Why are we now discussing decentralized AI inference instead of traditional centralized services? The reason lies in the fact that traditional services often lack transparency, verifiability, and community participation mechanisms, whereas the solution proposed by DGrid AI is centered around these issues. @dgrid_ai has built a three-layer architecture, including a decentralized routing and verification network, a unified API, a free market, and DAO governance system. This structure addresses current pain points in Web3 AI such as fragmentation, non-auditable inference, and inefficient value flow. In the DGrid network, node operators serve as infrastructure providers; anyone can run a node and earn $DGAI rewards by processing AI inference tasks. The governance mechanism allows participants holding $DGAI to influence the future development of the protocol through on-chain voting, including model whitelists, fee structures, and upgrade proposals. This means DGrid is not just a technology stack but a truly community-governed AI inference network. Another meaningful attempt is their Genesis Membership Program, which attracted over 5,000 subscribers within 24 hours of launch. Subscribers can unlock high-usage model invocation rights, API access, and dual-token rewards, demonstrating early users' interest and expectations in participating in building the ecosystem. However, realizing this vision still faces practical challenges, such as how to ensure the long-term stability of all nodes, how to balance the influence of different stakeholders in governance, and how to continuously attract model contributors and developers to the ecosystem. These are key questions DGrid needs to address in its development process, but its envisioned decentralized AI public infrastructure undoubtedly opens new avenues for the integration of Web3 and AI. @Galxe @GalxeQuest @easydotfunX