Micron Shares Are Falling, Google TurboQuant Precision Strike? Micron’s AI Memory Empire Is Facing a Test

TradingKey - On Wednesday, Micron Technology ( MU) shares fell 3.4%, marking the fifth consecutive trading session of declines.

The latest source of pressure may be an announcement from Google ( GOOGL) ( GOOG )—the company introduced the TurboQuant compression algorithm late Tuesday, which Google says can significantly reduce memory usage while improving the speed and efficiency of AI models.

The core breakthrough of TurboQuant lies in addressing the Key-Value cache (KV cache) bottleneck during the inference phase of AI models. As the context windows of large AI models continue to expand, historical data stored in the KV cache grows geometrically, becoming a central obstacle limiting model performance and costs.

Through a two-stage architecture of Polar Quantization (PolarQuant) and Error Correction (QJL), Google's new algorithm compresses the KV cache memory footprint to one-sixth of its original size without any loss in model accuracy, achieving up to an eightfold performance improvement.

Unlike traditional compression techniques, TurboQuant can be deployed directly on existing AI systems without the need to retrain or fine-tune models; testing shows it maintains a 100% retrieval recall rate in long-text benchmarks for open-source models such as Gemma and Mistral.

This 'zero-cost' efficiency gain has sparked market concerns that future demand for high-end High Bandwidth Memory (HBM) in AI servers could be weakened. As Micron is one of the world's three major HBM suppliers, its stock price took a direct hit.

Hidden Concerns Behind Micron’s High Growth

Meanwhile, market concerns regarding Micron are beginning to surface.

On the profitability front, Micron's gross margin is experiencing an unprecedented surge, with guidance for the next quarter indicating it will surpass 80%, a record high since the company's founding.

However, many Wall Street analysts have issued warnings, believing that Micron's margins may have hit a cyclical peak and are likely to see a subsequent decline of 1,000 to 2,000 basis points.

Bank of America ( BAC) analyst Vivek Arya noted in his latest report that while emerging demand from data center eSSDs and Nvidia's KV cache offloading is indeed supporting the NAND flash market, Applied Materials data shows that KV cache-related demand represents only a single-digit percentage of the total NAND market, making it difficult for Micron to sustain gross margins above 80% in the long term.

He expects Micron's gross margins to eventually revert to the pre-AI era historical high range of 60%-70%, suggesting that current earnings levels have already pulled forward growth from the foreseeable future.

Parallel to the looming peak in profitability are the long-term risks stemming from Micron's aggressive capacity expansion. Its capital expenditure plans for fiscal years 2026 and 2027—including the upgrade of its Tongluo facility in Taiwan and the construction of new domestic fabs in the U.S.—show a clear determination to bet on the memory chip market, but this significantly raises the risk of long-term overcapacity compared to its peers.

In terms of specific figures, Micron has sharply revised its capex forecast for fiscal 2026 (ending August 2026) from an initial $20 billion to $25 billion, with an additional $10 billion expected in fiscal 2027 for building or expanding wafer fabs in the U.S., Taiwan, and Japan.

Despite strong earnings growth expectations, the company's forward EV/EBITDA multiple is now below its historical median, creating a valuation structure that appears attractive but is highly sensitive to cyclical shifts. This implies that any marginal slowdown in demand or faster-than-expected capacity release could lead to a sharp valuation adjustment for Micron.

Memory Expansion Faces Power Equipment Shortage

More notably, Micron's massive expansion plans are facing the real-world challenge of infrastructure bottlenecks.

According to DigiTimes, citing industry sources, Micron plans to invest $24 billion in Singapore to expand NAND flash capacity; this project alone requires 400 to 500 power transformers, more than double the typical requirement for a standard wafer fab. This scale already exceeds the annual capacity of any single transformer manufacturer in Taiwan, and the shortage of heavy power equipment is becoming a core bottleneck for AI-driven semiconductor expansion.

Micron's massive demand for transformers reflects the extreme power intensity of next-generation memory chip plants associated with AI. Due to prolonged capacity constraints for High Bandwidth Memory (HBM) used in AI servers, major global memory chip manufacturers have simultaneously launched expansion plans, but the supply of power infrastructure required to support these factories is clearly failing to keep pace with demand.

Meanwhile, Samsung Electronics and SK Hynix have also announced massive expansion plans. The underlying logic is highly consistent: the consumption of HBM by AI servers has far outstripped the supply capacity of existing production lines.

Consequently, a wave of simultaneous factory construction has been triggered across Asia, the Americas, and Europe, with various memory chip projects competing for the same limited pool of heavy power equipment and raw materials.

The impact of this supply-demand imbalance is already visible in pricing and supply. Driven by the dual impact of surging semiconductor industry orders and rising costs for raw materials like copper, major heavy power equipment suppliers in Taiwan, such as Fuji Electric and Chuan Tai Electronics, have raised prices by 20% to 30%. Some transformer manufacturers, unable to meet the stringent delivery schedules and high-volume demands of semiconductor projects, have begun to outright refuse to provide quotes for such large-scale projects.

Industry insiders revealed that no single manufacturer can currently handle massive orders from the AI and semiconductor industries alone, and the entire supply chain is facing unprecedented pressure.