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@thevalueistApr 23, 2026 · 1:55 AM

$000660 $MU $SNDK $EWY $005930 KEY READ-THROUGHS FROM SK HYNIX 1Q26 EARNINGS CALL SK hynix’s 1Q26 earnings call was an unusually important cross-market signal because the company reported extreme operating leverage, broad-based price acceleration, and sustained supply tightness across HBM, conventional server DRAM, and NAND/eSSD rather than a narrow HBM-only beat. Revenue increased 60% QoQ and 198% YoY to KRW52.6 trillion, operating profit reached KRW37.6 trillion, operating margin expanded to 72%, and EBITDA margin reached 79%. The most important read-through is that AI infrastructure demand is now pulling the entire memory hierarchy tighter: HBM for accelerators, high-density server DRAM for CPU/server systems, SOCAMM/CXL for emerging inference architectures, and enterprise SSDs/QLC NAND for KV-cache and AI data storage. The call is positive for memory suppliers, semiconductor equipment, HBM packaging/test suppliers, and next-generation memory-interface ecosystems, but negative for memory-intensive buyers including hyperscalers, AI accelerator vendors, server OEMs, PC OEMs, and smartphone vendors where bill-of-materials inflation, allocation risk, and working-capital pressure are increasing. The highest-quality implication is that the market may need to underwrite a longer memory upcycle than in prior cycles, but the same evidence also raises medium-term overbuild risk as SK hynix and peers accelerate capacity into extraordinary margins. MEMORY SEMICONDUCTORS BROAD MEMORY PRICING POWER IS MUCH STRONGER THAN A NORMAL CYCLICAL RECOVERY (READ-THROUGH 1) Affected companies: SK hynix Inc (000660: Korea), Micron Technology (MU: U.S.), Samsung Electronics (005930: Korea). Directional impact and magnitude: Positive, high magnitude. Call support: SK hynix reported 1Q26 revenue of KRW52.6 trillion, up 60% QoQ and 198% YoY, with operating profit of KRW37.6 trillion and operating margin of 72%. DRAM shipments were “similar to Q4 levels,” but DRAM ASP rose by a mid-60% QoQ. NAND shipments declined approximately 10% QoQ, but NAND ASP rose by a mid-70% QoQ. Management stated that “prices for both DRAM and NAND rose meaningfully, led in particular by server DRAM and enterprise SSDs.” Transmission mechanism: The call confirms that the current earnings inflection is driven primarily by price and mix rather than volume. That is a more powerful read-through for memory suppliers than a normal cyclical bit-growth recovery because flat-to-down shipments still produced record revenue and record margins. For Micron and Samsung, the read-through is that DRAM and NAND contract pricing, especially in server and enterprise SSD, should continue to reprice upward into upcoming quarters. For SK hynix, the magnitude is even higher because the company has the strongest mix exposure to HBM and high-density server modules. Near-term trading catalyst: Positive earnings revision risk for Micron and Samsung memory segments, especially if investors had assumed spot DRAM moderation was signaling a near-term peak. The call directly pushes back against that view by showing very strong contract-market realization despite spot-market concerns. Longer-duration fundamental shift: The broader memory sector may be transitioning from a volatile commodity model toward a more structurally constrained AI infrastructure supply chain, particularly if high-density DRAM, HBM, and eSSD remain allocation markets. This supports a higher through-cycle margin assumption, although the sustainability of 70%+ operating margins should not be treated as normalized. MEMORY SPOT PRICE WEAKNESS IS NOT YET A RELIABLE PEAK-CYCLE SIGNAL (READ-THROUGH 2) Affected companies: SK hynix Inc (000660: Korea), Micron Technology (MU: U.S.), Samsung Electronics (005930: Korea), Nanya Technology (2408: Taiwan), Winbond Electronics (2344: Taiwan). Directional impact and magnitude: Positive for major server/AI-exposed memory suppliers, moderate to high magnitude; less positive for commodity/client-exposed memory suppliers. Call support: In response to a question on spot-price weakness, management stated that “the spot market itself takes up a very small part of the overall DRAM market” and that the products and volumes traded there “differ considerably” from SK hynix’s business. Management added that spot moderation “rather than being a sign of market peak-out, appears to be a temporary phenomenon resulting from some inventory entering the market from some distribution channels due to the recent price increase.” Transmission mechanism: The read-through is that investors should not mechanically extrapolate commodity spot weakness into HBM, server DRAM, or enterprise SSD contract pricing. This is most positive for SK hynix and Micron because both have meaningful exposure to data center memory, HBM, and high-value server products. It is directionally positive for Samsung as well, though Samsung’s broader exposure to mobile, consumer, and legacy memory makes the mix less clean. It