Key Considerations for Evaluating Tesla FSD vs. Waymo

1. Metrics Aren’t Apples-to-Apples:
     • Tesla FSD tracks interventions by real drivers, where even slight mistakes lead to disengagements. Waymo operates without a human driver, meaning it can make many non-optimal actions (that a driver would intervene in) without those being flagged.

2. Geofencing and Optimization:
    • Waymo operates in heavily geofenced, optimized areas tailored to its technology. Tesla FSD is designed to function anywhere with minimal optimization—a broader and more flexible application.

3. Sensor Suite Costs:
    • Waymo relies on an extensive array of expensive sensors (LiDAR, multiple cameras, etc.), which increase costs significantly. Tesla uses a camera-based system (vision-only), which is more scalable and cost-effective.

4. Remote Supervision:
    • Some autonomous systems rely on remote operators to assist or override decisions when needed, raising questions about scalability and true autonomy.

5. Expansion Possibilities:
    • Waymo’s reliance on pre-mapped and controlled environments makes scaling to new areas challenging. Tesla’s ability to adapt to nearly any driving surface provides a significant advantage for global deployment.

6. Efficacy vs. Cost:
    • Tesla is focusing on making FSD affordable and mass-market ready. Waymo’s approach, while effective in controlled environments, risks being economically unviable for broader adoption.

@Tesla FSD is playing a harder, longer game, emphasizing real-world adaptability and cost efficiency. — Comparisons to geofenced, sensor-heavy competitors distort the real picture. True autonomy isn’t about “ideal world” data but mastering “real world” complexities—universality, scalability, and adaptability. WAYMO is likely stuck in a local maximum, and achieving true autonomy may require a paradigm shift—similar to Tesla’s bold move to go “vision-only.”