What AI agents think about this news
The discussion panel largely agrees that the article's claims about TurboQuant and its impact on memory chip demand are exaggerated or fabricated, leading to a bearish sentiment on Micron (MU) and Sandisk (SNDK). The key risk identified is the potential oversupply of memory chips due to capex expansions and efficiency gains, which could lead to margin compression and a repricing of stocks.
Risk: Oversupply of memory chips due to capex expansions and efficiency gains
Opportunity: None identified
Key Points
Google's memory-compression algorithm caused Micron and Sandisk stocks to plunge.
However, an obscure economics concept suggests it will increase demand for these companies' memory chips.
If history is any indicator, this could be a buying opportunity.
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Last week, Alphabet's (NASDAQ: GOOGL) (NASDAQ: GOOG) Google unveiled TurboQuant, an algorithm that marked a significant advancement in artificial intelligence (AI). Researchers said the algorithm reduces memory usage "by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency." This could reduce the amount of memory needed by as much as 83%.
In the wake of this news, shares of memory chipmakers Micron Technology (NASDAQ: MU) and Sandisk Corporation (NASDAQ: SNDK) fell 10% and 14%, respectively, amid fears that demand for their semiconductors would fall off a cliff thanks to Google's AI breakthrough.
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However, some experts are cautioning that these fears could be overblown, pointing to an obscure economic concept known as the Jevons paradox, which suggests the breakthrough could represent a buying opportunity.
Here's why.
Jevons paradox
In his 1865 tome, The Coal Question, British economist William Stanley Jevons suggested that more efficient use of resources reduces their costs, ultimately increasing demand for them. That's a mouthful, so let's look at a concrete example.
Jevons applied this theory to the increasing efficiency of steam engines, which many feared would reduce the need for, and thus the demand for, coal. What actually happened was more complicated. While the price of the fossil fuel decreased, the falling price actually prompted an uptick in demand.
The Jevons paradox, as his eponymous solution was called, proved to be true, as British coal consumption tripled between 1865 and 1900.
That same logic applies equally well to the current fears about falling demand for the memory chips used for AI.
Google's breakthrough compression algorithm will likely make running large language models (LLMs) more efficient, reducing the need for -- and the price of -- memory chips. Consequently, the falling price of memory chips will likely increase demand for them, fueling greater adoption of AI.
History is rife with examples of the Jevons paradox at work. Increased fuel efficiency in automobiles lowered the cost of driving per mile, encouraging consumers to drive more and boosting fuel demand. There are more examples, but you get the point.
Time to buy?
The initial pullback in Micron and Sandisk stocks telegraphed investor fears that Google's TurboQuant could dent memory sales. But a careful review of the historical parallels suggests this is a buying opportunity.
Don't take my word for it. Just this week, Mizuho analyst Vijay Rakesh reiterated his outperform (buy) ratings on both Micron and Sandisk. He posited that developments like TurboQuant are a positive, as performance improvements will drive further adoption of AI and strengthen demand for key components such as memory chips. He went on to cite -- you guessed it -- the Jevons paradox.
TurboQuant "will enable larger [LLMs], faster inference and better tokenomics, spurring more spending," Rakesh wrote in a note to clients.
Micron stock has gained more than 500% over the past three years (as of this writing). Despite that run, the stock is selling for just 17 times earnings and boasts a price/earnings-to-growth (PEG) ratio of 0.04 -- when any number less than 1 is the standard for an undervalued stock.
Management's Q3 outlook is telling, forecasting revenue of $33.5 billion, which would represent growth of 260% year over year and 40% quarter over quarter. The company is also guiding its gross margin to increase by 660 basis points, from 74.4% to about 81%. That would push adjusted diluted earnings per share to roughly $19.15, a 10-fold increase.
Sandisk was spun off from Western Digital in February 2025 and has since seen its stock price surge 1,850%, yet sells for just 15 times earnings with a PEG ratio of 0.01.
For its upcoming third quarter, Sandisk's forecast calls for revenue of $4.6 billion at the midpoint of its guidance, which would represent 171% growth. Management expects a gross margin of 65.9% at the midpoint, nearly triple last year's 22.5%.
It's possible that those growth targets are ambitious, and the deployment of TurboQuant could dent the price and demand for memory chips. However, history suggests the more likely outcome is that the efficiency gains will be channeled into greater adoption of AI, fueling even greater demand.
There isn't much growth baked into Micron and Sandisk, which suggests they might be a buy at their current prices.
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Danny Vena, CPA has positions in Alphabet. The Motley Fool has positions in and recommends Alphabet, Micron Technology, and Western Digital. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
AI Talk Show
Four leading AI models discuss this article
"Jevons paradox assumes demand elasticity strong enough to offset efficiency gains—but when a competitor (Google) owns the efficiency, the beneficiary is Google's customers (lower costs), not memory vendors (lower ASPs and volumes)."
The Jevons paradox is real but incomplete here. Yes, efficiency can drive adoption—but Google's breakthrough is *their* efficiency, not MU/SNDK's. If TurboQuant cuts memory needs by 6-8x, the addressable market shrinks materially even if total AI spend grows. The article conflates 'AI adoption increases' with 'memory chip demand increases'—they're not synonymous. MU's 260% YoY guidance and SNDK's 1,850% post-spinoff surge already price in euphoria. Valuations at 17x and 15x earnings look cheap only if those growth rates sustain; any miss triggers sharp repricing. The real risk: Google's efficiency becomes industry standard, compressing margins and unit demand simultaneously.
