The more I look at @vangrid_io, the more I realize its true innovation lies in how it connects “data needs” with “people willing to collect them.” Imagine a simple scenario: A company wants to train a robot to navigate malls, warehouses, or neighborhoods in a specific city. What it may lack isn’t another 100,000 photos online. It might just need ground-level spatial data from a specific location, at a specific time, under specific environmental conditions. At this point, simply encouraging people to collect data freely creates a problem: People naturally go where it’s convenient. Popular commercial districts get photographed. City centers get covered. Tourist attractions get documented. In the end, data becomes increasingly concentrated in places that are already easy to access. Vangrid’s approach is Commissioned Capture. Requesters can specify exact locations and offer a bounty, with funds held in escrow. Contributors see the request and go collect the data themselves. After submission, if the requester approves, the reward is released. The most interesting part of this mechanism is how it transforms: “Where people are” into “Where data is genuinely missing” It effectively treats coverage as a dynamic variable that the network can actively adjust. If an area has little data, increasing the collection bounty incentivizes contributors to make the trip. So what Vangrid is building is a concrete, self-sustaining cycle: Someone needs data → posts a request → offers a bounty → someone collects it → data is delivered → reward is released This is an entirely different economic structure from simply encouraging people to “take more photos.” I believe this also highlights a frequently overlooked issue with spatial data networks: Having lots of data doesn’t mean the data is evenly distributed. The most valuable data often exists precisely in places people are too lazy to travel to. And the bounty solves exactly this question: “Why should I go there specifically?”