- Micron Technology (NASDAQ: MU) reported strong fiscal first-quarter results with revenue of $13.64 billion and adjusted EPS of $4.78, significantly beating analyst estimates, driven by robust AI-driven demand for memory chips.
- The company issued upbeat second-quarter guidance, forecasting adjusted EPS of $8.42 (plus or minus 20 cents) and revenue of $18.70 billion (plus or minus $400 million), nearly double prior Wall Street expectations amid tight HBM supply.
- As one of three major High-Bandwidth Memory (HBM) suppliers, Micron expects memory markets to remain constrained beyond 2026, plans to increase 2026 capital expenditures to $20 billion, and is prioritizing production for AI data centers to capitalize on sustained demand growth.

Micron (NASDAQ: MU) has emerged as a critical player in the semiconductor memory sector, particularly through its leadership in high-bandwidth memory (HBM) chips that support advanced artificial intelligence applications. As one of only three dominant suppliers alongside SK Hynix and Samsung Electronics, the company benefits from an oligopolistic market structure where HBM is indispensable for training and deploying generative AI models in data centers.
The company’s fiscal first-quarter results for 2026 demonstrated robust performance, with revenue reaching $13.64 billion, compared to $11.32 billion in the prior quarter and $8.71 billion in the same period last year. Adjusted earnings per share stood at $4.78, exceeding analyst expectations of $3.95 according to LSEG data. Operating cash flow rose to $8.41 billion, up from $5.73 billion sequentially and $3.24 billion year-over-year. CEO Sanjay Mehrotra highlighted record revenue and significant margin expansion across all business units, attributing this strength to technology leadership and operational execution in meeting growing AI-driven needs for memory and storage.
Looking ahead, Micron provided guidance for the second quarter indicating adjusted earnings per share of $8.42, plus or minus 20 cents, substantially above the Wall Street consensus of $4.78 per share. Revenue is projected at $18.70 billion, plus or minus $400 million, compared to the LSEG average estimate of $14.20 billion. Mehrotra noted that the outlook reflects substantial records in revenue, gross margin, earnings per share, and free cash flow, with business performance expected to strengthen further through fiscal 2026.
Supply constraints remain a defining feature of the memory market, driven by surging demand from AI data centers operated by large-scale cloud providers. Executives indicated that markets are likely to stay tight beyond 2026, with Micron anticipating it will fulfill only half to two-thirds of demand from certain key customers in the medium term. Chief Business Officer Sumit Sadana told Reuters that no customer is receiving their full requested volumes, with many receiving substantially less. This supply environment has driven negotiations for multiyear contracts and prompted an increase in planned 2026 capital expenditures to $20 billion, up from the prior $18 billion estimate.
Analyst Kinngai Chan from Summit Insights emphasized that AI-related demand serves as the primary growth driver, enhancing margins not only for AI-specific products but also for non-AI segments as supply is prioritized toward higher-value applications. The company has repositioned production capacity toward AI data centers, including the recent decision to exit direct consumer sales under the ‘Crucial’ brand. eMarketer analyst Jacob Bourne observed that this strategic shift positions Micron favorably among suppliers capable of addressing escalating component requirements for AI infrastructure.
Micron’s chips underpin a wide range of applications, from servers and personal computers to smartphones and automotive systems, yet the current boom is predominantly fueled by data center expansion and tight supply dynamics in advanced memory technologies. With strong demand signals persisting and capacity investments underway, the company is well-placed to capitalize on the ongoing expansion in AI-enabled computing.
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