While most of the attention is focused on AI models and chipmakers, there's another part of the story gaining momentum behind the scenes. More from EasyAssetManagement.
Artificial intelligence has become one of the defining structural investment themes globally.
What may have initially been seen as a software and semiconductor story has evolved into something far broader. The scaling of AI requires enormous investment across physical infrastructure, including data centres, power systems, networking architecture and connectivity technologies.
As hyperscalers and technology companies continue to step up AI-related capital expenditure, the broader AI infrastructure ecosystem continues to expand alongside it.
At EasyAssetManagement, we follow a thematic investment approach. We examine the broader economic landscape to identify the structural tailwinds driving lasting change across global markets and economies. Rather than focusing solely on the most visible beneficiaries of a trend, we follow the entire value chain to identify the companies best positioned to capture the long-term growth tailwinds, including the enabling technologies required to support them.
AI is one such theme.
A narrow view of AI investing might focus only on the companies building AI models or designing advanced semiconductors. We take a broader perspective, looking across the full infrastructure stack required to manufacture and enable the systems that make AI work.
That includes data centres, energy infrastructure, cooling systems, networking architecture and connectivity technologies and many more.
Within themes, we look for bottlenecks and constraints. We ask where demand is rising fastest, where infrastructure requirements are increasing, and which technologies become more important as AI systems scale.
One of the key bottlenecks within AI infrastructure is connectivity.
AI workloads require enormous amounts of data to move continuously between chips, servers, storage systems and networking equipment. Modern AI clusters rely on thousands of GPUs working together simultaneously, requiring extremely high-speed communication across systems.
As these AI environments become larger and more compute intensive, efficient data transfer becomes increasingly important.
Traditionally, data has moved through electrical connections using copper cabling, where information is transmitted via electrical signals carried by electrons. While this has worked effectively in the past, copper has become increasingly constrained as bandwidth requirements rise and systems scale. Electrical signals can degrade over distance, generate more heat, consume more power and become less efficient at the speeds required for next-generation AI infrastructure.
This is where optical connectivity technologies fit into the broader AI infrastructure theme.
Rather than using electrical signals, optical networking transmits data using light. These systems use lasers and fibre-optic technology to move information over distance at significantly higher speeds and with greater efficiency than traditional copper-based interconnects.
In practical terms, optics help improve data throughput, reduce latency and ease networking bottlenecks inside modern AI data centres.
While optics do not increase the computing power of AI clusters directly, they help improve the efficiency and utilisation of broader AI systems by enabling faster and more efficient communication across infrastructure. In a world where AI infrastructure requires enormous capital investment, anything that improves the return on that capital matters.
EasyAssetManagement holds exposure to the AI theme, including optics counters, within the EasyETFs AI World Actively Managed ETF and the EasyETFs Global Equity Actively Managed ETF, as well as part of the global equity exposure within our EasyETFs Balanced Actively Managed ETF.
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