此次CES上，英伟达可能带来企业AI第一次真正落地。 2026年消费电子展（CES）当地时间6日在美国拉斯维加斯正式开幕。在美股科技板块中，CES一直被 视为年初最重要的市场情绪催化节点之一，而今年，这一节点的重要性尤为突出。 多位参与CES的科技圈人士对第一财经表示，一方面，AI芯片需求仍处高景气区间；另一方面，资本市 场的关注焦点已从"算力是否紧缺"，逐步转向"AI何时、如何转化为可持续收入"，也正因如此，即使此 前英伟达股价下跌后的远期市盈率已经跌入了20倍区间，投资者大幅抄底买入的兴趣仍不如以往。在这 一背景下，英伟达在此次CES主办多场论坛——1月5日到8日每日"供应"，其CEO黄仁勋的CES演讲也 被视为一次关于AI下一阶段走向的关键定调。 综合目前释放的信息，英伟达在本届CES上的看点，或不止于新品发布，而更集中于AI商业模式的落地 路径。 企业级AI能否走向"可复制"的收入模式 过去一年，大模型能力已被充分验证。无论是文本、图像还是多模态模型，其技术上限已不再是争论焦 点。 大模型科技公司OpenAGI创始人秦增益博士对第一财经表示，目前对大语言模型仍持有乐观态度，虽然 它可能非最终技术范式， ...

物理AI是黄仁勋演讲的重中之重。他说，英伟达已为物理AI工作了8年。现场演示视频中，来自黄仁勋的声音说：物理AI的ChatGPT时刻即将到来。 - . s - - r e te . Career States l - - The September are and are and the state 3000人的剧场座无虚席，英伟达推出多款与物理AI有关的开源模型，并详细披露芯片平台Rubin的性能数据。 美国当地时间1月5日，在超威半导体AMD CEO苏姿丰即将在CES开幕前夜主题演讲的几个小时前，英伟达CEO黄仁勋率先在不远的酒店举行新年的第一场 演讲。 当天上午11点，距离英伟达发布会正式开始还有3个多小时，第一财经记者看到，在拉斯维加斯枫丹白露酒店的剧场中心外，观众已经开始排起了百米长 队。 3000人的剧场座无虚席，黄仁勋一身闪亮的皮衣入场，发布了多款与物理AI有关的开源模型，同时首次详细披露了新一代芯片平台Rubin的性能数据。 此外，还有一系列机架产品、超节点产品发布。从新发布的产品看，英伟达不再只依赖芯片上晶体管密度提升，而是着力网络处理、低精度浮点运算、数据 中心超节点等方面，来综合提升算力 ...

下面是CES 2026上，英伟达的主题演讲schedule, 从演讲的标题标题也大概能看出来这次CES，英伟 达对Physical AI的重视程度。 | | | | CES 2026 NVIDIA Schedule | | | --- | --- | --- | --- | --- | | Date | Time (Local, PST) | Location | Event Theme | Speaker | | 2026/1/5 | 11:30 a.m .- 3:00 p.m. | Fontainebleau Las Vegas, 2nd Floor / Virtual Live Stream | NVIDIA Live at CES 2026 with Founder and CEO | Jensen Huang, Founder & CEO, NVIDIA | | | | | Jensen Huang | | | 2026/1/6 | 9:00-10:00 a.m | | Powering the Industrial Al Revolution | Roland Busch, President & ...

英伟达业务版图再拓展。 黄仁勋曾多次公开强调，自动驾驶汽车不仅是"机器人技术的第一个主要商业应用"，更是一个"数万亿 美元级别的产业"。 36氪从多处获悉，英伟达内部正在孵化Robotaxi项目，这项决定在近期的一项All hands meeting（全员 大会）上宣布，将交由就职多年的高级总监Ruchi Bhargava负责。 知情人士告诉36氪，新项目将采用全新的一段式技术路线。该技术路线仅使用一个"端到端"神经网络， 核心是通过仿真技术形成的世界模型对神经网络进行强化训练，与特斯拉FSD采用的路线相似。 英伟达今年1月发布了Cosmos世界基础模型。该平台通过整合文本、图像、视频及传感器数据，生成遵 循物理规律的高质量合成视频数据，并已经经过2000万小时数据的预训练。 Cosmos世界基础模型的意义之一，便是能够拓展现实场景难以产生的复杂数据，来提升自动驾驶系统 的能力上限。 这条路线已经得到行业的初步认可，理想、小鹏等企业均已着手打造自己的世界模型。 消息人士向36氪表示，英伟达发力Robotaxi，其逻辑并非简单的业务扩张，而是想推出一个"Robotaxi 的技术样本"。 此前，英伟达已与通用、奔 ...

