What Perle is focused on is not "AI lacking data," but rather AI's ongoing lack of "responsible data." Right now, many people discussing AI are still fixated on model parameters, inference speed, and whether agents can work independently. However, when it comes to actual industry applications, the most critical issues are not as glamorous: where does the data come from, who labels it, how is it verified, and who is responsible for quality. This is also why I believe that the AI data track is not a supporting role; instead, it will increasingly resemble infrastructure. In the short term, the limits of models depend on algorithms, but in the long term, they depend on data. Especially as we reach stages like multimodal and RLHF, data is no longer just about quantity; it must be usable, verifiable, and auditable. Traditional crowdsourcing platforms can solve low-cost slice labor but cannot address high-quality cognitive labor. There is plenty of cheap data, but truly usable data that can be fed to models and consistently improve performance has always been a scarce resource. The past data production chain has resembled a black box: who labeled it, what criteria were used, was there expert review, and who takes the blame when deviations occur? Often, these questions remain unanswered. The result is that models appear very intelligent on the surface, but a closer look reveals illusions, biases, and instability. You can understand this as a very real contradiction: AI aims for industrialization, yet data production is still stuck in the workshop era. What makes Perle truly interesting is not just "moving labeling on-chain" in such a superficial way, but rather attempting to transform AI data production from fragmented labor into a scalable collaborative process. With experts in the loop, modular workflows, on-chain attribution, and native incentives, the logic becomes clear: first, clarify "who is qualified to participate," then break down tasks into executable and verifiable segments, and finally bind contributions and rewards, making data no longer a one-time delivery but a traceable, billable, and accumulative production process. This is crucial because what AI training has always lacked is not just data volume, but a high-trust data supply network. Whoever can standardize "quality" into a productive capacity will be closer to the upstream of the next round of the AI value chain. Therefore, I don't see Perle as an ordinary data platform; I prefer to understand it as a "data production coordination layer." It addresses not the model itself, but the invisible supply chain behind the model: how to organize expert resources, how to value contributions, how to verify results, and how to retain data assets with attribution. Web3 here is finally not just riding the AI narrative but is filling in the weakest link of traditional platforms—transparent pricing, on-chain settlement, and contribution attribution. Of course, this direction is not without risks. The most challenging aspect of AI data platforms has never been storytelling, but rather managing both sides simultaneously: one side needs a sufficiently dense supply of experts, while the other side must have genuine training demands that continue to pay. Without demand, the expert network will be idle; without quality, transparency on-chain is meaningless. Perle has not issued a token yet, which I actually see as a good thing. At least at this stage, the focus is still on product logic rather than inflating liquidity narratives. My judgment on this track is straightforward: competition in AI will increasingly resemble competition in manufacturing. Models are brands, computing power is factories, and data is the raw material and quality inspection system. The first two are already highly competitive, while the latter has just begun to be seriously priced. Whoever can turn high-quality data into a sustainable, verifiable, and incentivized infrastructure will not only serve AI but will also define how the next generation of the AI industry chain operates. What is worth watching about Perle is not whether it will ride the AI hype but whether it has the opportunity to make "data production"—the dirty and laborious work—into the most irreplaceable layer in Web3 AI. Many projects are working on talking agents. What is truly scarce may be the person who makes agents talk less nonsense. "— participating in @PerleLabs community campaign." #PerleAI #ToPerle