Meta cuts 600 AI roles amid broader tech reshuffle
Meta cuts 600 AI roles as Big Tech reshapes teams.
Meta is scaling back part of its artificial intelligence workforce, a move that underscores wider recalibration across Big Tech.
Planned cuts within Meta Superintelligence Labs
According to Bloomberg, citing an internal memo, Meta plans to eliminate about 600 roles within its AI group known as Meta Superintelligence Labs (MSL). The memo said Meta’s Chief AI Officer, Alexandr Wang, informed employees of the cuts in October. The company’s newly formed TBD Lab, which includes many recently hired, highly paid recruits, was reportedly not affected.
Hiring surge and big-ticket compensation
The reduction comes after an aggressive hiring push earlier this year. Reports say MSL onboarded roughly 50 staff, including high-profile recruits from Apple, Anthropic, xAI, Google, and OpenAI, with compensation packages said to reach as high as $100 million for some roles. Meta has also spent heavily to secure AI talent and partnerships, including links to data-labelling firm Scale AI, though full financial details have not been publicly disclosed.
Internal mobility and hiring pause
A source told Bloomberg that affected employees are being encouraged to apply for roles elsewhere within Meta, and that the company still intends to hire for certain AI teams. The adjustment follows a hiring pause reported by the Wall Street Journal on August 20, when Meta froze hiring for parts of its AI division. At the time, a company spokesperson told Reuters the pause was part of routine organizational planning tied to annual budgeting.
Industry-wide reshuffle
The cuts at Meta mirror a broader trend. Other major technology companies—from Amazon to Google—have trimmed AI-related roles or cited AI-driven shifts as they reorganize teams and priorities, even as they continue to invest in core AI research and product development.
Bottom line
Taken together, Meta’s move signals reallocation rather than retreat: tightening in some areas while continuing to channel resources into priority AI projects. As the industry refines its strategies, companies are balancing the cost of top-tier talent with the need to ship AI features, manage infrastructure spending, and navigate fast-evolving competitive pressures.
