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Ciena and Micron flagged as AI infrastructure picks

Ciena and Micron flagged as AI infrastructure picks

3 Key Points

  1. What happened

    Analysts frame Ciena as a prime AI infrastructure beneficiary, citing aggressive cloud provider and neo-scaler investment in high-capacity, low-latency optical networking for AI workloads, while Micron supplies memory and storage for AI data centers.

  2. Why it matters

    Both companies sit on the plumbing that AI data centers depend on, making them sensitive to how much customers will pay for each extra bit of bandwidth, according to the piece.

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Context & Analysis

The piece frames Ciena and Micron as two of the large US-listed companies most directly exposed to AI infrastructure spending. Ciena supplies optical and routing gear that hyperscalers lean on as GPU clusters swell and traffic spills across wide area networks. Micron provides the DRAM, high bandwidth memory and NAND storage that let GPUs, accelerators and data centers process and retain AI workloads at scale. Both are described as clear AI infrastructure plays, with Ciena positioned on the networking side and Micron on the memory and storage side. The full screen surfaced 31 more large US-listed AI infrastructure players that are not covered, and it raises a pricing-power question for Ciena — what happens if the buildout trend meets changing economics around how much customers are willing to pay for each extra bit of bandwidth. That question is presented as one of the tests for whether Ciena's AI buildout story could accelerate or stall.

FAQ
What parts of Ciena's business generate the most revenue?
Ciena generates most of its revenue from Networking Platforms at about US$4.8b, followed by Global Services at roughly US$700 million, Platform Software and Services at about US$400 million, and Blue Planet at around US$100 million.
Where does Micron make its money?
Micron earns around US$43.1b from its Cloud Memory unit, US$37.6b from Core Data Center, US$36.6b from Mobile and Client and US$15.9b from Automotive and Embedded.
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