If the results of AI cannot be verified, then it is essentially a black box service.

Many people are now discussing AI infrastructure, but they assume a premise: that the results are trustworthy. The reality is that users cannot verify whether the reasoning has been tampered with or confirm the execution path.
@dgrid_ai's solution is to introduce a verification layer through Proof of Quality, allowing nodes to mutually verify reasoning results. If an error occurs, staked assets will be penalized. This design directly ties the cost of errors to the economic model.
The biggest difference from traditional SaaS is that trust no longer comes from the brand but from the game-theoretic structure.
From a developer's perspective, this network is more like an AI RPC layer, where calling models doesn't require binding to a specific platform but is routed through the network to the optimal node for execution.
Of course, whether this mechanism can operate stably at large scale still needs time to verify.
But at least it addresses a real issue: can AI become a trustworthy computation rather than a black box output.
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