In decentralized systems, the real danger often lies not in code vulnerabilities, but in the "default choices" that are never questioned. This risk is amplified when artificial intelligence begins to participate in transaction execution, resource allocation, and even governance decisions. If an outcome merely "appears reasonable" but cannot be proven, reproduced, or audited, then so-called decentralized trust is simply outsourced to probability and models. This is why I believe the @inference_labs approach is crucial. @inference_labs focuses not on how intelligent the model is, but on whether the decision is responsible and traceable at the moment it occurs. By turning the reasoning process itself into a verifiable object, every output can answer "why this happened" and "whether it meets preset constraints." Errors are no longer hidden within the model but can be discovered, questioned, and corrected. Embedding capabilities like JSTprove directly into real-world systems allows zkML to move beyond the demonstration stage and truly enter production environments. In my view, @inference_labs represents a mature AI + Web3 direction: not pursuing unlimited expansion, but first establishing solid accountability and verification. #inference #KaitoYap @KaitoAI