# Decentralized Physical AI ## Physical AI Physical AI refers to artificial intelligence systems embodied in autonomous machines—such as robots or self-driving vehicles—that can perceive, understand, and perform complex actions in the physical world. Unlike traditional AI models that operate within digital confines, physical AI integrates spatial awareness and an understanding of real-world physics to interact seamlessly with its environment. ## The Role of DAOs in Physical AI A decentralized autonomous organization acts as the governance layer that aligns incentives. XMAQUINA is an example of such a DAO, designed to fund and guide projects in robotics and Physical AI. By using smart contracts and transparent protocols, DAO members can decide how resources are allocated to promising ideas and research, all without relying on a handful of decision-makers. This structure keeps the process open, democratically managed, and easily auditable. Bringing together the DAO model with Physical AI creates a community-driven ecosystem where humanoid robots and other AI-enabled machines can be built, tested, and improved collaboratively. This goes hand in hand with open-source and open data-sharing. The emphasis is on shared ownership and collective progress, where each participant has a say in which direction the technology moves. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://xmaquina.gitbook.io/xmaquina/glossary/decentralized-physical-ai.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.