- Microsoft shares have fallen about 10% over the past six months amid volatile trading.
- Azure posted roughly 40% year-over-year growth and is expected to keep accelerating in 2026 driven by AI demand.
- OpenAI projects $115 billion in losses through 2029, much of which will be spent on data centers that benefit cloud providers like Microsoft.
- Analysts view the upcoming earnings as a pivotal test; most price targets sit between $600 and $650, with Bernstein and Piper Sandler highest at $645 and $650.
- Morgan Stanley expects software spending growth to rise 9 basis points to 3.8% in 2026, CIOs forecast 7.3% growth for Microsoft, and Dan Ives of Wedbush calls the company a “core winner” for 2026 while Evercore ISI notes limited systemic risks.
Microsoft has traded unevenly over the last six months, with its stock down roughly 10% as analysts debate its next move. Market watchers point to accelerating AI demand, especially in cloud services, as the central driver for the company’s near-term prospects.
Azure grew about 40% year over year and analysts expect that momentum to continue into 2026 as enterprises scale AI projects. Investors see the next earnings report as a pivotal moment to confirm whether Azure can sustain higher growth.
OpenAI anticipates $115 billion in losses through 2029, and much of that spending will flow into data centers and cloud infrastructure that benefit hyperscalers such as Microsoft. Analysts view this indirect investment as a major tailwind for cloud revenue.
Most price targets for Microsoft fall in the $600–$650 range from current levels near $454, with Bernstein and Piper Sandler setting the highest targets at $645 and $650. A minority of firms remain more conservative, warning the stock could face resistance.
Reports from Morgan Stanley expect software spending growth to rise by nine basis points year over year to 3.8% in 2026, while CIOs forecast about 7.3% growth for Microsoft that year. Dan Ives of Wedbush calls the company a “core winner” for 2026 as Azure moves from pilots to broader enterprise deployments, and Julian Emanuel of Evercore ISI says systemic risks tied to the AI trade appear limited given strong hyperscaler balance sheets.
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