Whether it's Web3 AI or Web2 AI, we have already reached a crossroads from "competing in computing power" to "competing in data quality."

Written by: Haotian

On one side, Meta has spent $14.8 billion to acquire nearly half of Scale AI, and the entire Silicon Valley is exclaiming that the giant is re-pricing "data labeling" at an astronomical cost; on the other side, the soon-to-be TGE

@SaharaLabsAI is still trapped under the bias label of "riding on concepts, unable to self-verify" in Web3 AI. What has the market overlooked behind this huge contrast?

First of all, data labeling is a more valuable track than decentralized computing power aggregation.

The story of challenging cloud computing giants with idle GPUs is indeed exciting, but computing power is essentially a standardized commodity, with differences mainly in price and availability. While price advantages may seem to find gaps in the monopoly of giants, availability is constrained by geographical distribution, network latency, and insufficient user incentives. Once the giants lower prices or increase supply, this advantage will be instantly erased.

Data labeling, however, is completely different—it's a differentiated field that requires human wisdom and professional judgment. Each high-quality label carries unique expertise, cultural background, and cognitive experience, which cannot be "standardized" and replicated like GPU computing power.

A precise cancer imaging diagnosis label requires the professional intuition of a seasoned oncologist; a seasoned financial market sentiment analysis relies on the practical experience of a Wall Street trader. This inherent scarcity and irreplaceability give "data labeling" a moat depth that computing power can never reach.

On June 10, Meta officially announced the acquisition of 49% of data labeling company Scale AI for $14.8 billion, marking the largest single investment in the AI field this year. More noteworthy is that Scale AI's founder and CEO, Alexandr Wang, will also serve as the head of Meta's newly established "Super Intelligence" research lab.

This 25-year-old Chinese entrepreneur founded Scale AI in 2016 while still a dropout from Stanford University, and now the company he leads is valued at $30 billion. Scale AI's client list is a "who's who" of the AI world: OpenAI, Tesla, Microsoft, the Department of Defense, and others are long-term partners. The company specializes in providing high-quality data labeling services for AI model training, with over 300,000 professionally trained labelers.

You see, while everyone is still debating whose model scores higher, the real players have quietly shifted the battlefield to the source of data.

A "shadow war" over the future control of AI has already begun.

The success of Scale AI has exposed a neglected truth: computing power is no longer scarce, model architectures are becoming homogenized, and what truly determines the upper limit of AI intelligence is the carefully "tuned" data. What Meta has bought at a high price is not an outsourcing company, but the "oil rights" of the AI era.

There are always rebels in the story of monopoly.

Just as cloud computing aggregation platforms attempt to disrupt centralized cloud computing services, Sahara AI aims to completely rewrite the value distribution rules of data labeling using blockchain. The fatal flaw of traditional data labeling models is not a technical issue, but an incentive design issue.

A doctor spends hours labeling medical images and may receive only a few dozen dollars for their labor, while the AI model trained on this data is worth billions, yet the doctor receives not a penny. This extreme unfairness in value distribution severely suppresses the willingness to supply high-quality data.

With the catalyst of web3 token incentive mechanisms, they are no longer cheap data "migrant workers," but the true "shareholders" of the AI LLM network. Clearly, the advantages of web3 in transforming production relations are more applicable to data labeling scenarios than computing power.

Interestingly, Sahara AI coincidentally aligns with the TGE at the time of Meta's high-priced acquisition—whether it's a coincidence or a carefully planned move? In my view, this actually reflects a market turning point: whether Web3 AI or Web2 AI, we have already reached a crossroads from "competing in computing power" to "competing in data quality."

As traditional giants build data barriers with money, Web3 is constructing a larger "data democratization" experiment with Tokenomics.

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