Coinpaper 2026-03-30 12:55:25

SNDK, MU, WDC Stock Forecast as Google TurboQuant Disrupts AI Demand

Memory stocks are bouncing back in premarket trading, with Sandisk, Western Digital, and Micron all posting gains after last week’s pullback. Sandisk climbed over 2% to around $628 in premarket action after a 20% drop in the last 7 days. Micron rose about 1.5% , after a 19.59% drop in the last 7 days, too. Western Digital added roughly 1% to trade at $275 after a 13% drop in the last 7 days. The rebound follows a selloff earlier last week triggered by concerns over a new AI breakthrough. So, what changed? TurboQuant Sparks A Sudden Shift In Sentiment Last week, shares across the memory sector dropped after Google introduced TurboQuant, a compression algorithm designed to reduce memory usage in AI systems. The technology targets a key bottleneck in large language models known as the key-value cache. TurboQuant compresses this cache to just 3 bits without requiring model retraining. That detail matters. It means developers can reduce memory consumption without sacrificing accuracy or performance. In tests on models like Gemma and Mistral, the algorithm achieved a sixfold reduction in memory size. Even more striking, performance improved. Google reported up to an eightfold speed increase on H100 GPUs. That combination of efficiency and speed immediately raised questions. If AI systems need less memory, what happens to demand for DRAM and flash? How The Technology Actually Works The underlying approach relies on two techniques working together. First, PolarQuant rotates data vectors to improve compression quality. Then, the Quantized Johnson-Lindenstrauss method removes remaining errors. This design avoids the overhead seen in traditional quantization methods, which often add extra bits and reduce efficiency gains. As a result, TurboQuant delivers cleaner compression without hidden costs. The implications stretch beyond AI models. Vector search systems, which power large-scale search engines, could also benefit. That broad applicability explains why markets reacted quickly. Investors began to price in a potential shift in memory demand dynamics. Analysts Debate The Real Impact Despite the initial selloff, analysts remain divided on the long-term consequences. Wells Fargo analyst Andrew Rocha pointed out that growing context windows in AI models continue to drive massive data storage needs. TurboQuant directly addresses that cost curve. However, Rocha also questioned whether lowering memory requirements could reduce overall capacity demand. If systems run efficiently with less hardware, could that slow future orders? On the other hand, Lynx Equity Strategies analyst KC Rajkumar pushed back on that idea. He argued that supply constraints in memory markets still dominate the outlook. Compression may ease bottlenecks, yet it does not eliminate the need for large-scale memory infrastructure. Rajkumar maintained his long-term forecasts for Micron and reiterated a $700 price target. That stance signals confidence in sustained demand despite technological shifts. So, does efficiency kill demand, or does it unlock more usage? What Comes Next For Memory Stocks? Looking ahead, much depends on adoption. Google plans to present TurboQuant at ICLR 2026, with related methods appearing at AISTATS 2026. Those events could provide more clarity on real-world implementation. Now, will this technology scale across the industry, or remain limited to specific use cases? Memory stocks appear to be stabilizing for now. But the debate around AI efficiency versus hardware demand is far from settled.