By Charlie Garcia
Google's TurboQuant cracks the memory-chip cartel - the hardware-heavy AI thesis now looks like yesterday's news
One algorithm. Six times less memory. Zero accuracy loss. And the entire investment thesis for the AI hardware boom just got compressed along with it.
Memory-chip stocks were supposed to be the safe way to play AI. The picks and shovels. The people selling jeans to the gold miners.
Now the gold miners have figured out how to pan without jeans.
The entire memory sector is under pressure. Not because of poor earnings. But because of a research paper that a team from Alphabet's (GOOG) (GOOGL) Google published in April 2025.
That paper led to Google's unveiling last month of TurboQuant - a compression algorithm that reduces the memory an AI model needs by 6x. Makes it 8x faster on the same GPUs. Loses zero accuracy.
Here's how to explain this to your financial adviser: Every time an AI model has a conversation with you, it remembers what you've said. That memory lives on expensive chips. As conversations get longer, the memory required becomes enormous - hundreds of gigabytes per session.
That is why memory prices soared 7x in three months. Demand was insatiable. Supply was deliberately constrained. The memory producers were printing money the way the U.S. Treasury prints money - except with better margins and less apology.
TurboQuant compresses that memory by a factor of six. It's the same conversation and same accuracy, but with ?th of the chips. The analogy is shrinking a 4K image to the size of a postage stamp - except the picture is still 4K.
Cloudflare's CEO called this &#34;Google's DeepSeek moment&#34;- referring to China's disruptive AI model. The internet called it &#34;Pied Piper,&#34; after the fictional compression algorithm in HBO's &#34;Silicon Valley&#34; series.
Within 24 hours, developers had ported TurboQuant to Apple Silicon and every popular open-source AI library. A benchmark on the Qwen model showed perfect accuracy at every compression level. 100% exact match at much less memory. Its implementation requires no retraining. No fine-tuning. It drops into existing systems like a software update.
That is the part that should concern you if you own memory stocks. This is not a road map for 2028. This is a patch you can install before lunch.
Read: Why TurboQuant hammered memory stocks - and why &#34;Jevons' Paradox&#34; means the market might be wrong
The cartel meets the algorithm
The memory bull case rested on one assumption: AI demand for DRAM and NAND would outstrip supply indefinitely.
South Korea's Samsung (KR:005930) and SK Hynix (KR:000660) operated with what analysts politely call &#34;supply discipline&#34; and what everyone else calls a cartel. They refused to add capacity. Margins went vertical. Goldman Sachs told investors it was structural.
It was structural the way a dam is structural - until someone finds a way around it.
Google found a way around it. The algorithm is free. The paper is public. The code is already being replicated by developers in coffee shops on three continents.
If TurboQuant reduces memory demand by even half of what the paper claims, the fair value of the memory sector re-prices by 40% to 60%.
TurboQuant is the opening paragraph - not the conclusion - of the memory-stock correction. Micron Technology (MU) shares are up 28% so far this year - but pre-TurboQuant, the stock was up more than 60%. Rivals Sandisk SNDK, Western Digital WDC and Seagate Technology STX have also since come off their 2026 peaks.
The fine print - because there is always fine print
TurboQuant compresses the KV cache - the temporary memory storing mid-conversation computations. It does not compress the model's permanent weights. That is a harder problem. Google did not solve that. Not yet.
The research tested open-source models, such as Gemma, Mistral and Llama. Not Google's Gemini running at production scale, serving billions of requests.
&#34;Zero accuracy loss&#34; is a lab result. The gap between the lab and the data center is where reputations go to be tested and frequently to die.
But the market is not pricing the fine print. The market is pricing the implication.
If Google achieved 6x compression today, someone will achieve 10x tomorrow. Software efficiency improvements compound. Hardware scarcity assumptions do not survive compounding software improvements.
They never have. Ask anyone who sold magnetic tape in 1985.
Read: Micron and Sandisk stock have been in a slump. Buy the pullback, analyst says.
The 3 things nobody on television is discussing
