黄仁勋CES最新演讲：这，是所有人的机会

Core Insights - AI is transitioning from being a tool to becoming an integral part of all software applications, indicating a significant shift in how technology is utilized in various industries [3][4] - The concept of "double relocation" in AI signifies that it is moving from traditional applications to a new paradigm that includes both physical and digital environments [2][4] - The emergence of open-source models is democratizing AI, allowing a wider range of participants, including startups and researchers, to engage in AI development [5][8] Group 1: AI's Evolution - AI is no longer just a tool but is becoming foundational to all software, indicating a shift in application development [3] - The technology stack for software development is being completely overhauled, moving from CPU-based programming to GPU-based training, which allows for more dynamic and context-aware applications [4] - The modernization of approximately $10 trillion worth of computing infrastructure is underway to accommodate this new AI-driven approach, with significant venture capital flowing into this transformation [4] Group 2: Open-Source Models - The introduction of open-source models, such as DeepSeek R1, has sparked widespread interest and participation in AI development across various sectors [6][8] - The rapid growth in the download of open-source models indicates a global enthusiasm for AI, with contributions from startups, large companies, and academic institutions [8][9] - Open-source initiatives are seen as crucial for building trust among developers and fostering innovation in the AI space [9] Group 3: Physical AI - AI is evolving from being a digital assistant to a physical worker, capable of understanding and interacting with the real world [10][11] - The development of "physical AI" involves training AI to comprehend physical laws and realities, which is essential for applications like autonomous driving and robotics [11][12] - NVIDIA's Cosmos platform is designed to generate synthetic data for training AI in real-world scenarios, enhancing its ability to perform tasks in various environments [13][14] Group 4: Computational Power Upgrade - The introduction of the Rubin platform aims to address the challenges of computational power and cost associated with AI, significantly improving training efficiency and reducing operational costs [20][22] - Key advantages of the Rubin platform include a fourfold increase in training speed, a tenfold reduction in token costs, and a sixteenfold increase in context memory, enabling more complex tasks without loss of information [23][25][26] - The platform is designed to enhance energy efficiency, allowing for greater computational output with lower energy consumption, which is critical for the sustainability of AI operations [28][35] Group 5: Industry Insights - NVIDIA's CEO emphasizes the importance of competition, particularly from Chinese AI chip companies, as a driving force for innovation and improvement within the company [30][32] - The advice for robotics startups includes focusing on either broad technologies applicable across various sectors or specializing in specific verticals to create competitive advantages [33][34] - The energy demands of AI operations are acknowledged, with a focus on improving energy efficiency to ensure sustainable growth in the industry [35][36]