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.

Incumbent automakers evolved with 100+ separate computer modules, creating a complex system. Newcomers like Rivian and Tesla start with a centralized, "zonal" architecture. This clean-sheet design dramatically simplifies over-the-air updates, reduces costs, and enables more advanced, integrated AI features.

Apple crushed competitors by creating its M-series chips, which delivered superior performance through tight integration with its software. Tesla is following this playbook by designing its own AI chips, enabling a cohesive and hyper-efficient system for its cars and robots.

RJ Scaringe argues that successful, neural net-based autonomy requires a rare combination of ingredients: full control of the perception stack, a large vehicle fleet for data collection, massive capital, and GPU access. He believes only a handful of companies, including Rivian, Tesla, and Waymo, possess all the necessary components to compete.

Musk states that designing the custom AI5 and AI6 chips is his 'biggest time allocation.' This focus on silicon, promising a 40x performance increase, reveals that Tesla's core strategy relies on vertically integrated hardware to solve autonomy and robotics, not just software.

For a hyperscaler, the main benefit of designing a custom AI chip isn't necessarily superior performance, but gaining control. It allows them to escape the supply allocations dictated by NVIDIA and chart their own course, even if their chip is slightly less performant or more expensive to deploy.

Rivian's CEO explains that early autonomous systems, which were based on rigid rules-based "planners," have been superseded by end-to-end AI.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

Tesla's decision to stop developing its Dojo training supercomputer is not a failure. It's a strategic shift to focus on designing hyper-efficient inference chips for its vehicles and robots. This vertical integration at the edge, where real-world decisions are made, is seen as more critical than competing with NVIDIA on training hardware.

Initially criticized for forgoing expensive LIDAR, Tesla's vision-based self-driving system compelled it to solve the harder, more scalable problem of AI-based reasoning. This long-term bet on foundation models for driving is now converging with the direction competitors are also taking.