Part 10/15:
This shift enables Tesla to generate enormous datasets of virtual scenarios, vastly expanding the system's ability to handle rare and edge-case events that real-world data alone cannot cover.
Practical Constraints and Engineering Realities
Despite the promise, Tesla’s progress is bounded by practical engineering factors:
Hardware limitations: Model size, inference speed, and chip capabilities limit how large and complex models can get in production vehicles.
Compute resources: Training enormous networks with reinforcement learning requires astronomical computational power—Tesla’s data centers are built specifically to handle this.
Development cycle: Each iteration involves balancing feature completeness, safety, and time-to-market.