Shares of Micron Technology(NASDAQ: MU) were taken out to the woodshed in March, tumbling as much as 18.1%, according to data supplied by S&P Global Market Intelligence.
After the semiconductor specialist reported epic results and hit a new all-time high, an unexpected development in artificial intelligence (AI) technology sent investors scrambling for the exits.
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Micron reported the results for its fiscal 2026 second quarter (ended Feb. 26), and to say the results were stunning might be underselling it a bit. Revenue of $23.9 billion soared 196% year over year and 75% compared to Q1. This drove adjusted earnings per share (EPS) to $12.20, up 682% (not a typo). The bottom line was fueled by Micron’s gross margin, which more than doubled to 74.4% from 36.8% in the prior-year quarter.
The results surged past analysts’ consensus estimates for revenue of $20 billion and EPS of $9.31.
CEO Sanjay Mehrotra attributed the blowout to strong demand for its memory chips used in AI processing. Furthermore, the scarcity of these memory chips has driven prices through the roof. “The step-up in our results and outlook are the outcome of an increase in memory demand driven by AI, structural supply constraints, and Micron’s strong execution across the board,” Mehrotra said.
The stock had been on a tear, gaining 239% in 2025 and up 62% in the wake of its financial report. Micron seemed unstoppable — then the other shoe dropped.
On March 24, Alphabet‘s Google announced a groundbreaking compression algorithm that marked the next big step in the evolution of AI. “We introduce a set of advanced, theoretically grounded quantization algorithms that enable massive compression for large language models and vector search engines,” Google scientists said in the research paper.
One of the biggest bottlenecks in recent years has been the persistent shortage of memory chips — like those supplied by Micron. By creating a digital “cheat sheet,” this new algorithm reduces the amount of memory required to run large language models “by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency.” If the algorithm works as advertised (and we have no reason to believe it won’t), it could dramatically reduce the amount of memory needed by roughly 83%.
