Nobody’s really talking about the data layer in AI. What happens when AI starts training on AI-generated data? You get faster outputs. But you also get weaker models, worse feedback loops and a bigger trust problem. Researchers already call this “Model Collapse”, performance degrades when models are trained on synthetic data repeatedly. That’s the lane Perle is going after. $PRL is live and the idea is simple: as AI scales, clean and verifiable data becomes more valuable, especially as synthetic content floods the system. Perle is building around that layer. A human-verified, onchain-auditable data for enterprise and sovereign use cases where bad inputs actually have consequences. The team comes out of Scale AI, has raised $17.5M and is going after a part of the stack that still feels underpriced compared to model hype. They’ve also tied USD1 into contributor payouts, so rewards move in a stable onchain unit instead of something more fragmented. If AI keeps scaling, the bottleneck probably isn’t models. It’s the data. Glad to partner with @PerleLabs to highlight the side of AI most people still overlook.