Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

marsbit2026-06-01 tarihinde yayınlandı2026-06-01 tarihinde güncellendi

Özet

Wang Chuan: When the Neighbor Lao Wang Made 30x on Storage Stocks, How to Stay Anxiety-Free (Part 6) - The Trap of Commoditized Goods. This essay uses historical and current examples to analyze the cyclical and high-risk nature of the data storage industry. It begins with the 1990s rise and dramatic fall of Iomega, whose stock soared over 160x in 18 months before collapsing 97% from its peak, illustrating the fleeting success of storage "meme stocks." The core problem is that storage products, like DRAM and flash memory, are highly commoditized. This leads to extreme volatility: prices have plummeted over 80% multiple times, and company stocks often crash 95% or go bankrupt. The industry's dynamic is defined by "elastic demand facing heavy-asset, long-cycle, rigid supply." When demand spikes and supply is fixed, prices skyrocket, as seen recently with AI-driven demand for High Bandwidth Memory (HBM). Companies like Sandisk and Micron have reported massive revenue and gross margin jumps (e.g., Sandisk's gross margin rising from 22.5% to 78.3%) despite minimal increases in production volume. However, these high margins are self-defeating. They incentivize massive new capacity investments (hundreds of billions planned from 2026), with supply expected to surge by late 2027. Once new supply meets demand, prices and profits will crash, potentially leading to a scenario where "selling more results in earning less." The article debunks the safety of long-term supply agreements, c...

Author: Wang Chuan

This article is a continuation of Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Five) - The Bullwhip Effect.

1/ Memory company stocks are things that can easily thrill inexperienced youngsters to climax. Over thirty years ago, the primary medium for mobile storage in the computer industry was the 3.5-inch, 1.44 MB floppy disk. By the end of 1994, a company named Iomega launched a 100 MB removable hard drive, also known as the Zip drive, priced at $199. The Zip drive solved a major problem for consumers who needed to back up and transfer large files.

2/ Iomega's sales skyrocketed from $140 million in 1994 to $1.21 billion in 1996. Its stock price also surged from around $2 per share at the end of 1994 to an equivalent of $330 per share (accounting for stock splits) by May 1996, delivering a return of over 160x in a year and a half. I recall a sincere sigh from a netizen on a tech stock forum back then: "This is even more beautiful than sex!"

3/ After May 1996, Iomega's stock declined continuously, falling over 85% from its 1996 peak by the end of 1999. Eventually, in 2008, EMC acquired Iomega for $210 million, representing a 97% drop from its peak market capitalization of $7 billion in 1996. What does a 97% drop mean? It means that after an 85% drop, some speculators think they're getting a bargain and jump in, only to suffer another 80% decline.

4/ The main theories held by staunch Iomega bulls back then were twofold: i) Until 1996, potential competitors seemed weak and expensive. ii) There was a genuine possibility that the Zip drive could become a standard PC component, like the floppy disk drive. If this could attract hundreds of millions of users, with each Zip disk generating over ten dollars in profit, the future would be limitless. Iomega's stock became the first meme stock of the internet era to attract massive retail investor frenzy. A flood of capital created a self-reinforcing trend, burying many short sellers.

5/ Iomega's downfall was more complex than outsiders imagined. The latter half of 1996 saw a simple price correction; competitors were barely visible. Revenue in 1997 was $1.74 billion, with growth already noticeably slowing. CD-R, as a potential competitor, had become cheaper than Zip disks for high-end users. By 1998, CD burner prices were close to Zip drives, but a CD cost less than a dollar, far cheaper than a Zip disk, completely destroying Iomega's moat. While its 1998 revenue only fell 3% from 1997, gross margin dropped from 31% to 25%, turning profit into loss. The story was over.

6/ Storage industry products, epitomized by Dynamic Random-Access Memory (DRAM), are among the most commoditized in the tech sector. Commoditization means no brand premium, with prices fluctuating rapidly based on global supply changes. Historically, DRAM chip prices have plummeted over 80% within short periods at least six times: 1985, 1998, 2001, 2009, 2012, 2023, with numerous other instances of 30% to 50% declines in between. The stock price declines of memory companies are even more brutal than chip price drops, with 95% crashes or bankruptcies being commonplace. Micron's stock price in May 2025 was roughly the same as in June 2000, losing a full 25 years. Four major bankruptcies in the storage industry over the past three decades include: i) Mostek, 1986. ii) Qimonda, 2009. iii) Spansion, 2009. iv) Elpida, 2012. Countless smaller companies have also gone bankrupt.

