One thing that often gets overlooked in the development of Physical AI is where the data actually comes from. AI models can become more capable, but understanding the physical world requires more than digital information. It needs real-world observations, diverse environments, and reliable data that reflects how things actually work outside a computer. That’s why I find @vangrid_io interesting. The idea of turning everyday devices into part of a decentralized perception network opens up a different approach to collecting real-world data. Instead of relying entirely on a few centralized sources, a distributed network could help capture more diverse information from the environments around us. What matters here is not just collecting more data, but making that data useful, verifiable, and valuable for the systems that depend on it. Personally, I think this is an important part of the Physical AI narrative. Robots and autonomous systems need a better understanding of the real world before they can operate reliably at scale. Building that foundation requires more than powerful models. It also requires infrastructure that connects data collection, verification, contributors, and real-world applications. If decentralized networks can align incentives around producing useful data, they could become an important part of how Physical AI develops over time. The real opportunity is turning everyday observations into infrastructure that helps intelligent systems understand the world better.