Data rates for training: Meta invests $ 14.3 billion in Scale AI-a strategic AI move

Published on: June 13, 2025 / update from: June 13, 2025 - Author: Konrad Wolfenstein

Data records for training: Meta invests $ 14.3 billion in Scale AI-a strategic AI move-Image: Xpert.digital

Meta concludes 14.3 billion investment in Scale AI-second largest takeover of the company history

The investment is already finalized

Contrary to the original reports on ongoing negotiations, Meta Platforms has already completed the investment in Scale AI. The investment amounts to $ 14.3 billion and evaluates the start-up with over $ 29 billion. This makes it the second largest acquisition ever after the 19-billion dollar purchase from WhatsApp.

Alexandr Wang changes to Meta

A decisive aspect of the deal is the change from Scale AI founder Alexandr Wang to Meta. The 28-year-old CEO will lead a new “superintelligence” unit at Meta, which aims to achieve artificial general intelligence (AGI). Jason Droege, previously Chief Strategy Officer from Scale AI, takes over the management of the company as an interim CEO.

Wang, who left after only one year and founded Scale Ai at the age of 19 in 2016, became the youngest home -made billionaire in the world in 2021. His technical expertise and business sense have made Scale Ai one of the leading providers of AI training data.

Scale AI: The backbone of the AI industry

Scale AI has developed into an indispensable service provider for the AI industry since its foundation in 2016. The company specializes in data labeling and offers high-quality, annotated data records for the training of machine learning models. Customers include leading AI companies such as Openaai, Google, Microsoft and Meta themselves.

Business development and financial indicators

Scale AI has impressive growth: In 2024, the company generated sales of around $ 870 million and at the end of the year reached an annualized running rate of $ 1.5 billion. For 2025, Scale Ai plans to more than double sales to $ 2 billion.

Strategic importance for Meta

Zuckerberg's frustration with Metas AI position

Mark Zuckerberg is becoming increasingly frustrated by Meta's position in the AI race race. Competitors such as Openaai seem to be ahead of basic AI models and consumer applications. The weak admission of Metas Llama 4 AI models in April reinforced Zuckerberg's dissatisfaction.

Access to competitive information

By taking over, META gives potentially insight into the priorities of its competitors regarding data, since many AI laboratories scale AI commission for data services. This could give Meta a strategic advantage in the intensive AI competition.

The market for AI training data is booming

Market size and growth forecasts

The global market for AI training data rates was rated to $ 2.92 billion in 2024 and is expected to grow to $ 17.04 billion by 2032, which corresponds to an average annual growth rate of 24.9%. North America dominates the market with a share of 47.95% in 2024.

The broader market for data collection and labeling will grow even more: from $ 3.0 billion in 2023, it is expected to increase to $ 29.2 billion by 2032, which corresponds to a CAGR of 28.54%.

Competitive landscape

Scale Ai is in competition with Labelbox, Appen, Amazon Web Services (Sagemaker Ground Truth), Google Cloud Automl and Microsoft Azure Machine Learning. Despite the competition, Scale Ai has secured a leading position with its scalability, accuracy and innovation.

Regulatory considerations

Meta possibly structures the deal as a 49%participation instead of a complete takeover in order to avoid regulatory exams. The company is already in legal disputes with the Federal Trade Commission due to antitrust questions in connection with previous acquisitions.

A turning point for meta

With this strategic investment, Meta signals its serious will to catch up in the AI race race. The combination of financial investment and the recruitment of Alexandr Wang shows that Meta is willing to use considerable resources for his AI ambitions. It remains to be seen whether this strategy will be successful, but it underlines the central importance of high -quality training data for the future of artificial intelligence.

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