Physical AI is moving out of controlled environments and into the real world.
Robots don’t care about your coverage map. Neither do autonomous vehicles, drones, or machines moving between networks. So why are we still designing their connectivity around one?
Robots are navigating warehouses, streets, farms, hospitals, and industrial sites. Autonomous systems are making decisions at the edge. Drones are moving beyond fixed infrastructure. Connected machines are expected to keep operating as they move between locations, networks, and sometimes entirely different types of coverage. And that creates a connectivity problem that traditional IoT architectures weren’t built to solve.
“It worked on our Wi-Fi” isn’t a deployment strategy
Getting an AI-enabled device connected in a lab is one thing; keeping thousands of devices connected after they leave the lab is another. Physical AI systems don’t get to choose where they operate. They move through public and private networks. They encounter coverage gaps. They cross borders. They move between cellular and Wi-Fi. Increasingly, they may need satellite connectivity when terrestrial networks aren’t available.
The connectivity architecture has to account for that reality before the device ships. Because once a connectivity decision is embedded into hardware and deployed in the field, changing it isn’t exactly a quick configuration update. That’s where the old IoT model starts to show its limits.
Static SIMs. Single-network dependencies. Carrier contracts. Manual provisioning. They may work when devices stay in one place. They become considerably harder to manage when your fleet is actually a collection of autonomous machines moving through the world.
Physical AI needs connectivity that can adapt
The next generation of connected machines needs something different: a connectivity layer that can change as the device changes.
That means separating the device’s identity from any single carrier or network. With technologies such as SGP.32 and remote SIM provisioning, devices can maintain a consistent identity while connectivity can be managed and changed remotely. Instead of designing a device around one carrier and hoping the coverage holds up, the connectivity architecture can evolve with the deployment. And that’s important for Physical AI.
A robot shouldn’t become a different device because it crossed a network boundary. An autonomous vehicle shouldn’t need a hardware change because its route changed. A machine shouldn’t become unreachable because the network it was designed around isn’t available in a particular location. The identity stays. The connectivity adapts.
And cellular coverage isn’t the edge of the world
Even a multi-network cellular strategy has limits. There will always be places where terrestrial coverage doesn’t reach: remote infrastructure, offshore environments, rural areas, and machines operating far beyond traditional network footprints. That’s where satellite and NTN connectivity come into play.
The opportunity isn’t simply to bolt satellite connectivity onto an existing device. It’s to extend the same connectivity architecture beyond terrestrial networks, giving Physical AI systems another path to stay connected when the traditional network isn’t there.
That changes the way we should think about coverage.
The question isn’t: “Does this device have coverage?”
But rather: “What happens when it doesn’t?”
It’s important to make that distinction when connectivity is no longer supporting a stationary sensor, but rather an autonomous machine making decisions in the physical world.
Build for the network you need tomorrow
Physical AI deployments are going to get more distributed, more autonomous, and more mobile. The connectivity architecture underneath them needs to keep up. That means thinking beyond a single carrier, a single network, or a single coverage map from the beginning. It means building around:
- One device identity that isn’t tied to a single carrier
- Remote provisioning so connectivity can change without touching the hardware
- Multi-network connectivity across public and private infrastructure
- Satellite/NTN options when terrestrial networks aren’t enough
- A programmable connectivity layer that can adapt as the deployment evolves
Because the hardest connectivity problems in Physical AI won’t happen in the lab. They’ll happen after the device ships.
- When the robot crosses a border.
- When the vehicle leaves cellular coverage.
- When the machine moves from private 5G to public cellular.
- When the network you planned for isn’t the network that’s actually available.
That’s why Physical AI doesn’t stop at the coverage map.
See what connectivity for Physical AI looks like in the real-world
On October 8, 2026, Monogoto Co-Founder & CTO Maor Efrati joins Fierce Sensors for a live discussion on what it takes to build connectivity architectures that can actually scale with Physical AI.
The conversation will cover SGP.32 and remote SIM provisioning, multi-network architectures, satellite/NTN connectivity, and why connectivity decisions made today can determine how flexible your Physical AI deployment is tomorrow. Attendees will gain practical frameworks for assessing connectivity needs, actionable checklists for implementing multi-network strategies, and guidance on integrating remote SIM provisioning into existing workflows. You’ll leave with clear steps to future-proof your deployment and the tools to evaluate and adapt your connectivity architecture as your use case evolves.
October 8, 2026 | 11:00 AM ET | 60 minutes
