The shift to AI makes multi-sensor arrays (including LiDAR) more valuable. Unlike older rules-based systems where data fusion was complex, AI models benefit directly from more diverse input data. This improves the training of the core driving model, making a multi-sensor approach with increasingly cheap LiDAR more beneficial.

After proving its robo-taxis are 90% safer than human drivers, Waymo is now making them more "confidently assertive" to better navigate real-world traffic. This counter-intuitive shift from passive safety to calculated aggression is a necessary step to improve efficiency and reduce delays, highlighting the trade-offs required for autonomous vehicle integration.

By integrating Starlink satellite connectivity directly into its cars, Tesla can solve for internet outages that cripple competitors. This creates a powerful moat, ensuring its fleet remains operational and potentially creating a new licensable mesh network for other vehicles.

Waymo vehicles froze during a San Francisco power outage because traffic lights went dark, causing gridlock. This highlights the vulnerability of current AV systems to real-world infrastructure failures and the critical need for protocols to handle such "edge cases."

The latest Full Self-Driving version likely eliminates traditional `if-then` coding for a pure neural network. This leap in performance comes at the cost of human auditability, as no one can truly understand *how* the AI makes its life-or-death decisions, marking a profound shift in software.

Rivian's CEO explains that early autonomous systems, which were based on rigid rules-based "planners," have been superseded by end-to-end AI.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.

By eschewing expensive LiDAR, Tesla lowers production costs, enabling massive fleet deployment. This scale generates exponentially more real-world driving data than competitors like Waymo, creating a data advantage that will likely lead to market dominance in autonomous intelligence.

The AI's ability to handle novel situations isn't just an emergent property of scale. Waive actively trains "world models," which are internal generative simulators. This enables the AI to reason about what might happen next, leading to sophisticated behaviors like nudging into intersections or slowing in fog.

Autonomous systems can perceive and react to dangers beyond human capability. The example of a Cybertruck autonomously accelerating to lessen the impact of a potential high-speed rear-end collision—a car the human driver didn't even see—showcases a level of predictive safety that humans cannot replicate, moving beyond simple accident avoidance.