Micron Investment Thesis
Artificial intelligence (AI) is becoming a recurring workload across the economy. Micron supplies the memory that makes it run and the storage that keeps what it creates. We see strong earnings growth prospects at a modest valuation.
Updated
TLDR: INVESTMENT CASE
We are bullish on Micron: the valuation looks modest for the earnings growth and durability we expect.
Micron’s forward price-to-earnings ratio was about 7.2× as of September 10, 2026, far below the roughly 19–20× multiples for the S&P 500 and semiconductor index comparison, and NVIDIA’s 18.6×.
A valuation this low could be appropriate for a business in decline, with earnings expected to shrink. If those earnings can instead be sustained, it presents an attractive investment opportunity. If earnings can grow, the opportunity becomes exceptionally attractive.
We believe the AI wave gives Micron that growth potential. More AI use, larger models, and demanding workloads can expand memory and storage sales. Supply constraints and long-term customer contracts may help those profits endure. Together, they support our view that Micron offers more earnings growth and durability than its valuation implies.
Micron’s business in one minute
Micron designs and manufactures the memory and storage chips used in data centers, computers, phones, and vehicles. Its customers need chips that hold data while processors work and retain it afterward. Micron serves those needs through three main product groups: conventional DRAM, HBM, and NAND flash. [19]
Conventional DRAM
Dynamic random-access memory (DRAM) is a computer’s temporary workspace. It holds the data and applications a processor is actively using and loses that information when power is off. Micron sells it for servers, computers, phones, and vehicles. Here, “conventional” means DRAM other than HBM, including advanced server memory.
High-bandwidth memory (HBM)
HBM is a premium form of DRAM, stacked and placed close to AI processors to deliver data much faster. Micron’s HBM helps keep expensive graphics processing units (GPUs) and other AI accelerators supplied with the information they need to perform calculations. [1]
NAND flash storage
NAND retains data when power is off. Micron sells this flash memory and the solid-state drives (SSDs) built with it. They store everything from phone photos to AI training datasets, saved models, and business documents.
Micron’s product mix
In the third quarter of fiscal 2026, ended May 28, Micron reported $41.46 billion in total revenue. Its revenue mix spans the three product groups above, although it reports conventional DRAM and HBM together. [2]
DRAM, including HBM, contributed $31.3 billion and NAND $9.9 billion. Data center SSD revenue exceeded $5 billion within NAND. [3]
Reported figures are rounded and do not exactly reconcile to total revenue.
A revenue share measures dollars, not the amount of memory shipped or its share of profit. Higher prices can change the mix even without an equivalent increase in capacity. For investors, the key is whether Micron can grow earnings across the whole portfolio.
How AI turns into memory demand and higher earnings
AI needs more memory to keep processors productive
AI models repeatedly move large amounts of data between memory and processors. Holding the model requires capacity, while feeding it to the processor requires bandwidth. Generating answers can become limited by memory delivery rather than calculation speed, increasing the importance of memory relative to compute. There is no fixed memory-to-compute ratio across workloads, but AI servers typically carry much more memory and storage than conventional servers. Micron’s infrastructure white paper estimated six times the DRAM and 2.6 times the SSD capacity per AI server. [23] [4]
HBM demand also tightens conventional DRAM supply
Building more AI accelerators raises demand for HBM. Producing HBM uses the same underlying DRAM manufacturing resources but consumes more wafer capacity per bit of memory. Wafers are the silicon discs on which chips are made. Micron’s earlier-generation comparison put HBM at roughly three times the wafer requirement of conventional server DRAM. Micron says this wafer requirement relative to conventional DRAM increases with each new HBM generation. Shifting production toward HBM therefore leaves less capacity available for conventional DRAM, with newer generations adding to that supply pressure. [12] [3]
AI servers also need conventional DRAM around their accelerators. Growing server demand and constrained supply therefore reinforce each other, pushing prices up. TrendForce’s June 2026 survey estimated that conventional DRAM contract prices rose roughly 93–98% from the preceding quarter in calendar Q1. [24]
AI creates a second demand surge in NAND
NAND serves two distinct needs in AI. First, training datasets, saved models, company documents, and generated media need persistent storage: they must survive when power is off. HBM and conventional DRAM cannot provide that on their own. SSDs built with NAND retain this data and supply it to working memory when needed. [25]
Second, NAND can hold temporary working data that does not need to survive a shutdown. An AI system might move cached information from an earlier conversation out of HBM or DRAM while it is not being used. NAND costs less per bit, so keeping that information on an SSD frees expensive memory for active work. The trade-off is higher latency: retrieving it takes longer. This works when data is accessed less often or can be loaded ahead of time, so the capacity savings outweigh the delay. NAND therefore gains demand both as permanent storage and as a cheaper extension of working memory. [26]
Profits have grown faster than revenue and the share price
Micron’s fiscal Q3 2026 revenue rose 346% year over year, from $9.30 billion to $41.46 billion. Net profit grew even faster: up 1,398%, from $1.89 billion to $28.24 billion, using generally accepted accounting principles (GAAP). That is nearly 15 times the profit on roughly 4.5 times the sales. [2]
Improving margins explain the gap. Gross margin, the share of sales left after production costs, rose from 37.7% to 84.6%. Stronger pricing lets more revenue flow through to profit. Operating leverage adds another boost: research, administration, and other operating expenses grew only 30% while revenue more than quadrupled. Spreading those expenses across much larger sales lifted operating margin from 23.3% to 80.4%. [2]
The stock’s rally has been extraordinary too. Between the two fiscal quarter-end dates, May 29, 2025 and May 28, 2026, Micron’s closing share price rose from $96.80 to $923.52, a gain of 854% before dividends. Yet even that increase lagged profit growth. Diluted earnings per share rose from $1.68 to $24.67, or 1,368%, so the earnings growth outpaced the stock even after accounting for the share count. [2] [27]
A much higher share price can still buy more earnings per dollar when profits rise faster. Whether that makes Micron attractive depends on how much of this earnings base lasts and what comes next. We address that in the valuation section.
