It only takes 1% synthetic data to break an AI model. The problem? Most teams don't know how much of their training data was generated by another AI. The model race gets all the attention, but the data underneath it is quietly rotting. AI models are increasingly training on outputs from other AI models. At first it works, but then the details start disappearing. Judgments get weaker. That's the synthetic data loop, and it's already contaminating pipelines that don't even realize it's there. @PerleLabs is building around the opposite problem. Instead of chasing model benchmarks, they're focused on the data layer: human-verified, onchain-auditable data infrastructure built for enterprise and sovereign use cases where the source actually matters. The same "trust the source" logic carried over to their token distribution. Instead of letting bots farm the airdrop, they added palm-based biometric verification via @VeryAI to tie each claim to a real person. The real AI bottleneck won't be who builds the next model - it'll be whether the data feeding those models can be trusted at all. Pleased to partner with @PerleLabs. twitter.com/thedefiedge/status...