Common Is Not Shared: The Coordination Problem Hiding in Physical AI

Common Is Not Shared

^{Field Notes from the Last Mile - No. 5}

Everyone talks about the connectivity problem in Physical AI.

Get the machines online. Give them enough bandwidth. Keep latency low. Make the connection reliable.

All of that is necessary. We see the difficulty of doing it every day - in factories, construction sites, mines, yards, and other industrial environments.

But as Physical AI scales, I think an even harder problem emerges. The scarce resource may not be bandwidth. It may be coordination.

Consider what happens as an industrial site fills with autonomous systems from many different vendors: robots moving material, autonomous vehicles crossing the yard, cameras monitoring operations, drones inspecting equipment, and people working alongside all of them.

Each machine may be extremely sophisticated. Each may understand its own mission, its own location, its own environment.

But who has right-of-way when two independent systems need the same physical space? Who gets the next charging slot? Who can enter an exclusion zone? What happens when a robot from one vendor meets an autonomous vehicle from another?

These aren’t problems any one machine can solve on its own. Each system sees the world through the boundaries of its own software and sensors. Yet the physical resources they consume belong to the site as a whole.

That leads to a distinction I think will matter more and more as Physical AI moves from individual machines to entire industrial environments.

Common is not shared.

Software that twenty robot manufacturers each use independently is common infrastructure. A factory floor being navigated at the same moment by twenty machines is shared infrastructure.

Simulation environments can be common. Training tools can be common. Foundation models can be common.

But spectrum is shared. Charging capacity is shared. Physical space is shared. Right-of-way is shared.

These are fundamentally different kinds of infrastructure, with fundamentally different operating problems. Common infrastructure can be replicated - every vendor can run its own copy. Shared infrastructure cannot. It has to reconcile the demands of many independent actors, often in real time.

This is why I think the Physical AI infrastructure discussion eventually has to move beyond connectivity alone. Connectivity remains essential. So do positioning, timing, edge compute, identity, security, and operational visibility. But increasingly, their purpose isn’t only to connect machines. It’s to let machines coordinate.

We spent much of the first generation of Physical AI making individual machines smarter. That work is advancing fast. The next challenge may be what happens when all of those intelligent machines arrive at the same place.

The machines are getting smarter fast. The space between them isn’t - yet.

Who settles right-of-way on your site when the machines come from different vendors?

^{#PhysicalAI #IndustrialAI #Robotics #EdgeAI #RamenInc}

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