7/ The nature of the storage industry is elastic demand facing capital-intensive, long-cycle, rigid supply. When storage prices are too high, elastic demand naturally recedes, finding ways to circumvent it. However, when rigid supply comes online 18 months later, capacity must run at full tilt, and products must be sold immediately at any price to maximize profit. Once rigid supply slightly exceeds elastic demand, price drops occur immediately, sometimes violently.

8/ Looking back, the surge in the entire memory sector's stock prices starting in September 2025 was essentially because demand from cloud service providers for various memory types consumed by AI chips, especially High Bandwidth Memory (HBM), crossed a critical threshold. To lock in capacity for 2026 and 2027, cloud providers were willing to accept significant price hikes. As long as one or two buyers were sufficiently frantic, competitors were forced to follow suit. The panic over shortages quickly spread to smaller buyers and the consumer electronics sector. Learning from past price crashes, major memory makers didn't rapidly increase capacity. Instead, they opted to let prices skyrocket, exploiting the temporary advantage to make a killing while time was still on their side.

9/ In Q1 2026 (non-fiscal year), flash memory maker SanDisk's production cost was $1.288 billion, compared to $1.313 billion in the same period last year, meaning costs actually fell about 2% year-over-year, with the volume of storage produced remaining largely unchanged. However, Q1 2026 revenue was $5.95 billion with a gross margin of 78.3%, whereas the year-ago quarter saw revenue of $1.695 billion and a gross margin of only 22.5%. Therefore, the 251% revenue growth came primarily from corresponding storage price increases, not from selling more goods.

10/ Why did prices more than double without selling much more? Because market demand suddenly exploded, while supply was rigid, limited to what was available in the short term, leading to a scramble for limited supply that drove up prices. This implies a counterintuitive future phenomenon: When rigid supply finally catches up and rebalances with demand, flash memory prices and gross margins will inevitably return to previous levels. At that point, although sales volume increases, total sales revenue and net profit will actually decrease. The more you sell, the less you earn.

11/ Similarly, for Micron in the quarter from November 1, 2025, to the end of February 2026, operating costs were $6.1 billion, a less than 20% increase from $5.09 billion in the year-ago quarter. However, sales revenue was $23.86 billion, nearly triple the $8.05 billion from a year ago. Gross margin was 74.4%, compared to only 36.8% a year earlier. Micron's product line includes HBM, DRAM, and flash memory, making its capacity constraints and price dynamics more complex than SanDisk's, but it cannot escape the same underlying logic.

12/ For commoditized goods, high margins themselves destroy high margins, and high prices themselves erode marginal demand. Seeing gross margins above 70%, major memory makers could no longer remain passive. Starting in 2026, they invested tens of billions of dollars to increase capacity, though a significant portion of new capacity is expected to come online only in the second half of 2027.

13/ Those bullish on memory stocks might argue that memory companies have started signing long-term agreements with customers to lock in capacity prices, which should mitigate the risk of price collapse, right? The truth is, the less stable a relationship, the more eager parties are to sign long-term agreements. The reason for signing them is the temporary, intense need to avoid worst-case scenarios, creating a false yet fragile sense of security for both sides. However, when circumstances change substantially, the stronger party under the new conditions will generally find some excuse in the agreement and immediately renege. The so-called long-term agreements in the memory industry typically don't exceed five years. Once future memory capacity comes online and spot prices fall below long-term agreement prices, buyers can exploit various loopholes in the agreements to force memory makers to share the pain of the price drop immediately. Even if the contract is legally airtight, buyers can threaten to shift more business to competitors who offer better terms once the agreement expires. Memory companies will usually concede immediately to protect long-term interests. Long-term agreements signed between memory makers and customers are probably about as effective as the Molotov–Ribbentrop Pact (the 1939 Nazi-Soviet non-aggression treaty). When everyone senses risk and rushes to use formal agreements to hedge against it, don't naively think the risk is mitigated. This is precisely a signal that risk is increasing.

14/ There's also an asymmetry here: When all players are making huge profits, it only takes one new player indifferent to short-term economics and capable of sustaining long-term losses to enter the market and change the supply-demand dynamic; or one new technological innovation to emerge, drastically reducing demand. You cannot predict beforehand which specific factor will directly shift the balance. You just need to know that the risk of memory price declines relative to the possibility of further increases is now highly asymmetric. That's enough. Risk factors include but are not limited to: i) Economic recession due to rising interest rates and inflation. ii) Cloud service providers cutting capital expenditures on AI. iii) New memory capacity coming online faster than expected, especially from Chinese companies adept at massively ramping up capacity regardless of cost, like CMXT and YMTC. iv) New AI chip designs, model architectures, and software algorithms that can drastically reduce memory requirements. In fact, precisely because of soaring memory prices, smart minds worldwide are brainstorming at all levels—chip design, model architecture, software algorithms—to actively reduce memory demand.

