Many people believe AI will evolve infinitely as long as you give it enough GPUs and electricity. Wrong, dead wrong.

Large language models' evolution speed is hitting a wall right now. The reason is particularly ironic: high-quality data available on the internet has been completely consumed by these models. Now large models are starting to "devour" second-hand content generated by other models on the internet, and the result of this "inbreeding" is logical degradation and widespread hallucinations.

This raises an extremely hardcore industry proposition: when public data depletes, what will AI use to keep getting smarter?

There's only one answer: high-quality human feedback. That is, so-called expert-level data.

Regular annotators can tell you whether there's a traffic light in this image, but they can't tell AI where the logical loopholes are in a legal contract, nor can they correct complex smart contract code. This kind of workforce capable of producing "premium fuel" is currently locked down by Web2 giants, like Scale AI, valued at tens of billions of dollars.

The $17.5 million that Perle Labs secured isn't an investment in some Web3 concept at all—it's an investment in these veterans who came out of Scale AI, aiming to completely overturn this core productive relationship.

I've researched many projects, and what I fear most is the kind of "Web3 for Web3's sake." But Perle Labs' logic is extremely cold-headed: it's solving efficiency problems that Scale AI can't solve.

In a centralized model, maintaining a global expert pool means management costs grow exponentially. If you want to pay a doctor in London to annotate medical images, the fees, compliance reviews, and identity verification—these friction costs can eat up half your profits. The end result is that real experts won't bother participating, leaving only cheap labor "washing data."

Perle Labs is brutal. They directly leveraged Solana's high-performance foundation and moved this entire logic onto the chain.

Identity as reputation: You don't need to fill out application forms. Your on-chain behavior, your task accuracy, your NFT badges—these are your credentials.

Incentives as positive-sum games: Through tokenomics, a programmer in Africa or a legal expert in Asia can directly and losslessly obtain profit-sharing from model evolution.

This isn't just about issuing tokens—it's constructing a global intellectual settlement network. Capital is pouring money in because they see clearly: whoever controls the channel to AI's "high-purity fuel" will be the next era's gas station.

Many Twitter followers ask me: Duoduo, I check into Perle's S1 season every day—is it actually useful?

This question shows they haven't entered the industry yet. You need to understand Perle's task distribution mechanism.

Perle has an extremely stringent internal reputation system. Those simplest, most basic annotation tasks are actually the system's "stress test" of you. If you can't even do basic tasks well, the system directly classifies you as a low-value node, and you'll never access high-yield expert-level tasks.

This logic is called "intelligence-based proof of work."

In S1 season, those 1.7 million tasks weren't run for nothing—the system was profiling hundreds of thousands of global nodes. It's filtering out who are truly experts and who are farming bots. This selection process itself is Perle's most core data asset. When the S1 snapshot locked in, it didn't just lock in points—it locked in a global intellectual distribution map.

So when you see $PRL 's pre-market trading price on XT exchange, don't think it's inflated. That's the market pricing this "automated expert-screening protocol."

There's an iron law in crypto: when a project hasn't been fully discussed in terms of how it actually makes money, that's when expectation gaps are largest.

Currently, most people's understanding of Perle Labs is stuck at "just another airdrop project in the AI track." That's precisely where the opportunity lies.

Real cash flow closure: traditional AI projects survive by selling tokens to retail, while Perle-type projects can directly sell high-quality data to model companies. This external revenue is the hard logic supporting token price.

Institutional endorsement logic: institutions like CoinFund and Framework have extremely discerning eyes. They invest in "alternative infrastructure." In their view, Perle is the decentralized version of Scale AI, and Scale AI is valued at $14 billion.

Time node urgency: S1 snapshot has passed, mainnet and token issuance are approaching. This is when researching its next-phase tasks and acquiring higher-tier identity marks (like specific NFT badges) is what professional creators should be doing with their audiences.

80% of Web3 + AI projects on the market will eventually go to zero. Because they can't solve the "why use blockchain at all" problem.

But Perle Labs gave me a very satisfying logical closure: using decentralized anonymity and efficient settlement to unlock high-end intellectuals unwilling to show themselves on centralized platforms.

This is the next step in AI evolution.

I'll continue monitoring this project's on-chain dynamics and $PRL 's pre-market feedback. Feel free to criticize if you don't get the logic; if you do, join me in waiting for that inflection point to arrive.

— participating in @PerleLabs community campaign
#ToPerle # PerleAI