Google's TurboQuant Just Wiped Billions Off Memory Chip Stocks

After Google（GOOG） released research on a compression technology known as TurboQuant, Samsung and SK Hynix both lost more than 6% in Seoul in less than 48 hours. In US trade, Micron, Western Digital, and Sandisk all saw at least 7% declines. The idea is rather straightforward: if AI models require less memory to function, perhaps the memory boom is not as long-lasting as previously believed.

That premise's viability is a different matter.

What TurboQuant Actually Does

The approach targets a component of a big language model known as the key value cache, which saves previous computations so they don't need to be repeated each time a query is executed. According to Google, TurboQuant may reduce the amount of RAM needed to execute LLMs by a factor of six without sacrificing performance. Although the initial study was conducted last year, the business wrote about it last week on X.

The timing is crucial. From a lab viewpoint, this is not an unexpected revelation. It is a republic. It reveals more about investor mood than it does about the technology itself because markets are responding to something that technically previously existed.

Why the Market Sold First and Asked Questions Later

Memory chips had been having a really great run. As of Wednesday, both SK Hynix and Samsung were up more than 50% for the year. Kioxia had grown by a lot. The case for the bull was strong: spending on AI infrastructure was rising faster than supply, prices were high, profits were wide, and the head of SK Group had just said that the crunch would last until 2030.

That's the exact situation where one story does the most damage. There was no doubt in Ben Barringer's mind: "Memory stocks have had a very strong run and this is a highly cyclical sector, so investors were already looking for reasons to take profit."

Matthew Prince, CEO of Cloudflare, called TurboQuant "Google's DeepSeek" on X. This likely sped up the selloff more than the study itself. DeepSeek caused AI technology stocks to drop by a huge amount in a single day last year. That emotional link really worked.

The Jevons Paradox Debate Nobody Fully Agrees On

The Bull Case Analysts Are Reaching For

Shawn Kim, an economist at Morgan Stanley, used the Jevons Paradox, which is an economic theory from the 1800s that says using resources more efficiently tends to make people consume more, not less. If it costs less to run AI questions, more of them are run. Not less, but more questions mean more memory needs. Analysts at both JPMorgan and Citigroup said the same thing.

In fact, Kim's note was pretty positive about TurboQuant. It is good for hyperscalers' return on investment to make inferences more efficient, and generally, a lower cost per AI-generated token has made the market bigger. "A lower cost per token can also lead to higher product adoption demand," he said.

Ray Wang at SemiAnalysis was more honest about how hardware really works. Fixing a problem doesn't make the system less hungry for resources; instead, it makes it possible for the system to do more. "In order to make AI devices more useful, you need to fix a bottleneck. And the model for teaching will get stronger over time. When the model gets stronger, you need better tools to back it up.

The Bear Case Isn't Nothing

The thing is, people are worried about this and they have a reason to be. There are four companies. Amazon and Google are the leaders. And they are planning to spend a huge amount of money around 650 billion dollars on data center infrastructure this year. A big part of that money will go to memory chips. If something like TurboQuant works it could make a difference. Even if it just reduces the amount of memory needed for each server a bit that is still a lot of money when you are spending 650 billion dollars.

An analyst named Andrew Jackson from Ortus Advisors said that this development will probably not affect how much memory chips are needed because it is already very hard to get them.. That is only true if it is still really hard to get memory chips. If that changes then this development could make a difference. TurboQuant and similar techniques are what we are talking about here. The impact of TurboQuant, on memory chip demand is what matters.

What the Numbers Say Right Now

The drop is big, but it's important to keep things in perspective. In the past year, Samsung stock had gone up almost 200%. Micron and SK Hynix both went up more than 300%. With that as a background, a 6-7% drop in one day is painful but not fundamentally disastrous.

The total amount of memory that AI is expected to need has not changed. The promises made by hyperscalers to spend money are real and limited. And TurboQuant only works on improving the efficiency of inference. It doesn't affect the training side, since that needs more memory because of the size of the model, not the efficiency of inference.

Market Risks Worth Taking Seriously

It's not just TurboQuant that's dangerous. It's that this is the first of many efficiency breakthroughs that will smooth the memory demand curve as a whole. DeepSeek was also meant to be a one-time thing.

If companies like Google, OpenAI, and others keep putting out research that makes both training and inference more efficient, the supply crunch story starts to fall apart. It won't happen right away, but it will happen over the next 12 to 18 months. If investors bought SK Hynix at 50 times earnings because they thought the crunch would last until 2030, they would need to look at those models again.

In the short run, the damage to technology is real. Longer term, Thursday's selling didn't change the basic supply and demand picture for memory with a lot of bandwidth.

What Traders and Investors Should Do With This

For anyone with existing positions in memory names — the selloff looks like a profit-taking event dressed up as a thesis break. The underlying demand drivers are intact. But the concentration of AI infrastructure spending in a handful of hyperscalers means any efficiency signal from those same companies carries outsized market weight, fair or not.

Worth watching: whether Google actually deploys TurboQuant at scale in its own inference infrastructure. Publication and deployment are different things. If Waymo, Google Search, and Gemini start showing dramatically lower memory consumption per query in coming quarters, that's when the bear case earns a harder look.

Until then, this is a shakeout in an overheated sector. Uncomfortable, but probably not the start of something structural.