Future Teslas will contain powerful AI inference chips that sit idle most of the day, creating an opportunity for a distributed compute network. Owners could opt-in to let Tesla use this power for external tasks, earning revenue that offsets electricity costs or the car itself.

While often discussed for privacy, running models on-device eliminates API latency and costs. This allows for near-instant, high-volume processing for free, a key advantage over cloud-based AI services.

Ring founder Jamie Siminoff prioritizes cloud-based AI because on-device intelligence becomes obsolete too quickly. The rapid pace of AI advancement means that edge models "decay so quickly that by the time you actually ship that product, it's maybe no longer intelligent."

The seamless experience of an autonomous vehicle hides a complex backend. A subsidiary company, FlexDrive, manages a fleet for services like cleaning, charging, maintenance, and teleoperation. This "fleet management" layer represents a significant, often overlooked, part of the AV value chain and business model.

The true disruption from AVs isn't cheaper transport, but the transformation of cars into productive spaces—moving offices, hotel rooms, or media centers. This framing shifts the value proposition from cost savings to creating new revenue streams and unlocking vast amounts of consumer time, impacting even real estate.

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.

Samsara's AI systems, like in-cab cameras, are built to function without connectivity for extended periods (e.g., a week). They gracefully degrade and sync when back online, a crucial feature for industries like utilities construction working in areas without roads or cell signals.

A cost-effective AI architecture involves using a small, local model on the user's device to pre-process requests. This local AI can condense large inputs into an efficient, smaller prompt before sending it to the expensive, powerful cloud model, optimizing resource usage.