The next bottleneck for Physical AI may not be model intelligence. It may be the quality of the real world data those models can learn from. That is why @vangrid_io stands out to me. Robots need to understand actual environments, from warehouse layouts and station entrances to sidewalks and changing street conditions. Internet images can provide useful information, but they cannot fully represent every location or capture every change as it happens. Vangrid approaches this problem by turning smartphones into a distributed spatial data capture network. Instead of depending entirely on dedicated mapping hardware, the network can collect requested captures of specific places and turn them into 3D reconstructions with onchain provenance. I find the on demand approach particularly interesting. A robotics developer may need fresh spatial information about one particular environment rather than another enormous dataset covering places that are not relevant to the task. There is still an important challenge beyond collecting more data. The usefulness of this infrastructure will depend on capture quality, geographic coverage, consistency, and whether developers actually need the resulting information. For me, Physical AI is not just a competition to build smarter models or better robots. It also requires infrastructure that connects digital intelligence with reliable information about the physical world. That is the part of @vangrid_io I will be watching as the Physical AI ecosystem develops. The opportunity is to make real world spatial data easier to request, collect, and verify when intelligent machines need it.