OpenLedger aims to do more than just "attach blockchain to AI"; it seeks to reorganize three heterogeneous resources—data, models, and computing power—into a chain-based production relationship that can be priced, settled, and combined. Its underlying structure uses a layered ledger: the first layer is a rapid consensus based on Tendermint, used to write contribution hashes and revenue distribution instructions; the second layer is a verifiable storage area composed of a multi-chain DA network, storing desensitized data fragments, model weight differences, and computing node operation logs; the third layer is a heterogeneous execution environment bridge that maps external contract calls from Ethereum mainnet, Arbitrum, Sui, etc., to a unified event format using light clients and ZK proofs, ensuring consistency in cross-ecosystem revenue settlement. To reward "contribution" rather than "noise," OpenLedger has introduced a PoC (Proof of Contribution) scorecard: it maps indicators such as the accuracy improvement of the dataset, QPS enhancement of model inference, and the stability and energy efficiency of computing nodes to a weight range of 0–1, incorporating a time decay function to prevent one-time contributions from long-term exploitation. All contributions are divided into minimal atomic NFTs, supporting splitting, transfer, and merging, meaning a segment of annotation script or a model fine-tuning can be freely traded in the secondary market. Revenue settlement employs a "streaming profit-sharing" model, where each round of inference triggered by the caller results in micro-payments of OPEN tokens, with settlements made to corresponding NFT holders upon contract expiration, significantly reducing the risk of arrears and confusion associated with traditional royalty models. On the compliance front, OpenLedger uses verifiable credentials (VC) to label data owners with a "usable range" tag and confirms whether calls exceed authority using zero-knowledge boolean proofs, achieving "usable but invisible". For investors, the greatest imagination of this chain lies not in token prices but in the potential to incubate a truly meaningful "AI version of the App Store": data producers sell data fragments like songs, developers can pay to call as needed, and the final long-tail revenue is automatically distributed by the code. If this model takes hold, the profit structure of the AI industry chain will be completely rewritten—data and algorithms will shift from cost centers to tradable assets, inference services will transition from single platforms to multi-party markets, and the industry's moat will transform from "scale monopoly" to "contribution compounding."@OpenLedger #OpenLedger $OPEN
