The AI industry shifts focus to data annotation, with Web3 models challenging traditional giants.

As Meta acquires nearly half of Scale AI for $14.8 billion, the entire tech industry is discussing the repositioning of the "data labeling" value by giant companies. At the same time, some Web3 AI projects still face skepticism about concept hype and lack of substance. Behind this huge contrast, the market seems to overlook some key factors.

Data labeling is more valuable and has greater potential than decentralized computing power aggregation. While the idea of using idle GPUs to challenge cloud computing giants is appealing, computing power is essentially a standardized commodity, with the main differences being price and availability. These advantages may quickly disappear as giants lower prices or increase supply.

In contrast, data labeling is a differentiated field that requires human intelligence and professional judgment. Each high-quality label contains unique expertise, cultural background, and cognitive experience, which cannot be easily replicated like GPU computing power. For example, precise cancer imaging diagnosis labeling requires the professional intuition of experienced oncologists, while in-depth analysis of financial market sentiment relies on the practical experience of seasoned traders. This inherent scarcity and irreplaceability create a strong moat for the data labeling industry.

Meta's acquisition of Scale AI has attracted widespread attention. Scale AI's clients include several top AI companies, tech giants, and government departments, with over 300,000 professionally trained annotators. This deal exposes a neglected truth: in an era where computing power is no longer scarce and model architectures are becoming homogenized, it is the meticulously processed data that truly determines the upper limits of AI intelligence.

However, the traditional data labeling model has issues with unfair value distribution. For example, a doctor may spend hours labeling medical images but only receive a few dozen dollars in compensation, while the AI model trained on this data could be worth billions of dollars, yet the doctor cannot share in these earnings. This unfairness severely undermines the willingness to provide high-quality data.

Against this background, some Web3 AI projects are trying to rewrite the value distribution rules of data labeling using blockchain technology. Through token incentive mechanisms, they hope to transform data providers from cheap "data migrant workers" into true "stakeholders" of the AI network. This model has the potential to stimulate the supply of more high-quality data.

As traditional giants build data barriers with capital, Web3 is attempting to create a more open and democratized data ecosystem through token economics. AI projects in both Web2 and Web3 have already shifted from "competing computational power" to a new stage of "competing data quality." This market inflection point signifies an important direction for the future development of the AI industry.