is less positive for Nanya and Winbond because spot and client/consumer channels are more representative of their demand exposure. Near-term trading catalyst: Potential relief rally or earnings-support catalyst for memory stocks if market positioning had shifted toward a peak-cycle narrative on spot-price softness. Longer-duration fundamental shift: If server and AI memory increasingly trade under strategic allocation or long-term contracts rather than spot-market dynamics, conventional spot-price indicators may become less useful for valuing leading-edge memory suppliers. MULTI-YEAR MEMORY LTAS COULD REDUCE CYCLICALITY AND SUPPORT A STRUCTURAL RERATING (READ-THROUGH 3) Affected companies: SK hynix Inc (000660: Korea), Micron Technology (MU: U.S.), Samsung Electronics (005930: Korea), Microsoft (MSFT: U.S.), Amazon (AMZN: U.S.), Alphabet (GOOGL: U.S.), Meta Platforms (META: U.S.), Oracle (ORCL: U.S.). Directional impact and magnitude: Positive for memory suppliers, high magnitude if contracts include firm volume or pricing protections; negative to mixed for hyperscale buyers, moderate magnitude. Call support: Management stated that customer requests to secure medium- to long-term supply volumes have “significantly increased” and that customers now view “memory price and supply uncertainties as key business risks.” Management also said that a multi-year LTA should provide “supply stability to the customer and demand visibility and stable revenue structure to the seller.” Transmission mechanism: Binding or semi-binding LTAs would reduce demand uncertainty for memory producers and improve confidence in capacity investment economics. For SK hynix, Micron, and Samsung, stronger demand visibility lowers the probability of a sharp inventory-led downturn and could justify higher valuation multiples. For hyperscalers, the same mechanism is a cost and flexibility headwind because buyers may need to commit earlier, accept price floors, or provide stronger volume visibility to secure supply. Near-term trading catalyst: Any disclosure of LTA terms, prepayments, strategic customer commitments, or take-or-pay structures would be a positive catalyst for memory suppliers and a potential negative margin/capex catalyst for cloud buyers. Longer-duration fundamental shift: If AI memory supply is increasingly governed by strategic LTAs, the memory industry could become less volatile than prior cycles. That would be a major structural valuation event for SK hynix and Micron. The key risk is that LTAs signed near cyclical highs can protect suppliers only if customers continue to need the contracted capacity once new supply comes online. HBM LEADERSHIP REMAINS A MAJOR COMPETITIVE ADVANTAGE, BUT THE READ-THROUGH FOR AI CHIP VENDORS IS MIXED (READ-THROUGH 4) Affected companies: SK hynix Inc (000660: Korea), NVIDIA (NVDA: U.S.), Advanced Micro Devices (AMD: U.S.), Broadcom (AVGO: U.S.), Marvell Technology (MRVL: U.S.), Taiwan Semiconductor Manufacturing Co (2330: Taiwan). Directional impact and magnitude: Positive for SK hynix, high magnitude; mixed for AI accelerator vendors, moderate to high magnitude. Call support: Management stated that HBM competitiveness depends not only on DRAM process technology but also on “TSV and packaging” and execution across “performance, yield, quality, and supply stability.” Management said that for HBM4, SK hynix is working closely with customers and plans to ramp volume according to agreed schedules. Management also stated that “customers’ demand for the next 3 years far exceeds our current supply capacity.” Transmission mechanism: For SK hynix, this confirms continued pricing power and strategic relevance in the AI accelerator supply chain. For NVIDIA, AMD, Broadcom, and Marvell, the read-through is more nuanced. HBM supply tightness validates end-demand for AI accelerators, but it also creates volume risk, cost inflation, and potential allocation bottlenecks. AI accelerator vendors may be able to pass through some cost, but HBM scarcity can constrain shipment schedules, compress system-level margins, or force customers into longer lead-time commitments. For TSMC, HBM constraints are mixed: they validate AI accelerator demand and CoWoS-related packaging demand, but any HBM shortage can limit completed accelerator module shipments even if wafer capacity is available. Near-term trading catalyst: HBM4 qualification updates, HBM supply allocation commentary, and gross-margin guidance from AI accelerator companies become more important. Positive demand commentary may be offset by questions on HBM availability and bill-of-material cost. Longer-duration fundamental shift: HBM is becoming a strategic control point in AI compute. Accelerator vendors with privileged HBM allocation, co-design capability, and packaging ecosystem access should gain relative advantage. The call reinforces that the bottleneck in AI compute is not only leading-edge logic but also memory capacity, packaging yield, and system-level integration. SAMSUNG AND MICRON BENEFIT ABSOLUTELY FROM THE MEMORY UPTURN, BUT