Meta花費了$ 14.8B，以獲取近一半的人工智能權益

一方面，梅塔花費了148億美元，以獲取近一半的人工智能股權，整個矽谷都以高價重新定位了“數據標籤”，這使整個矽谷感到震驚

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”.

輸出：一方面，梅塔花了148億美元獲得了近一半的人工智能股權，整個矽谷震驚了這位巨頭以高價重新定位“數據標籤”。另一方面，即將到來的TGE @saharalabsai仍然被困在“騎行概念並無法證明自己”的Web3 AI偏見下。

What is the market ignoring behind this huge contrast?

這個巨大的對比背後的市場是什麼？

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

首先，與分散的計算功率聚集相比，數據標記是更有價值的軌道。

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.

使用閒置GPU來挑戰雲計算巨頭的故事確實令人興奮，但是計算能力本質上是一種標準化的商品，其差異主要在於價格和可用性。價格優勢似乎能夠在巨人的壟斷中找到差距，但可用性受地理分佈，網絡延遲和用戶激勵措施不足的約束。一旦巨人降低了價格或提高供應，這一優勢將立即消除。

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.

數據標記是完全不同的 - 這是一個需要人類智慧和專業判斷的差異化領域。每個高質量的標籤都具有獨特的專業知識，文化背景，認知經驗等，不能像GPU計算能力一樣“標準化”並複制。

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.

準確的癌症診斷註釋需要高級腫瘤學家的專業直覺；經驗豐富的金融市場情緒分析不能與華爾街交易員的實際經驗分開。這種自然的稀缺性和不可替代性給出了“數據註釋”，這是計算能力永遠無法達到的護城河深度。

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，它將以148億美元的價格收購數據標籤公司規模AI股票的49％，這是今年AI Field中最大的單一投資。更值得注意的是，Scable AI的創始人兼首席執行官Alexandr Wang還將擔任Meta新成立的“超級情報”研究實驗室的負責人。

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.

這位25歲的中國企業家是斯坦福大學輟學生，當時他在2016年創立了Scale AI。今天，他管理的公司的價值為300億美元。 Scale AI的客戶列表是AI行業中的“全明星陣容”：OpenAI，Tesla，Microsoft，國防部等都是其長期合作夥伴。該公司專門為AI模型培訓提供高質量的數據註釋服務，並擁有300,000多個專業培訓的註釋者。

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.

您會看到，雖然每個人仍在爭論誰的模型更高，但真正的玩家將戰場悄悄地轉移到了數據源。

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

控制人工智能未來的“秘密戰爭”已經開始。

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.

規模AI的成功揭示了一個被忽視的事實：計算能力不再稀缺，模型體系結構變得越來越均勻，而AI智能的真正上限是精心的“調諧”數據。梅塔以高價購買的不是外包公司，而是AI時代的“石油開採權”。

There is always a rebel in the story of Monopoly.

壟斷的故事中總是有叛逆者。

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.

就像雲計算聚合平台試圖顛覆集中式的雲計算服務一樣，撒哈拉AI試圖用區塊鏈完全重寫數據註釋的價值分佈規則。傳統數據註釋模型的致命缺陷不是技術問題，而是激勵設計問題。

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.

醫生可能只能在幾個小時的醫療圖像上獲得幾十美元，而接受這些數據培訓的AI模型價值數十億美元，但醫生沒有得到一分錢。這種非常不公平的價值分佈嚴重抑制了提供高質量數據的意願。

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.

隨著Web3令牌激勵機制的催化，它們不再是便宜的數據“移民工人”，而是AI LLM網絡的真正“股東”。顯然，與計算能力相比，Web3在轉換生產關係中的優勢更適合數據標記方案。

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恰好是以高價獲取的元元節點。這是巧合還是謹慎的計劃？我認為，這實際上反映了一個市場拐點：Web3 AI和Web2 AI都從“量計算能力”變為“量數據質量”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.

儘管傳統巨頭使用資金來構建數據障礙，但Web3正在使用令牌組學來構建更大的“數據民主化”實驗。