Finding
AI in physical systems improves as fast as its data loop: the operators with the largest fleets and training clusters pull ahead
AI now drives vehicles, moves warehouse inventory, forecasts solar output and dispatches batteries. What separates leaders is the loop: fleets collect real-world data, training clusters turn it into better models, and over-the-air updates send the models back. Tesla trains on more than 230,000 H100-equivalent GPUs at Giga Texas; Waymo runs more than 500,000 driverless rides a week; Amazon's warehouse robots are coordinated by AI routing.
The domains differ in what AI does. In vehicles and robots it perceives and acts in real time on edge hardware; in energy it forecasts and schedules; in factories it inspects, predicts failures and plans. The common constraints are physical: inference chips in every machine, training compute in data centers, and the power to run both.
Domains
AI by domain
| Domain | What AI does | Page |
|---|---|---|
| Vehicles and fleets | Driving, energy management, predictive maintenance, dispatch | AI in Vehicles and Fleets |
| Robotics | Perception, manipulation, learned policies | AI in Robotics |
| Energy and infrastructure | Forecasting, battery dispatch, grid operations | AI in Energy |
| Industry | Inspection, predictive maintenance, scheduling | Industrial AI |
Sources
Sources and scope
- Scope
- AI in Electrification
- Sources
- Tesla Q1 2026 deck; Amazon; Waymo; Komatsu (April 2026)
- Updated
- October 2026