SK HYNIX’S HBM POSITIONING CREATES RELATIVE COMPETITIVE PRESSURE (READ-THROUGH 5) Affected companies: SK hynix Inc (000660: Korea), Samsung Electronics (005930: Korea), Micron Technology (MU: U.S.). Directional impact and magnitude: Positive for the group, but relatively more positive for SK hynix; moderate negative relative read-through for competitors if HBM share gains lag. Call support: Management emphasized that SK hynix has maintained “the highest level of overall product competitiveness in time-to-market, cost, yield and performance” since HBM2E, and expects to lead with “next-generation products such as HBM4 and HBM4E.” HBM4E samples are planned for 2H26, with mass production targeted for 2027. The core die will use 1c nanometer technology, which management described as already mature in yield and mass-production capability. Transmission mechanism: The entire memory sector benefits from higher ASPs, but HBM share and qualification status will determine relative multiple expansion. Samsung and Micron should benefit from conventional DRAM and NAND price strength, but SK hynix’s commentary implies that its lead in HBM time-to-market, yield, customer trust, and supply stability remains intact. If customers continue to allocate the highest-value AI memory business disproportionately to SK hynix, Samsung and Micron may have less upside to blended gross margin than headline DRAM pricing would imply. Near-term trading catalyst: HBM4 qualification news, customer allocation disclosures, and 2027 HBM4E sampling progress will be more important than generic DRAM pricing for relative performance within memory. Longer-duration fundamental shift: The memory industry may increasingly split into 2 tiers: suppliers with leading HBM/advanced packaging execution and suppliers with more commodity-weighted DRAM/NAND exposure. That would structurally favor SK hynix and any competitor able to close the HBM execution gap. SEMICONDUCTOR CAPITAL EQUIPMENT AND ADVANCED PACKAGING SK HYNIX CAPEX ACCELERATION IS A CLEAR POSITIVE FOR FRONT-END EQUIPMENT AND EUV SUPPLIERS (READ-THROUGH 6) Affected companies: ASML Holding (ASML: Netherlands), Applied Materials (AMAT: U.S.), Lam Research (LRCX: U.S.), Tokyo Electron (8035: Japan), KLA (KLAC: U.S.), ASM International (ASM: Netherlands), Advantest (6857: Japan), Disco (6146: Japan). Directional impact and magnitude: Positive, high magnitude. Call support: Management said that 2026 CapEx is expected to “increase significantly” versus 2025, with the majority allocated to “infrastructure preparation centered on the Yongin Cluster and ramp-up of M15X” and “key equipment such as EUV tools.” Management also said that semiconductor infrastructure takes several years from construction to operation and that key equipment has substantial lead times. Transmission mechanism: A significant CapEx increase by SK hynix supports order visibility for lithography, deposition, etch, process control, inspection, metrology, test, and wafer-processing tools. ASML is the clearest beneficiary through EUV demand. Applied Materials, Lam Research, Tokyo Electron, ASM International, and KLA benefit from DRAM process migration, NAND layer expansion, and new cleanroom toolsets. Advantest and Disco benefit from more advanced memory test, thinning, dicing, and packaging complexity associated with HBM and high-density products. Near-term trading catalyst: Positive order, backlog, and shipment commentary from semicap companies tied to DRAM, NAND, and HBM equipment demand. Any confirmation of EUV pull-ins or advanced DRAM tool urgency would be especially positive for ASML and leading process-tool suppliers. Longer-duration fundamental shift: AI memory demand is extending the semicap cycle beyond logic/foundry into memory. This matters because memory CapEx had been more cyclical and more prone to downturns. A multi-year HBM, 1c/1d DRAM, 321-layer NAND, and Yongin buildout cycle improves revenue durability for semicap suppliers. HBM PACKAGING AND TEST BOTTLENECKS REMAIN STRATEGICALLY UNDERVALUED BY PARTS OF THE MARKET (READ-THROUGH 7) Affected companies: Hanmi Semiconductor (042700: Korea), BE Semiconductor Industries (BESI: Netherlands), Advantest (6857: Japan), Teradyne (TER: U.S.), Disco (6146: Japan), SK hynix Inc (000660: Korea), Samsung Electronics (005930: Korea), Micron Technology (MU: U.S.). Directional impact and magnitude: Positive for HBM packaging/test suppliers, high magnitude; positive but capacity-limiting for HBM producers. Call support: Management explicitly said HBM competitiveness is determined by “a combination of diverse technological capabilities, including TSV and packaging,” and that execution across “performance, yield, quality, and supply stability” is critical. It also said severe supply shortages affect “not only HBM but also the general purpose DRAM.” Transmission mechanism: HBM supply is not only a wafer-start issue. It is a packaging, bonding, TSV, stacking, test, and yield issue. Suppliers tied to HBM bonding, inspection, wafer thinning, test, and high-bandwidth package