Meta has systematically recruited the founding team of Thinking Machines Lab, the AI startup built by former OpenAI CTO Mira Murati, after she rejected a reported $1 billion acquisition offer. The most expensive individual hire, co-founder Andrew Tulloch, received a compensation package reportedly worth $1.5 billion over six years, making it potentially the largest single talent acquisition in technology history. The pattern reveals how competition for frontier AI researchers has escalated from millions to hundreds of millions to, in some cases, billions of dollars.

Why Is Meta Spending Billions on Individual Engineers?

Meta's aggressive recruitment strategy reflects the intense competition for AI talent at the frontier of the field. After Mark Zuckerberg's reported $1 billion offer to acquire Thinking Machines Lab outright was rejected, the company pivoted to recruiting the founding team one by one. Of the startup's original founding group, five have gone to Meta, three have returned to OpenAI, and one has joined Elon Musk's xAI. This exodus has left Mira Murati's company, which raised $2 billion at a $12 billion valuation in July 2025, significantly weakened despite maintaining its leadership structure.

The compensation figures circulating in the AI talent market have reached unprecedented scales. OpenAI's chief executive, Sam Altman, has acknowledged that signing bonuses of up to $100 million have been offered to lure top researchers. The escalation reflects a zero-sum dynamic in which every hire by one lab represents a direct loss for another. Meta can afford this competition; the company reported $201 billion in revenue for 2025, up 22% year over year, with $43.6 billion in free cash flow.

What Has Meta's AI Restructuring Actually Produced?

Meta's investment in talent acquisition is part of a broader restructuring of its AI organization. In June 2025, Meta paid $14.3 billion for a 49% non-voting stake in Scale AI and appointed Alexandr Wang, the 28-year-old former Scale AI chief executive, as its first chief AI officer. The restructuring has not been smooth. Yann LeCun, Meta's chief AI scientist for 12 years and one of the most influential figures in deep learning, departed in November 2025 after being asked to report to Wang.

"You don't tell a researcher what to do. You certainly don't tell a researcher like me what to do," LeCun told the Financial Times in January.

Yann LeCun, former Chief AI Scientist at Meta

LeCun's departure triggered a broader exodus. He subsequently raised $1 billion to found AMI Labs in Paris, drawing the founding team "almost entirely from Meta's AI research organisation". By August 2025, Meta Superintelligence Labs had been split into four groups: the TBD Lab for large language models, led by Wang; FAIR for fundamental research; a products and applied research division led by Nat Friedman, the former GitHub chief executive; and an infrastructure unit led by Aparna Ramani.

The first output from Meta Superintelligence Labs arrived on April 8 with the release of Muse Spark, a natively multimodal reasoning model that Meta described as the first step toward "personal superintelligence". The model now powers Meta AI across Facebook, Instagram, WhatsApp, Messenger, and the Ray-Ban Meta AI glasses. Notably, Muse Spark is closed-source, breaking with Meta's open-source Llama tradition and signalling that the intellectual property produced by the researchers Meta is hiring at extraordinary cost will not be shared freely.

How to Understand Meta's AI Strategy Shift

• Closed-Source Models: Meta moved away from its open-source Llama approach with Muse Spark, indicating that proprietary AI capabilities developed by newly hired talent will remain internal rather than released publicly.
• Organizational Restructuring: The creation of Meta Superintelligence Labs under Wang's leadership consolidated model development and dissolved the AGI Foundations team responsible for the Llama model family after Llama 4's lukewarm reception.
• Massive Capital Allocation: Meta is spending $115 to $135 billion in capital expenditure this year on AI infrastructure, including a $27 billion joint venture with Nebius for a gigawatt-scale data centre, alongside the $14.3 billion Scale AI investment.
• Workforce Reallocation: The 8,000 layoffs beginning on May 20 are explicitly framed as a reallocation, shedding roles in Reality Labs, recruiting, sales, and global operations to fund the AI pivot that Wang's division represents.

The talent acquisitions have come at a human cost. Approximately 600 roles were cut from FAIR and AI infrastructure units in October 2025. LeCun publicly stated that the AI team had "fudged" some of the results related to Llama 4, and he warned that "a lot of people have left, a lot of people who haven't yet left will leave".

A second model, internally codenamed "Avocado," is reportedly in development under tighter control within the TBD Lab. Its progress has been uneven; internal benchmarks showed it falling short of Google's Gemini in some evaluations, which contributed to the dissolution of the AGI Foundations team and the consolidation of model development under Wang.

The broader talent market reflects the intensity of competition among AI labs. Anthropic is winning what Fortune described as a "one-sided talent war" against both OpenAI, which retains 67% of its researchers, and Google DeepMind, which retains 78%. DeepMind has responded by enforcing six- to twelve-month non-compete clauses with full salary. The competition extends beyond compensation; it now involves the systematic dismantling of rival startups to acquire their founding teams.

The question for Meta is whether the talent it has assembled, at a cost that includes $14.3 billion for Scale AI, a reported $1.5 billion for a single engineer, the departure of one of the field's most respected scientists, and the systematic dismantling of another company's founding team, will produce AI capabilities that justify the investment. Thinking Machines Lab, for its part, has survived the exodus with new leadership, a $12 billion valuation, and the distinction of having produced a team so valuable that the world's largest social media company was willing to spend extraordinary sums to acquire its members.