Highlights

1. Rapid Growth: Nvidia’s automotive business revenue surged 69% year-over-year to $586 million, driven by the adoption of its self-driving technology.

2. Full-Stack Platform: The new DRIVE AGX Thor chip and DriveOS software combine to create a full-stack solution that powers next-generation autonomous features for major automakers.
3. Powering Industry Leaders: Beyond in-car platforms, Nvidia’s GPUs are essential for training autonomous vehicle models for key players like Tesla and Alphabet’s Waymo.

4. Trillion-Dollar Vision: CEO Jensen Huang frames the automotive segment as the first “multitrillion-dollar robotics opportunity” within his broader vision for “physical AI.”

Beyond Data Centers, Is Nvidia’s Auto Business Its Next Trillion-Dollar Bet?

While Nvidia’s record-breaking data center results continue to dominate headlines, another powerful growth engine within the company is accelerating rapidly. The chipmaker’s automotive division, long considered a secondary business, posted a staggering 69% year-over-year revenue increase, signaling that its influence is decisively expanding from the cloud to the concrete. This surge, fueled by a comprehensive strategy to become the central nervous system for autonomous vehicles, positions Nvidia not just as a supplier but as a foundational technology partner in the future of transportation.

The Full-Stack Approach to the Autonomous Revolution

Nvidia’s success in the automotive space is built on more than just selling silicon; it’s rooted in providing a complete, full-stack solution for self-driving technology. The company is now shipping its next-generation DRIVE AGX Thor system-on-a-chip (SoC), which, when paired with its DriveOS software, creates a powerful platform that automakers can integrate to power advanced driver-assistance and autonomous capabilities. This turnkey approach has attracted a growing roster of industry giants, including Toyota, Mercedes-Benz, Volvo, and BYD, all of whom are leveraging Nvidia’s platform to shorten development cycles and enhance vehicle safety and autonomy.

Powering the Brains Behind the Leaders

Nvidia’s automotive strategy is distinctly two-pronged. In addition to powering vehicles directly, its high-performance GPUs are the undisputed standard for training the complex AI models that enable self-driving. This gives the company a critical role even with companies that develop their own in-car systems. Tesla, for instance, is leaning more heavily on Nvidia’s chips for training its AV models after abandoning its own Dojo supercomputer project. Similarly, Alphabet’s Waymo, widely seen as the leader in the robotaxi space, utilizes Nvidia hardware to process and learn from its vast datasets, solidifying Nvidia’s indispensable position across the entire autonomous vehicle ecosystem.

A Vision for ‘Physical AI’

For CEO Jensen Huang, the automotive business is the leading edge of a much grander vision he calls “physical AI” or “embodied AI.” This concept describes a future where AI moves beyond digital realms to interact with and navigate the real world through robotics. He has identified the autonomous vehicle “revolution” as the first multitrillion-dollar opportunity within this new frontier. With an estimated revenue of $5 billion for the segment this fiscal year, Nvidia is laying the groundwork for what Huang believes will eventually become a trillion-dollar business, transforming every moving object, from cars to factory robots, into sophisticated, AI-powered machines.

Looking ahead, Nvidia has established a formidable and deeply entrenched position in the future of mobility. The company’s immediate challenge is to convert its numerous design wins with automakers into mass-market production revenue as new vehicle models featuring its Thor platform roll out. For investors and industry observers, the key metrics to watch will be the adoption rate of this next-generation hardware and the tangible progress of robotaxi services from partners like Tesla and Waymo, which will serve as the ultimate validation of Nvidia’s physical AI strategy.