Competition and Micron’s relative edge
Micron competes primarily with the Korean companies Samsung and SK hynix in DRAM and HBM, and with those companies and other flash suppliers in NAND. Customers choose on performance, power consumption, price, and the ability to deliver reliable products in volume. AI growth expands the opportunity, but Micron still has to win its share. [19]
We are generally bullish on memory competitors too, including Samsung and SK hynix. Expanding AI demand can benefit several suppliers. We favor Micron for its U.S. base and its combination of working memory and storage.
A U.S. base with a geopolitical advantage. Although Micron also manufactures extensively in Asia, it is headquartered in Boise, Idaho, and leads its major memory competitors in existing and planned U.S. memory manufacturing. Its domestic footprint includes production in Virginia and new fabs underway in Idaho and New York. [28] This aligns Micron with its customer base: U.S.-headquartered customers accounted for roughly 65% of fiscal 2025 revenue. [19] We see that alignment as a meaningful geopolitical advantage over Samsung and SK hynix. In a climate of shifting tariff policies and a U.S. push to bring semiconductor production home, Micron’s established domestic presence and expansion plans strengthen its position as a long-term supplier to American customers. [29]
A closer fit for investors who favor the dollar. Micron uses the U.S. dollar as the functional currency across all its operations, meaning the dollar is the economic basis on which it measures business performance. Its sales are predominantly in dollars, and it hedges foreign-currency cash and investments to protect their dollar value. [19] Samsung and SK hynix, by comparison, use the Korean won as their parent companies’ functional currency, tying their financial results to a different currency base. [30] [31] For investors who favor USD as a long-term store of value, we prefer Micron’s alignment with that base.
Both sides of the memory opportunity. Micron sells DRAM, including HBM, and NAND storage. We prefer that breadth to a NAND-focused investment such as Sandisk: Micron can benefit from the memory used to run AI and the storage used to retain its data. [22] This breadth is not unique to Micron. SK hynix also supplies DRAM and NAND, so our preference combines product exposure with Micron’s U.S. base and valuation. [21]
Why the valuation makes Micron attractive
A great business can still be a poor investment if its price already assumes exceptional results. We favor Micron because we believe its earnings can endure and grow beyond what the share price implies.
A price-to-earnings (P/E) ratio tells you how much investors pay for a dollar of annual profit. Trailing P/E uses the past 12 months. Forward P/E uses forecast earnings, generally based on analyst estimates. A low forward multiple can signal an opportunity, or a market that expects those forecasts to fall apart.
| Investment | Trailing P/E | Forward P/E | Source date |
|---|---|---|---|
| Micron (MU) [13] | 23.2× | 7.2× | Sep 10, 2026 |
| S&P 500 [14] | 22.8× | 19.3× | Sep 9, 2026 |
| Semiconductors (SOXX) [15] [16] | 64.9× | 19.6×* | Sep 9 / Sep 8, 2026 |
| NVIDIA (NVDA) [17] | 28.3× | 18.6× | Sep 10, 2026 |
Rounded published ratios, with differing dates and methods. *SOXX forward P/E uses the index it tracks as an approximation, while trailing P/E uses the fund’s holdings. These are illustrative comparisons, not uniform calculations. Read the valuation methodology.
The striking discount is in the forward numbers. Micron’s trailing multiple is roughly in line with the S&P 500 in this snapshot. On forecast earnings, it trades at less than half the multiple of every comparator. The gap between Micron’s own trailing and forward ratios also shows how much stronger the forecast earnings base is. Our thesis requires that stronger earnings base to endure and grow. A cheap multiple on a temporary profit peak would not necessarily justify the investment.
