面向通用机器人时代，英伟达推出了新一代"最强大脑"。 8月25日，英伟达宣布，基于Blackwell架构的Jetson Thor正式面向开发者开放。这是一款针对机器人的 超级计算机，可以为制造、物流、交通、医疗、农业和零售等行业的数百万台机器人提供算力支持。 据介绍，较上一代产品Jetson Orin，Jetson Thor的AI算力提升7.5倍，能效提升3.5倍。并且它能够运行各 种生成式AI模型，包括 Cosmos Reason、DeepSeek、Llama、Gemini、Qwen等通用模型，以及Isaac GR00T N1.5等机器人专用模型。 目前，Jetson Thor开发者套件已发售，定价3499美元，Jetson T5000模组购买1000片以上单价2999美 元。联影医疗、万集科技、优必选、银河通用、宇树科技、众擎机器人和智元机器人等企业已经率先使 用了Jetson Thor。 从电脑、数据中心再到机器人，英伟达的GPU算力平台触达了更多终端，并加速拓展。近年来，机器人 成为英伟达的重要业务线，每次都在发布会上压轴出现。 事实上，英伟达已经在机器人赛道投入多年，其创始人黄仁勋瞄准了具有广阔前景 ...

Core Viewpoint - The emergence of physical AI and robotics is set to revolutionize industries by connecting the physical and information worlds, with significant potential for growth in the trillion-dollar market of physical industries [3][5][32]. Group 1: Industry Insights - The IT industry's total scale is approximately $5 trillion, which is a small fraction compared to the global economy exceeding $100 trillion, indicating that the real value lies in industries that interact with the physical world such as transportation, manufacturing, logistics, and healthcare [3][5]. - The development of physical AI is crucial for enabling machines to operate effectively in the physical world, with robots serving as a bridge for this transition [5][32]. - China possesses unique advantages in the field of AI and robotics, including a large pool of AI researchers and developers, unmatched electronic manufacturing capabilities, and a vast manufacturing base for large-scale deployment and testing [5][32]. Group 2: Technological Developments - NVIDIA aims to create three types of computers to support robotics: embedded computers in robots, AI factory computers for data processing and model training, and simulation computers for generating data and testing robots [5][6]. - The collaboration between companies like宇树科技 and 银河通用 with NVIDIA has led to the development of advanced humanoid robots capable of performing complex tasks in industrial settings [6][8]. - The next generation of humanoid robots is expected to see exponential growth, with projections indicating a tenfold increase in production every three years, potentially surpassing the total output of industrial robotic arms [8][14]. Group 3: Market Potential - The humanoid robot market is anticipated to reach a scale that could exceed the combined output of all industrial robots, with estimates suggesting a market value of over 1 trillion yuan in the next decade [8][14]. - The current focus on humanoid robots is driven by their ability to integrate into human environments and perform a variety of tasks, which is essential for their widespread adoption [14][27]. Group 4: Challenges and Future Directions - Key challenges in deploying humanoid robots include enhancing their operational capabilities, particularly in tasks like object manipulation and sorting, which require precision and speed comparable to human workers [18][27]. - The gap between simulation and real-world application (Sim2Real) remains a significant hurdle, necessitating advancements in simulation accuracy and efficiency to ensure reliable robot performance in real environments [19][20]. - The industry is exploring various approaches to improve data generation and training processes, including the use of AI to automate synthetic data creation, which could significantly enhance the training of robots [11][20][22].

