Hanwha Asset Management launches fund targeting AI infrastructure bottlenecks

Hanwha Asset Management is launching a target-return fund that invests in "bottlenecks" emerging as the AI ecosystem expands. The fund allocates to theme-based ETFs covering semiconductors, power infrastructure, networking, fiber optics and cybersecurity, then shifts its portfolio toward short-term bond ETFs once it hits a 7% target return.

Hanwha Asset Management announced Friday it is accepting subscriptions for the Hanwha Dynamic AI Bottleneck Resolution Theme EMP Target-Return Fund.

The fund focuses on supply shortages and infrastructure constraints arising from AI ecosystem growth, splitting its allocation roughly 50-50 between key theme ETFs and short-term bond ETFs. Themes are selected using a bottom-up approach that analyzes financial statements, growth outlooks, investment trends and share price movements, with rebalancing carried out as market conditions warrant.

Core investment themes include semiconductors, AI power infrastructure, networking and fiber optics, and cybersecurity. The fund's managers expect investment demand to spread beyond semiconductors into power grids, communications networks and security infrastructure as AI data center expansion accelerates, and have selected industries accordingly.

Hanwha Asset Management said its strategy of continuous rebalancing is designed to keep pace with a market in which the dominant AI theme changes rapidly and volatility has increased.

Bottlenecks from AI infrastructure expansion are also appearing in the power sector. According to Hanwha Asset Management, electricity demand for operating data centers in the United States is projected to surge from 4 GW in 2024 to 123 GW by 2035. Semiconductor supply shortages driven by the broader AI transition are also pushing up export prices, the company said, pointing to growing demand across related industries.

Unlike a conventional equity fund, this is a target-return product that reduces its exposure to risk assets once the target return is achieved. Initially, the fund splits its investment between AI bottleneck-related ETFs and short-term bond ETFs; when returns reach 7%, assets shift primarily into short-term bond ETFs. The fund uses an ETF Managed Portfolio structure, building its portfolio through multiple ETFs rather than individual stocks.