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Micron’s Strong Results Highlight Surging AI-Driven Demand For Memory Chips

Micron shares surged in premarket trading on Thursday after the company reported third-quarter results that highlighted strong demand for memory chips driven by continued investment in artificial intelligence infrastructure.

Revenue reached $41.46 billion in the fiscal third quarter, up from $9.3 billion a year earlier and well above LSEG consensus estimates of nearly $36 billion.

The company also forecast revenue of around $50 billion for the current quarter, compared with $11.3 billion in the same period last year. Following the results, Micron shares climbed 16.4% in premarket trading, extending gains over the past year and lifting the company’s market value to about $1.2 trillion.

AI Data Centers Are Tightening The Memory Market

The company’s performance reflects a broader supply-chain shift. As hyperscalers and other large cloud operators pour capital into AI infrastructure, data centers are consuming vast quantities of memory chips. That has reduced availability for smartphones, PCs and other consumer devices, creating a supply imbalance that has lifted memory prices and supercharged Micron’s results.

Micron said Wednesday that it has signed 16 long-term agreements with customers spanning data centers and automakers, locking in sales for three to five years and generating expected financial commitments of $22 billion. For a cyclical industry long exposed to boom-and-bust demand swings, that kind of visibility is especially valuable.

RBC Capital Markets analysts estimated that about 40% of Micron’s revenue now comes from long-term contracts with minimum pricing built in. That structure should help cushion margins if demand softens over time, the analysts said, while also reducing the company’s exposure to abrupt pricing declines.

“Our base case is for current upcycle to continue through 2027, and SCAs give us added conviction regarding sustainability,” RBC analysts wrote, adding that they raised estimates, lifted their price target and reiterated an Outperform rating.

Tech Stocks Catch A Bid

Micron’s results also lifted sentiment across the semiconductor sector following a broader sell-off earlier in the week. In premarket trading, Qualcomm gained 12%, Intel rose nearly 6%, AMD advanced 3.6%, and Nvidia added 1.5%.

“U.S. equities have recovered some ground as Micron’s earnings have provided fresh reassurance that the AI investment cycle remains firmly intact,” said Capital.com senior market analyst Daniela Hathorn.

She added that continued demand from data centres and AI infrastructure customers suggests capital spending on artificial intelligence remains strong, helping restore confidence across semiconductor stocks after recent market weakness.

The latest results also highlight the increasingly important role memory chips are playing in the AI supply chain, alongside processors and software, as investment in artificial intelligence infrastructure continues to accelerate.

Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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