Western Digital Corporation

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Bullish +67

Did Alphabet Just End the AI Memory Boom?

AI-generated analysis

📉 Memory stocks faced a sharp sell-off after Alphabet published details on TurboQuant, with Sandisk dropping 11% and peers like Micron, Western Digital, and Samsung falling over 7%.

🧠 The new compression algorithm reduces the key-value cache memory footprint by nearly 6x without sacrificing accuracy, potentially lowering costs for AI inference.

⚖️ While the news triggered panic similar to the DeepSeek-driven sell-off in early 2025, TurboQuant targets inference rather than training, leaving High Bandwidth Memory (HBM) demand intact.

📈 Both Micron and Sandisk reported exceptional earnings recently, with revenue nearly tripling for Micron year-over-year and gross margins expanding significantly.

📉 Investor sentiment may be driven by headline-driven profit-taking in an overextended sector rather than a fundamental shift that destroys the bull case.

🔄 Historical cycles suggest the memory industry is driven by multi-year supply-demand dynamics, making normalization via capacity expansion a more likely risk than technology obsolescence.

🔮 The Jevons paradox effect could mean TurboQuant accelerates LLM adoption in edge devices, creating new memory demand that currently does not exist.

⚡ Testing on Nvidia H100 GPUs showed up to 8x acceleration in computing attention, benefiting Alphabet's cloud and search infrastructure directly.

💰 Sandisk is projecting 75–100 exabytes of AI-related storage demand by 2027 and has signed long-term supply agreements to meet the need.

📉 Micron currently trades at roughly 6x forward earnings, making it one of the cheapest large tech stocks in the S&P 500 after its recent slide.

⚠️ The ultimate end of the current memory cycle is more likely driven by normalizing supply-demand dynamics rather than a single algorithmic breakthrough.

Bullish Signals
  • Both Micron (MU) and SanDisk (SNDK) carry a Zacks Rank #1 (Strong Buy), indicating strong investment potential despite recent price fluctuations.
  • SanDisk delivered February-quarter earnings that beat guidance by 45%, with Q2 fiscal 2026 EPS of $6.20 beating forecasts by nearly 78% and revenue up 61% year-over-year.
  • Micron's May-quarter earnings guidance effectively doubled consensus estimates, trading at roughly 6x forward earnings, which is the cheapest forward P/E in the entire S&P 500 after its recent slide.
  • SanDisk management guided Q3 revenue to $4.4–$4.8 billion with gross margins of 65–67%, a significant expansion from the prior quarter's 29.9%.
  • TurboQuant achieved perfect scores on retrieval tasks across five standard AI models and reduced KV cache memory footprint by nearly 6x without sacrificing accuracy.
  • High Bandwidth Memory (HBM) remains essential for training workloads, which is where the most memory consumption occurs, continuing to drive strong revenue growth at Micron and peers.
  • By making AI inference cheaper and more memory-efficient, TurboQuant could accelerate LLM deployment into edge devices, smartphones, and IoT applications, creating entirely new memory demand that doesn't exist today.
  • Western Digital (WDAC) and Seagate have been rebounding as of the morning following the initial sell-off, suggesting investor interest is returning to the sector.
  • The company is actively signing long-term supply agreements and projecting 75–100 exabytes of AI-related storage demand by 2027.
Risk Factors
  • Memory stocks including Western Digital (WDAC), Seagate, Micron (MU), Sandisk (SNDK), Samsung, and SK Hynix each fell more than 7%, with Micron dropping 7% and SanDisk plunging as much as 11% following Google's release of the TurboQuant compression algorithm.
  • The article suggests that investor sentiment has turned pessimistic due to concerns that Alphabet's technology could destroy the bull case for the AI-driven memory sector, causing a swift sell-off despite rebound attempts by morning.
  • There is a significant risk that the narrative around the AI memory boom ending is an overreaction to headline-driven profit-taking rather than a fundamental threat to long-term revenue growth.
  • Western Digital and Seagate specifically faced selling pressure alongside peers, with stock prices falling over 7% following the news about Google's TurboQuant technology reducing memory footprint by 6x.
  • While High Bandwidth Memory (HBM) remains essential for training workloads, the sell-off echoes previous market panic episodes like the DeepSeek-driven crash in early 2025, indicating potential susceptibility to sentiment swings regardless of underlying fundamentals.
Full Analysis
Memory stocks faced significant declines following Alphabet's publication of a new compression algorithm called TurboQuant, which addresses memory bottlenecks in running large language models. Sandisk Corporation fell as much as 11%, Micron Technology dropped 7%, and Western Digital, Seagate, Samsung, and SK Hynix all saw losses exceeding 5% on the news. Alphabet's research detailed that TurboQuant can reduce the key-value cache memory footprint by nearly 6x without sacrificing accuracy, achieving perfect scores on retrieval tasks across five standard AI models and delivering up to 8x acceleration in computing attention on Nvidia H100 GPUs. While these results have shaken investor sentiment regarding the AI-driven memory bull run, market reactions suggest a potential mix of headline-driven profit-taking rather than a fundamental shift in demand dynamics. Despite the sell-off, Micron and Sandisk currently maintain Zacks Rank #1 Strong Buy ratings based on their recent financial performance and supply-constrained conditions. Micron delivered February-quarter earnings that beat guidance by 45% with revenue nearly tripling year-over-year and net income surging almost 10x; its forward P/E is roughly 6x, making it one of the cheapest in the S&P 500 after a recent decline. Sandisk reported Q2 fiscal 2026 EPS of $6.20, beating forecasts by nearly 78%, with gross margins expanding to 51.1% and management guiding Q3 revenue between $4.4 and $4.8 billion with margins of 65% to 67%. The company projects 75 to 100 exabytes of AI-related storage demand by 2027, supported by long-term supply agreements. Analysts suggest that the distinction between training and inference is critical, as TurboQuant compresses data during inference rather than training, leaving high-bandwidth memory essential for training workloads unchanged. The technology could also trigger a Jevons paradox where cost reductions accelerate AI deployment into edge devices, smartphones, and IoT, creating new memory demand categories not currently in existence. Experts caution that the memory cycle is driven by multi-year capital expenditure timelines and supply-demand dynamics rather than individual research papers, noting that Samsung, SK Hynix, and Micron have constrained supply in recent quarters to prevent a rapid crash similar to what occurred after DeepSeek's efficiency claims earlier this year. Ultimately, while TurboQuant represents a legitimate technical achievement for Alphabet’s operating costs, the long-term trajectory of memory stocks appears more dependent on cyclical normalization than technological obsolescence.