Building Safe Physical AI Systems for Defense, Autonomous Vehicles, and Robotics

A panel of leaders from Shield AI, autonomous vehicle development, and industrial robotics examines safety practices in physical AI, covering defense platforms, self-driving vehicles, and humanoid robots. The discussion compares frontier LLM lab practices with high-stakes physical deployments and explores verification, regulation, culture, and scaling. The speakers also address audience questions on risk management for non-deterministic systems.

■ Physical AI safety landscape
- Contrast with LLM lab incidents; defense, robotics, and autonomous vehicle context

■ Defense deployments and Shield AI
- Infrastructure and tools; air, maritime, and space domains; compliance versus urgent needs

■ Autonomous vehicle safety and scaling
- Verification, simulation, trucking pressures; robotaxi partnerships and announced scale

■ Robotics, humanoids, and industrial safety
- Regulations, deterministic controls, wheeled versus bipedal platforms, risk mitigation

■ Culture, prototypes, and Q&A
- Safety culture, prototype-to-scale transition, constraints, non-deterministic risk frameworks

The session is aimed at founders, engineers, safety and compliance professionals, and investors working on physical AI or high-stakes autonomy. It provides a high-level overview of how safety, verification, culture, and scaling are discussed across defense, autonomous driving, and robotics. It does not include product specifications, pricing, or step-by-step implementation guidance.