“The ChatGPT moment for general robotics is just around the corner.”

Jensen Huang, Nvidia’s founder, returns to that line often. The evidence continues to accumulate.

One idea followed us across these digital leaves all week…. 

The digital age began with hardware (analog), drifted into software, and is now swinging back toward the physical world.

Atoms yielded to bits. Bits are now returning the favor.

Put differently: The world is one big pendulum… we’re just along for the swing.

The AI era followed the same arc. It opened in code—lightweight, abstract, frictionless. As Jensen Huang keeps reminding us, it’s now crossing into matter.

Robots, autonomous vehicles, drones, and embodied agents are leaving simulations and entering factories, streets, and supply chains.

Algorithms are moving on from recommending disturbingly specific things you mentioned to your spouse five minutes ago to actually doing work.

They lift. They drive. They monitor. They decide.

That shift changes the question.

AI stops being something you download and starts being something you own—or don’t. And ownership determines who captures the upside, who absorbs the risk, and who writes the rules.

The intuitive assumption: control concentrates. A few players own the machines. The rest of the world rents the future.

That assumption is rational because it’s the way things have worked for as long as we can remember.

And yet, the internet already bent that logic in ways our instincts still haven’t caught up to.

Right now, a small corner of crypto is pointing the way forward—not because anyone (like me) is rooting for it, but because physical AI creates a coordination problem that demands a new kind of solution.

It’s called Decentralized Physical AI (DePAI).

DePAI coordinates robots, sensors, vehicles, and physical data through networks instead of single owners.

Intelligence emerges from shared data, incentives, and verification, allowing machines to work together across many operators without a central authority.

This isn’t theoretical. You’ve already used its granddaddy.

GPS.

GPS Was the Dress Rehearsal

For most of the 20th century, navigation followed the industrial playbook: central control.

Traffic engineers planned routes. Dispatchers coordinated fleets. When congestion hit, the solution was always the same—bigger control rooms, more rules, better planners.

Navigation felt like a problem that required a central brain.

Then came GPS.

At first, it was just a better map. A military timing system. In 1983, Reagan opened it to civilian use, and planners panicked. They warned of chaos. They worried coordination would collapse without centralized control.

Those fears made intuitive sense.

But they were wrong.

Once routing intelligence lived in every vehicle, decisions happened locally and continuously.

Traffic rerouted faster. Bottlenecks dissolved. Fleets became insanely more efficient. Ride-sharing, real-time logistics, and modern delivery networks wouldn’t exist without this shift.

Studies estimate GPS has generated nearly $2 trillion in value in the U.S. alone. Fuel use and travel time fell by 10–30%. Unimaginable efficiency gains have compounded across shipping, aviation, agriculture, emergency services, and daily life.

All without central control.

The planners said it wouldn’t work. Performance settled the argument.

The “ChatGPS” Moment For Robots

GPS changed the payoff structure. Once intelligence moved to the edge, re-centralizing control stopped improving outcomes. It made them worse.

Local decision-making with shared context beat centralized planning with delayed information along every dimension.

Physical AI creates the same scaffold.

Robots, vehicles, drones, and sensors operate locally in environments no single entity fully controls.

Lock intelligence and data inside one or two firms (or command centers), and everyone else pays a tax—slower adaptation, higher costs, brittle failures. The system underperforms.

But once shared, neutral infrastructure exists—shared data, shared verification, shared spatial context—the dominant strategy flips.

Contributing to the network beats walling it off. Opting out becomes costly. Centralization stops maximizing payoff.

That’s what DePAI enables.

Right now, DePAI teams are quietly building the real layers that matter:

• Data from the physical world

• Verification that it’s real

• Incentives to participate

• Shared spatial conditions

• Execution through machines

In the near term, one layer matters most: data.

Physical AI runs on real-world signals—movement, location, timing, environment.

Simulation helps. Digital twins help. Nvidia’s Omniverse and Cosmos are powerful tools.

But synthetic worlds only solve learning.

They don’t solve coordination. They can’t model live incentives, ownership boundaries, trust between independent operators, or adversarial reality.

They help machines learn how to act, not how to work together.

That’s the DePAI gap.

Capital still matters. These systems are expensive. But DePAI introduces something new: capital funds the system without fully commanding it. Ownership, coordination, and control become separate layers of the physical AI stack.

That separation is the opportunity.

And it’s why crypto matters—especially as the pendulum swings back to atoms.