Took a look at the OPEN tokenomics. From the token distribution, OpenLedger’s design is pretty aggressive: 61.71% allocated to the community and ecosystem, which is a very high proportion compared to current AI projects. But there are a few issues I’m genuinely concerned about. 1/ **Technical Challenges** Although the community allocation takes the largest share, it’s broken down into: - Proof-of-Attribution rewards - Inference rewards - OpenCircle grants - DataNet grants - Airdrops etc. Whether such a complex distribution mechanism can function effectively largely depends on whether OpenLedger’s attribution technology can truly and accurately track the impact of data on model outputs. From a technical perspective, OpenLedger’s Proof-of-Attribution system is its core selling point. By tracking which datasets influence model outputs, it directly rewards high-impact data contributors. Theoretically, it’s perfect, but the real-world implementation is extremely challenging. The decision-making process of AI models is essentially a black box—how can the contribution of a single data point be accurately quantified? 2/ **Ecosystem Development** The token unlock design is relatively reasonable: - 21.55% enters circulation at TGE - Team and investors have a 12-month lock-up period + 36-month linear vesting - Community tokens start linear unlocking from the first month This design avoids massive sell pressure at launch. In terms of use cases, OPEN is designed to handle gas fees, inference payments, governance, model deployment, and more. But the core question is: Can OpenLedger truly attract enough developers and data contributors to build the ecosystem? 3/ **Future Outlook** The biggest risk lies in the fact that traditional AI giants hold vast amounts of data and computing resources. The problems OpenLedger is trying to solve are rooted not just in technology, but also in the dynamics of vested interests and legal frameworks.