Behind Meta's sky-high acquisition of Scale AI: How Web3 AI can get rid of bias

Author: Haotian Source: X, @tmel0211

On one hand, Meta spent $14.8 billion to acquire nearly half of Scale AI’s equity, and the entire Silicon Valley was exclaiming that the giants used sky-high prices to re-price "data labeling"; on the other hand, @SaharaLabsAI, which is about to hold TGE, is still trapped under the Web3 AI bias label of "using concepts and being unable to prove itself". What exactly 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, and the difference lies mainly in price and availability. The price advantage seems to be able to find a gap in the monopoly of giants, but the availability is subject to geographical distribution, network latency and insufficient user incentives. Once the giants reduce prices or increase supply, this advantage will be wiped out in an instant.

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

An accurate cancer imaging diagnosis labeling requires the professional intuition of a senior oncologist; an experienced financial market sentiment analysis cannot be separated from the practical experience of Wall Street Trader. This natural 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 the shares of data labeling company Scale AI for US$14.8 billion, which is the largest single investment in the AI ​​field this year. What is more noteworthy is that Alexandr Wang, founder and CEO of Scale AI, will also serve as the head of Meta's newly established "super intelligence" research laboratory.

The 25-year-old Chinese entrepreneur was a Stanford University dropout when he founded Scale AI in 2016. Today, the company he manages is valued at $30 billion. Scale AI's customer list is an "all-star lineup" in the AI ​​industry: OpenAI, Tesla, Microsoft, the Department of Defense, etc. are all its long-term partners. The company specializes in providing high-quality data annotation services for AI model training and has more than 300,000 professionally trained annotators.

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

A "secret war" for control of the future of AI has begun.

The success of Scale AI exposes an overlooked truth: computing power is no longer scarce, model architectures tend to be homogenized, and what really determines the upper limit of AI intelligence is the carefully "tuned" data. What Meta bought at a sky-high price was not an outsourcing company, but the "oil mining rights" in the AI ​​era.

There are always rebels in monopoly stories.

 Just as cloud computing aggregation platforms try to subvert centralized cloud computing services, Sahara AI tries to use blockchain to completely rewrite the value distribution rules of data annotation. The fatal flaw of the traditional data annotation model is not a technical problem, but an incentive design problem.

A doctor may only get a few dozen dollars in labor fees for a few hours of annotating medical images, while the AI ​​model trained by these data is worth billions of dollars, but the doctor does not get a penny. This extremely unfair value distribution seriously suppresses the willingness to supply high-quality data.

With the catalysis of the web3 token incentive mechanism, they are no longer cheap data "migrant workers", but real "shareholders" of the AI ​​LLM network. Obviously, the advantage of web3 in transforming production relations is more suitable for data annotation scenarios than computing power.

Interestingly, Sahara AI happened to be in the TGE node acquired by Meta at a sky-high price. Is it a coincidence or a careful plan? In my opinion, this actually reflects a market inflection point: both Web3 AI and Web2 AI have gone from "volume computing power" to the crossroads of "volume data quality".  

While traditional giants use money to build data barriers, Web3 is using Tokenomics to build a larger "data democratization" experiment.