The supply bottleneck of AI infrastructure is spreading from GPUs, memory, data centers, and power systems to even more foundational hardware components. Goldman Sachs and Morgan Stanley have now turned their attention to MLCCs—the multi-layer ceramic capacitors long seen as common passive components.
In AI servers, MLCCs are responsible for stabilizing current, filtering noise, and are a key component in ensuring chip high-speed operation. As Nvidia’s next-generation rack architecture increases the usage of MLCCs per rack, their value is rapidly rising. Goldman Sachs estimates that the AI server MLCC market will grow over fourfold between 2025 and 2030, while industry capacity is increasing at a slightly higher than 10% annual rate, highlighting a supply-demand mismatch that is becoming a core variable in this cycle.
Moreso, the price cycle has already begun. Japanese industry leaders like Murata and TDK have initiated price hikes, and Japanese export data is starting to validate demand strength. For the capital markets, the logic around MLCCs is not complicated: demand is coming from AI servers and high-end automobiles, supply expansion is limited, and price increases can significantly enhance profit margins.
From chips to capacitors, the pricing power of the AI supply chain is trickling down to more granular and obscure links. Whether MLCCs will become the “next memory chip” still depends on whether AI server demand can continue to materialize; however, what is certain is that this once overlooked foundational component has now stepped into a new cycle of synchronized rising prices and demand.
The supply bottleneck in the artificial intelligence (AI) arms race is sequentially igniting opportunities across various hardware sectors. Following data centers, energy infrastructure, and storage chips becoming the focal points of capital attention, Wall Street giants Goldman Sachs and Morgan Stanley have simultaneously turned their focus to a long-underestimated foundational component: multi-layer ceramic capacitors (MLCCs). The two institutions predict that MLCCs will become the next key battleground for “synchronized rising prices and demand,” and this AI-driven growth cycle may be the largest in history.
Goldman Sachs analyst Daiki Takayama pointed out in a report that the AI server MLCC market size is expected to surge from about ¥215 billion (approximately $1.4 billion) in the fiscal year 2025 to about ¥920 billion (approximately $5.8 billion) in the fiscal year 2030, more than quadrupling with a compound annual growth rate of 34%. Goldman Sachs stated that the current AI-driven MLCC cycle “will be the largest and longest-lasting in history, and we believe it is still in its early stages.”
An MLCC can be understood as an extremely miniature, ultra-fast charging and discharging unit. Unlike regular batteries that store a large amount of energy and release it slowly, an MLCC stores minimal energy but can charge and discharge within extremely short milliseconds or even shorter. Its core function is to smooth power fluctuations and filter noise: absorbing sudden voltage spikes, rapidly replenishing current during voltage drops, providing stable power to sensitive chips, and blocking electrical interference that could disrupt digital signals.
The operational characteristics of AI servers make MLCCs indispensable. When AI models perform massive computations, the processor’s power demand can spike within microseconds and then quickly drop to near zero after the computation is complete. The power system itself struggles to respond promptly to such intense fluctuations. MLCCs are typically installed directly near AI chips and release energy instantly when power spikes occur, preventing server crashes. Since AI chips like Nvidia GPUs need to process billions of tasks simultaneously, a top-tier AI server rack may require up to 600,000 MLCCs working together to maintain system stability.
Goldman Sachs analyst Nelson Armbrust further pointed out that MLCCs have become the third most expensive component in the AI server bill of materials (BOM), only after the GPU and memory. The current overall MLCC market size is around $15 billion, with the server-related market accounting for approximately $1.3 billion, expanding at an 80% compound annual growth rate. In contrast, demand growth in other application areas such as automotive and smartphones has significantly slowed down. Daiki Takayama expects that the cost of MLCCs in the AI server BOM will gradually increase from the current approximately 0.5% to about 1%.
The key factor igniting market attention is the severe structural supply-demand imbalance the MLCC industry is facing. Goldman Sachs analyst Allen Chang clearly pointed out that the annual production capacity growth rate of the entire MLCC industry is only slightly above 10%. Furthermore, due to heavy reliance on internal production of equipment and materials by manufacturers, the expansion progress is limited by internal engineering resources, making it difficult to significantly accelerate. However, the demand impact from AI servers is not at the same level. Goldman Sachs projects that from the 2025 fiscal year to the 2030 fiscal year, the MLCC demand driven by AI servers will grow by approximately 4.3 times.
What is even more concerning to the market is that the strong demand for high voltage, high capacitance MLCCs driven by automotive electrification is still robust, with the per-vehicle MLCC usage continuing to increase. The two major demand pillars of AI servers and electric vehicles are jointly consuming the already limited additional production capacity. This has also led to customers actively seeking long-term supply agreements even as consumer electronics demand declines, to hedge against future shortage risks.