Why the discount exists
The bear case deserves a fair hearing. Memory has a history of brutal economic feedback loops. Shortages lift prices and profits. Producers build factories. New supply eventually arrives, customers stop stockpiling, and prices fall. Because manufacturing costs do not fall as quickly as revenue, profits can collapse. Near the top of that cycle, a stock can look cheapest precisely when its earnings are least sustainable.
Micron’s low forward multiple reflects substantial skepticism about how long its current level of earnings can last. Analysts can forecast a strong coming year while investors refuse to pay much for it because they expect weaker years afterward.
Where we disagree
We think the market is underpricing the duration of demand. AI adds a recurring need for memory across an expanding range of activities. The supply coming online must serve a market that is still growing when those factories finally deliver. Announcing capacity is easy compared with producing large volumes of memory that meet customers’ requirements, and demand does not stand still during construction.
We expect expanding demand and profitable capacity additions to support earnings growth over time, even if pricing moderates. At a modest starting multiple, that growth can reward shareholders without requiring a higher valuation multiple. Greater confidence in the durability of those earnings could provide additional upside.
Why we expect earnings to keep growing
More memory shipped, a larger share of sales from premium products, and attractive pricing can lift earnings.
Three overlapping themes support our growth view. They explain how demand could grow and should not be added together as separate forecasts.
AI scale, model size, and inference
When it comes to AI models, all else equal, bigger is better: more parameters give a model greater capacity to learn complex patterns. The push for greater capability is driving larger frontier models, with leading systems already reaching a trillion or more total parameters. [33] [32] These parameters, also called model weights, are the numerical values learned during training. To run a model, the system loads its weights into memory so processors can access them quickly. More weights mean more memory needed just to hold the model, before it starts answering a request. [5]
Inference, using a trained model to generate an answer, needs additional memory for a key-value (KV) cache. Think of this as saved working notes about the text the model has already processed. Reusing those notes avoids repeating calculations as it generates each new piece of an answer, reducing response latency. Longer conversations, larger documents, and more simultaneous users require more space for these caches, on top of the memory occupied by model weights. [5] [26]
AI agents extend this demand by working through tasks over many steps: planning, consulting documents, using tools, and checking results. Keeping useful context available between steps lets them respond faster without repeatedly rebuilding it. Larger models and growing caches therefore expand two distinct sources of memory demand, supporting our outlook for HBM and conventional DRAM, with NAND providing an additional tier for less immediately needed context. [4]
Wider adoption at current intelligence
Even without further improvements in AI capability, we expect wider adoption to drive substantial growth. Pew’s February 2026 survey found that 49% of U.S. adults use AI chatbots, but only 24% use them daily. [34] Agentic use is earlier still: in EY’s 2026 survey across 23 markets, 16% of respondents had used AI that acts autonomously on their behalf in the previous six months. [35]
The global opportunity is larger still. Microsoft estimated that 17.8% of the world’s working-age population used generative AI in Q1 2026, compared with 31.3% in the United States under the same measure. Adoption was just 15.4% in the Global South, versus 27.5% in the Global North. [39] We see substantial room for growth as access expands, tools become more affordable, and more people learn to use capabilities already available today.
We see considerable room for more people to adopt today’s tools and for occasional users to make them part of everyday work. Coding, research, customer support, and document analysis already offer useful applications. More users, more frequent requests, and more tasks delegated to agents increase inference volume. Serving that growing workload at acceptable speeds supports demand for more memory-equipped servers.
Increased demand beyond AI chats & coding agents
Image and video generation
Better visual quality, more reliable editing, and longer, more coherent videos can make AI useful across advertising, design, entertainment, and training. All else equal, generating detailed images and video generally puts more pressure on working memory than simple text generation: the system must hold and process rich visual information, with video adding many frames and the relationships between them. Higher resolution and longer clips expand that workload, while the resulting media adds to NAND storage needs. [6] [36]
OpenAI discontinued Sora, its AI video-generation product that turned text prompts into videos, to prioritize other work. That decision shows how competing demands for compute can sideline a promising product. In his July 2026 interview on Relentless, Sam Altman said of Sora, “It would have been super successful.” He explained that it was more important to “put the compute and the energy into coding agents.” [37] We see this as evidence that today’s video usage does not capture the full opportunity: even products their developers believe could succeed must compete for limited infrastructure and attention. More available capacity could allow additional video applications to be served alongside higher-priority workloads, expanding demand for memory and storage.