assembly should see structurally stronger demand as HBM moves from HBM3E to HBM4 and HBM4E. For Hanmi Semiconductor, the read-through is especially relevant because thermocompression bonding and HBM process equipment are central to scaling stacked memory. For Advantest and Teradyne, higher HBM complexity supports higher test intensity and longer test times. For BESI, advanced bonding adoption is a structural tailwind even if the precise HBM exposure varies by customer and process. Near-term trading catalyst: HBM capacity expansion headlines, new tool orders, and qualification milestones should support packaging/test equipment names. Any bottleneck commentary from memory or GPU companies could further increase investor focus on these suppliers. Longer-duration fundamental shift: The profit pool in memory is moving toward advanced packaging and system-level reliability. Packaging and test suppliers may deserve more durable AI-infrastructure multiples if they become persistent bottlenecks rather than cyclical tool vendors. AI COMPUTE, CLOUD, AND DATA CENTER INFRASTRUCTURE HYPERSCALERS FACE HIGHER AI INFRASTRUCTURE COSTS AND LOWER PROCUREMENT FLEXIBILITY (READ-THROUGH 8) Affected companies: Microsoft (MSFT: U.S.), Amazon (AMZN: U.S.), Alphabet (GOOGL: U.S.), Meta Platforms (META: U.S.), Oracle (ORCL: U.S.), Alibaba Group (9988: Hong Kong), Tencent Holdings (0700: Hong Kong), Baidu (9888: Hong Kong). Directional impact and magnitude: Negative near term, moderate magnitude; positive long term as AI demand validation, moderate magnitude. Call support: Management said customers are “prioritizing securing volume over pricing.” It also said memory price and supply uncertainties are now viewed by customers as “key business risks,” and that customer requests to secure medium- to long-term supply volumes have “significantly increased.” Transmission mechanism: Hyperscalers are the likely customers most exposed to multi-year AI memory commitments. Higher DRAM, HBM, and eSSD pricing increases data center CapEx per unit of compute and storage. Long-term allocation agreements reduce supply risk but also reduce purchasing flexibility and may lock buyers into elevated price levels. The near-term negative is higher infrastructure cost, greater working-capital intensity, and potential pressure on AI service gross margins. The long-term positive is that customers are committing because AI demand is sufficiently strong to justify securing scarce inputs. Near-term trading catalyst: Cloud CapEx revisions, AI depreciation expense guidance, and gross-margin commentary from hyperscalers. If cloud companies disclose elevated memory costs or prepayment commitments, the market may revisit AI ROI assumptions. Longer-duration fundamental shift: Memory is becoming a strategic input for AI service availability. Hyperscalers with balance-sheet capacity to secure multi-year supply should widen their advantage over smaller AI service providers, but at the cost of higher fixed commitments and greater capex intensity.

@thevalueistApr 22, 2026 · 11:01 PM

$000660 $MU $SNDK $EWY $005930 (Bloomberg) -- SK hynix Inc reported net for the first quarter that beat the average analyst estimate. FIRST QUARTER RESULTS Net 40.33 trillion won, estimate 29.39 trillion won (Bloomberg Consensus) Operating profit 37.61 trillion won Sales 52.58 trillion won (Bloomberg) -- SK hynix Inc reported net for the first quarter that beat the average analyst estimate. FIRST QUARTER RESULTS Net 40.33 trillion won, estimate 29.39 trillion won (Bloomberg Consensus) Operating profit 37.61 trillion won Sales 52.58 trillion won (Bloomberg) -- SK Hynix Inc. reported a five-fold jump in quarterly profit, underscoring the surging prices of the memory chips that underpin the boom in global artificial intelligence development. Operating profit for the March quarter jumped to 37.61 trillion won ($25.4 billion), a record high that compares with the average estimate by analysts for 35.7 trillion won. Sales nearly tripled to 52.58 trillion won. Favorable memory prices will continue on robust demand and SK Hynix’s capital spending would increase significantly this year, it said. The chipmaker is riding an AI spending boom that’s reshaping the memory sector and offsetting fears around supply chain disruptions stemming from war in the Middle East. The outperformance may fuel the stance of bullish investors, who argue that the advent of AI is igniting a super-cycle of demand that will help SK Hynix and its rivals break a decades-old cycle of boom and bust. Read More: Memory Stock Valuations Spark Debate Over ‘Supercycle’ Potential Hyperscalers from Meta Platforms Inc. to https://t.co/SpqvHNV5fi Inc. are spending hundreds of billions of dollars on AI hardware. That’s fueling earnings at SK Hynix, which is benefiting from its lead in high-bandwidth memory, critical for training and running AI models alongside Nvidia Corp. accelerators. SK Hynix and its competitors Samsung Electronics Co. and Micron Technology Inc. are also allocating more production capacity to lucrative