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 has revalued "data labeling" with sky-high prices; on the other side is the upcoming TGE of @SaharaLabsAI, still trapped under the Web3 AI bias label of "riding on concepts and unable to self-validate." What has the market overlooked behind this huge contrast?

First of all, data labeling is a more valuable sector 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. Price advantages may seem to find gaps in the giants' monopoly, but availability is restricted by geographic distribution, network latency, and insufficient user incentives. Once the giants lower prices or increase supply, such advantages will be instantly wiped out.

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

A precise cancer imaging diagnosis label requires the professional intuition of a senior 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 a 49% stake in data labeling company Scale AI for $14.8 billion, making it the largest single investment in the AI field this year. What is even 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 dropped out of Stanford University to establish Scale AI in 2016, and today his company is valued at $30 billion. Scale AI's client list is a "dream team" in the AI world: OpenAI, Tesla, Microsoft, and the Department of Defense are all 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 arguing about whose model scores higher, the real players have quietly shifted the battlefield to the source of the data.

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

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

There's always a rebel against monopoly.

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

A doctor spends several hours labeling medical images and may only receive a few dozen dollars in labor fees, while the AI models trained on this data are worth billions of dollars, and the doctor does not receive a penny. This extreme unfairness in value distribution severely suppresses the willingness to supply high-quality data.

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

Interestingly, Sahara AI happens to be at the node of Meta's expensive acquisition TGE. Is it a coincidence or a carefully planned move? In my opinion, this actually reflects a market turning point: whether Web3 AI or Web2 AI, they have already moved from "competing on computing power" to the crossroads of "competing on data quality."

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