If you break down AI development, data, models, and computing power are the three core pillars, and @dgrid_ai is more like pioneering a liquidity market for the computing power layer. In the past, DeFi solved capital liquidity, while dgrid attempts to solve the liquidity of computational resources.

In its design, computing power providers can earn returns through staking and network participation, while demand-side users pay usage fees on demand. This two-sided market structure allows for more flexible resource allocation. Compared to traditional cloud services' fixed pricing, this model is closer to real-time market dynamics.
At the same time, it ensures traceability of computational processes and credibility of results through on-chain recording and verification mechanisms. This is particularly important for AI inference, because the correctness of results directly impacts application effectiveness.
From a deeper perspective, this model is essentially financializing AI infrastructure. Computing power is no longer just a technical resource, but an asset that can participate in revenue distribution.
If this direction proves viable, the cost structure of future AI applications may change significantly. Developers can acquire more flexible resources at lower costs, and the innovation speed of the entire industry will be further unleashed.
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