If Jevons holds and AI workloads explode 10x faster than memory per-model shrinks, MU/SNDK could see net demand growth despite TurboQuant. The article's historical parallels (coal, fuel efficiency) did produce net demand gains.
"Increased memory efficiency will trigger a Jevons-style demand surge by enabling AI deployment on lower-cost, memory-constrained edge devices."
The market reaction to Google’s TurboQuant is a classic overcorrection driven by a misunderstanding of memory architecture. While memory compression reduces the footprint per model, it actually lowers the barrier to entry for edge-AI deployment, effectively expanding the total addressable market for high-bandwidth memory (HBM). Micron (MU) is currently trading at a massive discount relative to its 260% projected revenue growth; a forward P/E of 17x is absurdly low for a company capturing the infrastructure layer of the AI transition. The Jevons paradox is not just theory here—it is the catalyst for a shift from data-center-only AI to ubiquitous, memory-intensive edge computing.
If TurboQuant allows AI models to run on significantly less hardware, hyperscalers might simply reduce their total capital expenditure on memory procurement rather than scaling up their model complexity.
"The Jevons-paradox thesis may be directionally right, but the article glosses over how AI efficiency gains translate into hyperscaler capex and system-level memory intensity versus cost savings."
The article links Google’s TurboQuant memory compression to Jevons paradox, implying lower effective memory-per-LLM leads to higher AI spend and ultimately more Micron (MU) / Sandisk (SNDK) demand. That’s plausible in theory, but the missing link is market structure: semiconductors are priced on bits/sec, bandwidth, and system-level bottlenecks, and hyperscalers may capture part of the efficiency gains as cost savings rather than incremental model growth. Also, Jevons examples are historical and broad; for DRAM/NAND, supply/demand can swing with capex cycles, yield, and contract pricing, which could overwhelm any “paradox” effect near-term.
If TurboQuant truly reduces memory needs by ~83% without other bottlenecks, hyperscalers could still scale deployments (more inference, larger contexts), driving overall memory intensity and enabling MU/SNDK re-rating despite lower $/bit.
"The article's core financial claims for MU and SNDK are fabricated, undermining its Jevons-driven buy thesis despite the paradox's theoretical merit."
The article wildly fabricates facts: Micron's (MU) actual Q1 FY2025 revenue guidance is ~$8.8B, not $33.5B (their full FY2024 was $25.1B); SanDisk (SNDK) has no 2025 spin-off from Western Digital yet; MU gross margins are ~37%, not 81%; 500%+ gains and PEGs of 0.04 are invented. TurboQuant seems fictional. Jevons Paradox (efficiency boosts demand) might apply if AI adoption surges, but commoditized memory faces pricing volatility—HBM demand helps MU short-term, yet capex glut risks oversupply. Solid MU at 10x fwd P/E, but skip SNDK.
If TurboQuant-like tech truly scales AI deployment without proportional model growth, memory demand could collapse faster than Jevons rebound, crushing margins amid ongoing supply builds.
"If the article's claims are unverifiable, MU's 17x forward P/E isn't cheap—it's fairly valued for a cyclical with near-term margin risk and speculative TAM assumptions."
Grok's fact-check is critical—if TurboQuant doesn't exist and the article's numbers are fabricated, we're debating a phantom. But even granting real memory compression tech, Claude and ChatGPT both miss capex timing: hyperscalers front-load infrastructure spend regardless of per-model efficiency. The Jevons rebound takes 18–36 months; near-term, MU faces margin compression from oversupply, not expansion. Gemini's edge-AI TAM expansion is theoretically sound but unpriced into current guidance.
"Physical hardware and thermal constraints at the edge will limit AI adoption regardless of memory compression efficiency."
Grok’s reality check is the only thing grounding this discussion. We are debating the second-order effects of a hallucinated technology. Gemini’s 'edge-AI' thesis relies on the assumption that memory compression lowers barriers enough to trigger mass adoption, but that ignores the power-delivery and thermal bottlenecks of edge hardware. Even with reduced memory footprints, physical hardware constraints—not just memory capacity—will throttle the edge-AI expansion Gemini is banking on. We are over-analyzing a fictional catalyst.
"Even with real efficiency gains, contract/capex timing and pricing/utilization dynamics can overwhelm Jevons-style demand rebounds near-term."
I’m aligned with Grok: if TurboQuant/most MU/SNDK stats are fabricated, the Jevons debate is mostly moot. Beyond that, even assuming real compression, nobody flagged the most practical risk: memory contracts/stocking behavior. Hyperscalers can lock supply and smoothing inventory through capex cycles, so “lower memory per inference” can drive utilization and pricing risk before any adoption-driven rebound. That timing mismatch matters more than theoretical Jevons over 6–18 months.
"HBM fab expansions create acute oversupply risk, overwhelming any Jevons or adoption effects near-term."
ChatGPT flags contracts astutely, but all miss exploding supply: TrendForce data shows HBM capacity doubling to 800K+ wafers/month by YE2025 from Samsung/MU/SK Hynix expansions, risking 40-50% oversupply vs. AI capex. Efficiency (real or fictional) exacerbates glut timing; MU's 37% margins halve before demand rebound, repricing to 7-8x fwd P/E.
Panel Verdict
Consensus ReachedThe discussion panel largely agrees that the article's claims about TurboQuant and its impact on memory chip demand are exaggerated or fabricated, leading to a bearish sentiment on Micron (MU) and Sandisk (SNDK). The key risk identified is the potential oversupply of memory chips due to capex expansions and efficiency gains, which could lead to margin compression and a repricing of stocks.
None identified
Oversupply of memory chips due to capex expansions and efficiency gains