Signals of the current tight market have appeared on multiple fronts: delivery lead times for high-end MLCCs (high-capacity, high voltage specifications) have exceeded 20 weeks; low-capacity and consumer-grade MLCCs are affected by hoarding and duplicate orders, leading to a 20% to 40% price increase in spot and distribution channel prices; key raw material prices such as nickel and silver remain high, putting pressure on the cost of various products.
Price signals are rapidly strengthening. The price hike actions of Japan’s two leading companies, Murata Manufacturing and Taiyo Yuden, mark the official start of the MLCC price hike cycle. Murata, starting from April 1 this year, will raise the prices of MLCC products in AI servers and high-end automotive applications by 15% to 35%. Taiyo Yuden has also notified customers that it will adjust prices for multiple product lines starting from May, involving MLCCs, inductors, RF devices, FBAR/SAW devices, and aluminum electrolytic capacitors, citing continuous increases in costs of precious metals and other raw materials.
Trade statistics data released by the Japanese Ministry of Finance on May 28 validated this price hike trend from a macro perspective. The data shows that in April, the average export price of MLCCs increased by 3% month-on-month and 16% year-on-year; export volume increased by 10% year-on-year; and export value surged by 28% year-on-year. Goldman Sachs believes that this data confirms the signals released in the recent financial reports of Japanese MLCC manufacturers: all companies have confirmed that order momentum remains strong.
Looking at the timeline of the entire AI supply chain, Goldman Sachs’ analytical framework shows that the price hike of MLCCs significantly lags behind AI core components such as DRAM, NAND memory, ABF substrates, and copper-clad laminates (CCL). Therefore, Goldman Sachs predicts that among all AI components and materials, the price increase space for MLCCs is the longest, and the sustainability is the strongest. Goldman Sachs has revised its 2026 MLCC year-on-year price change forecast from approximately 0% to +5%, emphasizing that the actual future increase may far exceed this level.
For investors, the profit elasticity resulting from an MLCC supply-demand mismatch is not to be underestimated. Daiki Takayama estimates that a mere 5% price increase could theoretically drive Murata’s FY2027 operating profit up by around 13% and SolarEdge’s operating profit by as much as 37%.
Goldman Sachs projects that Murata’s FY2027 revenue will reach ¥10.5 trillion (approximately $66 billion), a 13% year-on-year increase, and SolarEdge’s revenue will reach ¥286 billion (approximately $1.8 billion), also with a 13% year-on-year growth. Goldman Sachs maintains a “Buy” rating on Murata, SolarEdge, and TDK. Its constructed Asian MLCC theme stock portfolio has recently started to strengthen, but compared to other popular AI themes, there is still significant room for catch-up.
Another heavyweight catalyst comes from Nvidia’s next-generation Vera Rubin AI rack. Morgan Stanley, after dissecting Nvidia’s latest VR200 rack, found that the significance of peripheral components in the latest BOM is rapidly increasing.
The value of MLCCs in a single rack has surged from approximately $1,530 in the previous GB300 era to around $4,320, a remarkable 182% increase. Although the absolute value of MLCCs still lags behind GPUs, memory, and PCBs, its growth rate in peripheral components is outstanding.
Morgan Stanley’s channel checks further indicate a significant increase in MLCC usage on compute and switch boards, with compute board showing a more pronounced growth. Additionally, the newly introduced BlueField and ConnectX modules will further increase the total MLCC usage per rack. This partially explains why the current demand for high-end AI server MLCCs is so strong, prompting several ODM manufacturers to actively stock up in preparation for the mass production and delivery of the Rubin rack in the latter half of 2026.
Market intelligence indicates that in the AI supercycle’s infrastructure arms race, as supply bottlenecks rotate, it has led to one market winner after another. Goldman Sachs’ latest assessment describes MLCCs as the “new memory chip”—marking the passive component subindustry that is currently at the inception of a cycle where both volume and price are rising simultaneously.
With the exponential impact of AI server and Nvidia Rubin rack demand, the high-end MLCC lead time has exceeded 20 weeks. The Japanese industry leader has initiated price hikes, official export data remains strong, all signals point to the same conclusion: this AI-driven MLCC supercycle is just getting started.
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MLCC Crisis: The New Front in the AI Infrastructure Arms Race and Crypto Market Implications
The crypto market has been captivated by the AI narrative, but recent developments reveal that the infrastructure arms race extends far beyond GPUs and memory chips. Goldman Sachs and Morgan Stanley’s simultaneous focus on multi-layer ceramic capacitors (MLCCs) marks a critical inflection point in the AI supply chain that could reshape investment landscapes across both traditional markets and the crypto ecosystem.
