Speaking of which, the OpenChat recently released by @ Openledger HQ, under the banner of "Proof of Attribution," claims to ensure that every contributor to AI generated content receives the appropriate rewards. How to achieve it specifically? 1) From the perspective of product implementation logic, OpenChat is indeed doing something with extremely high technical difficulty. Its core selling point is that every message you send, dataset you share, and model you fine tune will be recorded on the chain, and once used by others, you can earn profits. From a technical perspective, it is actually thanks to its "attribution engine", which is a high fidelity ∞ - gram language model that uses suffix arrays to perform millisecond level matching among trillions of tokens, breaking down each sentence into a token window, embedding it through vectorization logic such as BERT and Sentence-T5, and then accurately matching it to the original data source. Simply put, it means installing a "super microscope" for AI content generation, which can detect the "DNA" of each token. The user interface will underline which content comes from which dataset, and even the confidence score will be clearly marked. Every chat will trigger real-time on chain transactions, calculating the allocation of platform fees, data network fees, and model fees - you will receive the same amount of money based on how many sentences your data is used. (As shown in the attached picture) 2) After analyzing the product logic, OpenChat's business logic becomes very clear, which is an attempt to reconstruct the entire value distribution system for AI content generation. Traditional AI services are like "black box sharing" - when you call the OpenAI interface, you have no idea how the accounts are split behind the scenes. What OpenChat aims to do is to make every AI call a 'public sharing', where data contributors and model developers can receive real-time profits based on their contributions. To put it bluntly, Openledger abstracted three layers of technical architecture when building the AI technology sovereign data blockchain: the underlying on chain protocol, the middle layer attribution engine, and the top-level user application; It is obvious that OpenChat is a technical validation and experiment of the entire OpenLedger technology architecture. Through a chat interface, the abstract AI data that OpenLedger wants to transmit is contributed to the public ledger system and visualized. The goal is to verify that data calls can be fully transparent, the source of each token can be accurately traced, and value allocation can be automatically executed in real-time. 3) However, achieving precise tracking and on chain recording of large-scale data is not an easy task, especially in the design of "on chain design". OpenLedger has chosen a heavy solution of "real-time on chain" to ensure that every conversation can be split instantly. Just think about it, this will have a significant impact on gas costs and transaction frequency. The direct challenge faced by this design is the performance bottleneck. If OpenChat has tens of thousands of users chatting online at the same time, it may generate hundreds of thousands of attribution calculations and profit sharing transactions per second, and this throughput TPS requirement far exceeds the processing capacity of most public chains. Even on relatively high-performance sidechains, ensuring millisecond level attribution matching and real-time on chain settlement requires extremely high demands on the entire technology stack. The more realistic issue is economic feasibility. Every on chain transaction requires a gas fee, even if the single fee is low, the cost will become a burden for users when used on a large scale. Would users be willing to bear the additional costs incurred during each chat for the sake of "fair sharing"? In fact, Openledger can completely choose a compromise solution between batch settlement or periodic on chain, but since it has chosen real-time on chain and instant fair distribution, it can only be said that it is still trying to explore the extreme. It is not yet known whether the mechanism will be adjusted in the end, but such an attempt is quite meaningful.