Feng Huang Wang·2026-01-06 02:19Core Insights - The keynote by NVIDIA CEO Jensen Huang at CES 2026 highlighted a significant shift in AI development from "digital intelligence" to "physical AI," emphasizing the company's ambition to build foundational elements for this new era [1][14] - NVIDIA is responding to the surging demand for AI computing power by upgrading its computing platforms, aiming to capture a substantial share of the digital world's computational base [1] Group 1: AI Evolution and Open Source - Huang emphasized that the computing industry undergoes a platform shift every 10 to 15 years, with current transitions focusing on AI-centric applications and a redefined software development paradigm [2] - The transformation of the computing industry, valued at approximately $10 trillion over the past decade, is driven by a shift in global R&D budgets towards AI and significant venture capital investments [4] - Open-source models are revolutionizing AI, with NVIDIA investing billions in supercomputing clusters to advance open-source model development, achieving breakthroughs in various scientific fields [4] Group 2: Intelligent Agents and Physical AI - The next stage in AI capability evolution involves moving from large language models to intelligent agents that can reason and act, addressing previously untrained problems [5][7] - Huang highlighted the challenge of enabling AI to understand the physical world, necessitating a complete system comprising training, real-time inference, and high-precision physical simulation [7] Group 3: Innovations in AI Models - NVIDIA introduced Cosmos, an open-source world model that learns from vast amounts of video and real-world driving data, enabling realistic video generation and causal reasoning [8] - The first end-to-end autonomous driving system, NVIDIA AlphaMio, was developed using data generated by Cosmos, featuring reasoning capabilities to explain actions to passengers [8] Group 4: Vera Rubin Chip Architecture - The new NVIDIA Vera Rubin chip architecture addresses the explosive growth in AI model size and computing demands, with a redesign of all chips to work as a cohesive system [9][12] - The Vera CPU and Rubin GPU demonstrate significant performance improvements, with the GPU's AI floating-point performance reaching five times that of the previous Blackwell architecture [11] - The architecture's design allows for a reduction in the number of systems required to train a 100 trillion parameter model to one-fourth of that needed with Blackwell, while also significantly lowering inference costs [13] Group 5: Industry Collaboration and Future Vision - NVIDIA announced a partnership with Siemens to integrate its physical AI models with Siemens' digital twin platform, aiming for a comprehensive digital transformation across various industrial processes [14] - Huang concluded that autonomous vehicles represent just the beginning of the physical AI market, with similar technologies poised to drive a robotics revolution [14]