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.

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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.

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Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

by THEFUTURE.TEAM

October 13, 2026

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

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According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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