NVIDIA's long-term business model for automotive is not just selling hardware. By providing a full-stack platform (chips, OS, models), the company's ultimate goal is to capture a percentage of the revenue generated from the 13 trillion miles driven annually as they become autonomous.

NVIDIA's next-generation autonomous driving models will incorporate language, allowing them to reason through driving scenarios verbally. A user could ask the car why it is making a certain move, and the multimodal model, which combines vision and language, will explain its thought process in real time.

Despite widespread industry skepticism and slower-than-expected progress, NVIDIA's head of automotive, Jinju Wu, makes a bold prediction: Level 4 autonomy, where a car drives itself in most conditions, will become a mainstream, commodity feature available in consumer vehicles in less than five years.

NVIDIA is releasing an open-source, end-to-end AI software and hardware stack for autonomous driving. This strategy mimics Google's Android playbook: by enabling any automaker to build self-driving cars, NVIDIA aims to sell more of its onboard computers and dominate the chip market.

Waymo’s system starts with a large, off-board foundation model understanding the physical world. This is specialized into three 'teacher' models: the Driver, the Simulator, and the Critic. These teachers then train smaller, efficient 'student' models that run in the vehicle.

A pure "pixels-in, actions-out" model is insufficient for full autonomy. While easy to start, this approach is extremely inefficient to simulate and validate for safety-critical edge cases. Waymo augments its end-to-end system with intermediate representations (like objects and road signs) to make simulation and validation tractable.

While public focus is often on expensive sensors like LiDAR, Rivian's CEO states the onboard compute for AI inference is an order of magnitude more expensive than the entire perception stack. This cost reality drove Rivian to design its own chip in-house, enabling it to deploy high-level autonomy capabilities across all its vehicles affordably.

Waymo uses a foundation model to create specialized, high-capacity "teacher" models (Driver, Simulator, Critic) offline. These teachers then distill their knowledge into smaller, efficient "student" models that can run in real-time on the vehicle, balancing massive computational power with on-device constraints.

NVIDIA's flexible, multi-layered platform strategy allows automakers to choose between a full turnkey solution or select components. This enables NVIDIA to collaborate even with companies like Tesla, which design their own inference chips, by providing essential cloud, simulation, and training infrastructure.