What’s behind Meta’s bold hiring move—and how might it alter the global AI power dynamic?

Over the past week, Meta made headlines across the global tech sector after securing three top-tier researchers from OpenAI. While the media focused on the unexpected nature of the move, the deeper story reveals a strategic pivot: a global contest not merely over models, but over minds, platforms, and control of the next frontier—Artificial General Intelligence (AGI).

The Strategic Move

On June 26, 2025, leading outlets including Reuters, The Wall Street Journal, TechCrunch, and Yahoo Finance reported that Meta had successfully hired Lukas Biewald, Alexander Kolesnikov, and Xuehua Zhai—three senior researchers formerly based at OpenAI’s Geneva office, established just six months earlier. All three had previous experience at Google DeepMind and are regarded as key contributors to advanced machine learning research.

OpenAI confirmed the departures, and while Meta has not issued a formal statement, sources close to the company corroborated the reports.

AGI-Driven Incentives: Up to $100 Million Compensation Offers

The core takeaway for many: Meta is reportedly offering extremely generous compensation packages—rumored to reach as high as $100 million. According to The Wall Street Journal, Mark Zuckerberg himself played an active role in the recruitment process, including direct outreach, private meetings, and even personal hospitality at his Lake Tahoe estate.

OpenAI CEO Sam Altman responded with subtle sarcasm, suggesting that “this often doesn’t work,” while acknowledging that financial incentives have become unavoidable in today’s highly competitive talent market.

Why These Researchers Matter

This move aligns with Meta’s broader AGI strategy. The company has committed approximately $14.3 to $14.8 billion to Scale AI—where Alexander Wang is a leading partner—and recently launched a new division called “Superintelligence.” This internal group is tasked with accelerating development of future-generation large language models (LLMs), including Llama 4 and beyond.

Although Llama models have yet to dominate the market, Meta is positioning itself as a leader in open-source AI and infrastructure innovation—going head-to-head with OpenAI, Anthropic, and DeepMind.

Quantitative Analysis: Expenditures and Return Expectations

Meta’s 2025 capital allocation reflects its economic strength and clear strategic direction:

Projected capital expenditures for 2025: ~$65 billion

Portion allocated to AI and infrastructure: $40+ billion

Equity investment in Scale AI (Q1 2025): $3.5 billion at a $20B valuation

Number of AI engineers/researchers hired since 2024: Over 1,000

These numbers highlight the scale of Meta’s ambition. In contrast to OpenAI’s more conservative compensation model, Meta is setting new benchmarks for recruitment—potentially pressuring Microsoft, Anthropic, and others to respond, either through salary restructuring or differentiated value propositions.

Industry-Wide Implications

Such high-profile hires could reshape the AI talent market. The focus on elite researchers and soaring incentive packages risks centralizing talent within a handful of corporate giants. As innovation becomes tethered to those who can afford it, smaller startups, academic institutions, and open research communities could be pushed out of the competitive equation.

Those unable to match Meta’s financial muscle may be forced to pivot toward academic collaborations, niche research areas, or consortium-based development models.

Meanwhile, Meta’s open-source narrative—while compelling—raises regulatory and philosophical questions about platform control, public access, and long-term stewardship of open technologies.

Identity Crisis or Innovation Drive?

This move reflects a broader ideological divide: between cautious, regulation-oriented approaches to AGI and aggressive, tech-centric acceleration models. Meta clearly embraces the latter. Zuckerberg is keen to reclaim a leadership role in AI innovation—possibly to overcome the perception of lagging behind GPT-class models.

Yet within the research community, skepticism remains. Many argue that innovation stems from shared purpose and cross-disciplinary collaboration—not from isolated, high-stakes compensation.

Between Strategy and Impact: What’s Next for Meta?

Looking forward, critical questions emerge: Can Meta truly rival OpenAI’s technical achievements? Will Llama 5 outperform GPT-5 or Claude 3.5? Is the “concentration of talent” approach sustainable—or does it risk creating a bloated, top-heavy system prone to stagnation?

Meta is clearly prepared to absorb extraordinary costs to secure strategic advantage. However, long-term success depends not just on spending, but on execution—translating investment into resilient, scalable, and ethical AI breakthroughs.