NVIDIA Physical AI Platform

Jensen Huang's declaration that "every industrial company will become a robotics company" is not a prediction — it is a product strategy. NVIDIA's Physical AI platform, assembled across 2025 and 2026, represents the most comprehensive infrastructure stack for embodied AI development currently available to industry. At its core are four interlocking components: GR00T N1.7, a commercially licensed foundation model for humanoid robot control that has demonstrated 2x or greater task success compared to leading Vision-Language-Action alternatives on standardized benchmarks; Cosmos 3, a world foundation model that generates photorealistic synthetic environments and action trajectories for robot training without requiring physical hardware; Newton 1.0, an open-source physics engine specifically designed for dexterous manipulation simulation with high-fidelity contact modeling; and Isaac Sim 6.0 paired with Isaac Lab 3.0, providing the development environment where simulation and real-world deployment connect. The platform's reach is defined by its industrial partnerships. ABB, FANUC, KUKA, and YASKAWA — collectively operating over 2 million installed industrial robots worldwide — have integrated with the NVIDIA stack, meaning the GR00T and Cosmos infrastructure has a direct path to the largest installed robot base in the world. Application-specific deployments are already live: PeritasAI is using the platform for multi-agent surgical robotics; Aigen is applying it to precision agriculture; Maximo is running a 100-megawatt solar field under NVIDIA-powered autonomous coordination. The strategic importance of NVIDIA's physical AI platform extends beyond any single application. By providing shared simulation infrastructure, open-source physics primitives, and commercially licensable foundation models, NVIDIA has effectively lowered the barrier to entry for physical AI deployment across every industrial sector. GR00T N2, the next generation model, is already showing 2x-plus improvement over leading VLA models in benchmark evaluations.