It’s only been since June that Meta invested $14.3 billion in the data-labeling vendor Scale AI, bringing on CEO Alexandr Wang and several of the startup’s top executives to run Meta Superintelligence Labs (MSL). But the relationship between the two companies is already showing signs of fraying.
At least one of the executives Wang brought over to help run MSL — Scale AI’s former Senior Vice President of GenAI Product and Operations, Ruben Mayer — has departed Meta after just two months with the company, two people familiar with the matter told TechCrunch.
Mayer spent roughly five years with Scale AI across two stints. In his short time at Meta, according to those sources, Mayer oversaw AI data operations teams and reported to Wang but wasn’t tapped to join the company’s TBD labs — the core unit tasked with building AI superintelligence, where top AI researchers from OpenAI have landed.
However, Mayer disputes some details about his role, telling TechCrunch that his initial position was “to help set up the lab, with whatever was needed” rather than run data operations, and that he was “part of TBD labs from day one” rather than being excluded from the core AI unit. Mayer also clarified that he “did not report directly to [Wang]” and was “very happy” with his Meta experience.
Beyond the personnel changes, Meta’s relationship with Scale AI appears to be shifting. TBD Labs is working with third-party data labeling vendors other than Scale AI to train its upcoming AI models, according to five people familiar with the matter. Those third-party vendors include Mercor and Surge, two of Scale AI’s largest competitors, the people said.
While AI labs commonly work with several data labeling vendors – Meta has been working with Mercor and Surge since before TBD Labs was spun up – it’s rare for an AI lab to invest so heavily in one data vendor. That makes this situation especially notable: even with Meta’s multi-billion-dollar investment, several sources said that researchers in TBD Labs see Scale AI’s data as low quality and have expressed a preference to work with Surge and Mercor.
Scale AI initially built its business on a crowdsourcing model that used a large, low-cost workforce to handle simple data labeling, which is the process of tagging and annotating raw information to train AI models. But as AI models have grown more sophisticated, they now require highly-skilled domain experts—such as doctors, lawyers, and scientists—to generate and refine the high-quality data needed to improve their performance.
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Although Scale AI has moved to attract these subject matter experts with its Outlier platform, competitors like Surge and Mercor have been growing quickly because their business models were built on a foundation of high-paid talent from the outset.
A Meta spokesperson disputed the fact that there are quality issues with Scale AI’s product. Surge and Mercor declined to comment. Asked about Meta’s deepening reliance on competing data providers, a Scale AI spokesperson directed TechCrunch to its initial announcement of Meta’s investment in the startup, which cites an expansion of the companies’ commercial relationship.
Meta’s deals with third-party data vendors likely mean the company is not putting all its eggs in Scale AI, even after investing billions in the startup. The same can’t be said for Scale AI, however. Shortly after Meta announced its massive investment with Scale AI, OpenAI and Google said they would stop working with the data provider.
Shortly after losing those customers, Scale AI laid off 200 employees in its data labeling business in July, with the company’s new CEO, Jason Droege, blaming the changes in part on “shifts in market demand.” Droege said Scale AI would staff up in other parts of the business, including government sales — the company just landed a $99 million contract with the U.S. Army.
Some speculated initially that Meta’s investment in Scale AI was really to lure Wang, a founder who has operated in the AI space since Scale AI was founded in 2016 and who appears to be helping Meta to attract top AI talent.
Aside from Wang, there’s an open question around how valuable Scale is to Meta.
One current MSL employee says that several of the Scale executives brought over to Meta are not working on the core TBD Labs team.
Meanwhile, Meta’s AI unit has become increasingly chaotic since bringing on Wang and a wave of top researchers, according to two former employees and one current MSL employee. New talent from OpenAI and Scale AI have expressed frustration with navigating the bureaucracy of a big company, while Meta’s previous GenAI team has seen its scope limited, they said.
The tensions indicate that Meta’s largest AI investment to date may be off to a rocky start, despite that it was supposed to address the company’s AI development challenges. After the lackluster launch of Llama 4 in April, Meta CEO Mark Zuckerberg grew frustrated with the company’s AI team, one current and one former employee told TechCrunch.
In an effort to turn things around and catch up with OpenAI and Google, Zuckerberg rushed to strike deals and launched an aggressive campaign to recruit top AI talent.
Beyond Wang, Zuckerberg has managed to pull in top AI researchers from OpenAI, Google DeepMind, and Anthropic. Meta has also acquired AI voice startups including Play AI and WaveForms AI, and announced a partnership with the AI image generation startup, Midjourney.
To power its AI ambitions, Meta recently announced several massive data center buildouts across the U.S. One of the largest is a $50 billion data center in Louisiana called Hyperion, named after a titan in Greek mythology that fathered the God of Sun.
Wang, who’s not an AI researcher by background, was viewed as a somewhat unconventional choice to lead an AI lab. Zuckerberg reportedly held talks to bring in more traditional candidates to lead the effort, such as OpenAI’s chief research officer, Mark Chen, and tried to acquire the startups of Ilya Sutskever and Mira Murati. All of them declined.
Some of the new AI researchers recently brought in from OpenAI have already left Meta, Wired previously reported. Meanwhile, many longtime members of Meta’s GenAI unit have departed in light of the changes.
MSL AI researcher Rishabh Agarwal is among the latest, posting on X this week that he’d be leaving the company.
“The pitch from Mark and @alexandr_wang to build in the Superintelligence team was incredibly compelling,” said Agarwal. “But I ultimately choose to follow Mark’s own advice: ‘In a world that’s changing so fast, the biggest risk you can take is not taking any risk’.”
Asked afterward about his time at Meta and what drove his decision to leave, Agarwal declined to comment.
Director of product management for generative AI, Chaya Nayak, and research engineer, Rohan Varma, have also announced their departure from Meta in recent weeks. The question now is whether Meta can stabilize its AI operations and retain the talent it needs for its future success.
MSL has already started working on its next generation AI model. According to reports from Business Insider, it’s aiming to launch it by the end of this year.
Update: This story has been updated with comments from Mayer, who reached out to TechCrunch after publication.
