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.

Musk envisions a future where a fleet of 100 million Teslas, each with a kilowatt of inference compute, built-in power, cooling, and Wi-Fi, could be networked together. This would create a massive, distributed compute resource for AI tasks.

Tech giants often initiate custom chip projects not with the primary goal of mass deployment, but to create negotiating power against incumbents like NVIDIA. The threat of a viable alternative is enough to secure better pricing and allocation, making the R&D cost a strategic investment.

Elon Musk's Optimus project is predicted to become history's most successful product, overshadowing Tesla's automotive achievements. This suggests investors should evaluate Tesla as a robotics and AI company, not just a car manufacturer, for long-term growth.

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.

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.

Elon Musk is personally overseeing the AI5 chip, a custom processor that deletes legacy GPU components. He sees this chip as the critical technological leap needed to power both the Optimus robot army and the autonomous Cybercab fleet, unifying their core AI stack.

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.

Musk's decisions—choosing cameras over LiDAR for Tesla and acquiring X (Twitter)—are part of a unified strategy to own the largest data sets of real-world patterns (driving and human behavior). This allows him to train and perfect AI, making his companies data juggernauts.