Investors chasing the next AI -To The Moon - Tiger Brokers Online Investing Community - To The Moon

Investors chasing the next AI winner are increasingly asking whether memory specialists could eclipse the GPU titan. SK Hynix has carved out a commanding position in high-bandwidth memory — a linchpin of data-center performance — with HBM3E and HBM4 capacity largely sold out and pricing power that belies its cyclical heritage.

Meanwhile, SanDisk’s rebirth as an independent flash and storage play has been turbocharged by AI-driven NAND demand and a new High Bandwidth Flash (HBF) alliance with SK Hynix to address inference bottlenecks.

Yet this isn’t a pure rerating of Nvidia’s moat. Nvidia’s GPUs remain the compute engine of generative AI, with unparalleled end-to-end ecosystem control and profit margins that dwarf legacy memory peers (and memory stocks’ valuations remain volatile).

For long-term allocators, memory names offer value leverage to the AI infrastructure buildout, while Nvidia’s dominance in core compute arguably still merits a central weight in any AI bet.

Memory Boom: Can Micron’s Earnings Climb Even Higher?

TrendForce: First-quarter NAND Flash price estimates raised, with a sequential increase of 85–90%. Micron is about to release its earnings report—can storage stocks climb further? Will the storage chip “supercycle” extend into 2027 due to the proliferation of AI smartphones and PCs?

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Micron is about to release its earnings report—can storage stocks climb further?\nWill the storage chip “supercycle” extend into 2027 due to the proliferation of AI smartphones and PCs?","image":"https://community-static.tradeup.com/news/01fb544710bfb44a79866be5eeb61fbe"}],"images":[],"coverImages":[],"title":"","html":"<html><head></head><body><p>Investors chasing the next AI winner are increasingly asking whether memory specialists could eclipse the GPU titan. SK Hynix has carved out a commanding position in high-bandwidth memory — a linchpin of data-center performance — with HBM3E and HBM4 capacity largely sold out and pricing power that belies its cyclical heritage. </p>\n<p>Meanwhile, SanDisk’s rebirth as an independent flash and storage play has been turbocharged by AI-driven NAND demand and a new High Bandwidth Flash (HBF) alliance with SK Hynix to address inference bottlenecks. </p>\n<p>Yet this isn’t a pure rerating of Nvidia’s moat. 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SK Hynix has carved out a commanding position in high-bandwidth memory — a linchpin of data-center performance — with HBM3E and HBM4 capacity largely sold out and pricing power that belies its cyclical heritage. </p>\n<p>Meanwhile, SanDisk’s rebirth as an independent flash and storage play has been turbocharged by AI-driven NAND demand and a new High Bandwidth Flash (HBF) alliance with SK Hynix to address inference bottlenecks. </p>\n<p>Yet this isn’t a pure rerating of Nvidia’s moat. Nvidia’s GPUs remain the compute engine of generative AI, with unparalleled end-to-end ecosystem control and profit margins that dwarf legacy memory peers (and memory stocks’ valuations remain volatile). </p>\n<p>For long-term allocators, memory names offer value leverage to the AI infrastructure buildout, while Nvidia’s dominance in core compute arguably still merits a central weight in any AI bet.</p></body></html>","text":"Investors chasing the next AI winner are increasingly asking whether memory specialists could eclipse the GPU titan. SK Hynix has carved out a commanding position in high-bandwidth memory — a linchpin of data-center performance — with HBM3E and HBM4 capacity largely sold out and pricing power that belies its cyclical heritage.  Meanwhile, SanDisk’s rebirth as an independent flash and storage play has been turbocharged by AI-driven NAND demand and a new High Bandwidth Flash (HBF) alliance with SK Hynix to address inference bottlenecks.  Yet this isn’t a pure rerating of Nvidia’s moat. Nvidia’s GPUs remain the compute engine of generative AI, with unparalleled end-to-end ecosystem control and profit margins that dwarf legacy memory peers (and memory stocks’ valuations remain volatile).  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