Self-driving vehicles
Driving systems must process continuous camera and sensor streams, hold models in memory, and make decisions quickly. More capable onboard AI increases the opportunity for conventional DRAM and flash, while fleet data, simulation, and model training add server memory and storage demand. [9] [10] AI4 and AI5 are successive generations of Tesla’s own AI chips for running driving models inside its vehicles. Tesla’s January 2026 update targeted 9× the memory capacity for the next-generation AI5 versus AI4, with AI5 production then planned for 2027. [38] That roadmap illustrates how memory content can grow sharply even before accounting for more vehicles using advanced driving systems.
Robotics
Robotics could become an enormous new market for memory, with a scale that is difficult to estimate today. Useful, affordable robots could spread across factories, warehouses, and homes, opening demand far beyond today’s deployments. At the Future Investment Initiative in October 2024, Elon Musk predicted that humanoid robots would outnumber people by 2040. That is an ambitious forecast, but it illustrates how large the opportunity could become if general-purpose robots achieve mass adoption. [40]
Micron’s Sanjay Mehrotra connected that opportunity directly to memory in his June 2026 earnings remarks. He said humanoid robots carry 10 times the memory of an average L2+ vehicle, a car with advanced driver assistance, and forecast a “sustained, substantial multi-decade memory demand cycle” beginning later this decade. [3] More robots and high memory content per machine could compound into a major source of demand. Training and simulation would add demand in data centers supporting those fleets. [8] [7]
We see this as substantial long-term upside for Micron. The opportunity could be much larger than today’s visible applications suggest, especially if robots become useful across a broad range of physical work.
Miscellaneous use cases
Real-time translation, medical-image analysis, industrial inspection, scientific research, and personalized education offer further ways for AI to move beyond chats and coding agents. We expect these applications to add working-memory demand as models process information and NAND demand as useful data and results are retained. Individually smaller uses can become meaningful in aggregate. We also expect new applications that are difficult to predict today, particularly as capabilities improve. That unquantified upside broadens the opportunity beyond the largest workloads already visible.
Supply constraints and long-term contracts support durability
More specialized memory is harder to replicate
Memory has historically been viewed as a commodity: standardized products that customers can substitute for one another, leaving price and production cost to drive much of the competition. As advanced memory becomes more specialized, matching a competitor’s capacity alone is increasingly insufficient.
High-bandwidth memory illustrates the shift. It stacks multiple memory chips vertically, requiring precise manufacturing and packaging while meeting demanding bandwidth and power targets. [41] Customer approval is another hurdle: Micron’s June 2026 results described HBM4 in high-volume shipments for its lead customer’s platform while qualification samples were being sent to other customers. [2] For the subsequent HBM4E generation, Micron has outlined an option to customize the logic chip beneath the memory stack for individual customers. [42]
We believe this raises the difficulty of reproducing a competitive product and replacing an approved supplier. Performance, power efficiency, manufacturing yields, and integration with the customer’s processor all matter. While standard memory remains price-sensitive, and rivals can still qualify competing products, we believe growing specialization can strengthen Micron’s customer relationships and support more durable margins as these products become a larger part of its business.
Supply takes time to deliver
The strongest bearish objection is also the obvious one: extraordinary profits will attract extraordinary investment. We agree. Where we disagree is the assumption that new capacity will arrive into a market that has stopped expanding.
A factory announcement does not translate directly into saleable memory. Between the announcement and commercial output come construction, equipment installation, yield ramping (raising the share of chips that meet specifications), advanced packaging, and customer qualification. Each step can constrain how quickly new capacity turns into chips customers can actually use and buy. Expanding floor space may increase capacity, but it does not instantly remove those bottlenecks.
While supply works through those constraints, we expect demand to keep expanding. New capacity must catch up with the market’s future needs, making today’s shortage harder to resolve than current demand alone would suggest.
Micron’s June 2026 outlook called for tight DRAM and NAND conditions beyond calendar 2027. [3] That outlook supports our view that supply will struggle to keep pace with demand for an extended period, making a quick return to chronic oversupply less likely. Memory prices can soften along the way while the longer-term supply constraints persist.
The bullish case does not require supply to stop growing. It requires demand to absorb that growth while Micron earns attractive returns on the capital needed to serve it.
Customer commitments support capacity investment
Micron’s new long-term contracts exchange dependable memory supply for binding customer purchases over several years. Customers secure memory for their future products, while Micron gains advance commitments on how much they will buy and, in many agreements, limits on how far prices can fall or rise. Micron calls these strategic customer agreements (SCAs). [3]
The business model shift is in the strength of those commitments. Micron already had long-term customer agreements, but its fiscal 2025 annual report explained that prices, quantities, and other terms were typically renegotiated periodically as market conditions and customer demand changed. Customers were generally reluctant to lock in long-term fixed prices. [19] That left Micron committing capital to factories whose eventual sales volumes and prices remained exposed to the market. Binding purchases and agreed pricing limits give it a firmer basis for those investments.
