Netflix to Cut About 1,000 Jobs, or 6% of Workforce
publishers 62articles 68first reported 9 Oct, 13:30 UTCdeveloping since 9 Oct · 17 editionstop 25 #23
Netflix is laying off about 1,000 employees worldwide, equivalent to roughly 6% of its workforce, in its largest round of job cuts to date. The reductions will fall most heavily on the streaming company’s product, technology, legal and human resources teams.
Co-CEOs Ted Sarandos and Greg Peters announced the changes in a memo to employees, saying Netflix needed to be disciplined about how it prioritizes staff and resources. Managers were expected to notify affected employees soon.
“Our business is strong and growing, and we remain on track to deliver our full-year forecast,” Sarandos and Peters wrote. They said the company would continue investing heavily in areas intended to improve its service and support growth and innovation, while making “focused choices” across the business.
Netflix reported about 16,000 full-time employees at the end of 2025. That year, it generated $45.2 billion in revenue, up 16%, and $11 billion in net income, up 26%. For 2026, the company has projected revenue of $51 billion to $51.4 billion and an operating margin of 31.5%.
The previous major round of layoffs came in 2022, when Netflix eliminated about 450 positions after reporting its first subscriber decline in more than a decade. The company is scheduled to report third-quarter results on Oct. 20.
HOW THIS STORY WAS MADE
Written from 8 articles, 6 with the publisher's own text; 8 independent newsrooms once syndicated copies count as one; this version written 132 h after the record first saw the story; the editor kept it as written.
- Publishers
- 62
- Source articles
- 68
- Given to the writer
- 8, 6 with the article's own text
- Independent newsrooms
- 8
- This version written
- 132 h after the record first saw the story
- Second model (editor)
- kept as written
- Publication gate
- passed
- Human review
- none
WHAT THE EDITOR DID
The second model checked the writer's draft against the same sources and published it as written.
Written by a language model from the sources above, then checked by a second model that may only cut, attribute or correct. How it works →