The MLCC Revolution: More Than Just Passive Components
MLCCs, once considered mundane passive components, have emerged as the third most expensive item in AI server bill of materials (BOM), trailing only GPUs and memory. This isn’t merely a technical detail—it represents a structural shift in the economics of AI infrastructure. The value of MLCCs in a single Nvidia AI rack has surged 182% from the previous generation, reaching $4,320 per rack. This dramatic increase underscores how the AI arms race is creating bottlenecks at increasingly granular levels of the supply chain.
What makes MLCCs so critical? Their function is irreplaceable: stabilizing current, filtering noise, and enabling the high-speed operation of AI chips. With top-tier AI servers requiring up to 600,000 MLCCs working in concert to maintain system stability, these components have transformed from afterthoughts to essential infrastructure.
The Investment Case: A Perfect Storm of Supply-Demand Mismatch
Goldman Sachs projects the AI server MLCC market to grow from approximately $1.4 billion in 2025 to $5.8 billion in 2030—a staggering 34% compound annual growth rate. This demand explosion is occurring against a backdrop of severely constrained supply, with industry capacity expanding at only slightly above 10% annually. The resulting supply-demand imbalance creates textbook conditions for a pricing supercycle.
The profit potential is compelling. A mere 5% price increase could theoretically drive Murata’s FY2027 operating profit up by around 13% and SolarEdge’s by as much as 37%. For investors, this represents an opportunity to participate in a supply-constrained market where pricing power is firmly in the hands of manufacturers.
Crypto Market Implications: Reinforcing the AI Narrative
For crypto investors, the MLCC crisis reinforces several key themes:
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AI Infrastructure Remains King: This development validates the long-term investment thesis in AI-related projects, particularly those focused on infrastructure. The physical constraints of AI aren’t going away—they’re becoming more pronounced.
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Tokenized Hardware Opportunities: We may see increased interest in projects that tokenize semiconductor production capacity or offer decentralized alternatives to MLCC supply chains. The opaque and fragmented nature of the MLCC market creates fertile ground for blockchain-based solutions.
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Compute-Related Tokens Poised for Gains: Projects providing decentralized computing resources or alternative AI infrastructure models could benefit from the continued tightening of traditional supply chains. As physical bottlenecks worsen, the value proposition of decentralized alternatives strengthens.
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Supply Chain Transparency: The MLCC crisis highlights the opaque nature of critical hardware supply chains. Blockchain solutions offering transparency and traceability in semiconductor and component manufacturing could see increased adoption and valuation.
Risks and Overlooked Considerations
While the opportunity is significant, crypto investors must navigate several risks:
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Narrative Overextension: The market may overreact to the MLCC story, creating unsustainable hype around AI infrastructure tokens. Not all AI-related projects will benefit equally from hardware bottlenecks.
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Demand Volatility: The entire thesis hinges on sustained AI server demand growth. If AI adoption slows or plateaus, the MLCC supercycle could quickly reverse, dragging down related investments.
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Traditional Market Competition: As Wall Street increasingly focuses on MLCC manufacturers, these traditional companies may absorb capital that might otherwise flow into crypto alternatives.
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Implementation Challenges: Creating viable blockchain solutions for hardware supply chain issues is technically complex and may face significant regulatory hurdles.
Strategic Positioning for Crypto Investors
For sophisticated crypto investors, the MLCC crisis presents both direct and indirect opportunities:
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Select AI Infrastructure Tokens: Prioritize projects with actual utility in solving AI infrastructure bottlenecks, rather than those merely leveraging the AI narrative. Look for projects with demonstrated partnerships or use cases in the semiconductor supply chain.
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Monitor Tokenized Physical Assets: Watch for developments in tokenized semiconductor production capacity or MLCC futures. These could provide exposure to the physical bottleneck without direct traditional market involvement.
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DeFi Applications in Hardware Financing: Consider DeFi protocols that could provide financing for MLCC manufacturers or other constrained components in the AI supply chain.
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Cross-Chain Arbitrage: As MLCC prices diverge across regions, there may be opportunities for cross-border trade facilitated by crypto payments, creating niche DeFi applications.
Conclusion: The AI Arms Race Extends to the Microscopic Level
The MLCC crisis is far more than a niche component story—it’s a signal that the AI infrastructure arms race is reaching increasingly fundamental levels of the supply chain. For crypto investors, this represents both a validation of the AI narrative and an opportunity to identify projects that can address the physical constraints that traditional markets are only now beginning to appreciate.
As Goldman Sachs describes MLCCs as the “new memory chip,” we’re witnessing the birth of a new investment supercycle—one that will create both winners and losers across traditional and crypto markets alike. The question for crypto investors isn’t whether AI infrastructure matters, but which projects can most effectively bridge the gap between digital aspirations and physical reality.
The MLCC crisis is merely the latest manifestation of this fundamental tension, and it won’t be the last. As the AI revolution continues to unfold, the most valuable crypto investments will likely be those that address the most tangible constraints in the physical world.