AI
Tesla FSD vs Waymo
The two leading approaches to autonomous driving take opposite bets. Waymo uses lidar, radar and high-definition maps to run a polished driverless taxi service in limited areas; Tesla uses a camera-first, AI approach designed to scale cheaply to millions of existing cars.
| Metric | Tesla FSD | Waymo |
|---|---|---|
| Sensor approach | Cameras + neural nets (vision-only) | Lidar + radar + cameras + HD maps |
| Hardware cost per vehicle | ~$400 (sensors) | ~$10,000–$12,000+ |
| Paid driverless service | Robotaxi launched Austin 2025; scaling | Operating since 2020; 14M+ paid trips in 2025 |
| Geographic model | Aims to work anywhere, no pre-mapping | Geofenced, HD-mapped cities |
| Scaling thesis | Cheap hardware on millions of cars | High-cost vehicles, city by city |
Takeaway
Today Waymo runs the more mature, fully driverless service with millions of paid trips. Tesla's bet is that a cheap, vision-only system trained on a huge fleet can scale far faster and cheaper if it reaches reliability. Both can be true: Waymo leads now; Tesla's approach has the higher ceiling if it works.
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