15/ The memory industry, like other highly commoditized sectors, has another deadly trap: at the peak of the cycle, product profits are extremely high, but company P/E ratios are often very low, sometimes even single-digit. This appears to be a good value investment, but it's actually the moment of greatest risk. Because once commodity prices crash, previous profits quickly shrink or even turn into losses, rendering the low P/E ratio meaningless. There will always be simple-minded investors tempted by low P/E ratios who, without deeper thought, willingly plunge their entire savings into this colossal wealth incinerator during the industry's downturn.

Neighbor Old Wang is still lost in the dream of effortlessly striking it rich with memory stocks and exiting unscathed in the future. Don't disturb him. To be continued.

(To be continued)

---

All articles represent the author's personal views for reference only and do not constitute investment advice for the mentioned assets. Investing carries risks, and one must be cautious when entering the market.

İlgili Sorular

QWhat is the central 'trap' that the author identifies for investors in the storage industry and similar commoditized goods?

AThe author identifies the trap as a period of high profit margins and low P/E (price-to-earnings) ratios during the peak of the commodity price cycle. This can falsely appear to be a good value investment. However, the risk is highest here because once commodity prices collapse, the high profits quickly evaporate, making the previously low P/E ratio meaningless. Investors are lured by the low P/E and invest at the wrong time, leading to significant losses.

QAccording to the article, what is the fundamental nature of the storage industry that drives its volatile boom-and-bust cycles?

AThe fundamental nature of the storage industry, particularly using DRAM as an example, is elastic demand facing heavy-asset, long-cycle, rigid supply. Demand is price-sensitive and can retreat quickly when prices are high. In contrast, supply is capital-intensive with long lead times, and once capacity is built, producers are forced to run at full capacity regardless of price to maximize revenue. A slight oversupply relative to this elastic demand can cause prices to crash dramatically.

QUsing the case study of Iomega, what were the key factors that led to its ultimate decline after its initial success?

AIomega's initial success with its Zip drive was eroded by several factors. First, the emergence of cheaper alternatives like CD-R/CD-RW technology, where blank discs cost less than a dollar compared to Zip disks. By 1998, CD burner prices became comparable to Zip drives, making the Zip solution economically uncompetitive for many users. This destroyed Iomega's pricing power and competitive moat, leading to plummeting gross margins (from 31% to 25%), a shift from profit to loss, and a collapse in its stock price and eventual acquisition value.

QWhy does the author argue that long-term supply agreements (LTAs) signed by memory manufacturers with customers do not provide reliable protection against future price collapses?

AThe author argues that LTAs provide a false sense of security because they are often signed when relationships are unstable. Once market conditions change, the stronger party (often the buyer if spot prices fall below the LTA price) will seek loopholes or other means to force the memory maker to renegotiate or share the pain of lower prices. Buyers can threaten to shift future business to competitors. The author cynically compares the effectiveness of these agreements to the 1939 Molotov-Ribbentrop Pact (German-Soviet non-aggression treaty), implying they are not honored when inconvenient. The very act of everyone rushing to sign LTAs is a signal that risk is increasing, not decreasing.

QWhat is the paradoxical phenomenon the author predicts for memory companies when new supply eventually comes online to meet current high demand?

AThe author predicts a counter-intuitive outcome: when the rigid supply finally expands to meet demand, flash memory prices and gross margins will inevitably return to previous, lower levels. At that point, even though the companies are selling a much larger volume of storage, their total sales revenue and net profit will actually decrease. In other words, they will sell more but earn less.

İlgili Okumalar

Can DeepSeek Save China One Trillion Dollars?

