The Ultimate Stress Test: What San Francisco’s Blackout Taught Us About Autonomy
The recent mass power outage in San Francisco wasn’t just a municipal headache—it was a definitive moment for the future of autonomous driving.
While the city went dark, we saw two very different "futures" play out:
• The Geo-fenced Approach: Dozens of Waymos reportedly stalled at intersections, "bricking" when traffic lights went dark and remote assistance became overloaded. When the infrastructure failed, the system’s reliance on predictable environments became a bottleneck.
• The First Principles Approach: Meanwhile, Tesla’s FSD (Supervised) continued to navigate the chaos. Why? Because it doesn't rely on a "pre-solved" map of the world. It uses vision and neural nets to interpret the world in real-time—just like a human does.
Why "First Principles" is Winning
Elon Musk’s insistence on Vision-Only was mocked for years as "impossible." Critics said you need LiDAR. They said you need HD Maps.
But from a First Principles perspective:
1. The World is built for Vision: Our entire road system (signs, lights, lane markings) is designed for visual processing.
2. Generalization > Specialization: A car that can only drive where there is a 5G signal and a perfect map isn't truly autonomous; it’s a high-tech train on invisible tracks.
Seeing FSD handle unmapped, blacked-out intersections while others waited for a "server" to tell them what to do proves that solving the hard problem of general vision is the only path to true scalability.
It’s not always the easiest path, but it’s the right one.
