Nvidia is positioning itself as the computing backbone for autonomous vehicles, with CEO Jensen Huang announcing during the company's earnings call that Nvidia will power Uber's robotaxi fleet across nearly 30 cities and 4 continents by 2028. This partnership represents a significant expansion of Nvidia's automotive business, which generated a record $2.3 billion in revenue last fiscal year, a 39% increase from the prior period.

What Is Physical AI and Why Should You Care?

Huang introduced a new strategic focus during the earnings call: physical AI. Unlike traditional artificial intelligence that operates in software, physical AI refers to autonomous and robotic systems that operate in the real world. Huang explained that Nvidia's CUDA cores, which are parallel processing units capable of handling multiple tasks simultaneously, will enhance robotics, autonomous vehicles, and embedded medical instruments. This shift reflects where the technology industry believes the next wave of growth will occur.

The robotaxi opportunity is substantial. By powering Uber's fleet expansion across nearly 30 cities by 2028, Nvidia is positioning itself at the center of what could become a trillion-dollar market for self-driving transportation. The scale of this deployment underscores how critical computing infrastructure has become for autonomous vehicle development.

How to Understand Nvidia's Role in the Autonomous Vehicle Ecosystem

• Hardware Provider: Nvidia supplies the chips and processing power that enable vehicles to perceive their environment, make real-time decisions, and operate safely without human intervention.
• Software Integration: The company's CUDA cores allow multiple computational tasks to run in parallel, essential for processing sensor data from cameras, radar, and lidar systems simultaneously.
• Market Expansion: By partnering with Uber, Nvidia gains a direct pathway to deploy its technology at scale across multiple continents, validating its approach and generating recurring revenue.

Huang also addressed Nvidia's performance in the inference market, which refers to the computational work required to run trained AI models in production. He stated that the company was "growing share in inference very, very quickly" and expressed confidence in the adoption of Nvidia's Vera Rubin platform among AI companies. Vera Rubin is designed to handle the computational demands of inference workloads, making it relevant for companies deploying AI models at scale.

Why Is Automotive Revenue Growing So Rapidly?

The 39% year-over-year growth in Nvidia's automotive revenue reflects broader industry trends. Autonomous vehicles require enormous amounts of computing power to process sensor data, run machine learning models, and make split-second decisions. As companies like Uber accelerate their robotaxi timelines, demand for Nvidia's chips is accelerating alongside them. The partnership with Uber signals that Nvidia's technology is not merely theoretical; it is being deployed in real-world applications that will operate across multiple cities and continents.

The earnings call revealed that Huang views physical AI as the next major growth frontier for Nvidia. While the company built its reputation on graphics processing and data center computing, the shift toward autonomous systems and robotics represents a diversification that could sustain growth for years to come. The Uber partnership is the most concrete evidence to date that this vision is materializing into actual business.

Nvidia shares gained 1.17% during pre-market trading on Thursday following the earnings announcement, reflecting investor confidence in the company's strategic direction and the scale of the opportunities ahead.