Group 1 - The core judgment presented by Rev Lebaredian emphasizes that the IT industry has primarily enhanced capabilities in the "information space," while the greater value lies in the "physical world" sectors such as transportation, manufacturing, logistics, and healthcare [1][2] - The emergence of artificial intelligence enables machines to possess "physical intelligence," effectively connecting the physical and information worlds, with robots serving as a bridge for this transition [2][3] - China is uniquely positioned to excel in this transition due to its substantial number of AI researchers, unmatched electronic manufacturing capabilities, and a vast manufacturing base for large-scale deployment and testing [2][3] Group 2 - NVIDIA's mission is to develop computers specifically designed to tackle the "hardest problems," which includes advancing robotics and physical AI by constructing three types of computers: embedded robots, AI factory computers, and simulation computers [2][3] - Companies like Yushutech and Galaxy General are collaborating with NVIDIA, showcasing robots like the G1 Premium humanoid robot, which utilizes NVIDIA's Jetson Thor technology for complex tasks [3][4] - Yushutech's humanoid robot R1 incorporates NVIDIA's full-stack robotics technology, optimizing movement and control capabilities through high-fidelity simulation platforms [3][4] Group 3 - Yushutech recently launched a new humanoid robot priced at approximately 39,000 RMB, significantly lowering the barrier for consumer-grade humanoid robots, with plans for mass production by the end of the year [3][4] - The company also introduced the A2 robotic dog, weighing around 37 kg with a payload capacity of 30 kg and a range of 20 km, while focusing on developing dexterous robotic hands for executing daily tasks [4][5] - The concept of humanoid robots is viewed as a critical vehicle for general-purpose robotics, with the belief that as AI matures, the complexity of hardware requirements will decrease [3][4] Group 4 - The market for humanoid robots is projected to grow significantly, with expectations that their production value will increase tenfold every three years, potentially surpassing the total output of industrial robotic arms [5][12] - The next decade is anticipated to witness a robot market that could exceed the combined market sizes of automobiles and smartphones, although the growth will not be instantaneous [5][12] - To achieve large-scale deployment of robots, advancements in computational power, simulation capabilities, cost-effective hardware engineering, and a large-scale training system driven by synthetic data are essential [5][12] Group 5 - The current challenges in deploying humanoid robots at scale include the need for improved capabilities in task execution, particularly in areas like object manipulation and mobility [27][28] - The focus is on enhancing the robot's ability to grasp objects, move within environments, and accurately place items, which requires a precise target recognition and positioning system [27][28] - Addressing these technical bottlenecks could unlock a market worth hundreds of billions, with significant advancements expected within five years [27][28] Group 6 - NVIDIA emphasizes a simulation-first strategy in robot training, addressing the challenges of bridging the gap between simulation and reality (Sim2Real) [19][20] - The company is working on enhancing the accuracy of simulation tools and leveraging AI to improve simulation speed and efficiency, which is crucial for large-scale data generation and testing [20][21] - Collaboration with partners is essential to tackle the complexities of creating realistic virtual environments that accurately reflect physical parameters [20][21] Group 7 - The current lack of a unified model architecture in the robotics field is hindering overall progress, with companies exploring various directions to enhance their models [22][23] - Yushutech is investigating the use of video generation models to drive and align robotic arms, although challenges remain in scaling and achieving the desired versatility [22][23] - The integration of foundational models with robotic control and spatial understanding training is seen as a promising avenue for improvement [22][23]

Financial Data and Key Metrics Changes - Revenue more than doubled to $130 billion, with operating income and EPS growing by 147% [39][53] - Blackwell's rollout debuted with $11 billion in sales in the fourth quarter and more than doubled in the first quarter of the following year [38][39] Business Line Data and Key Metrics Changes - Nearly 100 NVIDIA AI-powered factories are currently being built, which is double the number from the previous year, with the average number of GPUs per factory also doubling [38] - The transition from Hopper to Blackwell represents a significant advancement in AI infrastructure, with Blackwell designed for real-time inference applications [37][39] Market Data and Key Metrics Changes - Demand for AI compute is surging due to innovative applications like ChatGPT and the proliferation of generative AI applications [58] - AI factories require tens of gigawatts of infrastructure to be built in the coming years, positioning NVIDIA uniquely to capture this opportunity [59] Company Strategy and Development Direction - NVIDIA is transitioning from a chip company to an AI infrastructure and computing platform company, focusing on building a full-stack data center scale platform [35][36] - The company is investing heavily in AI and robotics, with a multi-trillion dollar growth opportunity identified in these sectors [58][60] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in maintaining leadership positions despite competition, citing a large and growing footprint of data center infrastructure and a strong ecosystem of partners [55][56] - The company views AI as the future of computing, with every industry expected to be affected, including gaming [61] Other Important Information - NVIDIA has returned capital to shareholders through share repurchases and dividends, utilizing $33.7 billion for share repurchases and paying $834 million in dividends in fiscal 2025 [67] - The company completed a 10-for-1 stock split in fiscal 2025 and regularly reviews its capital return program [67] Q&A Session Summary Question: How can NVIDIA improve sales given competition? - Management highlighted a multi-trillion dollar growth opportunity with AI and robotics, emphasizing their leadership in the market and continuous innovation [55][56] Question: Where do you see growth and profit opportunities for NVIDIA? - Growth opportunities are identified in AI compute and robotics, with significant demand for AI infrastructure and autonomous vehicles [58][60] Question: What is NVIDIA's plan if there's a sudden loss of interest in artificial intelligence? - Management reiterated that AI is the future of computing and investments in AI will enhance features across products, including gaming [61][62] Question: What is NVIDIA's strategy for parallel quantum computing? - The company is advancing quantum computing through its CUDA Q platform, which integrates GPUs and quantum processing units [64][66] Question: Will NVIDIA increase its dividend or consider another stock split? - NVIDIA has returned capital through share repurchases and dividends, and the board regularly reviews the capital return program [67]

近日美国关于稳定币的《GENIUS法案》由参议院通过，法案要求稳定币要有足额储备，实行分级监 管，现存稳定币需在18个月内纳入合规体系。近日香港也通过了《稳定币法案》，明确了在香港发行稳 定币的相关要求。这背后是盘什么棋呢？关注我，听我细聊。 稳定币也是一种依托区块链技术的加密货币，与传统货币或资产挂钩，价格波动幅度较小，价值相对稳 定。它绕过了SWIFT系统，交易更便捷，交易成本更低，因此很受青睐，目前2/3的加密货币交易是用 稳定币作为报价货币的。 在通胀严重、资本管制严格、本币不稳定的地区，稳定币被大量使用。俄罗斯正在考虑推出一种与人民 币挂钩的稳定币以规避制裁。今年5月，维萨宣布与稳定币公司Bridge合作，以实现与拉丁美洲的跨境 支付。 最后，《GENIUS法案》对稳定币的定性是用于支付或结算，不是证券，也不是商品，至多可以作为数 字美元，应对各国央行数字货币的竞争，增强美元的可及性，扩大美元的影响力，但是其与短期美债绑 定，就难逃美债的命运，若发生赎回危机，会加剧金融市场波动。 比如加密货币暴跌时，投资者会纷纷赎回稳定币换回美元，发行商需要抛售短期美债储备来应对，这就 把风险传导到了短期美债市场。 ...

