In fact, AI storytelling is always a hot topic in the current market. Recently, Coinbase, which has attracted a lot of attention, has included Perle (PRL) in its listing roadmap. In my opinion, the focus of this wave is not on AI itself, but on the repricing of the data layer. In most of our understanding, AI is basically composed of computing power, models, and agents. Even the recent popularity of "crayfish" can never be separated from data. As the most essential part of AI productivity, data also faces many challenges. When problems such as data opacity, inability to verify authenticity, and AI data reuse arise, it is easy for AI to create "illusions". Gradually, it deviates from the real purpose of using AI and goes further and further away from reality. The further AI moves forward, the stronger its dependence on high-quality and verifiable data becomes. @What PerleLabs is actually doing is "putting it on chain". one ⃣ It is not doing data crowdsourcing, but providing expert level data. Medical data should be reviewed by doctors, and legal data should be reviewed by lawyers. It's no longer the low-cost tactic of human warfare. two ⃣ Transforming 'data credibility' into something verifiable Every piece of data and every contributor is traceable and recorded on the chain. three ⃣ Building a 'data reputation system' By accumulating credit over the long term, one can become a continuously profitable identity. Essentially, it is transforming 'data' from a consumable item into an asset. Data, as the core production material in the operation of AI, is what Perle is doing to add "proof of origin", "quality assurance", and "economic incentives" to the production materials. In fact, it is a bit like what Defi was doing initially, turning what was originally opaque and non combinable into verifiable and tradable assets on the chain. In my opinion, the signal of CB being launched this time is quite clear. The narrative of AI has shifted from models to data. Previously, people were more concerned about LLM, AI agents, and Compute, but now they have shifted their focus to data sources, data quality, and data ownership. If the essence of AI competition in the past was computing power and models, then the next stage of the moat is likely to be "trustworthy data". The more authentic the data is, the closer it is to life, and it will generate more practical application scenarios.