Quick Read
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Sandisk surged 781% in the first half of 2026, more than doubling the second-best S&P 500 performer, Micron, which gained just 297%.
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AI models require massive high-speed storage for training and inference, igniting NAND flash demand while disciplined supply keeps average selling prices climbing.
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Memory markets are historically cyclical, and after a cumulative 3,900% gain, investors should expect far greater volatility in the second half of 2026.
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The artificial intelligence boom has reshaped the stock market over the past two years. Graphics processors grabbed the headlines first, but AI infrastructure reaches far beyond chips that perform calculations. Every AI model also needs somewhere to store the mountains of data it creates, trains on, and retrieves. That has turned memory into one of the hottest corners of the semiconductor industry.
While investors expected companies like Micron Technology (NASDAQ:MU) to benefit, the market’s biggest winner has been Sandisk (NASDAQ:SNDK), whose stock has delivered returns that few investors imagined possible since becoming an independent company last year.
A Remarkable Run Since the Spinoff
Sandisk began trading as an independent company on Feb. 24, 2025, after its separation from Western Digital (NASDAQ:WDC), which allowed it to focus on NAND flash memory.
The stock debuted around $52 per share, and by the end of 2025, had climbed to approximately $224, delivering a gain of about 559% in just over 10 months. That performance looks almost modest compared to what followed.
Through the first six months of 2026, Sandisk has surged another 781%, producing a cumulative gain of more than 3,900% since its debut as a standalone company.
Even Micron — the second-best-performing stock in the S&P 500 this year — has gained less than half as much as Sandisk.
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AI is Creating a Storage Boom
Unlike companies designing AI processors, Sandisk manufactures NAND flash memory, the non-volatile storage found in solid-state drives (SSDs), enterprise storage arrays, smartphones, laptops, automotive systems, and embedded devices. As AI models become larger, they require more high-speed storage to house training datasets, inference databases, checkpoints, and archived information.