WebTim5

I’ve spent months training AI models on annotation platforms. Labeling images. Rating outputs. Correcting hallucinations. And the one thing nobody talks about? The people doing this work are invisible. @PerleLabs is changing that. Here’s why I believe in what they’re building 🧵 Most AI training platforms treat contributors like disposable labor. You label 500 images. You get paid pennies. Your identity doesn’t matter. Your expertise doesn’t matter. Tomorrow they replace you with someone cheaper. I’ve lived this. It’s broken. Then I found Perle. The first thing that hit me was the framing they don’t call it “Human-in-the-Loop.” They call it “Expert-in-the-Loop.” That one word changes everything. It means your domain knowledge isn’t just useful it’s the entire point. Here’s the problem Perle actually solves: AI models are now training on AI-generated content. The internet is flooded with synthetic text, images, and data. Models trained on this output start degrading. Researchers call it “model collapse.” The fix? Real human intelligence. Verified. Traceable. Permanent. And not just any humans. Perle connects AI companies with verified domain experts doctors validating medical data, lawyers classifying legal documents, engineers reviewing technical outputs. Every contribution is logged on Solana. Immutable. Auditable. Yours forever. Think about what that means. A radiologist who labels 10,000 medical images on Perle doesn’t just get paid they build a portable, on-chain reputation. That credential compounds over time. It follows them. It can’t be taken away. This is the future of professional credentialing for AI work. The founder @AhmedZRashad saw this problem from the inside. He was Head of Supply & Growth at Scale AI during its rise from $90M to $29B valuation. He watched the best contributors burn out while the platform scaled on volume over quality. So he left and built the opposite. That’s Perle. The Scale AI comparison matters. Scale built the data labeling industry. But their model optimizes for speed and volume anonymous workers, opaque pipelines, no contributor ownership. Perle flips that model: verified expertise, transparent provenance, contributors who actually own their work. @AhmedZRashad has this analogy I can’t stop thinking about: AI data today is in a “pre-Sarbanes-Oxley” state. No independent verification. No audit trails. Companies self-certify their training data quality. We wouldn’t accept unaudited financial statements. Why do we accept unaudited AI training data? This isn’t theoretical. Perle’s beta processed 1.7 million tasks in roughly two months. Their human-verified annotations outperformed Amazon Rekognition by over 70%. 1 million plus annotators. 1 billion+ scored data points. The proof is already in the numbers. The backing tells you the smart money sees it too. $17.5M raised $8.5M pre-seed led by CoinFund, $9M seed led by Framework Ventures. Framework’s Vance Spencer said it best: “The pace of AI progress will be driven more by better data than by simply scaling models.” And the momentum is undeniable. Coinbase added PRL to its listing roadmap this month. Season 1 snapshots are complete. But this isn’t about speculation it’s about what’s being built. A sovereign data layer where the humans who make AI smarter finally get recognized for it. I believe in Perle because I’ve been on the other side. I’ve done the annotation work. I’ve seen how the current system treats contributors. Perle doesn’t just fix the data quality problem it fixes the human dignity problem in AI training. That’s why I’m here. That’s why I’m staying. The next chapter of AI won’t be built by bigger models. It’ll be built by better data verified by real experts, recorded on-chain, owned by the people who created it. @PerleLabs @AhmedZRashad @rthurding @0x_dahlia gPerle 🫡 #PerleAI #ToPerle participating in @PerleLabs community campaign