尽管如此，美国时间8月27日发布的英伟达二季报，依旧非常亮眼： 总营收达467亿美元，同比增长56%； 净利润为264.2亿美元，同比增长59%。 "本季度未向中国客户销售H20产品。" 知道投资者关注中国业务，在二季报新闻稿中，英伟达特地在很前面位置主动提及这一点。 超过英伟达在一季报给出的指引，也超市场预期。 在三季度的指引中，在不包含向中国客户交付H20产品的相关营收的情况下，预计总营收也能到达540亿美元，毛利率也会继续走高。 但是，明眼人都知道中国AI市场的分量和前景。而除了H20芯片，英伟达执行副总裁兼首席财务官科莱特・克雷斯还透露，中国市场在数据中心营收中的 占比环比下降至低个位数。 财报公布后，英伟达股价一度下跌，也是担心没有中国亮点。 在财报后的电话会议上，分析师也问及这一问题。CEO黄仁勋必须"画饼"： "今年中国市场为我们带来的机遇规模约为500亿美元。若我们能凭借具备竞争力的产品充分把握这一机遇，且鉴于全球AI市场整体均在增长，预计中国 市场规模每年有望实现50%左右的增长。" 英伟达现在赚钱可以不用靠中国市场，但长期不行。 更多关于这家全球最强大芯片公司二季报的信息，让我们仔细看一下 ...

Core Viewpoint - Nvidia's new Jetson Thor chip significantly enhances the computational power and data processing capabilities for robotics, enabling more complex tasks to be performed directly on the device rather than relying on cloud processing [3][4]. Group 1: Nvidia's Jetson Thor Chip - The Jetson Thor chip, based on the Blackwell architecture, offers a peak performance of 2070 TFLOPS at FP4 precision, representing a 7.5 times improvement over the previous Orin chip and a 3.5 times increase in energy efficiency [4]. - The chip allows robots to handle high-resolution, high-frequency sensor inputs directly, potentially shifting many tasks from cloud processing back to local execution [5]. Group 2: Robotics Deployment Models - Current robotics typically utilize a hybrid deployment model combining cloud and edge processing, with edge systems focusing on real-time tasks and cloud systems handling more complex reasoning tasks [4]. - The reliance on cloud processing introduces latency issues that can affect the safety and feasibility of high-frequency tasks, such as rapid decision-making and continuous grasping [4]. Group 3: Competitive Landscape - Domestic companies are also developing their own solutions, such as Diguo Robotics' RDK S100 development kit and Hezhima Intelligent's chips for humanoid robots, which focus on real-time control and multi-modal data processing [6][7]. - The advantages of domestic chips include higher cost-effectiveness and tailored services that cater to local market needs, providing differentiation in scene optimization [7].

当地时间8月25日，被英伟达称为机器人"新大脑"的Thor芯片，以开发者套件的形式正式发售，售价为 3499美元（约2.5万元人民币）。这款芯片旨在支撑具身智能机器人与物理世界进行实时、智能的交 互。 "机器人需要丰富的传感器数据和低延迟的 AI 处理来支撑实时运行，这必须依靠强大的AI算力和内存 来并行处理来自多个传感器的数据。"英伟达表示。 具身智能机器人面临的算力不足问题已引发业内关注。英伟达上一代Orin芯片被众多机器人厂商使用， 但其最高算力仅为275Tops（每秒275万亿次运算）。在一些业内人士看来，如果机器人在场景中自主运 行，这一算力规模远远不够。 官方数据显示，基于Blackwell架构GPU的新一代Thor芯片，在FP4精度下提供高达2070 TFLOPS（每秒 2070万亿次浮点计算）的峰值算力，比上一代Orin芯片提升了7.5倍，能效（单位功耗下的算力输出） 提升3.5倍。此外，其CPU性能比上代提升了3.1倍，内存128GB，提升了2倍。 自2014年以来，英伟达快速绑定机器人领域的开发者。公司8月25日披露，目前已经有超过200万开发者 加入英伟达机器人技术生态，覆盖制造业、食品 ...

