A 3D model can look convincing and still leave important questions unanswered. Where did the data come from? When was the environment captured? Has the original observation been altered? And what evidence supports the reconstruction? These questions matter when spatial data is used beyond visualization. For Physical AI, a robot may rely on its representation of a real environment to navigate, inspect a site, or interact with objects. A detailed model is useful, but detail alone does not establish provenance or data integrity. This is the distinction I find important in the approach behind @vangrid_io. The goal is not simply to turn smartphone footage into 3D scenes. It is to build a path from physical world observation to spatial data with evidence that downstream systems can inspect. Capture provides the observation. Reconstruction gives it spatial structure. Cryptographic fingerprints and Merkle based attestations can help preserve evidence of data integrity and provenance. Independent observations can add another layer of corroboration when assessing whether a representation is consistent with the real environment. These mechanisms solve different problems. A cryptographic proof does not automatically mean a scene is accurate, and multiple captures do not guarantee that every detail is correct. What they can provide is a more transparent basis for evaluating the data instead of asking users to trust an output blindly. That distinction could become increasingly important as AI systems move from generating answers to taking actions in the physical world. The quality of spatial intelligence will depend not only on how precisely an environment is reconstructed, but also on how clearly the underlying evidence can be traced and evaluated. For me, that is the more meaningful challenge in spatial infrastructure: making real world data not just usable, but verifiable.