passenger cars, automotive services, and auto parts declined by 0.99%, 0.88%, and 1.29% [3][31]. Investment Recommendations - The focus is on high-quality domestic brands that are accelerating in smart technology and globalization, with recommendations including Geely, BYD, Xiaopeng Motors, and others. In the parts sector, specific recommendations include companies involved in new energy and smart driving technologies [4][7][12]. Supplier Payment Terms - Multiple automakers, including Dongfeng, GAC, and Geely, have committed to standardizing supplier payment terms to within 60 days, responding to new regulations aimed at supporting small and medium enterprises. This shift indicates a move from price competition to "payment term competition," which may lead to the exit of financially weaker players from the market [5][10]. Huawei Developer Conference - The upcoming Huawei Developer Conference on June 20, 2025, is anticipated to catalyze advancements in embodied intelligence, particularly in AI and robotics. The focus will be on hardware innovations that could significantly impact the industry [6][16]. Motorcycle Market - The motorcycle market is seeing growth, with sales of 250cc and above motorcycles reaching 93,000 units in April 2025, a year-on-year increase of 28.0%. The cumulative sales from January to April 2025 reached 298,000 units, up 58.3% year-on-year [17][19]. Heavy Truck Market - Heavy truck sales in May 2025 were 83,000 units, a year-on-year increase of 6.0%. The expansion of the vehicle replacement subsidy program is expected to stimulate demand for new trucks [21][22]. Tire Industry - The tire industry is experiencing a high operating rate, with domestic PCR tire operating rates at 78.11% and TBR rates at 62.46%. The demand for tires remains strong, supported by both domestic and international markets [25][51].

Core Viewpoint - Xiaopeng Motors has launched its self-developed Turing chip in its new model, the Xiaopeng G7, which is positioned to enhance the company's capabilities in the competitive smart driving market [1][2]. Group 1: Product Launch and Specifications - The Xiaopeng G7 is available in two versions, Max and Ultra, with a pre-sale price of 235,800 yuan. The G7 Max features dual NVIDIA Orin-X chips with a computing power of approximately 500 Tops, while the G7 Ultra is equipped with three Turing chips [1]. - The effective computing power of one Turing chip is equivalent to three NVIDIA Orin-X chips or two Tesla FSD chips, with ongoing optimizations expected to enhance this capability further [1][2]. - The G7's total effective computing power exceeds 2,200 Tops, significantly surpassing industry standards and meeting the requirements for L3-level intelligent driving assistance [1]. Group 2: Chip Development and Strategy - Xiaopeng invested over 10 billion yuan and spent five years developing the Turing chip, which began with team formation in late 2020 and achieved successful functionality verification within 40 days last year [2][3]. - The company aims to improve the efficiency of computing power usage, distinguishing its self-developed chip from generic industry chips that may include unnecessary computational capabilities [3]. - The Turing chip is designed to support both intelligent driving and smart cockpit functionalities, marking a significant evolution in automotive AI capabilities [4][5]. Group 3: Market Positioning and Competition - The G7 is strategically positioned in the competitive 250,000 yuan SUV market, traditionally dominated by Tesla's Model Y, which sold over 100,000 units in China in the first five months of this year [8]. - Xiaopeng's G7 has already received over 10,000 orders within 46 minutes of its pre-sale launch, indicating strong market interest [8]. - The company acknowledges the challenges of competing in a market that prioritizes practicality and space over technological performance, suggesting that sustained growth will depend on market acceptance [9]. Group 4: Research and Development Investment - Xiaopeng plans to allocate approximately 4.5 billion yuan of its R&D budget this year towards AI development, with total R&D expenditures nearing 10 billion yuan [10]. - The company is confident in achieving profitability by the fourth quarter, supported by its high R&D investment and the successful launch of the G7 [10]. - Xiaopeng aims to establish a competitive edge in the AI automotive sector, emphasizing the importance of innovation and a unique development path [10].

Group 1 - BYD and Changan have unified their payment terms to 60 days, while new players like NIO and Li Auto have not yet responded [4][5] - Xiaomi's YU7 model faces plagiarism accusations, but the company claims its design is original and backed by experts stating it does not infringe on patents [7][8] - BYD's salary levels have surpassed Huawei's, with significant investments in AI and a commitment to improving brand perception amid shareholder criticism [10][11] Group 2 - Ren Zhengfei of Huawei stated that the U.S. has exaggerated Huawei's achievements, emphasizing the need for continuous improvement in chip technology [13] - TSMC is accelerating its U.S. factory construction while slowing down projects in Japan and Europe due to market demand fluctuations [14] - BYD and other Chinese manufacturers are gaining ground in the autonomous driving sector, posing a threat to Tesla's market position [15] Group 3 - The Zhiyuan Research Institute showcased a four-legged robot designed to assist visually impaired individuals, successfully guiding them in complex environments [17] - Tencent Music announced a $12.6 billion acquisition of Himalaya, marking a significant move into the online audio sector [19] - Xiaopeng Motors is set to unveil its G7 model featuring the Turing AI chip, which boasts advanced processing capabilities [26] Group 4 - Huawei is preparing to launch its Pura 80 series smartphones, featuring advanced imaging technology and expected to start at around 5000 yuan [32] - Ideal Auto has established two new robotics divisions, focusing on space and wearable robots, indicating a strategic shift towards AI integration [34] - Gree Electric's president mentioned that several business segments are ready for potential spin-offs, reflecting a strategy to enhance market competitiveness [35]