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Meta’s AI Ambitions: Hidden Strengths in the Superintelligence Race

Despite internal challenges and a delayed grand model launch, Meta possesses key advantages in data, talent, and compute that could propel it forward in the AI race against OpenAI and Anthropic.

News Published 17 July 2026 4 min read Maya Turner

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While Meta might not appear to be a leading contender in the race for superintelligence, the company holds significant, often overlooked, advantages that could allow it to close the gap with industry giants like OpenAI and Anthropic. A recent analysis by Semianalysis suggests that Meta’s current position, despite internal restructuring and a perceived delay in launching a flagship model, is stronger than many assume, primarily due to its strategic accumulation of data, talent, and computing power.

Data Goldmine

One of Meta’s most potent, and controversial, assets is its vast repository of user data. The company controversially implemented software to record employee computer activity, not for surveillance, but for training its AI. This initiative has yielded a rich dataset of individuals performing various tasks, a resource that competitors often have to piece together through partnerships. Semianalysis likens this to creating a “top-tier RL environment startup” within Meta, spearheaded by a former Scale AI co-founder. Following a significant restructuring, approximately 3,000 engineers have been redirected to focus on reinforcement learning environments, leveraging this unique data for developing advanced AI agents, akin to OpenAI’s Codex or Claude Code.

Massive Compute Capacity

Meta’s substantial investment in data centers positions it as a formidable force in the AI compute landscape. The company is constructing several massive data centers, each with over 1 gigawatt of capacity. While Meta may not rival hyperscalers like Google, Microsoft, and Amazon in sheer infrastructure scale, its capabilities are highly competitive when measured against frontier AI labs. Projections indicate that Meta could possess more computational power than Anthropic and OpenAI combined by the end of the year, a critical factor for training increasingly complex and powerful AI models.

Acquisition of Top Talent

Last summer, Meta aggressively pursued AI talent, offering substantial compensation. The company recruited at least 14 senior researchers from leading AI labs, including Anthropic and Google, and notably acquired Scale AI for $14 billion to secure Alexandr Wang. While assembling a team of top experts doesn’t guarantee seamless collaboration—rumors of internal tensions have surfaced—Meta has demonstrably gathered some of the brightest minds in the field. If these teams can overcome internal challenges, their collective expertise represents a significant strategic advantage.

Navigating Internal Challenges

Despite these strengths, Meta faces internal hurdles. A year ago, the company was reportedly hiring AI talent at an unprecedented rate, offering multi-million dollar salaries and acquiring entire companies. However, a subsequent layoff of 8,000 employees and reports of a strained work environment have raised questions about internal morale and strategic direction. The success of Meta’s AI ambitions hinges not only on its resources but also on its ability to manage these internal dynamics and maintain focus. Failure to navigate these challenges could risk derailing its progress.

The Path Forward

While Meta’s initial AI model, Muse Spark, did not meet expectations and lagged behind competitors like Deepseek v4 Pro and Kimi K2.6, its long-term potential remains high. The combination of proprietary data, significant compute resources, and a concentrated pool of top AI talent provides a strong foundation for future development. The company’s ability to effectively harness these advantages will determine its trajectory in the competitive landscape of superintelligence development.

Key facts

Aspect	Detail	Significance
Data Acquisition	Recording employee computer activity for AI training	Provides a unique, large-scale dataset for training AI agents, surpassing competitors needing external data.
Compute Power	Construction of multiple 1+ GW data centers	Expected to exceed Anthropic and OpenAI’s combined compute capacity by year-end, crucial for model training.
Talent	Recruitment of top AI researchers and acquisition of Scale AI	Assembled a high-caliber team, though internal cohesion remains a factor to monitor.
Internal Climate	Layoffs and reported strained work environment	Potential risk to focus and execution despite strong resource allocation.

This development matters to ReviewArticle readers as it highlights a potential shift in the AI landscape. Meta’s strategic investments, particularly in proprietary data and compute, could lead to significant advancements in AI agents and large language models, directly impacting the tools and technologies available to developers and businesses.

Source: Nadie apostaría hoy por Meta en la carrera de la superinteligencia, pero juega con más ventaja de la que pensamos para ganarla – Xataka: https://www.xataka.com/robotica-e-ia/nadie-apostaria-hoy-meta-carrera-superinteligencia-juega-ventaja-que-pensamos-para-ganarla