New tech, TurboQuant addresses the high cost and bottleneck of KV caches in AI, which typically grow rapidly as conversational context increases. In short, this further push performance to next level. Doesn't mean it pull down demand of memory. personally, I felt this like digital camera where photo/ video can be captured with higher quality and users are further pushing on higher content and others accessories equally increase. hence, this is just a tech breakthrough to enjoy higher performance.  each other components are still important and require. So this should mean demand are still there.

Market always excited over news. Memory is physical hardware that will not obsolete with turboquant functions as an optimization tool.

On March 25, $Alphabet(GOOGL)$ unveiled a new AI memory compression algorithm, TurboQuant, claiming it can reduce memory requirements during large language model inference by sixfold while increasing computational speed by eightfold, all without sacrificing accuracy. Specifically, as AI models grow more powerful, context windows continue to expand, and key-value (KV) cache storage grows geometrically, becoming a core bottleneck for both performance and cost. TurboQuant leverages PolarQuant and error correction (QJL) to maintain full model accuracy and a 100% retrieval recall rate. The technology can be directly deployed on existing AI systems, raising market concerns that demand for memory chips could be weakened. As a result, shares of

From my perspective, the selloff in $Micron Technology(MU)$ $Seagate Technology PLC(STX)$ $Western Digital(WDC)$ $SanDisk Corp.(SNDK)$ looks more like a knee-jerk reaction. Google Research’s TurboQuant is impressive, but the market is oversimplifying it into “less memory = less demand,” which I don’t fully agree with. The key point for me is that TurboQuant only compresses inference-side KV cache, not HBM used for training or model weights. Lower costs typically drive higher usage — meaning more queries, longer context, and larger models. That’s why I see $Alphabet(GOOGL)$ a

rotation back to other sectors after memory went to ATH. that's how institutions trap retail investors.

buy dip opportunity

🌟🌟🌟 Google's TurboQuant has just pulled off the ultimate "Deep Seek moment" for the AI industry & the market's reaction has been nothing short of a panic attack. The "Magic":  Released on 24 March 26, this algorithm claims to shrink AI memory usage by 6x & boost performance by 8x without sacrificing accuracy. The panic:  Markets worried that if AI needs 80% less memory, demand for chips from Micron & Samsung would evaporate. The Reality Check:  Analysts call this a classic efficiency paradox.  Making AI cheaper doesn't kill demand.  It makes it explode as companies run more models, larger batches & longer contexts. Buy the Dip? Short term pain:  Stocks like SK Hynix & Micron fell 3-6% as investors took profits. Fundamental strength: The co

1. Google’s newly released turboquant reduces memory usage pressure for larger models 2. Memory stocks are very highly valued at this time 3. The investment thesis in memory stocks has not changed at this time

Google drops [笑哭]

Jevons Paradox

This is actually a very important debate for the entire AI semiconductor supply chain, not just memory stocks like Micron Technology, SanDisk, Western Digital, and Seagate Technology. The key question is simple but very powerful: > Does AI efficiency reduce hardware demand, or does it increase total usage? Historically in tech, the answer is usually the second one. --- What TurboQuant actually affects From what analysts are saying, TurboQuant mainly: Optimises KV cache Improves inference efficiency Reduces memory per query Does NOT reduce training memory Does NOT reduce HBM demand significantly Mostly affects inference VRAM / system memory So Morgan Stanley’s view makes sense: HBM (used in training GPUs) should not be heavily affected. This means companies most exposed to HBM and AI tra

👍 Very nice 👌 All the best

80% booked for HBM4 Good sign for Technology 

Google has unveiled a new algorithm that significantly reduces memory usage, triggering a broad selloff across memory-related stocks. $Alphabet(GOOG)$   $Micron Technology(MU)$   $SanDisk Corp.(SNDK)$   $Seagate Technology PLC(STX)$   $Western Digital(WDC)$   According to Google, its new TurboQuantlp1 p algorithm can reduce LLM memory requirements by up to 6×, while boosting computation speed by 8×. However,

Micron is a stock that I will keep buying when it drops more in the next 3 years

Woow time for tryout 

After the close on Wednesday, memory giant $Micron Technology(MU)$ released its fiscal Q2 2026 results, covering the three months ending February 26. Compared with analyst expectations, Micron’s Q2 results significantly exceeded forecasts: Among them, Micron’s Q2 revenue reached $23.9 billion, surging 196% year over year and far exceeding analysts’ expectations of $19.7 billion: Micron’s revenue is primarily driven by DRAM (dynamic random-access memory), accounting for nearly 80% of total revenue, while NAND (non-volatile memory) makes up close to 20%: DRAM can be seen as the “short-term memory” of computers and servers, temporarily storing data in use. It is fast, but data is lost when power is cut. NAND is slower, but offers larger capacity and re