1. AI capex deceleration: The $200 billion hyperscaler build-out just got cheaper. That sounds bullish for AI companies. It is bearish for everyone selling them hardware. The capex boom propping up GDP growth just got a deflationary haircut from a research paper.
2. The K-shaped economy just widened: AI is deflating its own supply chain. Meanwhile, oil is comfortably north of $100 a barrel, with an Iranian toll booth on the Strait of Hormuz.
The digital economy is getting cheaper to run - exactly as the physical economy is getting more expensive to operate.
The U.S. Federal Reserve is trapped between two forces moving in opposite directions. TurboQuant just made the deflationary force stronger and the rate-cut decision harder.
3. South Korea: Samsung and SK Hynix represented roughly 30% of the country's Kospi Index. If the memory premium structurally compresses, Korea's current account deteriorates and the won (USDKRW) weakens.
Moreover, a critical American ally becomes less economically stable at the worst possible geopolitical moment. SK Hynix just announced an $8 billion ASML equipment order and a U.S. stock listing to expand production of the exact memory type that Google's algorithm just made 83% less necessary. Timing is everything. Sometimes the timing is terrible.
You cannot TurboQuant an oil barrel
Avoid memory-chip stocks. Micron. Sandisk. Western Digital (WDC). Seagate. And Samsung and SK Hynix through any Korean ETF. The broad semiconductor funds, such as VanEck Semiconductor ETF SMH and iShares Semiconductor ETF SOXX, carry embedded memory exposure that is now toxic.
What benefits: companies that deploy AI, not those supplying the hardware. Cheaper inference means faster adoption. Faster adoption means revenue for the companies using AI, not the companies selling the memory to run it.
More: There's a new ETF for memory stocks. History suggests that might be an ominous sign.
What does not change: the physical economy. Oil. Copper. Gold. Defense. Critical minerals. Nuclear fuel. You cannot compress a barrel of oil with an algorithm. You cannot algorithmically reduce the amount of tungsten in a penetrator round. You cannot write software that replaces the 45 million barrels per day of new production the International Energy Agency says the world needs by 2050.
Google just published an algorithm that reduces AI memory requirements by 83%. The algorithm is free. The takeaway for investors: Own what cannot be compressed.
Charlie Garcia is founder and a managing partner of R360, a peer-to-peer organization for individuals and families with a net worth of $100 million or more. He is the author of the &#34;Capital Mischief&#34; Substack. Follow him on X here.
More from Charlie Garcia:
Big Tech's AI fantasy hits a nuclear wall: No fuel, no welders - and no Plan B
The 'smart money' fled software stocks after Citrini's viral AI doomsday report. Here's where it's going.
This top stock picker spotted Nvidia and GLP-1s early - and made over 200%. Here's what he's buying now.
Kevin Warsh isn't who investors think he is - how you can profit from their mistake
-Charlie Garcia
This content was created by MarketWatch, which is operated by Dow Jones &amp; Co. MarketWatch is published independently from Dow Jones Newswires and The Wall Street Journal.
(END) Dow Jones Newswires
04-06-26 0750ET

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Google's TurboQuant algorithm significantly reduces memory requirements for AI models, compressing memory by six times without accuracy loss. This development threatens the memory-chip sector, which had relied on high demand for DRAM and NAND. The algorithm's public availability and rapid adoption by developers could lead to a 40-60% revaluation of memory stocks. While TurboQuant addresses temporary memory, permanent weight compression remains unsolved. The implications of this technology may lead to decreased capital expenditures in AI hardware, impacting companies reliant on memory sales.

- Google's new TurboQuant algorithm significantly reduces memory needs for AI, creating a major shift in the memory-chip market.  
- The algorithm enables sixfold compression of memory use without sacrificing accuracy, leading to a potential revaluation of memory sector stocks by 40% to 60%.  
- This disruption means decreases in demand for traditional memory solutions, making companies supplying AI hardware less favorable investments compared to those utilizing AI technologies effectively.

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Google just sucker-punched these highflying tech stocks - don't let the relief rally fool you