In 2014, Meta (then Facebook) made waves by acquiring WhatsApp for $19 billion—a deal that brought billions of users into its fold and cemented its position as a mobile-first communications leader. Now, more than a decade later, Meta is making another consequential move—one that could prove even more transformative in the long run. The company’s decision to invest $14.3 billion in Scale AI and bring its 28-year-old CEO, Alexandr Wang, in-house to lead a new “superintelligence” team is a calculated attempt to dominate the next frontier: large language models (LLMs).
While WhatsApp gave Meta user reach and data scale, its latest AI-centric bet positions the company to control the cognitive layer of the internet. If successful, this could define the next era of computing, making the WhatsApp acquisition look relatively narrow by comparison.
Owning the Intelligence Layer
LLMs are becoming the foundation for a new kind of digital infrastructure. From productivity tools and coding assistants to creative platforms and enterprise automation, these models are quickly evolving from experimental novelties to mission-critical systems. The company that controls the development, fine-tuning, and deployment of frontier models will not only gain a competitive edge but potentially define the standards for how human-AI interaction unfolds.
Meta’s investment in Scale AI is a significant step in this direction. Scale is not just a data-labeling company; it is a strategic asset in the AI supply chain, playing a vital role in preparing high-quality, domain-specific data for training models. By bringing Scale’s infrastructure under its roof, Meta is effectively shortening the cycle time between research, training, and deployment, allowing it to iterate faster than competitors.
Just as WhatsApp gave Meta access to communication networks, Scale AI could give Meta access to the very neurons of digital intelligence.
Platform Leverage Over Network Effects
WhatsApp delivered powerful network effects. But LLMs provide something even more valuable: platform leverage. They are not tied to a single use case or platform but can scale horizontally across multiple products, industries, and user personas.
Meta is clearly envisioning a future where its proprietary models—such as Llama 4 and the long-rumored “Behemoth”—power not just chatbots but the entire backend intelligence layer of Facebook, Instagram, WhatsApp, Quest, and enterprise solutions. With Alexandr Wang now steering its AI strategy, Meta is aiming to build a vertically integrated LLM stack: from data sourcing and annotation to compute infrastructure and global deployment.
This approach not only enhances model quality but opens new monetization avenues, ranging from developer APIs to enterprise licensing and AI-powered ads. In contrast, WhatsApp, even with its massive user base, has offered limited monetization potential outside of business messaging and payments.
The Wang Effect: Execution at Scale
Alexandr Wang is not a traditional AI researcher. His strengths lie in operational excellence, fast-paced scaling, and enterprise-grade execution. This reflects a strategic shift within Meta: the LLM race will not be won by research alone, but by translating innovation into productized intelligence at a global scale.
Wang’s presence brings a startup mentality to Meta’s AI efforts—something that could serve as a counterbalance to the more academic or research-centric cultures seen in traditional AI labs. Moreover, Meta’s willingness to offer \$100 million+ compensation packages to lure top AI talent signals that it is willing to outspend, outbuild, and outmaneuver its competitors.
If Meta can successfully bring together the best minds in LLM development, pair them with an optimized data and compute pipeline, and integrate these efforts across its user platforms, it will have built not just a model, but a moat.
From Utility to Ubiquity
WhatsApp’s value lies inits communication utility. But Meta’s LLM strategy targets ubiquity. A successful model doesn’t merely assist users; it becomes embedded in every touchpoint—from auto-generated content and smart recommendations to real-time translation, synthetic media, and developer ecosystems.
This positions Meta to become more than a platform—it becomes the AI operating system of the web. And as AI systems become the gateway for accessing knowledge, creativity, and commerce, Meta could control the lens through which billions of people engage with the digital world.
Final Thoughts
Meta’s WhatsApp acquisition was a watershed moment in mobile technology. But the move to hire Alexandr Wang and double down on large language models could be far more consequential. It is an audacious bet—one with considerable risks, including fierce competition from OpenAI, Google, and Anthropic. Yet, if Meta can execute at scale, deliver competitive models, and fully leverage its ecosystem, it will not just participate in the LLM race—it may define the rules of the game.
In doing so, Meta may achieve something even WhatsApp could not offer: cognitive infrastructure ownership at a global level.
