DGrid also has an underestimated point: it transforms "AI usage behavior" into a complete economic system.

In this system, each role is clearly defined. Users are demand-side participants, nodes are computing power providers, model developers can participate in supply, and $DGAI acts as an intermediary connecting all actions.
Users pay tokens to access inference services, nodes earn rewards based on their contributions, and they need to stake to ensure behavior reliability. This design creates a self-sustaining incentive loop.
The impact on the industry is that it turns AI from a tool into a sustainable economic network. Value is not captured by the platform but distributed among participants.
From an experience perspective, this structure is fairer. The costs you pay correspond to real computational resources. The capabilities you provide are directly priced rather than being compressed by the platform.
More importantly, this model provides infrastructure for AI Agents and automation applications. When calls, settlements, and verification are all on-chain, machine collaboration truly becomes scalable.
If it continues to evolve, projects like @dgrid_ai could become the foundational layer for AI on the blockchain, rather than just a single application. That’s where its long-term value lies.
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