Core Viewpoint - NVIDIA is positioned as a leading giant in the AI and accelerated computing landscape, evolving from a GPU manufacturer to a critical infrastructure company that shapes the future of AI and computing [1][3][29]. Group 1: Evolution of NVIDIA - NVIDIA started as a graphics processing unit (GPU) provider for gaming and professional visualization, but has transformed into a comprehensive computing platform provider [3]. - The introduction of CUDA in 2006 revolutionized parallel computing, leading to the development of the DGX system and marking the beginning of the AI revolution [3][4]. - NVIDIA's acquisition of Mellanox in 2019 enhanced its capabilities in data center networking, allowing for the creation of unified computing units [4]. Group 2: AI Infrastructure and Market Potential - The future AI infrastructure is likened to essential resources like electricity and the internet, with AI data centers referred to as "AI factories" that generate valuable outputs [5]. - NVIDIA's founder, Jensen Huang, highlighted the vast market opportunity, estimating that a $300 million chip industry could leverage a $1 trillion data center market [5]. Group 3: CUDA and Its Impact - CUDA is central to NVIDIA's success, enabling a vast ecosystem of libraries and applications that drive user engagement and developer innovation [9][10]. - The limitations of general-purpose CPUs in AI are emphasized, with CUDA allowing for targeted hardware design that accelerates performance significantly [9]. Group 4: Advanced Computing Systems - The introduction of the Grace Blackwell supercomputer represents a significant leap in computing power, capable of horizontal and vertical scaling [17][20]. - The GB300 upgrade promises a 1.5x increase in inference performance and doubled network connectivity, showcasing NVIDIA's commitment to continuous improvement [17][18]. Group 5: Collaborative Manufacturing and Innovation - The production of the Grace Blackwell supercomputer involves collaboration with various Taiwanese manufacturers, highlighting the importance of the semiconductor supply chain [24][26]. - The final product integrates over 1.3 trillion transistors and showcases the technological prowess of the Taiwanese semiconductor industry [27]. Group 6: Future Outlook - NVIDIA's strategy of continuous self-disruption and innovation positions it to dominate the future of computing, moving from chips to platforms and ultimately to infrastructure [29].

Core Viewpoint - The article discusses the advancements in humanoid robotics and the role of NVIDIA in developing the necessary technologies, particularly focusing on the concept of "Physical AI" and the importance of simulation data for training robots [1][7][41]. Group 1: NVIDIA's Role in Robotics - NVIDIA is positioning itself as a key player in the humanoid robotics industry by developing a series of platforms and models, including the Cosmos training platform and the Isaac GR00T N1 humanoid robot model [3][4][19]. - The company has created a comprehensive ecosystem for humanoid robot development, including high-performance computing (DGX), simulation platforms (Omniverse), and inference chips (Jetson Thor) [19][31]. - NVIDIA's strategy involves not only selling hardware but also providing software tools and services to enhance the capabilities of humanoid robots [41][42]. Group 2: The Concept of Physical AI - The term "Physical AI" refers to the next wave of AI development, where robots are expected to understand physical laws and interact with the real world autonomously [8][41]. - Unlike traditional industrial robots that perform specific tasks, humanoid robots aim to understand and make decisions based on their environment, showcasing a significant leap in intelligence [10][13]. - The training of these robots requires vast amounts of simulation data that mimic real-world physics, filling the gap where real-world data is scarce [16][17][18]. Group 3: Simulation Data and Its Importance - Simulation data is crucial for training humanoid robots, as it allows for the creation of realistic scenarios that adhere to physical laws, which is essential for effective learning [16][18]. - The article compares real data to "real exam questions" and simulation data to "mock exams," emphasizing the need for high-quality simulation data to ensure effective training [18]. - NVIDIA's experience in gaming and simulation technologies positions it well to provide the necessary tools for creating this simulation data [23][30]. Group 4: Historical Context and Future Directions - NVIDIA's journey in high-performance computing has evolved from gaming to various high-value applications, including mobile devices, autonomous driving, and now humanoid robotics [32][39]. - The company has learned from past ventures, such as its experience with mobile processors, to focus on more promising markets like AI and robotics [36][38]. - As the demand for "Physical AI" grows, NVIDIA aims to solidify its position by offering integrated solutions that combine hardware and software for the robotics industry [41][43].