Currently, a common problem with AI training data is: it's too cheap.大量复制粘贴的观点、几分钱的机械标注，结果就是噪音被无限放大，模型越训越平庸，最后就是平均值的堆砌。

There's an interesting idea—turn data annotation from pure labor into a genuine economic game. Using a betting mechanism to judge, where participants have actual gains and losses, as well as reputation risks, so that signals become scarce, accurate, and truly trustworthy. In simple terms, it’s about making the incentive mechanism itself a filter for signals. This logic is very similar to the economic design approach in blockchain: optimizing system quality through aligned interests.