In June 2026, Micron disclosed 16 signed SCAs, typically running from 2026 through 2030, with automotive agreements generally lasting three years. They covered roughly 20% of DRAM volume and one-third of NAND volume over their terms. Their “take-or-pay” provisions require customers to purchase specified volumes or pay under the contract. The largest agreements generally include minimum and maximum prices, called floors and ceilings. Other agreements have fixed prices or remain exposed to market pricing. [3]
That shifts more of the risk of expansion onto customers seeking assured supply. Purchase commitments make production planning more predictable, and price floors provide protection when market prices weaken. Customers benefit because dependable memory access helps them plan their own product launches. Micron’s July announcement of automotive agreements with Qualcomm, DENSO, and Hyundai Mobis illustrates that demand for greater supply and pricing visibility. [18]
Much of Micron’s output remains outside these commitments, and price ceilings limit some upside. Even so, we see this shift toward committed sales as a meaningful improvement in the business model: greater confidence in future volumes and pricing can support better investment decisions, more durable earnings, and a stronger case for a higher valuation.
What could break the thesis?
We would first reconsider our bullish view if Micron’s valuation discount narrows substantially, even with an unchanged business outlook. We also monitor regulation, AI economics and capabilities, Micron’s execution, and competing technologies.
1. The valuation discount narrows
Even if the business outlook remains intact, a higher valuation can weaken the investment case. We would begin reassessing if Micron’s forward price-to-earnings ratio approaches that of the broader semiconductor industry (SOXX) or the broader market (S&P 500). At those levels, the relative undervaluation central to our thesis would begin to fade. Continued growth could still support attractive returns, but we would need to judge whether the remaining upside justifies the price.
2. Regulations force a slowdown
Governments could restrict frontier model development, limit AI use in sensitive industries, or make data center approvals harder to obtain. Even with strong customer demand, tighter rules could delay training, deployment, and infrastructure investment, slowing the growth in memory purchases. We would watch whether restrictions remain narrow or begin to constrain AI adoption broadly.
3. Frontier labs or hyperscalers face financial strain
Strong demand does not guarantee attractive economics. Frontier labs developing the most capable models and hyperscalers operating vast cloud networks are making enormous commitments. Anthropic has committed more than $100 billion to AWS technologies over ten years. Microsoft’s July outlook put calendar 2026 capital expenditure at roughly $175 billion, following a shift in lease classification. [43] [44]
AI could see strong demand and widespread adoption yet remain unprofitable if the revenue it generates cannot cover computing, electricity, model development, and infrastructure replacement costs. If investors lose confidence in future returns, equity and debt financing could dry up. Labs might cut their computing commitments, while cloud providers could delay new capacity, weakening demand for Micron’s memory.
Currently, the profitability evidence is mixed, with an encouraging caveat. August reporting put OpenAI’s Q2 operating results deeper in loss. Bloomberg reported that Anthropic achieved positive adjusted operating income in Q2, based on preliminary figures shared with investors. That is evidence that a leading lab can cover costs on that measure, although it does not establish sustained net profitability or positive free cash flow. [45] [46]
More durable profits would reduce this concern, but stronger competition could still erode pricing and returns. Chinese labs could win business from American providers, shifting some infrastructure spending away from Micron’s customer base, which was roughly 65% U.S.-headquartered by fiscal 2025 revenue. China’s restrictions have impaired Micron’s sales to data centers in China, and Reuters reported plans to exit most of that market while retaining certain customers. [19] [47] Although Micron may still help fill some of the demand in China, it likely captures less of that opportunity under these restrictions. We therefore see a shift toward Chinese AI infrastructure as a risk to Micron’s share of memory spending, with the impact depending on where workloads run and which hardware they use.
4. AI progress hits a technical wall
Progress could plateau across several routes: larger models and more training compute, reinforcement learning that rewards successful answers, training on AI-generated data, or giving models more time and attempts to reason. If these techniques deliver increasingly small improvements, new applications could remain out of reach and labs could have less incentive to expand training and inference infrastructure.
There is already evidence of diminishing returns in specific settings. A 2025 study found that extra reasoning attempts and successive rounds of refinement eventually produced smaller gains on math and science benchmarks. [51] Another study, published at NeurIPS 2025, found that the reinforcement-learning methods it tested made correct answers more likely without establishing new reasoning abilities beyond those the starting model could already demonstrate given enough attempts. [52] These findings do not establish a permanent ceiling: a June 2026 preprint reported broader reasoning gains by combining targeted teaching with reinforcement learning. [53] We see a risk that further progress becomes harder and more expensive, not proof that useful advances have ended.
