Nvidia CEO Jensen Huang stated that physical AI could represent an industry opportunity ten times larger than digital AI. The physical AI he refers to encompasses robotics, autonomous vehicles, drones, and intelligent machines capable of perceiving, reasoning, and acting in real-world environments.

Huang noted that Nvidia's current annual revenue from robotics, autonomous driving, and related physical AI businesses is nearing $10 billion, and he expects this business to grow to $100 billion over the next decade; this is a tenfold growth target for Nvidia's own physical AI revenue, not a precise comparison of realized revenue or the overall digital AI market size.

Nvidia achieved $96.2 billion in revenue for the second quarter of fiscal year 2027, ending July 26, 2026, representing an 18% quarter-over-quarter increase. In its earnings report, the company highlighted "physical AI rollout" alongside frontier model labs and open model ecosystems, stating that they collectively accelerate the construction of AI infrastructure. Currently, physical AI accounts for only a small portion of Nvidia's total revenue, but it is viewed by management as the next phase of growth.

Nvidia sees autonomous driving as the first scalable entry point for physical AI. Huang believes the automotive market is large enough, with vehicle forms and sensor architectures relatively standardized and commercial value sufficiently clear, allowing for a faster formation of the data, chips, software, and mass production cycle compared to general humanoid robots.

Its technological path does not rely solely on robotics chips but rather on a "three-computer" system: AI factories responsible for training world models, simulation systems like Omniverse generating and testing virtual environments, and onboard or robotic computers executing real-time perception and control. Nvidia covers training, testing, and deployment simultaneously through GPUs, data center networks, simulation software, autonomous driving computing platforms, and edge chips.

The market mechanism involves buyers such as automotive manufacturers, robotics companies, industrial automation firms, warehousing and logistics operators, drone manufacturers, and manufacturing plants; they purchase not just chips but also training computing power, simulation tools, onboard computing, sensor data processing, and safety verification capabilities. Funding will flow towards GPUs, edge AI chips, industrial robots, machine vision, digital twins, synthetic data, and safety certification; traditional low-automation manufacturers, robotics firms lacking data loops, and suppliers relying solely on low-cost hardware assembly will face pressure from technological upgrades and capital expenditures.

Source: Public Information