Meta花费了$ 14.8B，以获取近一半的人工智能权益

一方面，梅塔花费了148亿美元，以获取近一半的人工智能股权，整个硅谷都以高价重新定位了“数据标签”，这使整个硅谷感到震惊

output: On one hand, Meta spent $14.8 billion to acquire nearly half of Scale AI’s equity, and the entire Silicon Valley was shocked that the giant repriced “data labeling” at a sky-high price; on the other hand, the upcoming TGE @SaharaLabsAI is still trapped under the Web3 AI bias label of “riding the concept and unable to prove itself”.

输出：一方面，梅塔花了148亿美元获得了近一半的人工智能股权，整个硅谷震惊了这位巨头以高价重新定位“数据标签”。另一方面，即将到来的TGE @saharalabsai仍然被困在“骑行概念并无法证明自己”的Web3 AI偏见下。

What is the market ignoring behind this huge contrast?

这个巨大的对比背后的市场是什么？

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

首先，与分散的计算功率聚集相比，数据标记是更有价值的轨道。

The story of using idle GPUs to challenge cloud computing giants 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 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.

使用闲置GPU来挑战云计算巨头的故事确实令人兴奋，但是计算能力本质上是一种标准化的商品，其差异主要在于价格和可用性。价格优势似乎能够在巨人的垄断中找到差距，但可用性受地理分布，网络延迟和用户激励措施不足的约束。一旦巨人降低了价格或提高供应，这一优势将立即消除。

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 replicated like GPU computing power.

数据标记是完全不同的 - 这是一个需要人类智慧和专业判断的差异化领域。每个高质量的标签都具有独特的专业知识，文化背景，认知经验等，不能像GPU计算能力一样“标准化”并复制。

An accurate cancer image diagnosis annotation requires the professional intuition of a senior oncologist; an experienced financial market sentiment analysis cannot be separated from the practical experience of a Wall Street trader. This natural scarcity and irreplaceability give “data annotation” a moat depth that computing power can never reach.

准确的癌症诊断注释需要高级肿瘤学家的专业直觉；经验丰富的金融市场情绪分析不能与华尔街交易员的实际经验分开。这种自然的稀缺性和不可替代性给出了“数据注释”，这是计算能力永远无法达到的护城河深度。

On June 10, Meta officially announced that it would acquire 49% of the shares of data labeling company Scale AI for $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.

6月10日，Meta正式宣布，它将以148亿美元的价格收购数据标签公司规模AI股票的49％，这是今年AI Field中最大的单一投资。更值得注意的是，Scable AI的创始人兼首席执行官Alexandr Wang还将担任Meta新成立的“超级情报”研究实验室的负责人。

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 client 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.

这位25岁的中国企业家是斯坦福大学辍学生，当时他在2016年创立了Scale AI。今天，他管理的公司的价值为300亿美元。 Scale AI的客户列表是AI行业中的“全明星阵容”：OpenAI，Tesla，Microsoft，国防部等都是其长期合作伙伴。该公司专门为AI模型培训提供高质量的数据注释服务，并拥有300,000多个专业培训的注释者。

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

您会看到，虽然每个人仍在争论谁的模型更高，但真正的玩家将战场悄悄地转移到了数据源。

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

控制人工智能未来的“秘密战争”已经开始。

The success of Scale AI exposes an overlooked truth: computing power is no longer scarce, model architectures are becoming more homogenous, and the real 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” of the AI era.

规模AI的成功揭示了一个被忽视的事实：计算能力不再稀缺，模型体系结构变得越来越均匀，而AI智能的真正上限是精心的“调谐”数据。梅塔以高价购买的不是外包公司，而是AI时代的“石油开采权”。

There is always a rebel in the story of Monopoly.

垄断的故事中总是有叛逆者。

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

就像云计算聚合平台试图颠覆集中式的云计算服务一样，撒哈拉AI试图用区块链完全重写数据注释的价值分布规则。传统数据注释模型的致命缺陷不是技术问题，而是激励设计问题。

A doctor may only get a few dozen dollars for a few hours of labeling medical images, while the AI models trained with these data are worth billions of dollars, but the doctor does not get a penny. This extremely unfair distribution of value has seriously suppressed the willingness to provide high-quality data.

医生可能只能在几个小时的医疗图像上获得几十美元，而接受这些数据培训的AI模型价值数十亿美元，但医生没有得到一分钱。这种非常不公平的价值分布严重抑制了提供高质量数据的意愿。

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 labeling scenarios than computing power.

随着Web3令牌激励机制的催化，它们不再是便宜的数据“移民工人”，而是AI LLM网络的真正“股东”。显然，与计算能力相比，Web3在转换生产关系中的优势更适合数据标记方案。

Interestingly, Sahara AI happened to be in the TGE node of Meta acquired 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”.

有趣的是，撒哈拉AI恰好是以高价获取的元元节点。这是巧合还是谨慎的计划？我认为，这实际上反映了一个市场拐点：Web3 AI和Web2 AI都从“量计算能力”变为“量数据质量”的十字路口。

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

尽管传统巨头使用资金来构建数据障碍，但Web3正在使用令牌组学来构建更大的“数据民主化”实验。