Why physical AI needs a distributed nervous system

We’ve all seen robots that can run, jump, and dance. But motion alone doesn’t create value – it creates demos. Value comes from reliable, repeatable work in the real world.

Locomotion is no longer the bottleneck in robotics – dexterity is. Creating value means interacting with the physical world in real time, where systems must sense, perceive, and act under tight power and latency constraints. In these environments, performance isn’t measured by what a system can demonstrate, but by what it can reliably do – again and again, in unpredictable, high-stakes conditions.

What works in the cloud breaks down in the physical world.

Physical AI cannot only rely on a centralized brain. It must be designed as a distributed nervous system, with intelligence embedded across sensors, edge processors, and actuators – close to where data is generated, and decisions are executed. This shift fundamentally changes how we partition workloads, architect compute, and co-design hardware and software for deterministic, real-time operation.

This session will examine why the shift from a centralized brain to distributed intelligence is inevitable, how real-world constraints break today’s AI architectures, and why trust – built through system design and an open ecosystem – will determine which physical AI platforms scale.