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The departures and subsequent reorganizations are 2025 developments. They should not be treated as a complete picture of MSL’s status in August 2026.
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What Meta was trying to build
Meta created Meta Superintelligence Labs in 2025 alongside its existing AI organizations, including the Fundamental AI Research (FAIR) group. The broader structure included a smaller frontier-model effort known as TBD Lab, product and applied-research teams, and infrastructure groups.
WIRED reported that the initial MSL roster included nearly two dozen researchers. Alexandr Wang, formerly chief executive of Scale AI, became a central leader after Meta invested $14.3 billion for a reported 49% stake in Scale AI (Associated Press). Nat Friedman was positioned as a co-leader focused on products and applied research. Meta later announced former OpenAI researcher Shengjia Zhao as chief scientist (TechCrunch).
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The Scale transaction was a corporate investment, not money paid to individual researchers. Conflating the two produces a misleading picture of Meta’s recruiting costs.
How large were the offers?
Reports in June and July 2025 said selected candidates were offered compensation packages worth as much as $300 million over four years, with more than $100 million in first-year total compensation in some cases (WIRED). These were reported total packages, potentially combining salary, equity, retention awards and other incentives—not universal salaries or guaranteed day-one cash.
TechCrunch specifically warned that the widely repeated “$100 million signing bonus” description was misleading: the offers were generally broader multimillion-dollar arrangements (TechCrunch). The reporting does not establish that every recruit received a nine-figure package. Some prominent targets reportedly rejected Meta’s approaches (TechCrunch).
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Who left, and what is actually known?
The clearest account concerns three MSL hires identified by WIRED. Other departures were reported later but should not automatically be counted as defections from the core TBD Lab.
| Person | Reported status | Public explanation | What remains unknown |
|---|---|---|---|
| Avi Verma | Left Meta and reportedly returned to OpenAI after less than a month | No public explanation established | Whether role, management, compensation or a counteroffer mattered |
| Ethan Knight | Left Meta and reportedly returned to OpenAI after less than a month; he had joined Meta from xAI | No public explanation established | The private reasons for reversing the move |
| Rishabh Agarwal | Announced his departure from MSL in August 2025 | Said he wanted “a different kind of risk” after 7.5 years at Google Brain, DeepMind and Meta | His destination and whether workplace conditions influenced the decision |
| Chaya Nayak | Reportedly moved from Meta to OpenAI special initiatives | No detailed public explanation in the cited report | Whether the move was connected to MSL |
| Aurko Roy | WIRED later reported that he had left Meta in July 2025 | No detailed reason reported | His precise role and destination |
| Rohan Varma | TechCrunch reported that he announced a departure in late August 2025 | No detailed reason reported | Whether he belonged to core MSL |
Sources for the personnel reporting include WIRED’s August 26 report, its later account of Meta recruiting (WIRED), and TechCrunch.
Why “mysterious reasons” is an incomplete explanation
No reporting cited here shows that the three MSL researchers shared one grievance, or that they left because of Mark Zuckerberg, Wang, poor models, or a single contractual dispute. Several organizational explanations are plausible, but they remain hypotheses unless tied to a specific person.
Mission versus product pressure
Researchers recruited for frontier work may value scientific autonomy, while Meta also needs AI to improve products such as Facebook and Instagram. A mismatch between long-horizon research and rapid product deployment can make a role less attractive even when computing resources and pay are exceptional.
Rapid hiring and unclear structure
Meta reorganized AI groups during the campaign and had to clarify leadership as hiring accelerated. Reporting described bureaucracy, changing reporting lines and the challenge of dividing responsibilities among FAIR, TBD Lab, product teams and infrastructure. Those conditions can create friction, but they are not proof of why any one person resigned.
Personal and ordinary hiring reversals
Relocation, family circumstances, counteroffers, contract terms and simple buyer’s remorse can all affect a move. Meta spokesperson Dave Arnold characterized some decisions as normal outcomes of an intense recruiting process, in which people ultimately decide to remain in their existing jobs (WIRED).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How rivals interpreted the campaign
OpenAI CEO Sam Altman told employees that Meta was trying to buy talent and contrasted “mercenaries” with “missionaries” (WIRED). That is a competitor’s argument, not a neutral finding. It does, however, frame the strategic question: can compensation and compute create durable commitment, or do mission, team identity and research culture matter more?
What happened after the first resignations?
Meta continued recruiting, including senior figures such as Zhao and Yang Song (WIRED). TechCrunch later reported additional departures and tensions involving the Scale AI relationship (TechCrunch). Axios reported in October 2025 that Meta reorganized the superintelligence organization and cut roughly 600 positions from a broader group of several thousand roles (Axios).
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Those reports show continuing organizational change, not a verified 2026 headcount, retention rate or definitive outcome. The available evidence does not establish that MSL collapsed, nor does it prove that the strategy succeeded.
How to judge whether the strategy worked
- Retention: six-, 12- and 24-month retention, including how many recruits returned to former employers.
- Research output: significant papers, models, benchmarks or technical breakthroughs, whether published or proprietary.
- Product impact: measurable improvements to assistants, recommendations, generative tools or advertising.
- Organizational stability: clear roles and durable reporting lines across FAIR, TBD Lab, product and infrastructure teams.
- Economic return: value created relative to compensation commitments and the Scale AI investment.
- Cultural fit: whether researchers received the autonomy and mission they expected while working inside a product company.
The defensible conclusion
Meta proved it could attract attention and prominent AI talent with extraordinary offers. The early exits—especially two reported returns to OpenAI within a month—are a meaningful retention warning, but not proof of a failed lab. They show something narrower and more important: money can open the door, yet it cannot by itself resolve mission, management, autonomy, geography or career-fit questions.
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