Google TurboQuant Just Makes Memory Obsolete? MU & SNDK Overblown?
$Micron Technology(MU)$ and $SanDisk Corp.(SNDK)$ fell about 7%, $Western Digital(WDC)$ and $Seagate Technology PLC(STX)$ fell 4%. That's all because of TurboQuant.
Google Research has quietly published TurboQuant — a compression algorithm that makes AI inference 8× faster and uses 6× less memory, with zero accuracy loss and no retraining required.
Morgan Stanley is calling it "another DeepSeek moment." The market reacted immediately: memory stocks sold off hard.
Is the panic justified?
TurboQuant only compresses the KV cache — the temporary memory buffer that stores key-value vectors during inference, growing linearly with context length.
It does not touch model weights stored in HBM, and it has zero impact on training workloads. This distinction matters enormously for how you think about the memory trade.
Why this is a big deal for Google?
Google Research originated TurboQuant — giving $Alphabet(GOOG)$ a first-mover deployment advantage in its own cloud infrastructure (GCP) and AI products (Gemini).
Lower inference cost per token directly improves the unit economics of Google's AI services, expanding margins on every Gemini API call.
TurboQuant also accelerates large-scale vector search — a core component of Google Search's AI features and Vertex AI's retrieval workloads.
The efficiency gain means Google can offer longer context windows (competitive moat) without proportional cost increases — widening the gap with rivals who lack this optimization.
Momery stock overblown or demand really decreases?
The fear is if AI needs 6× less memory per workload, demand for HBM collapses.
History suggests efficiency gains in compute don't reduce demand — they expand it. When the cost per AI query drops, hyperscalers reinvest in larger models, longer context windows, and higher query volumes. The "saved" memory simply gets filled by more ambitious workloads. Morgan Stanley explicitly cites this as limiting downside risk to GPU and HBM volumes.
Why $Micron Technology(MU)$ faces additional pressure?
Micron's sell-off isn't purely algorithmic panic. The company simultaneously reported Q2 capex of $5.39B in FY2026 Q1 — up 68% year-over-year. That level of capital commitment amplifies investor anxiety: any softening in AI memory demand expectations creates outsized financial risk for a company this leveraged to the build-out thesis.
How do you view Google’s newly released TurboQuant?
Is this pullback in memory stocks a buy-the-dip opportunity?
Or has the investment thesis fundamentally changed?
Leave your comments to win tiger coins!
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While the technology significantly reduces the physical memory footprint required for AI, most analysts view this pullback as a "buy-the-dip" opportunity rather than a fundamental breakdown of the investment thesis.
Despite the immediate price drop, several factors suggest the "Memory Supercycle" is not over:
Targeted Scope: TurboQuant primarily targets inference workloads rather than the high-bandwidth memory (HBM) used in the resource-heavy training phase.
Structural Shortages: The broader market is still grappling with a "global memory crisis" driven by capacity reallocation toward AI and geopolitical supply chain disruptions. Analysts at IDC and Morgan Stanley suggest shortages could persist into 2027.
Jevons Paradox