Core Viewpoint - The article discusses the controversy surrounding NVIDIA's H20 AI chip, which is perceived to potentially contain backdoor features that could compromise security, particularly in the Chinese market. The CEO, Jensen Huang, faces the challenge of proving the chip's safety while navigating complex geopolitical tensions between the U.S. and China [1][4][6]. Group 1: Chip Performance and Market Impact - The H20 chip's performance is only about 20% of NVIDIA's flagship H100, leading to it being referred to as a "crippled version," yet it is still allowed to enter the Chinese market [6][19]. - China contributes significantly to NVIDIA's revenue, accounting for 22% of its global income, which amounts to $17.1 billion, with a growth rate of 66% [6][19]. - Following the controversy, NVIDIA's revenue in China plummeted by 42%, while orders for Huawei's Ascend chips surged by 300% [19]. Group 2: Regulatory and Geopolitical Tensions - The Chinese government raised concerns about the H20 chip potentially having tracking and remote shutdown capabilities, demanding technical proof from NVIDIA within 48 hours [6][11]. - U.S. legislation requires regulated chips to include location verification and remote shutdown features, raising suspicions about the H20 chip's design [9][11]. - Jensen Huang's visit to Washington resulted in a deal where NVIDIA committed to a $50 billion investment in the U.S. in exchange for expedited export approval for the H20 chip [17]. Group 3: Trust and Market Dynamics - Chinese companies, including Baidu and iFlytek, have halted H20 chip purchases and are exploring domestic alternatives, indicating a rapid shift in market dynamics [18][19]. - The share of domestic chips in AI computing procurement is projected to rise from 5% in 2022 to 40% by 2025, while NVIDIA's market share in China is expected to drop from 95% to 50% [21]. - The article emphasizes that trust is the most critical factor in the chip market, suggesting that even high-performance chips cannot restore confidence once it is lost [22].

Core Viewpoint - The article discusses the competitive landscape of the autonomous driving industry, focusing on NVIDIA's challenges in maintaining its market position against emerging Chinese companies and the shift towards self-developed chips by major automakers [5][15][50]. Group 1: NVIDIA's Market Position - NVIDIA's market capitalization has reached $4 trillion, making it the world's most valuable company, but it faces increasing competition from Chinese automakers who are trying to reduce reliance on NVIDIA's technology [5][15]. - General Motors' executives have expressed concerns about NVIDIA's autonomous driving solutions, indicating potential issues in their collaboration [7][8]. - Other automakers, such as Mercedes-Benz, have also reported that NVIDIA's autonomous driving performance is lagging behind that of Chinese startups like Momenta [10][11]. Group 2: Challenges in Chip Delivery - NVIDIA's latest Thor chip has faced multiple delays, impacting key clients like Li Auto, which has resulted in significant sales losses estimated at around 6 billion yuan due to postponed vehicle launches [18][19]. - The delays in chip delivery have prompted companies like Xiaopeng to pivot towards self-developed chips, as they can no longer rely on NVIDIA's timelines [20][24]. - The challenges faced by NVIDIA in delivering the Thor chip are attributed to design flaws and the complexity of automotive-grade chip production, which differs from consumer electronics [34][42][46]. Group 3: Shift Towards Self-Developed Chips - Major Chinese automakers are increasingly investing in self-developed chips to reduce costs and enhance compatibility with their AI technologies, with companies like NIO and Xiaopeng already making significant progress [25][35][37]. - The self-development of chips is seen as a strategic necessity for automakers to maintain competitiveness in the rapidly evolving autonomous driving market [38][39]. - The article highlights that the development of self-developed chips is a long-term commitment, with significant investments and risks involved, but it is becoming essential due to supply chain uncertainties [26][27][30]. Group 4: Competitive Landscape - The competition in the autonomous driving software space is intensifying, with Chinese companies like Momenta and Qingtou Zhihang rapidly advancing their technologies, often outpacing NVIDIA's offerings [51][53]. - NVIDIA's corporate culture and operational structure may hinder its ability to adapt quickly to the demands of the automotive industry, contrasting with the agile approaches of Chinese startups [52][54]. - The article suggests that the future of autonomous driving will likely see a shift towards more localized solutions, with Chinese companies capturing a larger share of the market as they innovate faster and align more closely with automotive needs [55].

Core Viewpoint - The article discusses the challenges faced by NVIDIA in the automotive sector, particularly in the context of its partnerships with major car manufacturers and the increasing competition from Chinese companies developing their own chips and software solutions [3][5][18]. Group 1: NVIDIA's Automotive Business Challenges - NVIDIA's automotive business, while significant, accounts for less than 2% of its total revenue of $130.5 billion, indicating that it is a relatively small segment for the company [11][58]. - The collaboration between NVIDIA and General Motors has faced internal criticism, with GM executives describing NVIDIA's autonomous driving solutions as "very scary" [5][6]. - Other automakers, such as Mercedes-Benz, have also expressed dissatisfaction with NVIDIA's performance, leading to a shift towards competitors like Momenta for autonomous driving solutions [9][11]. Group 2: Competition from Chinese Companies - Chinese automakers are increasingly developing their own AI chips, with companies like NIO and Xpeng already delivering their self-developed chips, posing a significant threat to NVIDIA's market share [19][30]. - The article highlights that the delay in NVIDIA's Thor chip delivery has prompted companies like Xpeng to pivot towards their self-developed chips, indicating a loss of confidence in NVIDIA's ability to meet delivery timelines [24][25]. - The competitive landscape is shifting, with Chinese companies rapidly advancing in autonomous driving software and hardware, making it difficult for NVIDIA to maintain its previous dominance [66][68]. Group 3: Implications of Chip Development - The development of self-research chips by automakers is seen as a strategic necessity, driven by the need for cost reduction and better integration with AI capabilities [45][49]. - The article notes that the challenges faced by NVIDIA in delivering the Thor chip have inadvertently accelerated the self-development of chips among leading Chinese automakers [31][30]. - The long development cycle for automotive chips, which can take up to four years, contrasts sharply with the faster-paced software development cycles seen in the industry [33][50]. Group 4: Cultural and Operational Differences - NVIDIA's corporate culture, which emphasizes long-term technological advancements, may not align with the immediate delivery needs of automotive clients, leading to operational friction [51][62]. - The article points out that NVIDIA's team in China lacks decision-making power compared to its larger U.S. team, which may hinder its responsiveness to local market demands [65]. - The disparity in urgency and operational focus between NVIDIA and its automotive partners has created a gap that competitors are eager to exploit [67][68].

