Before discussing on-chain AI, perhaps we should first ask a more fundamental question: if the reasoning results of AI cannot be verified, what kind of trust is the so-called on-chain intelligence actually built upon? Currently, many AI applications still run on black-box infrastructure, with models performing inference off-chain. Users can only passively accept the output results, unable to verify the computation process or confirm whether intermediate steps have been tampered with. This mode inherently conflicts with the verifiability and auditability emphasized by blockchain, and also forces on-chain applications to re-depend on centralized trust when introducing AI capabilities. @inference_labs is working around this critical gap. They focus not on retraining a more powerful model, but on building a verifiable decentralized inference infrastructure, reshaping the integration of AI and blockchain from the ground up. Inference Labs aims to make AI inference processes transparent, auditable, and trustable through protocols, so that future smart contract calls will not just access ordinary data interfaces, but will include cryptographic proofs of the computational results. This structure shifts AI output from subjective trust to mathematical verifiability, providing a more solid foundation for on-chain automated decision-making. When inference possesses verifiable properties, AI can truly become a native component of on-chain systems, rather than a black-box tool outsourced to centralized services. This leap will determine whether AI and blockchain can achieve deep integration, rather than just superficial feature stacking. @Galxe @GalxeQuest @easydotfunX