A map can tell you where things are. Spatial data can help explain how those things relate to each other. That distinction becomes important when AI starts operating in the physical world @vangrid_io For a human, navigating a warehouse or a busy facility often feels intuitive. We naturally interpret distances, obstacles, pathways, and the relationship between objects. For an autonomous system, that understanding has to come from data, perception, and reasoning. And the environment is rarely as simple as a clean digital map suggests. This is why I find the idea of a dedicated spatial data layer for Physical AI compelling. Vangrid's approach focuses on human-collected ground truth, with multi-view ingestion, cryptographic provenance, and spatial data access infrastructure. The interesting part is how these pieces could contribute to a more useful representation of real environments. A collection of coordinates alone doesn't necessarily describe the context a system needs. Geometry, perspective, capture conditions, and changes over time can all influence how spatial information should be interpreted. There is no universal dataset that will fit every autonomous application either. Different systems will need different levels of detail and different forms of spatial context. That makes the underlying data infrastructure an important part of the stack, not just a supporting feature. Physical AI is often discussed through the lens of smarter models and better hardware. But understanding the environment those models interact with is another problem entirely. That's the layer I'm interested in watching @vangrid_io build.