Waymo alternates major upgrades between hardware and software. Its 6th generation system introduces a custom vehicle and a cheaper, simpler sensor stack, but runs largely the same software as the 5th generation. This demonstrates software generalizability and de-risks the launch of new hardware.

According to its co-CEO, Waymo has moved beyond fundamental research and development. The company believes its core technology is sufficient to handle all aspects of driving. The current work is an engineering challenge of specialization, validation, and data collection for new environments like London, signaling a shift to commercial deployment.

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. Waymo augments its end-to-end learning with structured, intermediate representations (like objects and road concepts). This provides crucial knobs for scalable simulation, safety validation, and defining reward functions.

Rivian's CEO explains that early autonomous systems, which were based on rigid rules-based "planners," have been superseded by end-to-end AI. This new approach uses a large "foundation model for driving" that can improve continuously with more data, breaking through the performance plateau of the older method.

The transition from Gen 4 to Gen 5 was a discontinuous jump that enabled rapid expansion. Waymo made a "big bet on AI," replacing a system of many smaller, specialized ML models with a single, generalizable AI backbone. This new architecture, trained on diverse national data, was the key to scaling beyond specific pre-mapped areas.

Waymo's CEO argues it is a deceptive assumption that Level 2/3 driver-assist systems exist on a continuous spectrum with Level 4/5 full autonomy. The hardest parts of building a 'rider only' system are fundamentally different, requiring a qualitative jump in technology.

Waymo decouples major hardware and software upgrades. Its 6th generation platform introduces a new custom vehicle and a cheaper, simpler sensor stack, but runs the same proven 5th generation software. This "tick-tock" approach allows them to validate a new hardware platform while relying on a mature, generalizable software stack.

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.