From another perspective, @dgrid_ai is addressing a more fundamental issue: how AI resources are priced in the market.

In the current environment, model prices are usually set by the platform, and users can only passively accept them. But in DGrid, inference costs are dynamically calculated and adjusted based on factors like computational complexity and latency, effectively building a "real-time pricing system."
This has a key impact on the industry. AI is no longer a fixed-price service but begins to resemble a compute market that automatically adjusts according to supply and demand.
From the user experience standpoint, this mechanism is more flexible. You can choose between cost and performance, rather than being locked into a single solution. For developers, this flexibility can significantly reduce integration costs.
At the same time, nodes will also compete. Those with lower latency and more stable performance will be able to handle more tasks and earn more rewards. This competitive mechanism will drive continuous improvements in network efficiency.
If this model proves successful, the future AI pricing system may no longer be controlled by a few platforms but instead be determined by market dynamics. This aligns more closely with Web3 principles.
@Galxe @GalxeQuest @easydotfunX @wallchain #Ad #Affiliate