Meta Superintelligence Labs Faces New Wave of Departures
Hao Zhang, an assistant professor at the University of California, San Diego (UCSD) and AI systems researcher, announced on Wednesday that he has officially left his position at Meta Superintelligence Labs' TBD Lab. Earlier this year, he had applied for academic leave to join Meta TBD Lab, and he is now ending this tenure to embark on a "new mission."
In a post on social media, Hao Zhang described this as "the best summer I have had in years," and specifically mentioned one of the team leaders, @yinghai, as "one of the best infrastructure leads, with deep technical skills and ambitious goals, and very adept at creating an environment for the team to focus on R&D." He also highlighted his experience working with @_tim_brooks (Tim Brooks, whom he referred to as "one of the greatest minds who pioneered the entire video generation industry"). Hao Zhang stated that he contributed to the development of Meta's Muse image and video generation model's inference capabilities and witnessed its rapid scaling, praising the recently released consumer AI product Muse as "one of the most impressive consumer-grade intelligent agent products currently available."
Hao Zhang's departure is not an isolated case but the latest in a series of high-profile exits from Meta Superintelligence Labs since its establishment this year. According to previous reports, within about two months of Zuckerberg announcing the formation of the lab, at least eight employees had left, including Chaya Nayak, who had worked at Meta for ten years (now at OpenAI), and Birch Mart, who had been with the company for twelve years (now at Anthropic), with at least two others returning to OpenAI.
Another notable departure occurred around September 9: Andrew Tulloch, a core member of TBD Lab, which is considered central to Zuckerberg's AI strategy and led by Scale AI founder Alexandr Wang, announced his departure. Tulloch was a star researcher personally recruited by Zuckerberg from Thinking Machines Lab, and reports indicated that his compensation package was as high as $1.5 billion over six years (this figure is disputed, but even if discounted, it remains a rare high-salary contract in the tech industry). Tulloch specifically waited until after Meta successfully launched its new generation of open-source AI models and the consumer AI assistant Muse before officially leaving; his next steps and whether he will continue AI research at another institution are currently unclear.
Reports indicate that despite Zuckerberg offering "nine-figure" compensation packages to attract top talent, this aggressive pay strategy has also caused dissatisfaction among long-time Meta employees, being seen as one of the factors leading to a tense team atmosphere and ongoing talent loss. The reports quote opinions suggesting that the company needs to make substantial improvements in the work environment to truly retain talent. Meta has responded to this wave of departures with indifference, stating, "Some people choose to stay in their original positions rather than jump to new companies... this is normal."
From the talent market perspective, top AI research talent is currently in extreme demand, with competition among leading institutions like Anthropic, OpenAI, Google, SpaceX AI, and Meta entering a model of "sky-high salaries + short-term high-intensity sprint projects"—many researchers choose to participate in key projects at popular labs in a manner similar to Hao Zhang's "academic leave + short-term joining," and after achieving phase results (such as the release and rapid scaling of Muse), they opt to leave, returning to academia or other institutions. This flow model benefits individual researchers (allowing them to accumulate experience from top projects and receive high compensation) and employers willing to exchange high salaries for short-term explosive output; however, for employers, this also means that the retention period of core team talent is significantly compressed, posing a continuous risk of losing organizational knowledge and technical continuity.
It is noteworthy that Hao Zhang's departure occurred shortly after Meta successfully launched the new generation of open-source AI models and the consumer AI assistant Muse, and it bears a striking resemblance to Andrew Tulloch's timing of leaving "after the product launch," a pattern that has repeatedly appeared in recent departure cases from TBD Lab.
Source: Public Information
ABAB AI Insight
Since Zuckerberg's high-profile establishment of Meta Superintelligence Labs this year, the lab has been characterized by a dense flow of talent—starting with the initial attraction of top researchers like Andrew Tulloch with "nine-figure" compensation packages, to the recent departures of several senior employees including Chaya Nayak and Birch Mart within just a few months. Hao Zhang's departure, framed as a "return from academic leave," continues the lab's historical pattern of "high salary recruitment—intensive sprint—exit after project completion."
From a resource mobilization perspective, Meta has invested an unprecedented scale of compensation capital in this talent competition—reports suggest that Andrew Tulloch's compensation package alone was as high as $1.5 billion over six years, a scale of personal compensation contracts that is extremely rare in tech industry history. Scholars like Hao Zhang, who join temporarily under academic leave, leverage their academic reputation and technical expertise accumulated at top universities to gain access to cutting-edge industrial projects (like the scaling of the Muse model) in the short term, essentially engaging in a "capital exchange for scarce talent's short-term output" transaction.
A comparable historical case is the cycle of "high salary recruitment—project sprint—rapid talent loss" that repeatedly occurred among several tech giants in Silicon Valley around the 2010s, often accompanying similar high salary hiring and high turnover phenomena, such as the early formation of star teams in autonomous driving and chip design. The current AI industry is in a phase of intense talent competition among top labs, with record-high salary levels but generally weak organizational retention capabilities, and this series of departures from Meta Superintelligence Labs is a concentrated reflection of this industry-wide contradiction.
This event essentially reflects that the scarce resource of top AI talent is being fiercely contested by capital with unprecedented intensity: when compensation scales reach levels like "$1.5 billion over six years," it is effectively concentrating the entire company's capital resources on a very small number of individuals deemed to possess key technical capabilities, attempting to exchange short-term capital accumulation for certain outputs during the project sprint phase. However, mechanistically, this reliance on purely capital accumulation to attract talent inherently lacks stability—once phase project goals (like the successful launch of Muse) are achieved, the technical reputation and choice power held by individual researchers will further enhance, making it easier for them to move to the next institution offering more attractive conditions, forming a paradox where the more concentrated the capital, the higher the talent mobility.
ABAB News · Cognitive Law
High salaries can attract talent to join, but cannot guarantee retention.
The launch of a project signals the time to exit.
The more capital concentrated on individuals, the easier it is for talent to switch jobs.