We would remain bullish even without further capability improvements: existing AI has substantial room for adoption across businesses, consumers, and countries, as discussed in our growth case. A technical plateau would nonetheless reduce the growth potential, especially for applications that require capabilities today’s models cannot reliably deliver.
5. Efficiency or architecture reduces memory needs
We generally expect efficiency gains to increase total memory consumption over time. Cheaper computing has historically enabled wider adoption and more demanding software. [55] For example, U.S. annual private investment in computers and peripheral equipment rose from about $39 billion in 1990 to $271 billion in 2025, roughly sevenfold in nominal dollars. Over the same period, quality-adjusted prices for that equipment fell about 95%, meaning buyers received far more capability for their money. Cheaper computing coexisted with a much larger hardware market. [54] We expect a similar demand response in AI: lower costs make more tasks worth automating, expanding demand for compute and the memory supporting it.
This is the Jevons paradox argument, where greater efficiency expands usage enough to increase total resource consumption. [11] As an example, if each task uses half as much memory for the same duration, but task volume triples, total memory use over time rises 50%.
Efficiency could nonetheless improve faster than demand grows. Compression, using fewer of a model’s connections or parameters, and smaller specialized models could reduce memory per task faster than usage expands. Accelerators could also require less HBM or favor lower-cost memory. In that scenario, AI adoption could keep growing while total memory requirements fall or grow more slowly than our thesis assumes.
6. Micron falls behind on execution
Strong demand only helps if Micron can deliver reliable products at competitive costs. It could bring new fabrication plants, or fabs, online more slowly than competitors, allowing rivals to capture demand first. Construction delays, equipment shortages, hiring difficulties, and budget overruns could delay output or make new capacity less profitable. Opening a fab is only the first step: Micron must then bring it to efficient volume production. [19]
Execution risk also spans conventional DRAM, HBM, and NAND. Transitions to new manufacturing processes could take longer than planned, low yields could raise the cost of each usable chip, and quality problems or delayed customer qualification could derail product launches. HBM adds particularly demanding stacking, packaging, and thermal requirements, making it one important example of this broader risk. [19] [41]
These failures could cost Micron orders and margins even with competitive product designs and a growing market. We would watch fab schedules, production volumes, customer approvals, and whether new capacity delivers the expected cost improvements.
7. CXMT and increased competition
ChangXin Memory Technologies (CXMT) is the Chinese memory producer we watch most closely in DRAM. China has lagged the U.S.-led AI infrastructure ecosystem in advanced chip access and HBM supply, but it is a manufacturing juggernaut. Its industrial scale and funding capacity make rapid expansion plausible if domestic producers master competitive memory production. That could pressure Micron’s market share and pricing. [48] [50]
CXMT is already a conventional DRAM competitor, but it has not solved competitive HBM production at scale. Domestic supply still falls short of China’s memory demand, according to August reporting citing Apacer’s CEO. CXMT cannot yet satisfy their domestic market’s needs, let alone remove the global shortage. [49] SemiAnalysis’s June model estimated roughly 25% overall yield for CXMT’s eight-layer HBM3, and September reporting also described yields near 25%. If these estimates are accurate, roughly three-quarters of potential output is lost across chipmaking and packaging, leaving fewer saleable stacks to cover production costs. That would put CXMT at a substantial manufacturing disadvantage: SemiAnalysis expects less HBM output from the same wafer capacity than established suppliers and very low margins compared with conventional DRAM. Such low yields signal difficulty producing reliable HBM economically at scale, making yield improvements critical to expanding supply and strengthening margins. [48] We take the longer-term threat seriously: domestic shortages and poor initial HBM yields do not prevent conventional DRAM expansion today or better HBM execution later.
8. Competitors out-innovate Micron
Samsung, SK hynix, or another producer could release memory with better bandwidth, capacity, power efficiency, or cost that Micron cannot match quickly enough. Customers could switch suppliers or demand discounts, weakening Micron’s share and margins. A commercially viable new memory or storage technology could also replace part of the DRAM, HBM, or NAND market before Micron builds a competitive offering. [19]
Note that this is a different risk from algorithms or system architectures reducing memory needs. Here, customers still need memory or storage, but a rival’s superior product or a substitute technology captures the spending.
Conclusion & next steps
Micron offers strong growth prospects at a valuation we consider too low for that opportunity. Expanding memory demand can grow the business, while supply constraints and customer contracts can support the durability of its profits. We are investing in that combination.
Sources & methodology
This is our investment interpretation of the sources below. Financial figures use Micron’s fiscal Q3 2026 disclosures. Product examples explain potential demand, not guarantees that customers will choose Micron components. The valuation snapshot was compiled September 10, 2026 using the source dates in the table and methods below. The explanation of the valuation discount is our interpretation, not a measurement of investors’ motives. Management forecasts are identified as such. No share price target or assumed HBM revenue split is used.
