One thing about Physical AI that often gets overlooked is where the training data comes from. A model can process millions of digital records, but understanding a real street, building or object requires information about the physical environment itself. This is where @vangrid_io takes a different route. Rather than treating spatial data collection as a job reserved for specialized teams, Vangrid lets contributors use smartphones to capture real-world locations and turn that footage into 3D reconstructions. The interesting part is how the pieces connect. ➜ A real location has data value ➜ Contributors use their phones to capture it ➜ The footage is reconstructed into spatial representations ➜ Buyers can review the result before accepting it ➜ Blockchain attestations provide verifiable records of network activity There’s also a privacy consideration. Vangrid has described on-device blurring of faces and license plates, an important step when collecting footage from public environments. Its reported network activity has surpassed 1.25M captures, 450K users, 4,700 attestations and $600K in settled USDC. Still, network size alone doesn’t guarantee useful training data. Accuracy, coverage, privacy and actual demand will matter just as much. What makes this model worth following is the possibility of creating a shared source of spatial data instead of making every AI developer collect everything from scratch. The digital world has plenty of information. Now the challenge is helping AI understand the physical one. That’s the problem @vangrid_io is working to solve.