Investing in Scale AI could be a stroke of genius and may help Meta (META.US) embark on a long-term bull market trajectory.

The analyst Geneva Investor of the investment research platform Seeking Alpha recently stated that if the social media company Facebook and its subsidiary Instagram continue to invest in the AI startup Scale AI, it could significantly increase their exposure to artificial intelligence-related businesses and act as a "positive catalyst" for the stock price of this tech giant led by Mark Zuckerberg. "The significant investment in Scale AI could accelerate the integration of META's own AI applications and their monetization path," said Seeking Alpha analyst Geneva Investor in an email to clients. "Synergies may come from more optimized data labeling compared to other competitors like OpenAI, leading to major improvements in META's own models, or in breakthroughs in defense and military applications in collaboration with the US government - the two companies have already collaborated on the Defense Llama project. So I look forward to seeing what happens next." Acquiring Instagram and WhatsApp proves Meta's keen investment vision Jonathan Weber, a thought leader at the investment portfolio community Cash Flow Club, also shares a similar view and believes that the rumored investment of up to $10 billion in Scale AI is completely affordable for Meta given its cash reserves. Weber wrote in an email, "The management has shown excellent performance in identifying and investing in quality targets in the past, including the acquisition of Instagram more than ten years ago, so I think the risk of overpaying is not significant." If the rumored investment of about $10 billion truly materializes, it would be one of Meta's largest financing deals. In 2014, Meta (known as Facebook at the time) acquired WhatsApp for $19 billion; in the earlier 2012, it spent about $1 billion in cash and stocks 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 machine learning/artificial intelligence large-scale systems. Its data labeling technology supports the R&D of AI large models and AI developers' systems for major clients such as Microsoft Corporation and OpenAI, and has emerged as a key player in the global generative AI wave since 2023. In its latest round of financing in 2024, the company's overall valuation reached about $14 billion, with major investors including Meta and Microsoft Corporation. Although Scale CEO Alexandr Wang may not be as well-known as OpenAI's Sam Altman or Anthropic's founder Dario Amodei, his company has become the absolute leader in the data field of the three pillars of AI - chips, talent, and data. This startup company provides AI model training data labeling services to tech companies like Meta and OpenAI through a large outsourcing team, assisting in the development of custom AI applications. According to sources, Scale is increasingly recruiting highly educated experts with PhDs in philosophy, biomedical, and physics to participate in the development of complex models. Earlier this year, it was reported that Scale AI was seeking a potential acquisition deal that could increase its valuation to $25 billion. With the global wave of AI applications sweeping the world, Scale AI is expected to help Meta build killer applications Since the emergence of the DeepSeek-R1 open-source AI large model with core attributes of "low cost" and "high performance", a wave of AI application deployment or local access of powerful AI large models such as DeepSeek, Claud, and LIama has surged in Silicon Valley and Wall Street. In the financial market, investors have flocked to software stocks this year, betting that software companies will have performance data underpinned by the AI boom that is several times stronger than the current one, and those companies with strong performance growth are expected to replicate the epic 1000% surge in NVIDIA Corporation's stock price curve since October 2022. Looking ahead at the trends in AI applications, the new paradigm of "extremely low cost AI training/inference" ushered in by DeepSeek's comprehensive leadership greatly simplifies AI workloads from training to deployment - significantly reducing AI training costs and token costs at the inference end. In the future, killer generative AI applications covering various industries at B-end or C-end, as well as the highly probable "AI agents" that significantly increase human social productivity, are likely to experience explosive growth, which is why global funds are pouring into software stocks recently. Digital advertising is Meta's core revenue engine, with 3 billion users as its foundation. Meta's AI advertising tools and Meta AI have helped Meta's advertising business revenue grow beyond expectations for several quarters. In the field of digital advertising where Meta relies on, Meta's powerful open-source AI large models and various generative AI software tools have driven advertisers to reach a larger potential user base, providing Meta advertisers and users with a new AI-based advertising recommendation experience. This is also a key logic why Wall Street analysts widely expect Meta's stock price to continue to rise. Therefore, in the context of global companies vying for the high ground of AI applications, Scale AI may help Meta build "killer" AI applications. ScAs a global leader in data annotation and model evaluation, Scale AI has a firm grasp on the "raw material" of the AI era. Scale AI is able to provide industry-leading data annotation and model evaluation platforms, while Meta is eager to commercialize the Llama series of large-scale models and deeply integrate Meta AI into the social, advertising, and hardware ecosystem. Scale AI can be seen as the final key piece of Meta's "AI moat": the computing power (NVIDIA Corporation AI GPU + self-developed ASIC + global large-scale data centers), large models (Llama family), and data (Scale AI) trinity.Data can be said to be the "fuel" of generative AI. Scale AI combines large-scale crowdsourced labeling with automatic quality control through the Data Engine, providing multiple modal data such as images, text, and 3D point clouds. The labeling accuracy is claimed to be over 99.9%. If Meta ultimately completes its strategic investment of about $10 billion - only about a quarter of its annual free cash flow - the two sides will form a closed loop in key areas such as high-quality, traceable massive training data, enterprise and government market model development, and model security alignment, possessing the necessary conditions to foster "killer" applications.