. @ coinbase has just added @ PerleLabs to its cryptocurrency roadmap, and the market is starting to stir again? The integration direction of AI+Crypto has shifted from combining computing power to integrating agent applications, and may eventually have to continue to tackle the hard bone of "data supply chain services": The recent GEO scandal on 315 has surprised many people. It turns out that the input of AI models can really be "polluted" through specific content. There is a saying that there is an embarrassing situation of "model collapse" in the AI circle, where the entire network is filled with a large amount of AI generated waste. Relying on these contents to train AI models is like "inbreeding" in the digital world, which will lead to the model becoming increasingly useless as it iterates. What should we do? The niche of Perle that we are going to talk about today is to solve this problem. The backbone of its team comes from the global data annotation oligopoly ScaleAI, which is the web2 AI data service giant with a valuation of 30 billion US dollars and has won orders from the US Department of Defense. Back then, @ scale_AI helped big companies solve the problem of "how to mass produce data". Now, these people are entering web3, and their goal has changed to "how to prove that these large-scale data are absolutely trustworthy"? Many people may be surprised, why do we have to use Crypto to solve it? One fundamental reason is that the further the AI race rolls back, the higher the premium for high-quality human feedback RLHF. A data annotation platform like ScaleAI, while obtaining large-scale data, inevitably has a "black box form". It is difficult to determine whether the individual feeding data behind the screen is a licensed specialist doctor, a temporary worker on the Southeast Asian assembly line, or even a data waste synthesized by the AI model itself? In the context of conversational AI models, such errors can still be tolerated, but if AI agents are applied to medical diagnosis, autonomous driving, and even high-frequency financial transactions, the impact of upstream training data bias will become apparent. So, Perle and the ScaleAI team know better than anyone what the data supply chain will look like at the end of ScaleAI's large-scale logic. The goal of entering Crypto is to leverage Crypto's data traceability and on chain features to specialize in vertical data supply business serving future segmented scenarios. For example, using the incentive method of Crypto to encourage doctors to annotate medical data, having lawyers review legal documents, and then directly uploading the review behavior and reputation accumulation of these experts on the blockchain. Big customers not only buy the data itself, but also the clear and auditable real human verification chain behind this data. Of course, the decentralized computing power and data track once mass-produced billions of FDV behemoths, but now most of them are deeply mired in token economics. In this context, when it comes to PRL, the breakthrough lies in the essence of its business: it does not need to rely solely on expectation management to sustain its existence, but instead uses traditional business logic and cash flow defenses to play a clear card in the crypto arena, which may be very different.