Micron Technology briefly became more valuable than Meta Platforms after reporting a 346% revenue surge driven by extreme demand for memory chips used in AI data centers. Memory has become a critical bottleneck in AI infrastructure; customers are now locking in supply with $22 billion（約3.5兆円） in long-term deposits, fundamentally shifting the market from cyclical pricing to structural scarcity—a dynamic comparable to Nvidia's rise as the essential hardware layer for AI.
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Micron's fiscal third-quarter earnings report showed revenue climbing 346% year over year to $41.5 billion（約6.6兆円）, sending its stock up 18.4% and lifting its market capitalization to $1.398 trillion（約220兆円）—briefly exceeding Meta's $1.392 trillion（約220兆円） and nearing Tesla's $1.4 trillion（約220兆円）. The company guided revenue for the current quarter to approximately $50 billion（約8兆円）, nearly 15% above analyst consensus of $43.7 billion（約7兆円）.
Why it matters
Memory chips, particularly high-bandwidth memory (HBM) used with AI processors, have emerged as a critical bottleneck in AI infrastructure—servers cannot function at scale without them. AI data center operators are now paying premiums and signing long-term contracts with $22 billion（約3.5兆円） in upfront deposits to guarantee supply, a shift away from memory's historical boom-and-bust pricing cycle. The four largest US technology companies—Amazon, Meta, Microsoft, and Alphabet—are set to spend a combined $725 billion（約120兆円） on AI infrastructure this year, nearly all of which depends on the memory Micron and two other manufacturers (Samsung and SK Hynix) produce.
What to watch
Micron's adjusted gross margins reached 84.9% in May, up from 39% a year earlier and forecast to reach 86% in the current quarter—a dramatic improvement from negative 33% margins three years ago. Micron is the only US-based manufacturer of HBM chips; supply constraints have already forced Apple to raise prices on Mac and iPad lines, and Apple's stock fell nearly 6% on Thursday. Whether Micron can sustain its valuation depends on whether AI infrastructure spending holds and how quickly new memory supply erodes current pricing power.
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