Google releases KV cache compression technology, storage demand is expected to be hit, and the US stock storage sector collectively falls

# Ye Zhen

Source: Wallstreetcn

Google’s AI memory compression technology **TurboQuant** has arrived, claiming to cut large-model cache memory usage by 6x and boost performance by 8x — instantly triggering market panic. Memory giants including Micron Technology and Western Digital plunged more than 5% during intraday trading. Yet Wall Street investment banks are calling for **“buying the dip”**. Analysis suggests historical experience shows compression algorithms have never fundamentally altered overall hardware procurement volumes. Citing the **Jevons Paradox**, Morgan Stanley points out that an efficiency revolution will not suppress hardware demand, but instead unlock a far larger scale of AI deployment.

Google’s new AI memory compression technology has not only sparked excitement in the tech world over a revolution in underlying computing efficiency, but also led to a sharp valuation re-rating in the U.S. memory chip sector. However, Wall Street institutions see a buying opportunity amid the panic.

On Wednesday, hit by expectations that the technology could drastically reduce AI hardware demand, U.S. memory chip stocks sold off sharply during the session. By the close, the Memory Chip & Hardware Supply Chain Index fell 2.08%, with leading firms such as Western Digital and Micron Technology closing notably lower, reflecting a defensive market reaction to the demand outlook.

Yet while the tech community hails this breakthrough as the **“real-life Pied Piper”** and **“Google’s DeepSeek moment”**, Wall Street banks have taken a starkly different stance. Several analysts argue the actual impact of the technology is being overpriced by the market, and urge investors to buy memory stocks on the pullback.

Despite stunning compression efficiency shown in lab tests, from the perspective of macroeconomics and the real evolution of computing deployment, this technology — designed to break AI memory bottlenecks — may ultimately not destroy storage demand, but instead act as a catalyst for further industry expansion.

## Memory Sector Tumbles in Response

Following Google’s release of the TurboQuant memory compression algorithm, concerns over long-term demand for storage hardware spread rapidly, triggering sell-offs in related assets.

During Wednesday’s session, the memory chip sector declined across the board. Western Digital plunged as much as 6.5%, Micron Technology 4%, Western Digital over 4%, and Seagate Technology over 5%. As market sentiment stabilized late in the day, losses narrowed for individual stocks.

By the close, Western Digital and Micron both fell more than 3.4%, Seagate closed down 2.6%, and Western Digital’s drop shrank to 1.6%. The Memory Chip & Hardware Supply Chain Index ended at 113.03 points, after touching an intraday low of 109.

The immediate cause of market panic was Google’s claim that TurboQuant can reduce cache memory usage during large language model operations by at least 6x **without sacrificing accuracy**. In an AI arms race highly reliant on hardware scale expansion, any technological advance that could cut physical memory purchases was enough to put pressure on an already highly valued chip sector.

## “Real-Life Pied Piper” and “Google’s DeepSeek”

Within the tech industry, the launch of TurboQuant is seen as a major milestone in solving the high operating costs of large language models. Built specifically to address bottlenecks in **key-value cache (KV Cache)** in AI systems, its core function is to compress bulky cache data down to just 3 bits.

According to media reports, Google uses a two-step compression method: first, it converts data vectors to polar coordinates via **PolarQuant** to eliminate extra normalization overhead, then uses the quantized **QJL** algorithm to remove residual errors.

In tests using open-source models such as Gemma and Mistral, the algorithm not only achieved a 6x memory reduction but also delivered up to an **8x performance improvement** on NVIDIA H100 GPUs compared to unquantized 32-bit systems.

The impressive figures sparked heated online discussion, with the technology dubbed the **“real-life Pied Piper”** — a reference to the fictional startup in the hit HBO show *Silicon Valley* that upended industry rules with a lossless compression algorithm.

Matthew Prince, CEO of Cloudflare, and others have called it Google’s **“DeepSeek moment”**, arguing it could, like DeepSeek, drastically lower AI operating costs through extreme efficiency gains.

## Wall Street Unafraid, Calling for “Buy the Dip”

Facing tech-sector euphoria and secondary-market selling, Wall Street banks have remained calm, viewing the market reaction as excessive.

KC Rajkumar, analyst at Lynx Equity Strategies, questioned the “disruptive” nature of the technology. In a note to clients, he stated media coverage of the technology has been exaggerated.

He noted that current inference models already widely use 4-bit quantized data, and Google’s claimed 8x performance improvement is measured against outdated 32-bit models. He emphasized that such advanced compression techniques only alleviate computing bottlenecks and will not undermine robust memory and flash demand over the next three to five years, supported by supply constraints. Accordingly, he maintained a $700 price target and Buy rating on Micron Technology, explicitly recommending “buying on the pullback triggered by Google’s news.”

Andrew Rocha, analyst at Wells Fargo, similarly noted that while TurboQuant directly targets the memory cost curve of AI systems, historical experience shows compression algorithms have never fundamentally changed overall hardware procurement volumes, and the fundamental demand for AI memory remains strong.

## Jevons Paradox Revisited: Long-Term Demand May Be Boosted

Beyond arguing the market overreacted, institutions have re-evaluated TurboQuant’s impact from a longer-term economic perspective.

Morgan Stanley noted in its analysis that TurboQuant **only applies to key-value caches during inference**, leaving model training tasks and high-bandwidth memory (HBM) for model weights completely unaffected. The technology’s core value is to increase per-GPU throughput, allowing the same hardware to support longer contexts or larger batch sizes.

Morgan Stanley further cited the **Jevons Paradox** to explain the dynamics: improvements in technological efficiency typically lower usage costs, thereby stimulating much greater total demand. By drastically reducing the service cost per query, TurboQuant allows models previously limited to expensive cloud clusters to run locally, effectively lowering barriers to large-scale AI deployment.

This means efficiency gains will unlock more AI use cases previously unfeasible due to cost constraints. The bank concluded that the technology reshapes the cost curve of AI deployment, and its long-term impact on computing and memory hardware is **not negative, but neutral to positive**.

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