Core Insights - The application of artificial intelligence (AI) has become a focal point in the global automotive industry, with significant strategic initiatives being launched by various countries to leverage AI for automotive advancements [4][5][11] Group 1: Strategic Initiatives - The European Commission has introduced two strategies: "Applied AI" and "Scientific AI," aimed at accelerating AI applications in industries including automotive, with a focus on autonomous driving and innovative models [5] - In the U.S., tech giants like NVIDIA and Tesla are leading the charge in AI for automotive, with Tesla planning to invest over $10 billion in 2024 for the development of its Autopilot system [6] - South Korea and Japan are focusing on cross-industry collaboration to create smart ecosystems, with initiatives like AI-connected electric vehicles and alliances among automakers to develop autonomous driving technologies [7] Group 2: Market Trends and Data - According to EU statistics, only 13.5% of companies with 10 or more employees in the EU are currently using AI in their operations, indicating significant room for growth [5] - The global autonomous driving market is projected to exceed $200 billion by 2030, highlighting the potential for smart vehicles to become the mainstream mode of transportation [11] Group 3: Technological Challenges - Despite rapid advancements, the automotive industry faces technical challenges, particularly in the reliability of autonomous driving systems under complex conditions and extreme weather [9] - Data privacy and cybersecurity issues are becoming increasingly prominent, with concerns over user data collection and potential hacking threats to vehicle systems [9][10] Group 4: Regulatory Landscape - There are discrepancies in regulations regarding autonomous driving across different regions, with the U.S. having varying state laws and the EU imposing strict data flow regulations, complicating global deployment of smart vehicles [10] - Calls for unified international regulations and standards are growing, as current disparities hinder the global development of the smart automotive industry [10]

Core Viewpoint - NVIDIA has made significant advancements in robotics by releasing multiple open-source technologies, including the Newton physics engine, which enhances robots' physical intuition and reasoning capabilities, addressing key challenges in robot development [1][4][10]. Group 1: Newton Physics Engine - The Newton physics engine aims to solve the challenge of transferring skills learned in simulation to real-world applications, particularly for humanoid robots with complex joint structures [4]. - It is an open-source project managed by the Linux Foundation, built on NVIDIA's Warp and OpenUSD frameworks, utilizing GPU acceleration to simulate intricate robot movements [4]. - Leading institutions such as ETH Zurich and Peking University have already begun using the Newton engine, indicating its adoption by top-tier robotics companies and universities [4][3]. Group 2: Isaac GR00T N1.6 Model - The Isaac GR00T N1.6 model integrates the Cosmos Reason visual language model, enabling robots to understand and execute vague commands, a longstanding challenge in the industry [5][6]. - This model allows robots to convert ambiguous instructions into actionable plans while performing simultaneous movements and object manipulations [6]. - The Cosmos Reason model has surpassed 1 million downloads, and the accompanying open-source physical AI dataset has exceeded 4.8 million downloads, showcasing its popularity and utility [6]. Group 3: Training Innovations - The Isaac Lab 2.3 developer preview introduces a new workflow for teaching robots to grasp objects, utilizing an "automated curriculum" that gradually increases task difficulty [8]. - This approach has been successfully implemented by Boston Dynamics' Atlas robot, enhancing its manipulation capabilities [8]. - NVIDIA has collaborated with partners to develop the Isaac Lab Arena, a framework for large-scale experiments and standardized testing, streamlining the evaluation process for developers [8]. Group 4: Hardware Infrastructure - NVIDIA has invested in hardware advancements, including the GB200 NVL72 system, which integrates 36 Grace CPUs and 72 Blackwell GPUs, already adopted by major cloud service providers [9]. - The Jetson Thor, equipped with Blackwell GPUs, supports multiple AI workflows for real-time intelligent interactions, with several partners already utilizing this technology [9]. - Nearly half of the papers presented at CoRL referenced NVIDIA's technologies, highlighting the company's influence in the robotics research community [9]. Group 5: Comprehensive Strategy - NVIDIA's "full-stack" approach, encompassing open-source physics engines, foundational models, training workflows, and hardware infrastructure, is redefining the landscape of robotics development [10]. - The advancements suggest that the integration of robotics into everyday life may occur sooner than anticipated [11].

