Nvidia(US:NVDA) Xin Lang Cai Jing·2026-01-07 07:07Core Insights - The core insight from the event is that the robotics industry is approaching a critical moment similar to that of ChatGPT in large models, indicating a significant technological breakthrough is imminent in the field of robotics and physical AI [3][39]. Group 1: Robotics and Physical AI - The CEO of NVIDIA, Jensen Huang, believes that the ability of generative video models to understand and generate complex actions indicates that the foundational capabilities for driving robots are nearing maturity [3][39]. - Huang emphasizes that the next two to three years will witness major breakthroughs in robotics, particularly in humanoid robots, which have historically faced commercialization challenges [4][48]. - The application of AI in robotic systems will make it easier for robots to learn actions through demonstrations, reducing the complexity and cost of programming [11][47]. Group 2: Rubin Platform and AI Factory - The newly launched Rubin platform enhances training efficiency by four times and reduces token costs by ten times, pushing the logic of "computing power equals productivity" to its limits [4][40]. - Compared to the previous generation (Blackwell), Rubin's training efficiency is four times greater, meaning tasks that previously took four months can now be completed in one month or with a quarter of the GPU resources [14][51]. - The token generation cost has decreased by ten times with Rubin, which is attributed to improved energy efficiency, algorithm optimization, and faster chips [17][52]. Group 3: Energy Bottlenecks - Huang acknowledges that energy will always be a bottleneck in any industry, especially in the rapidly growing AI sector, where both training and operation require significant energy [20][57]. - The energy efficiency of NVIDIA's products has improved tenfold from Hopper to Blackwell and again from Blackwell to Rubin, but the demand for energy will always outstrip supply [21][57]. Group 4: Market Dynamics and Competition - NVIDIA has established deep partnerships with three HBM suppliers, ensuring a controlled supply chain despite the serious storage bottleneck [22][58]. - The H200 product remains competitive in the Chinese market, but NVIDIA recognizes the need for continuous innovation to maintain this competitiveness against strong local players like Huawei [24][59]. - Huang emphasizes the importance of an open ecosystem, stating that NVIDIA collaborates with all major AI companies, which is crucial for maintaining its competitive edge [32][66]. Group 5: Future Innovations and Collaborations - NVIDIA is working on integrating AI and physical AI into Siemens' EDA tools, which will enhance chip design and simulation capabilities [55][66]. - The company is exploring the feasibility of deploying AI factories in space, leveraging abundant energy and optimal cooling conditions [61][62]. - Huang believes that the collaboration with Groq will lead to the creation of new product categories aimed at novel application scenarios [22][58].