The artificial intelligence boom is exposing a critical vulnerability in the global hardware supply chain, and it has nothing to do with advanced graphics processing units (GPUs) or high-bandwidth memory. The bottleneck lies in a component so ubiquitous and inexpensive it is often called the “rice of the electronics industry”: the multilayer ceramic capacitor (MLCC).
As AI data centers scale up to meet the computational demands of frontier models, the sheer volume of MLCCs required is triggering what analysts describe as a historic supply crunch. This dynamic is creating a strategic opening for Chinese component manufacturers, who are rapidly expanding production to capture market share as established market leaders pivot toward higher-end segments.
The “Largest and Longest” Cycle in History
The scale of the impending shortage is staggering. According to a recent research note from Goldman Sachs cited by SCMP, the industry is entering the “largest and longest” MLCC cycle in history. The fundamental driver is the exponential increase in component density required by AI infrastructure.
A standard enterprise server typically requires around 2,000 MLCCs. In contrast, an AI server demands up to 28,000 units, a 13-fold increase. The requirements for next-generation hardware are even more intense. Nvidia’s upcoming Rubin architecture, designed to power the next wave of generative AI, reportedly utilizes 12,000 MLCCs per board alone.
These tiny components, which regulate voltage and filter noise in electronic circuits, have quietly become the third-largest cost component in AI servers, trailing only the GPUs and memory modules. Goldman Sachs projects that the MLCC market will grow by 4.3x by 2030, driven almost entirely by the proliferation of AI data centers and the electrification of the automotive sector.
A Strategic Shift by Market Leaders
The global MLCC market has long been dominated by a handful of Japanese and South Korean conglomerates, notably Murata Manufacturing, Samsung Electro-Mechanics, and Taiyo Yuden. These companies possess the advanced materials science and precision manufacturing capabilities required to produce the highest-capacitance, smallest-form-factor MLCCs.
However, the AI boom is prompting a strategic realignment among these market leaders. Recognizing the immense profitability of high-end components required for AI servers and electric vehicles, companies like Murata and Samsung are increasingly dedicating their production capacity to these premium segments.
This pivot upmarket is creating a supply vacuum for standard- and mid-range MLCCs, components that remain essential for consumer electronics, telecommunications equipment, and industrial machinery. As top-tier manufacturers allocate fewer resources to legacy product lines, downstream buyers are facing longer lead times and higher prices.
China’s Component Makers Seize the Opening
This supply-demand imbalance presents a significant opportunity for China’s domestic MLCC manufacturers. Companies such as Guangdong Fenghua Advanced Technology, Chaozhou Three-Circle (Group), and Shenzhen EYANG Technology are aggressively ramping up production to fill the void left by their international competitors.
The market’s anticipation of this shift is already evident. In mid-June, Chinese A-share MLCC stocks surged, with several companies hitting their daily trading limits after Goldman Sachs released its report. Investors are betting that Chinese firms will not only capture the abandoned mid-range market but also gradually improve their technological capabilities to compete in the high-end AI server segment.
This dynamic aligns perfectly with Beijing’s broader strategic objective of achieving self-reliance in critical technology supply chains. While much of the focus has been on advanced semiconductor manufacturing, securing the supply of foundational components like MLCCs is equally vital for the resilience of China’s electronics industry. As EastFrontier has previously reported, Chinese chip giants SMIC and Hua Hong are already posting record revenue as the AI boom overrides US sanctions, and the MLCC opportunity represents the next layer of that domestic supply chain buildout.
The Taiwan Factor
The supply crunch is further complicated by the geographic concentration of MLCC production. Taiwan’s supply chain plays a significant supporting role, with Taiwanese firms such as Yageo and Walsin Technology also positioned to benefit from the surge in AI-driven demand. Digitimes reported that data center operators are already placing orders with Taiwanese MLCC suppliers at a pace that has not been seen in years, with delivery lead times for high-end components stretching to over 20 weeks.
This geographic dimension adds a layer of geopolitical complexity. As tensions between China and Taiwan remain elevated, any disruption to Taiwanese production capacity could further exacerbate the global shortage. This risk is not lost on major AI hardware buyers, who are increasingly seeking to diversify their component supply chains across multiple geographies.
The Road Ahead
The MLCC supply crunch underscores the complex, interconnected nature of the AI hardware ecosystem. While the spotlight remains firmly on the companies designing the most powerful AI accelerators, the realization of the AI revolution depends entirely on the availability of billions of unglamorous, microscopic capacitors.
For Chinese manufacturers, the current cycle offers a rare chance to accelerate their growth and move up the value chain. If they can successfully scale production while improving quality control and technological sophistication, they may emerge from this supply bottleneck not just as alternative suppliers but as indispensable players in the global buildout of AI infrastructure.
The broader lesson is that the AI revolution is a supply chain story as much as it is a software story. Every layer of the hardware stack — from the most advanced GPUs to the most basic passive components, must scale in lockstep. The MLCC shortage is a reminder that the weakest link in that chain will ultimately determine the pace of the AI buildout.