The dark battle for the trillion-dollar data labeling market has already begun.

June 2025: Meta acquires a 49% stake in Scale AI for $14.3 billion, marking the largest single investment in the history of AI data.

August 2025: The decentralized data labeling platform @PerleLabs completes a seed round of financing led by Framework Ventures, raising a total of $17.5 million (Perle's investors also include top institutions like Coinfund, NGC Ventures, and Hashkey).

In addition to direct business competition, Perle's founder Ahmed Rashad is a former core member of Scale AI, having served as the head of supply chain and growth at Scale AI from 2019 to 2022.

Questions arise: First, why is the AI data labeling business so valuable? Second, what is Perle's core competitive advantage compared to Scale AI?

1/ Why is the AI data labeling business so "valuable"?

From a technical perspective, the quality of AI model training directly depends on the accuracy of data labeling. Whether it's GPT, Claude, or other large models, they all require vast amounts of high-quality labeled data to "learn."

A deeper reason lies in the reconstruction of the commercial value chain. Traditional software development is code-driven, where programmers write code and machines execute it. In the AI era, it is data-driven; whoever controls high-quality labeled data controls the "brain" of the AI model.

Taking Scale AI as an example, its client roster is impressive, including tech giants like OpenAI, Meta, Microsoft, and Google, as well as government agencies like the U.S. Army and Air Force. In 2024, Scale AI's revenue reached $870 million, with projections of over $2 billion in 2025.

Whoever controls the data controls the future of the AI market.

2/ What is Perle's core competitive advantage?

Scale AI is not without its flaws. After Meta's investment, a series of negative chain reactions were triggered:

Google originally planned to pay Scale $200 million this year but has now cut off cooperation.

OpenAI has begun to gradually reduce its collaboration with Scale.

Microsoft and xAI have paused projects due to concerns over data confidentiality.

The core contradiction lies in the fact that data labeling companies need to serve multiple competitors to achieve scalable profitability, but once they become deeply tied to one, they lose the most essential value of "neutrality."

In contrast, as a decentralized data labeling platform, Perle does not face similar issues. Perle is built to support any team that constructs or fine-tunes AI models, and this openness allows more AI teams to benefit.

In addition, Perle's core advantages include:

Innovative incentive mechanisms: In traditional models, data labelers often play the role of "workers," while Perle Labs establishes a verifiable work history through a token incentive mechanism, ensuring that high-quality work receives its due rewards.

Long-tail capability: As model capabilities grow stronger, the key to performance often lies in the long-tail edge cases. Perle transforms data labeling into an accessible, rewarding, and community-owned process, potentially offering advantages in handling these complex scenarios.

Finally, echoing what I said before, the current battle for attention has been accelerated; everyone needs to find early and underreported quality projects, publish articles in advance to secure their positions, or risk falling into meaningless competition.

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