"DeepSeek and the $1 Trillion Infrastructure Question" The article examines whether DeepSeek's AI optimization breakthroughs could potentially save China $1 trillion in future AI infrastructure costs. The analysis begins with Nvidia's upcoming Vera Rubin AI platform, costing ~$7.8 million, where memory (HBM4/LPDDR5X) constitutes $2 million—a 435% cost increase in one year, highlighting how AI hardware spending is shifting toward expensive memory components. DeepSeek's approach works in the opposite direction. Through three key technical innovations showcased in DeepSeek V4, the company dramatically improves hardware efficiency: 1. **Memory Compression (MLA)**: Re-engineers the attention mechanism to compress long-context memory (KV Cache) by over 90%, drastically reducing expensive HBM usage. 2. **Selective Activation (MoE)**: Employs Mixture-of-Experts architecture where only a small fraction of parameters (e.g., 49B out of 1.6T in V4-Pro) are activated per token, allowing most parameters to reside in cheaper memory/SSD. 3. **Computation Caching**: Reuses previously computed results via cache hits, replacing expensive GPU computations with cheap memory reads. Combined, these optimizations allow the same hardware to produce approximately 4x more tokens, effectively reducing required hardware investment by 75%. DeepSeek's pricing reflects this: a 10-billion token workload costs ~$522 monthly versus ~$9,000-$10,000 for competitors. The $1 trillion savings projection stems from McKinsey's estimate that global AI infrastructure will require ~$5.2 trillion investment by 2030. As China's daily token consumption grows toward quadrillions, even marginal efficiency gains scale massively. With a conservative 4x throughput improvement, China could avoid building tens of thousands of AI data centers equivalent to ~7 trillion RMB ($1 trillion) in saved investment. Critically, this strategy shifts dependency from scarce, expensive GPU/HBM—where China lags—toward more accessible storage, caching, and systems engineering where domestic suppliers like CXMT are gaining strength. Rather than "replacing Nvidia," DeepSeek rebalances AI's value chain away from monolithic hardware dependency. Ultimately, DeepSeek's technical breakthroughs could lower the barrier to AI adoption across Chinese industries by making advanced capabilities affordable at scale—transforming who can access next-generation AI.

marsbit20 dk önce

Can DeepSeek Save China One Trillion Dollars?

marsbit20 dk önce

Overturning the Mainstream Approach to Hallucinations: Metacognition is the New Solution for Large Models to Break the Hallucination Barrier

This paper, "Hallucinations Undermine Trust; Metacognition is a Way Forward," proposes a paradigm shift in combating AI hallucination. It argues that the current mainstream approaches—striving for omniscience by scaling data/models or having AI abstain from uncertain answers—are fundamentally flawed. The former has inevitable knowledge gaps, while the latter imposes a crippling "utility tax," requiring the rejection of many correct answers to achieve high accuracy, due to models' poor "discrimination" (the ability to distinguish correct from incorrect answers internally). The core contribution is redefining hallucination not as "being wrong," but as "expressing false information with unwarranted certainty." The proposed solution is **Faithful Uncertainty** or **Metacognition**: enabling AI to accurately perceive its internal uncertainty and honestly express it in its language (e.g., using hedging phrases when unsure). This creates a more reliable assistant that provides useful information while signaling its confidence, minimizing harm from errors. The paper emphasizes that metacognition is critical for the era of AI Agents. Without it, Agents cannot intelligently decide when to use tools like search engines, leading to inefficiency and misuse. Key implementation challenges are highlighted: the "bootstrapping paradox" of training with static uncertainty data, the "alignment distortion signal" where human preference training suppresses internal uncertainty cues, and the difficulty of causally evaluating true metacognition vs. its superficial imitation. The paper concludes that the goal should not be an infallible AI, but one that is honest about the limits of its knowledge, thereby building user trust through transparent communication of its certainty.

marsbit25 dk önce

Overturning the Mainstream Approach to Hallucinations: Metacognition is the New Solution for Large Models to Break the Hallucination Barrier

marsbit25 dk önce

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

Bitcoin has recently declined, hitting a two-month low near $66,123, while Ethereum fell to a three-month low around $1,837. Analysts suggest the drop is not merely due to factors like ETF outflows or MicroStrategy's selling but reflects a deeper issue: Bitcoin is losing a broader asset competition. In a near-zero interest rate environment, Bitcoin previously thrived as an outlet for investor dissatisfaction with inflation and limited options. However, the market landscape has shifted. Bitcoin now occupies an "awkward middle ground," facing competition on three fronts. For inflation hedging, investors prefer gold, energy stocks, and commodity producers—assets with tangible backing and clearer pricing power. For growth exposure, AI-related companies with actual revenues and profits are more attractive. Even within crypto, investors can choose stablecoins, exchanges, or infrastructure firms tied directly to adoption, offering clearer business models and leverage. Thus, Bitcoin is no longer the top choice for hedging, growth, or crypto exposure. This shift is evident in market reactions: despite recent warnings about persistent inflation from a Fed official, Bitcoin did not rally as it might have in the past. Instead, capital flowed to assets with direct commodity or energy exposure. The recent ETF outflows and MicroStrategy sales are symptoms, not causes, of this new reality. Investors are becoming more selective, demanding clearer value propositions beyond mere scarcity. The emerging bear case for Bitcoin is not about it being a bubble or failed technology, but that scarcity alone is no longer sufficient.

华尔街日报28 dk önce

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

华尔街日报28 dk önce

İşlemler

Spot
Futures
活动图片