Weeks after Meta poured $14.3 billion into Scale AI and hired its founder, Alexandr Wang, to lead its new AI division, the startup has laid off 200 full-time employees — about 14% of its staff — in a significant restructuring move aimed at trimming excess and restoring operational focus.
The job cuts were announced by Scale AI’s interim CEO Jason Droege in a memo to employees on Wednesday. Droege, who stepped in after Wang joined Meta as its Chief AI Officer, acknowledged that the company had ramped up its generative AI business too rapidly, leading to layers of bureaucracy and inefficiencies that, he said, clouded the company’s mission.
“While that felt like the right decision at the time, it’s clear this approach created inefficiencies and redundancies,” Droege said in the memo. “We created too many layers, excessive bureaucracy, and unhelpful confusion about the team’s mission.”
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The layoffs come amid rising questions about Scale AI’s profitability and sustainability, particularly after it expanded its workforce to 1,400 globally and relied heavily on contracts from major players like OpenAI, Google, and Microsoft. But in the last year, OpenAI has significantly wound down its collaboration with Scale, and Google has reportedly begun cutting ties with the startup following its deepening alliance with Meta.
Bloomberg first reported the number of job cuts, which Scale AI later confirmed. Alongside the layoffs, the company is also ending contracts with 500 contractors, reducing the scale of its non-employee workforce as part of a broader pivot.
Despite the shake-up, Droege assured employees that Scale AI remains a well-funded and well-resourced company. He emphasized that the layoffs were not about survival but strategic recalibration.
“These changes will make us more nimble — enabling us to react more quickly to shifts in the market and customer needs,” he said.
The restructuring is aimed at streamlining the company’s generative AI division, cutting down from 16 “pods” to five key focus areas, and merging various business development roles into a single Demand Generation team. The company said it would deprioritize GenAI projects that generate little revenue or show low growth potential as part of its effort to become more efficient and move toward profitability — a point Droege appeared to acknowledge in his memo.
However, after publication, a company spokesperson disputed the notion that Droege called the company unprofitable, while refusing to clarify the statement.
Going forward, Scale AI plans to double down on its enterprise and public sector divisions. Droege said the company would “significantly increase headcount” in the second half of 2025 within these units, including across its international public sector business.
Founded in 2016 by Alexandr Wang, Scale AI made its name as a critical behind-the-scenes player in the AI revolution, providing data labeling and infrastructure services used to train large models for OpenAI, Google, and others. Its reputation surged as demand for generative AI exploded in the wake of OpenAI’s ChatGPT, but that growth appears to have outpaced strategic planning.
The Meta-Scale partnership, which included Meta’s acquisition of key Scale talent and Wang’s appointment to head Meta Superintelligence Labs, has raised eyebrows in the industry. Insiders say the alliance may have triggered nervousness among other big tech clients like Google and Microsoft, who are now reportedly scaling back ties with Scale due to potential conflicts of interest.
Scale spokesperson Joe Osborne said affected workers were being offered severance and that the company would continue to invest heavily in new AI initiatives.
“We’re streamlining our data business to help us move faster and deliver even better data solutions to our GenAI customers,” he said, adding that hiring in government and enterprise AI sectors would remain a priority.
Droege, in his message to staff, expressed gratitude to those affected by the cuts, describing their contributions as instrumental to Scale’s growth. However, the move underscores the growing pains many AI firms are experiencing as they shift from early hype and experimentation to monetization and long-term strategy.
The development also signals a sobering moment in Silicon Valley’s AI gold rush — even well-capitalized startups are beginning to tighten their belts, realizing that breakneck expansion without profitability or focus can be as dangerous as falling behind.