Core Viewpoint - Intel is selling its stake in Mobileye for $900 million, which includes a direct buyback of $100 million, potentially leading to total proceeds of $1 billion. This move reflects Intel's strategic shift amidst challenges in the semiconductor and autonomous driving industries [1][2][3]. Group 1: Intel's Strategic Shift - Intel has faced significant challenges in recent years, particularly against competitors like AMD, Apple, and Nvidia, leading to a need for strategic adjustments under new CEO Chen Lifang [2]. - The sale of Mobileye shares is part of a broader strategy to optimize assets and focus on core business areas, particularly data center and AI chips, which are seen as future growth points [3][5]. Group 2: Mobileye's Market Position - Mobileye, acquired by Intel for $15.3 billion in 2017, has seen a decline in competitiveness as the market shifts towards fully autonomous driving solutions. The company has lowered its revenue expectations for 2024 to between $1.6 billion and $1.68 billion, down from previous estimates [3][5]. - Despite its challenges, Mobileye still has a cash flow, making it a target for asset optimization by Intel [3]. Group 3: Industry Dynamics - The sale of Mobileye shares highlights a shift in the automotive industry, where car manufacturers are increasingly seeking to regain control over technology and software, moving away from reliance on suppliers like Mobileye [8][10]. - The changing landscape indicates a move from a hardware-dominated model to one that emphasizes software and service revenues, with projections suggesting that by 2030, over 50% of automotive revenue will come from services and software [8][9]. Group 4: Future Implications - The transaction may signal the beginning of a broader industry reshuffle, as companies adapt to new market realities and seek to establish more flexible partnerships [11][12]. - The evolving dynamics suggest that smaller players may struggle to survive unless they can secure ongoing orders from car manufacturers or develop software monetization capabilities [12][13].

Core Viewpoint - The article emphasizes that Horizon Robotics is not merely a chip company but aims to become a software company, focusing on integrated hardware-software systems for autonomous driving, particularly in urban environments [2][4][5]. Group 1: Company Background and Development - Horizon Robotics was founded in 2015, and its founder, Yu Kai, recognized the nearing end of the mobile internet boom, leading the company to focus on robotics chips instead of algorithms [7]. - The company faced significant challenges in 2019, including a reduction in workforce by one-third, but pivoted to focus solely on the automotive sector [7][8]. - Collaborations with major automotive manufacturers like Changan and Li Auto have been pivotal in establishing trust and achieving market penetration [8][10]. Group 2: Market Position and Achievements - Horizon Robotics has achieved over 8 million units of front-mounted production and has a market share of 33.97% in the L2 autonomous driving computing solutions market for domestic brands [13]. - The company claims that one in three smart vehicles is equipped with its technology, projecting rapid growth towards 10 million units [14]. Group 3: Future Vision and Strategy - Horizon Robotics aims to redefine the essence of smart driving, focusing on both functional and emotional value, similar to how smartphones evolved beyond basic communication [15]. - The company plans to transition from L2 to L3 and L5 autonomous driving capabilities, requiring significant computational power and extensive real-world data [18][21]. - The introduction of the Horizon Smart Driving (HSD) system, featuring the Journey 6P chip, is set to enhance urban autonomous driving capabilities and is expected to be launched in 2025 [24][28]. Group 4: Technological Approach and Innovation - The HSD system utilizes a one-piece end-to-end technology architecture, ensuring high performance and efficiency in data processing [24]. - Horizon Robotics emphasizes the importance of a robust software-hardware integration strategy, akin to successful models like Apple and NVIDIA, to maintain a competitive edge [23]. Group 5: Market Dynamics and Competitive Landscape - The article discusses the competitive landscape, highlighting that while other companies may have launched similar technologies earlier, Horizon Robotics prioritizes product quality over timing [30]. - The company is aware of the potential pitfalls of technological advancements leading to homogenization in the market and focuses on building a sustainable competitive advantage through consistent R&D efforts [20].