Meta To Cut 600 AI Jobs In Superintelligence

PALO ALTO, CA — Meta executives on Wednesday said the company plans to cut hundreds of jobs in its artificial intelligence division as it tries to balance the cost of keeping up with rivals in the field, such as OpenAI and Google, with delivering results, according to reports.

The layoffs in the Superintelligence Lab were announced in a memo by Meta chief AI officer Alexandr Wang. They come as the AI boom continues to drive the stock market and as some analysts warn of an AI stock market bubble.

"By reducing the size of our team, fewer conversations will be required to make a decision, and each person will be more load-bearing and have more scope and impact," Wang wrote in the memo, which was reported by Axios. As many as 600 employees will be laid off.

U.S. employees will learn by 7 a.m. Pacific time today whether their jobs are affected, Wang said in the memo, according to Axios, which reported the cuts will be in the company's FAIR AI research, product-related AI, and AI infrastructure units, while sparing the newly formed TBD Lab unit.

CNBC reported that Meta notified at least some employees on Wednesday that Nov. 21 would be their termination date and, until then, they're in a "non-working notice period." Meta informed them that, "During this time, your internal access will be removed and you do not need to do any additional work for Meta." The company encouraged them to use the time to search for another role at Meta, and that the company will pay 16 weeks of severance, plus two weeks for every completed year of service, "minus your notice period."

The cuts, which Axios earlier reported, come at an intensely competitive time for Meta, which has pitted itself for the past three years against major rivals in the race for AI dominance. Companies, including OpenAI, Google, Microsoft, and Facebook began aggressively recruiting talent.

In an effort to keep pace with competitors in the breakneck race for AI technology, Zuckerberg went on a hiring spree to stack his company with top talent, according to reports. The competition remains fierce.

"More than 1,300 AI startups now have valuations of over $100 million, with 498 AI “unicorns,” or companies with valuations of $1 billion or more, according to CB Insights," MSNBC reported Wednesday. "Megacaps like Amazon, Meta, and Microsoft are spending billions on data center buildouts."

Although META reported a 10 percent jump in shares during the second-quarter earnings on Wednesday, expenses nipped at gains, and the company has struggled to keep up with rivals.

The New York Times said job cuts are an effort to deal with problems by cleaning up the organizational bloat that resulted from three years of building up Meta's AI efforts too quickly, and help Meta develop AI products more nimbly. That includes Wang, hired earlier this year.

The company's AI spending spree included investing $14.3 billion for a 49 percent stake in Scale AI and hiring its CEO, Wang, to lead a new research lab.

Scale AI recently shut down an entire team of contractors in its Dallas office, marking the latest restructuring at the startup since Meta's investment in June, according to reports from Times of India. The shuttered team had over a dozen members focused on generalized tasks, such as improving the writing ability of AI chatbots.

In addition to data scientists and researchers, Scale AI operates a workforce of contractors in the United States and across Kenya, the Philippines and Venezuela, where workers manually label images, text and video for machine learning applications, according to Forbes.

The data labeling process involves identifying and annotating objects in images, transcribing audio, or categorizing text to create the training datasets that teach AI models to recognize patterns. Observers attributed the layoffs to the need for more specialized knowledge to train datasets in domains like healthcare, finance and legal services, demanding a nuanced understanding beyond basic image recognition.

Scale AI's acquisition was said to address Meta's most pressing challenge in the tech race — access to the specialized datasets required to train competitive large language models.