Why Micron Stock Slumped 18% in March

Shares of Micron Technology(NASDAQ: MU) were taken retired to the woodshed successful March, tumbling arsenic overmuch arsenic 18.1%, according to information supplied by S&P Global Market Intelligence.

After the semiconductor specializer reported epic results and deed a caller all-time high, an unexpected improvement successful artificial quality (AI) exertion sent investors scrambling for the exits.

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Micron reported the results for its fiscal 2026 2nd 4th (ended Feb. 26), and to accidental the results were stunning mightiness beryllium underselling it a bit. Revenue of $23.9 cardinal soared 196% twelvemonth implicit twelvemonth and 75% compared to Q1. This drove adjusted net per stock (EPS) to $12.20, up 682% (not a typo). The bottommost enactment was fueled by Micron's gross margin, which much than doubled to 74.4% from 36.8% successful the prior-year quarter.

The results surged past analysts' statement estimates for gross of $20 cardinal and EPS of $9.31.

CEO Sanjay Mehrotra attributed the blowout to beardown request for its representation chips utilized successful AI processing. Furthermore, the scarcity of these representation chips has driven prices done the roof. "The step-up successful our results and outlook are the result of an summation successful representation request driven by AI, structural proviso constraints, and Micron's beardown execution crossed the board," Mehrotra said.

The banal had been connected a tear, gaining 239% successful 2025 and up 62% successful the aftermath of its fiscal report. Micron seemed unstoppable -- past the different footwear dropped.

On March 24, Alphabet's Google announced a groundbreaking compression algorithm that marked the adjacent large measurement successful the improvement of AI. "We present a acceptable of advanced, theoretically grounded quantization algorithms that alteration monolithic compression for ample connection models and vector hunt engines," Google scientists said successful the probe paper.

One of the biggest bottlenecks successful caller years has been the persistent shortage of representation chips -- similar those supplied by Micron. By creating a integer "cheat sheet," this caller algorithm reduces the magnitude of representation required to tally ample connection models "by astatine slightest 6x and delivers up to 8x speedup, each with zero accuracy loss, redefining AI efficiency." If the algorithm works arsenic advertised (and we person nary crushed to judge it won't), it could dramatically trim the magnitude of representation needed by astir 83%.