HBM, spurring shortages and price spikes in conventional memory. That’s further boosting revenue for memory makers. In response to the demand, Hynix said in January that its capital expenditure in 2026 will rise significantly from 30.2 trillion won in 2025. Last month, it announced a plan to spend the equivalent of $8 billion on cutting-edge extreme ultraviolet lithography chipmaking tools from ASML Holding NV. Larger rival Samsung plans to spend more than 110 trillion won on chip capacity expansion and research this year, devoting a record amount of capital toward an effort to seize the lead in AI semiconductors. Shares of SK Hynix have climbed around 90% this year after more than tripling in 2025, outrunning Samsung. Samsung appears to be pulling ahead in the next-generation HBM4 arena, however. It was first to commercially ship HBM4 chips and it showcased its cutting-edge HBM4E at Nvidia’s GTC event. The memory makers’ stocks are still trading at a fraction of the valuation multiples of other top AI chip names including Nvidia and leading global foundry Taiwan Semiconductor Manufacturing Co., however. Skeptics say the lower valuation is merited due to the lumpy nature of memory earnings. For now, the exponential rise in prices over past months is tough to ignore. The benchmark contract price for 8 gigabits of DDR4 PC memory has risen for 11 consecutive months, hitting roughly $13 last month, according to market tracker DRAMeXchange. The steady uptrend has boosted margins across SK Hynix’s core business. Higher NAND flash prices also contributed. Read More: Rampant AI Demand for Memory Is Fueling a Growing Chip Crisis And the average selling price of DRAM climbed 60.8% in the first quarter from the previous quarter, while that of NAND rose 55.3%, according to estimates by Mirae Asset Securities Co. Demand for enterprise solid-state drives has also surged alongside data center investment. While pledging to expand capacity, SK Chairman Chey Tae-won cautioned in February that losses remain a possibility in the future in an era of rapid technological shifts. He also highlighted mounting infrastructure challenges. He said SK Group is now exploring building power plants alongside AI data centers, as failure to meet energy demand could be “disastrous.” Some investors also point to the rise of Chinese memory chipmakers as a potential threat to the dominant players. “We are, in a way, in a new paradigm for memory,” Jorry Noeddekaer, London-based head of global emerging markets and Asia at Polar Capital, said before the results. But “this idea that you will never ever see cyclicality in memory — we are not buying that.”

@aleabitoredditApr 17, 2026 · 7:52 PM

Genuinely thanks for nice comments. I share my ideas for free in the end though since I want to help out the retail community. $TSEM hit triple digit return... so that's 16 different names YTD. So my YTD hit 1525%+ as a result. Just to recap all the endless abuse and harassment along the way: 1. $AXTI - "Pump and Dump", "Scam Chinese Stock", Got banned from WSB $RDDT after Mods got mad investors actually made money AXT going from $12->$80. 2. $AAOI - "Pumping stock with no fundamentals, Meme stock" 3. $SIVEF - "Pump and Dump" "Meme Stock" 4. $LITE - "Photonics Bubble" 5. $IQE - "Just pumping low MC stocks" 6. $AEHR - "Stock with negative revenue growth, why is anyone following this guy and not paying $2,000+ for my subscription?" 7. $CRCL - "TA says it's going down to $30" 8. $EWY - "Just from followers" (hint, it's the South Korean Index) 9. Unimicron - "Idea is useless give me US stocks" 10. Nitto Boseki - "Idea is useless give me US stocks" 11. $OSS - Stealing Ideas (no, my synthesis around Venezuela was novel) 12. $GDRZF - "You're a terrible human trying to profit off of the War in Venezuela" 13. $RPI - "Meme stock all because of a Meme Trader" (FT, European Media). 14. $SOI - "Pump and dump", "no novel idea" (random analysts) 15. $ALRIB - "Pumping low MC stocks" (no, it's $MSFT quantum information discovery) 16. $TSEM - "Pumping based on followers alone" (bro it's $25B+, these are institutions) Or how about... the idea around fundamentals was right all along? And I'm just sharing information synthesis/discovery before institutions find out about them. Retail and media should be celebrating when 16+ different ideas return 100%+ YTD, since stocks are positive sum. Everyone from retail, the companies, and local economies benefits. Instead, negativity is through the roof and people keep trying to diminish/downplay the ideas like frontrunning the photonics supercycle… even when they actually turn out right? The trolls are starting to get to me, from $IREN folks creating new accounts every day just to send IRL threats, to European media disinformation about "pumping and dumping"... since I do read every comment. But notice... how 95% of things keep going up? And institutions like Point72 and Apollo end up buying the names I mention? Comments like this do make it helpful to stay on X, and I do enjoy taking victory laps on the haters.

