Physical AI needs more than maps. It needs fresh, verifiable data from the places maps often miss. Think warehouse interiors, stairwells, loading bays and crowded market alleys. These environments change constantly, yet collecting detailed 3D data traditionally requires expensive equipment and specialized teams. @vangrid_io is taking a different approach: turn everyday smartphones into a distributed spatial data network. Here’s how the model works: → A requester funds a capture bounty with USDC on Base or Arc. → A local contributor records the location using a phone. → Faces and license plates are anonymized on-device before footage leaves the phone. → Each capture receives a cryptographic fingerprint, with records grouped into Merkle trees and attested on Base through EAS. → Once the capture is accepted, the requester receives the reconstructed 3D model and the contributor earns their reward. The important distinction is that this isn’t just about collecting more footage. It’s about making spatial data traceable, independently verifiable and economically sustainable. The explorer provides a way to inspect capture attestations and USDC settlements, while the bounty model connects real-world data demand with the people who can supply it. For robotics, autonomous systems and AI world models, better ground-level data could make a meaningful difference. I’m watching how @vangrid_io develops this infrastructure. The next generation of Physical AI will need more than intelligence. It will need a reliable view of the physical world. Explore the network, docs and explorer to see how it works.