A platform reviewing its own decision is not much of an appeal. Imagine getting a video demonetized, appealing it, and having the appeal reviewed by the same team using the same guidelines that flagged it in the first place. You are not really getting a second judgment. You are getting the first judgment twice. That is the single point of failure @GenLayer is trying to remove. Athens used large juries chosen by lot so that controlling the verdict became impractical. GenLayer takes that idea and adds something an ancient jury could not: model diversity. One randomly selected validator proposes an answer. Other validators independently rerun the work and evaluate it using different AI models. If they agree, the verdict applies immediately. But there is an appeal window. A challenger can bring in a larger jury, with validators who have not voted yet. The jury grows with each appeal, while validators have money at stake. Vote correctly and you earn rewards. Vote incorrectly and you risk your stake. So overturning a verdict does not become easier because someone keeps appealing. It becomes more expensive to keep defending a wrong result. That combination is what I find interesting. It is not just more validators. It is random selection, independent model perspectives, economic incentives and an escalating appeal process working together. The goal is not to find one model that never gets things wrong. It is to make one wrong authority much harder to control. That is a very different way to think about consensus. Read how Optimistic Democracy actually works: docs.genlayer.com/understand…