The industry impact brought by the launch of DGrid AI is also reflected in its reshaping of the trustworthy verification mechanism for AI inference.

In the existing AI inference system, users often rely on the reputation of service providers and the black-box operation mechanisms behind the scenes to verify the authenticity of results. This approach is particularly fragile in high-risk, compliance-intensive application scenarios.

@dgrid_ai introduces a Proof of Quality (PoQ) mechanism that places the quality verification of inference tasks within a decentralized network.

Under this mechanism, inference results are not determined by a single node but are instead randomly sampled and checked by dedicated verification nodes. Rewards or penalties are then issued to nodes based on the comparison of the results with pre-set standards on the blockchain.

This verification system, designed based on cryptography and game theory, not only enhances the credibility of inference results but also turns quality assurance into an auditable on-chain activity, providing a traceable trust foundation for AI inference services.

This trustworthy verification mechanism has profound long-term industry implications because it directly addresses the increasing demand for the reliability and security of inference outputs.

Especially in scenarios such as financial risk control, medical diagnosis, and legal consulting, where decision accuracy is strictly required, the verifiability of inference quality is a prerequisite for widespread industry adoption.

Through this on-chain verification mechanism, $DGAI enables developers and end-users to no longer rely solely on the reputation guarantees of centralized providers but to independently verify the validity of inference results through transparent algorithm rules and verification processes.

This trustworthy mechanism not only improves service quality but also promotes the industry’s focus on interpretability and auditability, driving AI inference services toward higher-standard industrial-grade solutions.

@Galxe @GalxeQuest @easydotfunX