@thevalueistMar 4, 2026 · 6:20 PM

$MU $SNDK $EWY $NVDA SOURCE SUMMARY AND INTERPRETATION TheValueist frames NAND-based SSDs as a “Hard Asset Low Obsolescence” (HALO) product and contrasts that characteristic with GPUs and HBM/DRAM. The central claim is that a 5-year-old NAND SSD remains “almost as effective” as a new SSD for many use cases, with the main differences being capacity density and power within a given physical footprint, and with the primary true degradation mode being write-related wear-out (“burnout”). The post then links this low functional obsolescence to lower economic depreciation versus GPUs and HBM/DRAM. The tagged tickers ($MU, $SNDK, $EWY, $NVDA) imply an investment framing across memory suppliers (Micron; broader Korea memory exposure via EWY), flash product ecosystems (SanDisk branding historically associated with NAND storage), and the dominant AI accelerator supplier (Nvidia). The HALO framing mixes 2 separate concepts that typically need to be disentangled in hardware underwriting: functional obsolescence (whether the device can still perform the required job) versus economic obsolescence (whether the device remains cost-competitive versus newer alternatives given space, power, performance, and reliability constraints). The post is directionally strongest on functional obsolescence for storage capacity, but materially weaker as a blanket statement about economic depreciation once density, interface evolution, fleet reliability policies, and $/GB deflation are incorporated. TECHNICAL ASSESSMENT OF “LOW OBSOLESCENCE” IN NAND SSDs NAND SSD utility is primarily capacity provision plus a latency/IOPS service envelope. Storage is not “consumed” by operation in the same way compute throughput is; the same stored bit remains a stored bit until the media fails or data is migrated. As a result, if an SSD meets minimum interface compatibility and reliability requirements, it can remain serviceable across long periods, particularly in read-heavy or moderate-write workloads. This underpins the intuitive truth in the post: the job of persistent storage is comparatively stable, and many software stacks can tolerate wide variance in peak SSD specifications so long as tail latency and failure behavior remain acceptable. However, “almost as effective” depends heavily on the workload class and the reference point for “effectiveness”: 1.Interface and platform compatibility can create step-function changes in usefulness. SATA SSDs versus NVMe SSDs are not interchangeable for high-throughput or high-parallelism workloads; even within NVMe, PCIe generation transitions can change achievable bandwidth and queue behavior. A 5-year spread can encompass large sequential throughput differences and meaningful changes in latency under load, particularly at higher queue depths. 2.Quality-of-service (QoS) and tail latency, not peak bandwidth, tend to be the binding constraint in enterprise deployments. Controller firmware, garbage-collection behavior, write amplification, thermal throttling, and sustained performance at high fill levels can vary substantially across generations. “Usable” may remain true, but “on par” can fail in any environment where p99 or p999 latency is monetized (databases, high-frequency logging/telemetry pipelines, high-performance caching layers, training-data staging that gates expensive GPU time). 3.Density is not a footnote in datacenters; it is often a primary driver of replacement. Even if a prior-generation SSD still performs acceptably, a newer drive with materially higher TB per slot can reduce the number of devices, backplanes, servers, or enclosures required to deliver a given capacity target. That reduces platform overhead (controller CPU, memory, NIC ports, cables), improves operational simplicity, and can lower power per TB even if the newer device has higher peak power. 4.Wear-out (“burnout”) is only 1 failure vector. SSD retirement decisions in hyperscale and enterprise fleets are driven by a combination of warranty horizon, predicted failure curves, data retention margins, media error rates, firmware risk, and operational policies designed to avoid correlated failures. A 5-year-old drive can be perfectly functional yet still be decommissioned due to fleet risk management. This creates economic depreciation even when functional obsolescence is low. Netting these points, NAND SSDs do exhibit low functional obsolescence in capacity-centric roles and in tiers where performance is not the limiting factor. The claim becomes progressively less accurate as the workload becomes performance-sensitive, density-constrained, or governed by strict fleet reliability policies. COMPARATIVE OBSOLESCENCE: GPUs VS HBM VS DRAM VS NAND GPU obsolescence is fundamentally driven by rapid improvement in performance per watt, memory bandwidth per accelerator, interconnect bandwidth, and the introduction of new architectural features that are directly monetizable in AI training and inference (throughput, model size feasibility, latency, and cost per token). In frontier AI, a prior-generation GPU can remain functional, but can become economically dominated quickly when newer platforms deliver materially better training time, higher utilization, and lower energy per unit of work. The economic penalty of being compute-limited is often larger than the capex savings from extending GPU life, especially when model training time is strategic and when scarce engineering time is valued. This creates structurally higher economic obsolescence pressure than for storage. HBM should be treated differently from both DRAM and NAND because it is typically packaged with accelerators and is not a freely redeployable module in most deployments. HBM value therefore inherits the accelerator platform’s obsolescence curve. Even if the HBM die itself remains “good,” the economic life of that memory is gated by the economic life of the accelerator it is attached to. In AI training, where HBM capacity and bandwidth are binding constraints, the platform step-ups (higher HBM capacity, bandwidth, and improved memory subsystem efficiency) translate directly into higher feasible batch sizes, larger models, and improved time-to-train economics. That linkage tends to steepen depreciation versus free-standing storage assets. Discrete DRAM (server DIMMs) sits between these extremes. DRAM is physically durable and can remain functional for long periods, but economic and practical reuse can be constrained by platform transitions (socket changes, DDR generation changes, speed-bin requirements, power/thermal envelopes) and by the fact that incremental DRAM capacity or bandwidth can be required to support newer CPU generations and higher core counts. As a result, DRAM can experience meaningful economic obsolescence during platform refreshes even if the modules themselves remain functional, but it generally retains more redeployability than HBM and can have longer effective life than GPUs in many non-AI server contexts. Relative ranking by typical economic obsolescence pressure in performance-critical AI datacenter contexts tends to be: GPU (highest) ≈ HBM (tied to GPU) > platform-dependent DRAM > NAND SSD (lowest), with the caveat that NAND can move up this ranking when storage performance or density becomes the bottleneck to GPU utilization. DEPRECIATION MECHANICS: WHY “LOW OBSOLESCENCE” DOES NOT NECESSARILY MEAN “LOW DEPRECIATION” The post’s key financial leap is from low functional obsolescence to “significantly less depreciation.” That implication is plausible under a narrow definition of depreciation as “loss of usefulness,” but can be misleading under the definitions that matter for underwriting cash flows and equity value. 1.$/GB deflation drives economic depreciation even when usefulness remains. NAND has historically exhibited persistent cost-per-bit improvement, which tends to flow through to lower $/GB pricing over time. A 5-year-old SSD may be fully usable but can still be economically “depreciated” in resale value because newer SSDs offer more capacity per dollar and per slot. This is depreciation via price discovery, not via functional failure. 2.Density and system-level overhead convert into TCO penalties for older drives. If more older drives are required to hit a capacity target, the incremental chassis slots, servers, HBAs, PCIe lanes, switch ports, and operational complexity can dominate the media cost. In those cases, the older SSD’s “utility per slot” is inferior, and economic depreciation is driven by platform costs rather than NAND wear. 3.Reliability policy creates a cliff independent of observed performance. Many operators retire drives at or before warranty expiration, or at conservative wear thresholds, to manage tail risk. That retirement policy creates an economically finite life even if the drive could function longer. Lower obsolescence can extend this life at the margin, but does not eliminate a lifecycle cap. 4.Secondary markets can reduce new demand and still produce high depreciation. A robust used SSD market can exist precisely because drives remain functional; however, that same market can depress new-unit demand and compress new-unit pricing power while still marking used units at steep discounts to new due to warranty, endurance uncertainty, and generational $/GB improvements. Low obsolescence can therefore coincide with high price depreciation. In contrast, GPUs can display the opposite configuration in tight markets: high functional and architectural obsolescence pressure, but temporarily low price depreciation or even appreciation when supply is constrained and demand is urgent. Under that regime, observed depreciation becomes more a function of supply scarcity and monetizable demand than of “durability.” IMPLICATIONS FOR DEMAND SHAPE AND CYCLICALITY IN NAND If NAND SSDs are treated as HALO assets by buyers, several demand-shape implications follow, with mixed consequences for suppliers: 1.Lower replacement-driven baseline demand. A smaller portion of unit demand would be tied to mandatory refresh cycles, shifting more of NAND demand toward incremental capacity additions and new system shipments. This can increase sensitivity to macro-driven capex cycles and to periods of digestion after major build-outs, because incremental demand can be deferred more easily than mandatory replacement. 2.Higher elasticity to price declines through “buy-versus-wait” behavior. Low obsolescence means existing assets remain acceptable longer, enabling buyers to delay purchases in anticipation of further $/GB declines. That dynamic can steepen downcycles and slow recoveries unless supply is aggressively curtailed. 3.More inventory risk at the system level, not just at the wafer level. Because products remain functional, channels and OEMs can carry inventory without fear of technical irrelevance, but they remain exposed to price erosion. This can exacerbate the classic memory “inventory overhang” problem: inventory does not become obsolete, so it can persist and keep pricing under pressure until worked down. https://t.co/DQouH5avv5 storage demand may be front-loaded. AI build-outs can require large local NVMe capacity for dataset staging, checkpoints, and caching, plus large-scale object storage. Once a fleet is provisioned, ongoing demand becomes dominated by data growth and expansion rather than refresh. If the storage layer is not the performance limiter, refresh may be slower than GPU refresh, producing a digestion pattern in NAND shipments after initial deployment waves. These dynamics are consistent with NAND’s historical behavior as a cyclical commodity: low functional obsolescence does