Goldman Sachs Projects MLCC Upcycle Through 2030 on AI Data Center Buildout
In brief
- Goldman Sachs extended MLCC upcycle projection to 2030 driven by hyperscaler AI infrastructure spending
- 2026 MLCC pricing forecast revised from flat to 0-5% increase
- Data center electrical component stocks outperformed hyperscaler stocks by 85 percentage points since early 2025
- Goldman maintains Buy ratings on Murata Manufacturing and Nantong Jianghai Capacitor
Goldman's revised outlook
Goldman Sachs revised its 2026 MLCC pricing forecast upward, moving from a flat outlook to a predicted increase of 0-5%. The bank also extended its AI-driven MLCC upcycle projection to roughly 2030, a notable stretch from prior expectations that pegged the cycle ending around 2028. This revision reflects confidence in sustained hyperscaler buildout.
The shift matters because component cycles typically compress over time. Companies like Microsoft, Google, Amazon, and Meta have collectively committed hundreds of billions of dollars to building out AI infrastructure, creating a structural tailwind for manufacturers.
Market positioning and headwinds
Data center electrical component stocks have achieved an 85 percentage point absolute outperformance versus hyperscaler stocks since early 2025. Investors have already priced in some of this demand. Goldman maintains a Buy rating on Murata Manufacturing with a 5,400 yen target price. The bank also rated Nantong Jianghai Capacitor Co., a Chinese manufacturer with direct exposure to data center power infrastructure buildout, as Buy.
Yet risks loom. MLCC pricing increases of 0-5% are modest, and if the AI buildout slows or hyperscalers pull back on spending, the upcycle thesis could evaporate quickly. Geopolitical tensions add another layer of uncertainty. US-China tensions, particularly for Chinese component manufacturers, face potential tariff escalations or export controls that could disrupt supply chain dynamics.
Goldman's latest projection anticipates 220% global power demand growth by 2030, a sharp revision from a previous estimate of 175%. That gap underscores how AI infrastructure is reshaping energy forecasts — and the component supply chains that underpin them.
Frequently asked questions
What are multi-layer ceramic capacitors and why do AI data centers need them?
MLCCs are electronic components that store and regulate electrical power in circuits. AI data centers require vast quantities of them because they're essential for power distribution, voltage regulation, and signal integrity in the dense computing environments that train and run AI models. Hyperscalers building out infrastructure create sustained demand.
Why did Goldman extend its MLCC cycle forecast to 2030?
Goldman revised its outlook because major tech firms (Microsoft, Google, Amazon, Meta) have committed hundreds of billions to AI infrastructure buildout. This sustained capital spending extends the demand cycle for capacitors beyond prior expectations, which had pegged the upcycle ending around 2028.
What risks could derail the MLCC upcycle?
The pricing increases Goldman projects (0-5%) are modest, so any slowdown in AI buildout or pullback in hyperscaler spending could collapse the thesis quickly. Geopolitical tensions between the US and China also threaten Chinese manufacturers with tariff escalations or export controls.