Core Insights - Nvidia is incubating a new Robotaxi project led by senior director Ruchi Bhargava, utilizing an end-to-end technology approach that leverages simulation to enhance neural network training, similar to Tesla's Full Self-Driving (FSD) strategy but with a stronger technical foundation [4][5] - The project is supported by Nvidia's Cosmos world model, which integrates various data types to generate high-quality synthetic video data, having completed 20 million hours of pre-training [4] - Nvidia plans to invest $3 billion in the Robotaxi project, contrasting with Waymo's cumulative investment of approximately $12 billion to achieve its current operational scale [5] Company Strategy - The aim of Nvidia's Robotaxi initiative is not merely business expansion but to validate its full-chain engineering capabilities from GPU chips to physical AI models, thereby defining the infrastructure and ecological standards for the next generation of "physical AI" [5] - The US Robotaxi market is expected to accelerate by 2025, with Waymo operating in seven cities and Tesla launching services in Austin and the Bay Area, achieving significant download numbers [5][6] Competitive Landscape - Despite facing challenges in autonomous driving software, Nvidia possesses significant advantages, including its self-developed DRIVE Thor chip with a computing power of 2000 TOPS, enhancing end-to-end model inference efficiency [6] - Industry experts note that the Robotaxi market is still in its early stages, with Waymo operating around 700 vehicles and Tesla deploying only a few dozen in Austin, indicating that Nvidia's entry is timely and offers opportunities to compete for technological leadership [6] Financial Outlook - Nvidia's net profit for Q2 2025 is projected to reach $26.4 billion, providing substantial funding for long-term research and development [6] - Jensen Huang, Nvidia's CEO, emphasizes that autonomous vehicles represent a major commercial application of robotics and a multi-trillion-dollar industry [6]

Group 1: Conflict Between OpenAI and Tesla - The conflict between OpenAI CEO Sam Altman and Tesla CEO Elon Musk has become a hot topic in Silicon Valley, with Musk accusing Altman of deviating from OpenAI's original mission after its commercialization [1] - Musk has filed a lawsuit against Altman for allegedly breaching the founding agreement, while also establishing xAI to compete directly with OpenAI [1] - Altman has countered Musk's claims by revealing emails that suggest Musk attempted to take control of OpenAI and has been obstructing its progress since being denied [1] Group 2: OpenAI's Autonomous Driving Technology - Altman has hinted at new technology that could enable self-driving capabilities for standard cars, claiming it to be significantly better than current approaches, including Tesla's Full Self-Driving (FSD) [3][4] - However, Altman did not provide detailed information about this technology or a timeline for its development, indicating that it is still in the early stages [5] - The technology is believed to involve OpenAI's Sora video software and its robotics team, although OpenAI has not previously explored autonomous driving directly [6][7] Group 3: Sora and Its Implications for Autonomous Driving - Sora, a video generation model released by OpenAI, can create high-fidelity videos based on text input and is seen as a potential tool for simulating and training autonomous driving systems [10] - While Sora's generated videos may not fully adhere to physical principles, they could still provide valuable data for training models, particularly in extreme scenarios [10][11] - The concept of "world models" in autonomous driving aligns with Sora's capabilities, as it aims to help AI systems understand the physical world and improve driving performance [11][21] Group 4: OpenAI's Investments and Collaborations - OpenAI has made investments in autonomous driving companies, such as a $5 million investment in Ghost Autonomy, which later failed, and a partnership with Applied Intuition to integrate AI technologies into modern vehicles [12][15] - The collaboration with Applied Intuition focuses on enhancing human-machine interaction rather than direct autonomous driving applications [15] - OpenAI's shift towards multi-modal and world models indicates a strategic expansion into spatial intelligence, which could eventually benefit autonomous driving efforts [16][24] Group 5: Industry Perspectives on AI and Autonomous Driving - Experts in the AI field, including prominent figures like Fei-Fei Li and Yann LeCun, emphasize the need for AI to possess a deeper understanding of the physical world to effectively drive vehicles [19][20] - NVIDIA's introduction of the Cosmos world model highlights the industry's focus on creating high-quality training data for autonomous systems, which could complement OpenAI's efforts [22][24] - The autonomous driving market is recognized as a multi-trillion-dollar opportunity, making it a critical area for competition between companies like OpenAI and Tesla [24]