Valuation methodology
Published ratios rounded to one decimal. S&P 500 means the Standard & Poor’s 500 index. SOXX is the iShares Semiconductor exchange-traded fund. *Its forward figure is Ionic Research’s next-12-month multiple for the underlying semiconductor index, used as an approximation. The trailing figure is iShares’ portfolio P/E. These are illustrative comparisons, not a synchronized or uniform calculation.
Micron and NVIDIA use Stock Analysis’s published trailing and forward ratios. S&P 500 uses StreetStats’ trailing and forward 12-month earnings. Forecast horizons, earnings adjustments, how holdings are weighted, how loss-making companies are counted, and update times can differ across providers. In particular, SOXX’s two figures should not be read as an implied earnings growth rate. This is a dated snapshot, not a live quote.
Earnings and share-price growth
Financial growth compares fiscal Q3 2026 with fiscal Q3 2025 using GAAP results and unrounded reported amounts. Share-price growth uses closing prices before dividend adjustments on the corresponding quarter-end dates, May 29, 2025 and May 28, 2026. Percentage changes are rounded to the nearest whole percent. This compares quarterly earnings growth with the price change between quarter ends, not a change in trailing or forward P/E. The 2026 results were released June 24, after the stock-price endpoint.
Source references
- Micron · High-bandwidth memory overview
- Micron · Fiscal Q3 2026 results, June 24, 2026
- Micron · Fiscal Q3 2026 prepared remarks (PDF) · Product revenue: p. 8. HBM4: p. 4. Customer agreements: pp. 2 and 3. Supply outlook: p. 5. Robotics memory content and demand outlook: p. 5.
- NVIDIA · Memory management for agentic inference
- NVIDIA · Model memory requirements
- OpenAI · Video generation models as world simulators, February 15, 2024 · Technical research, used as an architectural example.
- NVIDIA · Cosmos for physical AI development
- NVIDIA · Jetson Thor memory specifications
- NVIDIA · Autonomous vehicle development
- NVIDIA · DRIVE development platform specifications
- Jevons paradox · Economic concept and background
- Micron · Fiscal Q2 2025 presentation, HBM manufacturing trade-off (PDF)
- Stock Analysis · Micron valuation ratios, September 10, 2026
- StreetStats · S&P 500 valuation, September 9, 2026
- iShares · SOXX portfolio P/E, September 9, 2026
- Ionic Global Research · Semiconductor index forward P/E, September 8, 2026
- Stock Analysis · NVIDIA valuation ratios, September 10, 2026
- Micron · Automotive customer agreements, July 16, 2026
- Micron · Fiscal 2025 Form 10-K · Customer agreements and periodic renegotiation of pricing and quantities, competition, international manufacturing and customer locations, sales and functional currencies, treasury hedging, and foreign-exchange risks.
- Micron · U.S. manufacturing and research investment plans, June 12, 2025 · Planned capacity and customer support.
- SK hynix · Company profile and DRAM/NAND portfolio
- Sandisk · Company overview and flash storage business
- Micron · AI and machine learning infrastructure white paper (PDF) · Illustrative AI versus conventional server memory and storage capacity, not a universal ratio per unit of compute.
- TrendForce · Calendar Q1 2026 DRAM industry revenue and contract pricing survey, June 1, 2026
- Micron · Memory and storage in AI data centers
- NVIDIA · How to reduce KV cache bottlenecks with NVIDIA Dynamo · Moving cached working data between GPU memory, CPU memory, and storage.
- Yahoo Finance · Micron historical closing prices · May 29, 2025: $96.80. May 28, 2026: $923.52. Cross-checked against the site’s historical price dataset, retrieved September 11, 2026.
- Micron · U.S. manufacturing expansion · Existing memory production and domestic investment plans.
- White House · Semiconductor import policy, January 14, 2026 · Tariff reviews and incentives for domestic manufacturing.
- Samsung Electronics · 2025 consolidated financial statements (PDF) · Note 2.4, parent-company functional currency.
- SK hynix · SEC registration filing · Functional currency and foreign-exchange exposure.
- NVIDIA · Vera Rubin infrastructure for trillion-parameter models, March 16, 2026
- Moonshot AI · Kimi K2 model specifications · One trillion total parameters, with 32 billion activated per token.
- Pew Research Center · Americans and AI, June 17, 2026 · Survey of 5,119 U.S. adults, February 17–23, 2026. Chatbot use: 49%, including 24% daily.
- EY · 2026 AI Sentiment Report findings · Survey of 18,152 adults across 23 markets. Autonomous AI use in the previous six months: 16%. This broader category includes autonomous services as well as software agents. Markets are equally weighted, and some samples represent internet-enabled populations. It is a separate adoption measure from Pew’s U.S. chatbot survey.
