Investing in Scale AI is a stroke of genius and may help Meta embark on a long-term bull market trajectory

According to the Zhitong Finance APP, Geneva Investor, an analyst stationed at the investment research platform Seeking Alpha, recently stated that if social media giant Meta Platforms (META.US), the parent company of Facebook and Instagram, continues to invest in AI startup Scale AI, it could significantly enhance its exposure to AI-related businesses and provide a "positive catalyst" for the stock price of this tech giant led by Mark Zuckerberg to enter a long bull market.

"This substantial investment in Scale AI is significant and may accelerate the integration of META's own AI applications and its monetization path," noted Seeking Alpha analyst Geneva Investor in an email to clients. "Synergies may arise from more optimized data labeling compared to peers like OpenAI, greatly improving META's proprietary models, or achieving breakthroughs in defense and military applications in collaboration with the U.S. government—both companies have reached a partnership on the Defense Llama project. So I am looking forward to what happens next."

Acquisition of Instagram and WhatsApp proves Meta's investment acumen

Jonathan Weber, an opinion leader in the investment community Cash Flow Club, shares the same view and stated that this rumored investment of up to $10 billion is entirely manageable given Meta's cash flow reserves.

Weber wrote in an email: "The management has performed excellently in identifying and investing in quality targets in the past, including the acquisition of Instagram over a decade ago, so I believe the risk of overpaying is not significant."

If this investment—rumored to be around $10 billion—materializes, it will rank among Meta's largest financing deals. In 2014, Meta (then still known as Facebook) acquired WhatsApp for $14 billion; earlier, in 2012, it spent about $1 billion in cash and stock to acquire Instagram, and then in 2014, it acquired Oculus VR for about $2 billion.

Scale AI, founded by Alexandr Wang in 2016, provides data labeling services for large machine learning/AI systems. Its data labeling technology supports the AI large models and developer ecosystems of major clients like Microsoft (MSFT.US) and OpenAI, and has emerged as a key player in the global generative AI wave since 2023. The company previously reached an overall valuation of approximately $14 billion in its latest funding round in 2024, with investors primarily including Meta and Microsoft.

Scale CEO Alexandr Wang may not be as well-known as OpenAI's Sam Altman or Anthropic founder Dario Amodei, but his company has become the absolute leader in the data sector among the three pillars of AI—chips, talent, and data This startup provides data annotation services required for AI model training to tech companies like Meta and OpenAI through a large outsourced team, and assists in developing customized AI applications. According to insiders, Scale is increasingly recruiting PhDs and highly educated experts from interdisciplinary fields such as biomedical sciences and physics to participate in the development of complex models.

Earlier this year, reports indicated that Scale AI was seeking a potential acquisition deal that could raise its valuation to $25 billion.

The wave of AI applications is sweeping the globe, and Scale AI is expected to help Meta create killer applications

Since the emergence of the DeepSeek-R1 open-source AI large model, which combines the core attributes of "low cost" and "high performance," Silicon Valley and Wall Street have been shocked, leading to a global wave of deploying or locally accessing heavyweight AI large models like DeepSeek, Claud, and LIama. In the financial markets, investors have flocked to software stocks this year, betting that software companies will have performance data several times stronger than current levels as a core pillar under the AI boom, and those companies with strong performance growth are expected to replicate the epic surge of 1000% seen in Nvidia's stock since October 2022.

Looking ahead at AI application trends, with the new paradigm of "ultra-low-cost AI training/inference" led by DeepSeek significantly streamlining the full range of AI workloads from training to deployment—AI training costs have drastically decreased, and token costs at the inference end have plummeted—killer generative AI applications covering both B-end and C-end industries are likely to experience explosive growth, which is why global funds have recently flowed into software stocks.

Digital advertising is the core revenue engine for Meta, and its 3 billion users are the foundation. Meta's AI advertising tools and Meta AI have consistently helped Meta's advertising business revenue exceed expectations for several quarters. In the digital advertising sector that Meta relies on, the powerful open-source AI large model launched by Meta, along with various generative AI auxiliary software tools, enables advertisers to reach a larger potential user base, providing a new AI-based advertising recommendation experience for Meta's advertisers and users. This is also an important logic behind Wall Street analysts' general expectation that Meta's stock price will continue to rise.

Therefore, for Meta, in the context of global companies competing for AI application dominance, Scale AI may help Meta create "killer" AI applications. As a leader in data annotation and model evaluation globally, Scale AI holds the "data production materials" of the AI era. Scale AI can provide an industry-leading data annotation and model evaluation platform, while Meta is eager to commercialize the Llama series of large models on a large scale and deeply embed Meta AI into its social, advertising, and hardware ecosystems. Scale AI can be seen as the last key piece of the puzzle for Meta's "AI moat": the integration of computing power (Nvidia AI GPUs + self-developed ASICs + large-scale data centers covering the globe), large models (Llama family), and data (Scale AI) Data can be said to be the "fuel" of generative AI. Scale AI combines large-scale crowdsourced labeling with automated quality inspection through its Data Engine, providing multimodal data such as images, text, and 3D point clouds, with an accuracy rate claimed to be over 99.9%. If Meta ultimately completes a strategic investment of approximately $10 billion—accounting for about a quarter of its annual free cash flow—the two parties will form a closed loop in key areas such as high-quality, traceable massive training data, enterprise and government market large model development, and model safety alignment, which are necessary conditions for generating "killer" applications