While everyone is focused on model size and performance, have you ever wondered what is truly trustworthy behind AI? AI predictions and decisions are now involved in more and more critical areas, from automated trading in financial markets to autonomous decision-making by robots. However, the lack of verifiable reasoning credentials means these systems still function as opaque black boxes in key scenarios. The emergence of @inference_labs aims to break this opacity by building a decentralized "Inference Proof" layer, allowing every model output to be accompanied by verifiable cryptographic proof, fundamentally enhancing the trustworthiness and auditability of automated systems. This is not just wishful thinking; technically, they are integrating distributed zkML slicing, zero-knowledge circuit conversion toolchains, and the Proof of Inference protocol to enable external on-chain review of reasoning data and logic without revealing model details or trade secrets. This design protects intellectual property while providing a traceable and verifiable output path for high-risk applications. During this process, the team has completed multiple rounds of institutional funding and established collaborations with several decentralized AI protocols, using foundational infrastructure to promote industry standardization. Proof of Inference is already running on the testnet and plans to deploy the mainnet version within days, indicating that a truly verifiable AI reasoning network is gradually coming to fruition. The most pressing question is: when AI outputs come with verifiable credentials, can they still be regarded as unpredictable black box tools? If every inference can be audited and traced, are we truly entering a new era where AI reasoning and economic logic become transparent? The answer to this question will determine the trust landscape for future autonomous agents and decentralized systems. @Galxe @GalxeQuest @easydotfunX