When AI systems proliferate, minor inaccuracies tend to compound into bigger problems across the network. A verification layer that cross-validates outputs against multiple models can solve this. By running responses through different models and structuring the results, you get clearer, more trustworthy signals instead of raw, unverified AI data. This approach transforms how we think about AI reliability in decentralized systems.