- By Prateek Levi
- Mon, 20 Oct 2025 03:25 AM (IST)
- Source:JND
Scale AI has quietly laid off an entire team of contractors at its Dallas office this week, signalling another round of restructuring since Meta poured $14.3 billion into the startup in June.
The affected group, known internally as the New Projects Organization (NPO), consisted of just over a dozen contractors responsible for improving the writing skills and overall performance of AI chatbots. Their sudden dismissal reflects a larger strategic shift within the company — and across the AI industry — toward more specialised, expert-driven training work.
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According to a statement shared with Business Insider, Scale AI explained the move as part of a transition to higher-skill data training. “Scale wound down a small experimental onsite programme in Dallas staffed by a contract workforce,” said Natalia Montalvo, a company spokesperson. “This reflects an industry shift toward higher skill, expert data work. It only affects a small fraction of our overall workforce and has no impact on customer delivery.”
Severance and Support for Affected Employees
The laid-off workers received four weeks of severance pay and healthcare coverage through the end of October, according to an internal email from HireArt, one of Scale AI’s staffing partners. The company also encouraged displaced employees to apply for work on its Outlier platform, a gig-based system where freelancers train AI models remotely.
“As you navigate this transition, we want to ensure you are aware of alternative opportunities,” the email said.
Scale AI confirmed that it remains committed to expanding its expert-level training programmes, while other contractor teams continue operations in Dallas.
The layoffs are the latest in a string of cutbacks since Meta’s investment and mirror moves by other AI firms like xAI, which recently reduced its team of generalist AI tutors to prioritise more technical expertise.
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As AI systems become increasingly advanced, companies like Scale are rethinking their labour models – replacing generalist contributors with domain experts who can help push machine learning systems toward higher reasoning and accuracy.