gn gm everyone LLMs have consumed a massive portion of the internet. But understanding text is very different from understanding the physical world. Robots need more than maps and dashcam footage. They need detailed, ground-level spatial data covering sidewalks, staircases, alleys, buildings, and the environments where autonomous systems actually operate. This is the problem @vangrid_io is working to solve. Vangrid is building a decentralized spatial data network that can turn real-world captures into structured 3D environments. The idea is to connect data contributors with AI developers and robotics teams that need accurate representations of physical spaces. What makes this interesting is the potential incentive layer: contributors can supply useful spatial data, while data buyers can fund collection based on their specific needs. Verification and provenance also matter because AI systems need more than a large dataset; they need data they can trust. As Physical AI advances, high-quality spatial data could become as important as training data was for LLMs. That's why @vangrid_io is worth watching. The opportunity isn't just collecting more data. It's building the infrastructure that helps machines understand the world beyond a screen. The big question is whether Vangrid can deliver the coverage, accuracy, and scale that real-world AI applications demand.