Behind Meta's exorbitant acquisition of nearly half of Scale AI's equity, how can Web3 AI eliminate bias?

Whether it's Web3 AI or Web2 AI, we have reached the crossroads of "computing power" and "data quality."

Written by: Haotian

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

@SaharaLabsAI is still trapped under the Web3 AI bias label of "hitching onto concepts, unable to self-verify." Behind this huge contrast, what has the market actually overlooked?

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 fascinating, but Computing Power is essentially a standardized commodity, with differences mainly in price and availability. Price advantages seem to find gaps in the monopoly of giants, but availability is limited by geographic distribution, network latency, and insufficient user incentives. Once the giants lower prices or increase supply, this advantage will be instantly erased.

Data annotation is completely different - it is a differentiated field that requires human intelligence and professional judgment. Every high-quality annotation carries unique expertise, cultural background, cognitive experience, and so on, which cannot be "standardized" and replicated like GPU Computing Power.

An accurate cancer imaging diagnosis requires the professional intuition of experienced oncologists; a seasoned analysis of financial market sentiment relies on the practical experience of Wall Street traders. 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 the data labeling company Scale AI for $14.8 billion, making it the largest single investment in the AI field this year. More notably, 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 found Scale AI in 2016, and today his company is valued at $30 billion. Scale AI's client list is considered an "all-star lineup" in the AI field, with long-term partners including OpenAI, Tesla, Microsoft, and the Department of Defense. The company specializes in providing high-quality data labeling services for AI model training, employing over 300,000 professionally trained labelers.

You see, while everyone is still arguing about whose model has a higher score, the real players have quietly shifted the battlefield to the source of the data.

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

The success of Scale AI has revealed an overlooked truth: Computing Power is no longer scarce, model architectures are becoming homogenized, and what truly determines the upper limit of AI intelligence is the data that has been carefully "trained". Meta did not buy a outsourcing company for an exorbitant price, but rather the "oil rights" of the AI era.

There are always rebels in the story of monopoly.

Just as the cloud computing aggregation platform attempts to disrupt centralized cloud computing services, Sahara AI seeks to fundamentally rewrite the value distribution rules of data annotation using blockchain. The fatal flaw of traditional data annotation models is not a technical issue, but rather an incentive design issue.

A doctor spends hours annotating medical images and may receive just a few dozen dollars in service fees, while the AI models trained on this data are worth billions of dollars, 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 the web3 token incentive mechanism, 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 suitable for data labeling scenarios compared to Computing Power.

Interestingly, Sahara AI coincidentally aligns with the TGE of the node that Meta acquired at a high price. Is it a coincidence or a well-planned strategy? In my opinion, this actually reflects a market turning point: whether it's Web3 AI or Web2 AI, we have moved from "competing on Computing Power" to the crossroads of "competing on data quality."

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