- Hugging Face · Stable Video Diffusion memory requirements · Working-memory demands from generating multiple video frames.
- Relentless · Sam Altman: How to Start a Startup, July 25, 2026, 56:24–56:31 · Altman’s assessment of Sora and the decision to prioritize coding agents. Timestamped transcript. His explanation concerns resource allocation and product priorities. Additional video demand enabled by more capacity is our investment interpretation.
- Tesla · Q4 and FY 2025 update, January 28, 2026 (PDF) · AI & Software: AI5 memory and compute targets, with production then planned for 2027.
- Microsoft AI Economy Institute · Global AI diffusion in Q1 2026, May 7, 2026 · Estimated generative AI use among people aged 15–64, based on anonymized telemetry adjusted for device-market share, internet penetration, and population. Regional comparisons use Microsoft’s definitions. This differs from Pew’s self-reported chatbot usage measure and EY’s autonomous-AI survey.
- FII Institute · FII8 executive report, October 2024 (PDF) · Event organizer’s account of Elon Musk’s prediction that humanoid robots would outnumber people by 2040. This is Musk’s forecast, not an adoption assumption used to estimate Micron revenue.
- Micron · HBM4 product overview · Vertical stacking, manufacturing precision, bandwidth, and power requirements.
- Micron · Fiscal Q1 2025 prepared remarks, December 18, 2024 (PDF) · Page 4: planned customer-specific logic base chips for HBM4E. Implications for supplier substitution and margins are our interpretation.
- Anthropic · Amazon compute agreement, April 20, 2026 · More than $100 billion committed to AWS technologies over ten years, with up to five gigawatts of new capacity.
- Microsoft · Fiscal Q4 2026 earnings call, July 29, 2026 · Calendar 2026 capital expenditure outlook of approximately $175 billion after a shift from finance to operating leases. This is company-wide capital expenditure, not a standalone measure of AI spending.
- Wall Street Journal · OpenAI’s second-quarter sales and losses, August 18, 2026 · Private-company financial reporting, also summarized by SiliconANGLE. Not independently audited by this site.
- Bloomberg · Anthropic’s Q2 revenue and positive adjusted operating income, August 14, 2026 · Preliminary investor figures, subject to revision. Adjusted operating income is distinct from net income and free cash flow.
- Reuters · Micron’s planned retreat from China’s server-chip market, October 17, 2025 · Impact of the 2023 critical-infrastructure restrictions, with exceptions for certain international customers. This does not establish the memory supplier behind every Chinese AI model.
- SemiAnalysis · CXMT’s DRAM expansion and HBM manufacturing constraints, June 23, 2026; ChosunBiz · CXMT’s reported HBM3 yield difficulties, September 9, 2026 (Japanese translation) · Approximately 25% overall eight-layer HBM3 yield is an analyst estimate and industry-source report, not a company disclosure or a yield for all CXMT products.
- Tom’s Hardware · Apacer CEO on memory supply and China’s domestic shortfall, August 2026 · Industry commentary on domestic demand exceeding supply, not a quantified CXMT order backlog.
- CEPR / Richard Baldwin · China’s manufacturing scale, January 17, 2024 · Economy-wide industrial capacity. The possibility of rapid competitive memory expansion is our investment judgment.
- Wang et al. · Scaling over Scaling: Exploring Test-Time Scaling Plateau in Large Reasoning Models, May 2025, revised June 2025 · Preprint analyzing diminishing returns from parallel attempts and sequential refinement, with experiments on AIME, MATH-500, and GPQA. Findings concern the models, methods, and benchmarks tested.
- Yue et al. · Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?, NeurIPS 2025 · Compares reward-trained and starting models with both single and many attempted answers. Evidence about the tested training setups, not a universal limit on reinforcement learning.
- Cai et al. · Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model, June 21, 2026 · Preprint reporting gains from targeted teacher guidance followed by reinforcement learning. Counterevidence to interpreting earlier limits as permanent.
- BEA via FRED · Annual U.S. private investment in computers and peripheral equipment; corresponding quality-adjusted price index · Investment: $38.643 billion in 1990 and $270.729 billion in 2025, measured in nominal dollars. Price index: 2,221.569 and 103.723 respectively, a 95.3% decline. This compares spending and quality-adjusted hardware prices within the same U.S. investment category, not global computing expenditure, energy efficiency, or memory consumption. The trends do not isolate efficiency’s causal contribution to market growth.
- NBER · Buyer benefits from innovation in mainframe computers in the 1980s, 1995; NBER · Why Do Computers Depreciate?, 2004 · Evidence on falling computing prices, increased purchasing, and more demanding software and networks. These studies do not directly measure a rebound in total memory consumption.