That’s according to a report Friday (March 13) from Reuters, which noted that the cuts come as the tech giant looks to offset its heavy spending on artificial intelligence infrastructure, while also preparing for what it hopes will be more efficiency from AI-assisted workers.
The company has not set a date for the job cuts and the scope of the layoffs is still not final, the report added, citing sources familiar with the matter. These sources, who spoke anonymously as they were not permitted to share the information, said that top executives at Meta had recently shared the plans with managers and told them to start figuring out how to cut back.
“This is speculative reporting about theoretical approaches,” Meta spokesperson Andy Stone told Reuters in response to queries about the plan.
The report noted that if Meta does let go of 20% of its workforce, it will mark the largest round of layoffs since the company’s “year of efficiency” cuts in 2022 and 2023. The company had nearly 79,000 workers at the end of 2025, per its most recent filing.
Meta laid off 11,000 staffers in November 2022, or around 13% of its workforce, before cutting another 10,000 jobs a few months later.
As Reuters pointed out, Meta CEO Mark Zuckerberg has over the past year pushed the company to compete more forcefully in the AI space, offering massive compensation packages to attract leading AI researchers.
The company has been making some acquisitions in the AI space. These include its planned purchase of Chinese startup Manus and, as of last week, Moltbook, a social networking platform for AI agents.
The company also plans to spend between $115 billion and $135 billion this year as it races to construct “data centers, chips and infrastructure capable of supporting increasingly powerful AI models,” as PYMNTS wrote last week.
This is a level of spending that puts Meta in the company of some of the biggest investors in AI infrastructure, including Amazon, Google and Microsoft.
“The difference is that those companies operate cloud businesses that directly monetize the computing power they build,” the report added. “Training models for internal use is only one part of the equation. The same infrastructure can also be rented to thousands of enterprise customers. Meta does not have that outlet.”
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