How can institutions get AI exposure in equities?

Portfolio manager behind new AI ETF outlines how AI can be accessed in different equity exposures and why he thinks AI still has room to run

How can institutions get AI exposure in equities?

Since 2023 a simple rule seems to have been applied to equity markets. If a company is perceived to have positive exposure to generative AI, its stock should outperform. Arguably the best evidence for that rule is the so-called “magnificent seven” of Apple, Microsoft, Alphabet, Meta, Nvidia, Tesla, and Amazon which together returned over 100 per cent in 2023, driven in large part by their exposure to AI.

This year, their performance has diverged in large part due to perceived positive or negative developments in AI. The somewhat botched rollout of Google’s Gemini spooked some investors, as did Apple’s acknowledgement that they needed to allocate more resources to AI. Only four of the “magnificent seven” have maintained their momentum this year, largely because they continue to generate positive AI news: Meta, Amazon, Nvidia, and Microsoft.

But AI is a story playing out beyond just the upper echelons of mega-cap technology names. Peter Hofstra, senior vice president, co-head of equities – research, and portfolio manager at CI Global Asset Management and the lead manager on the new CI Global Artificial Intelligence ETF which seeks to deliver AI exposure through an actively managed portfolio. He outlined some of the other sectors and names which could offer positive exposure to AI and argued why he believes the theme still has room to run.

“When you look at the percentage of IT spend from all firms going into AI, it’s still tiny. The spend that’s going to AI has been projected to be around two per cent of total IT spend for 2024,” Hofstra says. “As much as [AI] has grabbed headlines in part because of things like Chat GPT, it’s actually still such a small part of the overall tech spend. So we see AI as having long legs.”

Hofstra’s view on AI impacts is multi-sectoral. Looking at service businesses like call centres, for example, he sees AI driving efficiency and expediting service delivery. Healthcare, too, is a sector where AI applications appear to be limitless, from collecting and analyzing data, to helping patients and doctors manage primary care.

The new CI ETF was launched primarily with a portfolio of technology and communications companies. Hofstra explains that these sectors are currently the big spenders on AI infrastructure, the ones building out the compute power, communication linkages, and storage requirements that are needed for the widespread adoption and implementation of AI. Those include Nvidia, Amazon, Meta, and Microsoft who are the largest spenders on AI right now.

Hofstra argues that AI is not just a mega-cap play. He offers two names as examples of lesser-known AI-exposed stocks: Gitlab, which uses AI to help developers write code, and Supermicro, which develops servers and computers that are key underpinnings in AI infrastructure.

Other subsectors like data center REITs appear to offer some additional exposure to AI, given the scale of the data storage and computational power that will be required to fully integrate AI into the global economy. Hofstra also notes that some utilities companies could be positively exposed to AI because all that computing will draw a lot of power, potentially initiating a paradigm shift in power consumption which could jump-start relatively rangebound utilities stocks.

Even as he highlights the sectors and themes that could gain tailwinds due to AI, he contrasts this trend with the dot com bubble of the 1990s. Where that era was an investor driven bubble that paid little attention to underlying company fundamentals, those ‘big four’ names at least are spending billions to build and expand their AI infrastructure.

Despite his positivity, Hofstra does not deny that there could be short-term volatility or corrections as the pace of investor interest potentially dislocates from the pace of earnings growth. Nevertheless, he believes that AI spending will be far higher in five years than it is today, making this a longer-term play.

“My own experience of investing over the last 20 plus years tells me that the market underestimates long-term growth,” Hofstra says. “If you look at Microsoft over the past 15 years, for example, it has generated dramatic returns. This is the most well-known company in the world, yet it massively outperforms the market. Why does that happen? It’s because people have a hard time giving credit for longer-term growth, growth beyond the next year or two. If we’re right on that, that’s how this product would create better than market returns, is if the tail on this growth is higher than what people are willing to give credit for, with that tail being three, five, even ten years out.”

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