Gm gVangrid VanGrid: Turning Real World Data Into an Economy for AI AI models can process information at incredible speed, but understanding the physical world requires something more: accurate data from real environments. Robotics and autonomous systems need to know what places actually look like, how spaces are structured, and how real environments change. That’s the gap @vangrid_io is exploring. How VanGrid connects AI to the physical world: «People capture spatial data using smartphones. AI agents and other requesters create funded bounties for specific locations. Contributors complete the tasks and submit their captures for review. Once accepted, contributors receive USDC payments.» The interesting part is the demand driven model. Instead of collecting random footage and hoping someone finds it useful, requesters can define exactly what data they need. Contributors then fulfil real requests and get rewarded for useful work. This creates a potential new role for everyday people in the AI economy: becoming suppliers of real world data. But the model will depend on more than participation. Accurate captures, reliable verification, consistent demand, and fair payments will determine whether it can scale. The bigger picture is worth paying attention to. AI agents can request information. Humans can gather it on the ground. VanGrid is working to connect those activities through a coordinated payment system. The next data economy could reward people not only for what they create online, but also for helping machines understand the world around us. That’s the physical AI opportunity I’m watching with VanGrid.