MLCC Super Cycle – The Other AI Squeeze

Research evidence excerpt

MLCC Super Cycle – The Other AI Squeeze

IdeaM

Executive summary

The MLCC industry today feels like the legacy DDR4 DRAM two years ago, which

experienced unprecedented, AI-driven supply constraints, triggering structural price hikes.

These shortages arose as leading DRAM manufacturers redirected fixed capacity toward

higher-margin AI hardware (e.g., HBM), away from legacy products. We see several

reasons why MLCC constraints are structural this time:

• Existing MLCC capacity (e.g., EV-related) is not fungible with AI servers, given

large differences in specifications, packaging, power conversion, and capacitance–

voltage requirements.

• Long lead times. Greenfield MLCC capacity requires ~2 years from commitment to

production. Unlike semiconductors, equipment is customized in-house, limiting

rapid efficiency gains.

• New wave of agentic AI demand. Inference growth and the CPU-intensive nature

of agentic AI add incremental demand beyond GPUs. Technology: Rise of the AI

Agent – Global Implications (19 Apr 2026)

• Required returns. Manufacturers require sustained pricing upside to justify new

capacity; most target output growth of ~10%–15% p.a. while avoiding speculative

investment.

• High entry barriers. High-capacitance, low-ESL MLCCs for servers and autos face

strict qualification constraints, limiting competition from lower-end producers.

• 2026 vs. 2017. The commodity part of the MLCC industry will likely face

shortages similar to the 2017-18 super cycle driven by a demand surge from EV

adoption and smartphone content (5G and iPhone), although current dynamics are

more structural, supported by durable AI demand.

What's changed?

How AI is impacting MLCCs? MLCCs are evolving from commodity components into

strategic resources.