The physical world is mapped with centimeter-level accuracy. The digital world (the signal quality, latency, congestion, and reliability that every connected system depends on) remains fragmented, stale, and largely invisible.
This isn’t a minor inconvenience. It’s a multi-billion dollar blind spot that destroys value across two critical domains.
For Telcos: $9B Spent on Bad Data
Mobile network operators spend over $9 billion annually on network monitoring and optimization. Yet their primary tool remains the drive test, a decades-old method where vans with specialized equipment drive predetermined routes to measure coverage.
The result is data that is:
- Sparse: tests only happen when someone actively runs them
- Stale: conditions change minute-to-minute, but datasets update quarterly
- Self-reported: operators measure coverage; users experience something different
- Unverified: few systems validate measurements cryptographically
Companies like Ookla and OpenSignal have improved on drive tests, but they still rely on active, user-initiated measurements. The fundamental problem remains: you only see the network where and when someone decides to look.
What’s needed is continuous, passive, always-on sensing that reflects how people actually experience connectivity, not artificial test conditions.
For Physical AI: Blind Machines Don’t Scale
The stakes escalate dramatically when machines enter the equation.
Autonomous vehicles, delivery drones, warehouse robots, and edge AI systems all require continuous connectivity to offload computation, coordinate movement, and maintain safety links. When connectivity drops, missions fail. The average annual cost of connectivity failures for autonomous systems runs between $5,000 and $19,000 per unit.
These systems can perceive physical obstacles like walls, curbs, and pedestrians. But they are completely blind to digital obstacles: dead zones, latency spikes, congested cells, handover failures. A drone that loses its data link mid-flight isn’t navigating anymore. It’s falling.
