Investors had been betting that a shortage of memory chips would continue well into next year © Bloomberg
Tim Bradshaw in London and George Steer in New York
Published
15
US memory chip stocks have lost almost $100bn in market value this week, hit by new Google research that pointed to an easing of the AI-driven hardware shortage that had sent chipmakers’ shares to record highs.
Shares in US memory maker Micron have shed more than $70bn in market capitalisation since last Friday’s close, down 15 per cent, amid a broad sell-off on Wall Street.
Sandisk, the maker of flash memory devices that was the best-performing stock in the S&P 500 last year, lost around $15bn in value during the week, while storage companies Western Digital and Seagate each lost billions.
“These stocks have had tremendous runs so it’s rational for any marginal news” to dent their shares, said Travis Prentice, chief investment officer at Informed Momentum Company, a California-based asset manager.
The memory stocks rally “doesn’t look like it’s over yet but expectations are high, so it makes sense to take some profits, especially in a troubled market environment”, he added.
Investors had been betting that a shortage of memory chips, which are vital components in the data centres that run advanced AI models, would continue well into next year.
This had made memory chip and storage providers the biggest Wall Street beneficiaries of AI this year, as Big Tech companies including Nvidia, Microsoft and Alphabet lost ground due to concerns about excessive capital expenditures.
However, the Google Research paper published this week has shaken investors’ confidence that AI will continue to demand so much storage capacity.
The search group’s TurboQuant algorithm promises to radically compress AI models without compromising the accuracy of their outputs, meaning they can be run on computers with much less memory.
Analysts at Morgan Stanley said on Thursday that efficiency improvements such as TurboQuant could reduce the infrastructure needed to run AI models.
“If models can run with materially lower memory requirements without losing performance, the cost of serving each query drops meaningfully, resulting in more profitable AI deployment,” Morgan Stanley analysts wrote. “Thus, models that need cloud clusters can fit on local hardware, effectively lowering the barrier to deploying AI at scale.”
However, analysts were not convinced this week’s sell-off was warranted. The implications for memory and computing were “neutral near term”, Morgan Stanley added, as lower AI costs would likely increase overall demand.
After falling throughout the week, Micron and other US memory providers saw their stocks rally slightly on Friday morning.
Until this week, Micron had seen its value more than quadruple in value over the past year, thanks to its position as the leading US provider of high-bandwidth memory, a key component alongside Nvidia’s chips in the vast AI data centres needed to power services like Google’s Gemini and OpenAI’s ChatGPT.
Soaring demand for computing power for AI and the growing complexity of AI systems such as Anthropic’s Claude Code have absorbed much of the available supply of memory, causing shortages elsewhere in the electronics supply chain. On Friday, Sony said it would raise PlayStation 5 prices by as much as 20 per cent, in part due to higher memory component costs.
Developments in AI have buffeted Wall Street this year, as investors worry the technology will disrupt large parts of the economy. New tools and model launches have hammered stocks in sectors from wealth managers to insurance brokers and property services.
On Friday, cyber security stocks fell sharply following reports that Anthropic’s forthcoming model has much greater capabilities and could render existing cyber defences obsolete. CrowdStrike and Palo Alto Networks fell more than 6 per cent, while Cloudflare dropped over 4 per cent.
Copyright The Financial Times Limited 2026. All rights reserved.

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The whole AI bubble is about to burst like the biggest bubble in history.

Can anyone point to any serious tangible benefits from AI?

In the future yes. Today not really and certainly not requiring trillions of current investments that are obsolete the moment they’re installed.
A market of hysterical people that now operates like TikTok algorithms.
Anything can become “Dubai chocolate” or some awful Asian doll whose name I can’t remember.
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In this ever inflated market $ 100b isn't going to offer any tangible savings for consumer buyers, let alone corporate. Only people loosing out right now are prospective retirees. Only seems to be a small cabal winning right now..
They'll just increase the size of the now compressed models in a benchmark arms race surely?
Well Deepseek already showed us that the “bigger is better” approach in AI might not the way forward…. Smaller models, less energy, less chips…. So this obviously need to be reflected in the stock price.
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The big debate in memory is if it remains cicular in nature & if this is the peak of a demand cycle. Memory stocks have crashed periodically when companies build excess capacity or demand crashes - the last two just in 2023 and 2019. Since Micron released nvidia size earnings last week the share price dropped 23%, mostly before the Google research paper came out as the fear that they are at peak cycle has taken hold and shorts have increased, though most analysts raised price targets.

Because of their cyclical nature (like airlines) memory stocks are not afforded the valuations of other tech companies, they are well below the S&P average. Forward P/E is as low as 4, with huge revenues set to last until at least Q427 which makes the company look cheap - the question is if it holds.

The revenue Micron is generating is huge, their earnings smashed through the most optimistic estimates with huge increases forecast for next quarter - which are pretty much locked in as they are already contracted and sold out. Memory demand has shot up accross all types of memory, particularly high-spec DRAM and HBM used by AI. The current market conditions are a huge change to previos cycles, so a return to the 'norm' is unlikely but new capacity will come online in 18 - 24 months which is worrying investors.

If AI demand stays on track this should not be an issue. The new capacity in construction has been planned for years and is mid stage, it is not just a reaction to the rally in the last year. The capacity also represents modernisation required to build the next generation of memory, older tech will become redundant. HBM use is set to increase - particularly with agentic AI. Morgan Stanley see no sign of demand deacreasing after speaking with industry insiders.

The Google research is interesting - and may be what AI needs thrive. At the moment it is very expensive to run AI, a chat with ChatGPT has surprising variable costs. Breakthroughs like Googles will be needed for AI, particularly long tasks, to be economicaly viable. It may be an example of Jeevons paradox - where increased efficiency leads to increased demand.

Is the lower valuation fair? The market doesn't care about that, but it is prepared to give companies like Tesla or Palantir very haigh valuations when they make far less money - assuming almost impossible future potential. Photonics companies which are also considered 'AI infrastructure' have much higher valuations, yet are exposed to similar risks.

Valuation is difficult, AI has made this unchartered territory. If Micron can show more permanent revenue - say through long term commitments (which it is beginning to sign), then it is undervalued & should be rated higher. If it remains a cyclical stock then it will remain volitaile - as the bears have shown this week. But make no mistake, it is currently one of the most profitable companies in the world and will be for some time.
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Id imagine any savings just get soaked up by more thinking and larger contexts (it speeds up compute). Itll just allow for smarter AIs: the demand for intelligence for the foreseeable future will be insatiable.
Selling memory stocks on TurboQuant misses the point. This is a KV-cache efficiency improvement, not a death blow to AI memory demand. It feels like the same misunderstanding we saw in the DeepSeek panic: confusing better inference efficiency with weaker demand, when in practice lower cost often drives more usage: Jevons Paradox. That is exactly why BofA and Morgan Stanley defended names like Micron and Sandisk: the market is extrapolating far too much from a narrow optimisation.
While this news is welcome, I fear it is largely unwarranted.

The memory shortages are caused by directly by AI hardware build-outs having already booked memory production capacity.

Unless they start cancelling orders for memory, the shortages aren’t going anywhere.