Dolgov describes a flywheel where real-world deployment generates data → grounds simulator → simulator generates hard cases for critic → critic scores and improves agent → smarter agent dep…

Autonomous vehicles have moved from demo to product at scale — Waymo's 220M+ miles, 17x safety advantage, and 4M weekly miles demonstrate the technology is commercially viable and entering…

Deploying AI in the physical world faces four structural gaps vs digital AI: irreversible cost of errors (human lives), millisecond latency constraints, no internet-scale pre-labeled data,…

Dolgov argues that achieving full autonomy requires climbing an exponential ladder of reliability 'nines' where each additional nine takes 10x more effort, and that every AI breakthrough ma…

Dolgov argues that camera-only sensing hits a performance ceiling far below superhuman levels required for full autonomy, and that fusing cameras, lidar, and radar provides complementary ph…

Dolgov argues that closed-loop simulation (where agent acts, sees world response, acts again) is absolutely vital for safety-critical physical AI, and that building a high-fidelity generati…

In safety-critical physical AI, the defensible advantage lies in evidence-grade evaluation frameworks and closed-loop simulation flywheels (agent, simulator, critic) that compound real-worl…

Dolgov presents Waymo's latest safety data: 17x better than human drivers on serious-injury crashes across 220M+ fully autonomous miles, preventing a serious injury every 8 days — arguing t…

Dolgov applies Sutton's 'bitter lesson' to argue that structure which fights scale loses, but structure that channels scale (like physics, rules of road, object behaviors) wins — Waymo's 's…