not immunize the category from volatility; it can amplify it by enabling demand deferral and by prolonging inventory liquidation. IMPLICATIONS FOR MU, SNDK-RELATED FLASH EXPOSURE, EWY MEMORY COMPLEX, AND NVDA For Micron and the broader memory complex, the post implicitly argues that NAND should be viewed as a longer-lived asset class than GPUs and HBM/DRAM. From an equity-underwriting standpoint, the more actionable inference is that NAND revenue streams may be structurally less supported by forced refresh and more reliant on incremental capacity growth and new platform shipments. This tends to increase the importance of supply discipline, mix (enterprise SSD versus client/consumer), and differentiation at the controller/firmware/qualification layer. It also increases the relevance of hyperscaler procurement strategies, including qualification breadth and willingness to extend asset life. For Korean memory exposure (EWY as a proxy for Samsung Electronics and SK Hynix weightings), the key distinction is product mix. HBM economics are platform-linked and can exhibit a faster upgrade cadence, higher barriers to entry, and tighter supply-demand balance than commodity NAND. NAND’s HALO characteristics can therefore be a relative negative for suppliers if it reinforces commoditization and demand deferral, while HBM can remain structurally advantaged due to tight coupling with accelerator upgrades. DRAM sits between, with AI-driven content growth supporting demand but with platform transitions and reuse constraints shaping replacement dynamics. For Nvidia, the post’s contrast supports the view that accelerator demand is driven by rapid economic obsolescence and performance/watt improvement, enabling recurring upgrade cycles and sustained willingness to pay. That said, the same logic highlights a potential medium-term risk channel: as each generation materially improves, older GPUs can cascade into lower-tier inference and enterprise use cases, potentially forming a secondary supply that competes with some segments of new demand. The net effect depends on the slope of demand growth, software enablement, and the degree to which new model architectures require the newest features and memory footprints. KEY COUNTERPOINTS AND LIMITATIONS OF THE POST’S CLAIMS Several limitations materially affect the conclusion that NAND experiences “significantly less depreciation” than GPUs and HBM/DRAM: •The statement conflates durability (physical operability) with economic depreciation (value and cost-effectiveness). NAND can be durable and still depreciate rapidly in market value due to $/GB deflation and density improvements. •“Almost as effective” is workload-specific and becomes less true in performance-gated environments where storage latency/throughput affects expensive GPU utilization. •Fleet retirement policies and warranty horizons can impose effective economic lives that are not meaningfully longer than other datacenter components, even if the media could function longer. •DRAM modules, unlike HBM, can have meaningful redeployability; the post’s grouping of HBM/DRAM may overstate DRAM’s obsolescence relative to NAND in non-frontier workloads. •GPU depreciation is not monotonic; scarcity regimes can invert expected depreciation, making used GPUs retain value unusually well despite high architectural obsolescence pressure. MONITORING FRAMEWORK TO VALIDATE OR DISPROVE THE “HALO” EFFECT IN PRACTICE The HALO thesis is testable through observable behaviors in procurement, fleet management, and secondary markets. Key indicators include: •Evidence of extended SSD deployment lives in hyperscaler and enterprise fleets (increasing average age of installed SSD base, slower replacement cadence, rising share of re-used drives internally). •Secondary-market pricing and volumes for enterprise NVMe SSDs versus new-drive ASP trends; widening discounts with stable volumes would support “usable but economically depreciated” dynamics. •Mix shift toward higher-density SSD form factors and higher TB per device as a driver of replacement; accelerated densification-driven refresh would weaken the “low depreciation” framing at the system level. •NAND bit shipment growth versus hyperscaler capex and AI cluster deployments; front-loaded NAND demand followed by digestion periods would align with low replacement demand. •Storage performance bottleneck signals in AI infrastructure (increased adoption of higher-performance SSD tiers, increased spend on storage networking, stronger preference for new generations tied to tail-latency guarantees). Rising performance sensitivity would reduce the HALO advantage. •NAND supplier commentary and customer behavior around purchase timing (greater tendency to delay buys during downtrends, more pronounced inventory corrections), consistent with deferrable demand enabled by low functional obsolescence. BOTTOM LINE The post accurately highlights that NAND SSDs often retain functional utility for longer periods than performance-sensitive compute accelerators, and that storage assets can remain serviceable even as newer generations offer better density and efficiency. The post overreaches when it converts that observation into a general conclusion of “significantly less depreciation,” because NAND’s economic depreciation is frequently driven by $/GB deflation, density economics, and operational reliability policies rather than by loss of basic functionality. For investors, the most relevant implication is not that NAND is inherently “safer,” but that low functional obsolescence can shift NAND demand toward incremental growth and away from forced refresh, increasing sensitivity to digestion cycles and supply discipline, while GPUs and HBM remain more tightly coupled to rapid platform upgrade cycles where performance improvements are directly monetizable.