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November 9, 2026
Daily Newz Post · 500 Yale Avenue North, Seattle, WA 98109
Business · World

Microsoft reports $92.3 billion quarterly revenue amid workforce reductions

Azure cloud growth of 48% drove the results, which arrived days after the company offered buyouts to roughly a quarter of its staff.

By Jason Kovalenko ·

Illustration for: Microsoft reports $92.3 billion quarterly revenue amid workforce reductions
Illustration generated for this article, not a news photograph.

Microsoft recorded total revenue of $92.3 billion for the September quarter, exceeding analyst expectations of $90.6 billion. The company reported profit of more than $36.6 billion, surpassing the $35.1 billion projected by FactSet. These figures were disclosed in a securities filing released on Wednesday.

The financial performance followed a major restructuring effort initiated just two days prior. Microsoft offered voluntary buyouts to approximately 60,000 employees, representing roughly 25% of its workforce. This move aligns with the company's increased spending on artificial intelligence infrastructure. It marks the second voluntary buyout offer the company has made this year, following an April proposal that allowed 7% of its US staff to retire early.

Growth in the Azure cloud computing division fueled the strong results. That business unit expanded by 48% during the quarter. Satya Nadella, the company's chief executive, stated in a filing that Microsoft is delivering greater capability, choice, control, and cost-efficiency for customers across its entire product stack.

The company's global headcount stood at 223,000 at the end of June, a decrease from 228,000 at the end of 2025, according to regulatory documents. Microsoft also announced a new Surface Ultra laptop earlier this month. The device runs on Nvidia's artificial intelligence chips and carries a price tag above $2,500. Apple introduced a similar touchscreen MacBook in the same week.

These hardware releases reflect a trend among tech firms to build devices that handle more AI computing locally. This approach reduces reliance on remote cloud datacenters, where processing AI tasks is slower and costs more.

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