As artificial intelligence (AI) establishes itself as the disruptive theme for economies and portfolios for the decade to come, the need for strategies targeting opportunities across the value chain has become a necessity for investors.
In this interview, Zeno Mercer, senior research analyst at VettaFi, discusses the macro drivers behind AI adoption, the significance of capital and operational expenditure cycles and the broader societal and economic shifts AI is expected to catalyse.
Mercer also explains how index design plays a critical role in navigating the AI ecosystem. From the rising importance of inference and edge computing to the role of mergers and acquisitions, he highlights how a well-constructed index can provide access to the full breadth of opportunities emerging from one of the most transformative themes in global markets.
What is the investment case for AI and how has this evolved based on current macro conditions?
The investment case for AI mirrors humanity’s centuries‑long march of invention. It fuses physical and digital intelligence, moving us from simple tools to intelligent partners poised to disrupt the status quo.
Ultimately, there are many different ways for investors to look at this. You have this capital expenditure (capex) cycle right now and then there is the ongoing inference (real world utilisation of AI) and operational expenditure of AI. These are increasingly going to become a greater percentage of GDP. Currently, related expenditure is around 1-3% and it will climb to 5%, maybe 10% of global GDP. Longer term, it is possible this climbs to 25% of total GDP. So, by that standard, questions around valuations or the opportunity are missing the bigger picture that we are going to be seeing one of the biggest adoption curves in history. It is going to impact everybody in every industry. It is not only a technological innovation; it is almost a hedge to what else it is going to be impacting.
From an investment standpoint, we believe it is important to consider not only those developing disruptive technologies but also companies in other fields outside of technology that are deploying AI at scale. For instance, the ROBO Global Artificial Intelligence Index (THNQ) is composed of approximately 65% AI infrastructure companies and 35% application companies.
Index providers are offering increasingly granular access to AI. Which subsector is most compelling?
The obvious first-stage winners have been players such as Nvidia and data centres. We believe the subsectors with greater exposure to inference are more immune to open-source innovations, which could disrupt mega-cap business models – especially as we start to see super intelligence in our palms on personal devices, which would ironically reduce dependency on mega-cap names.
When we launched the THNQ Index, we envisioned a multi-faceted “body” of digital intelligence, and that continues to evolve. As capabilities increase, so does the complexity and integration in the physical world.
Companies involved in connectivity, cybersecurity and managing edge and cloud inference, as well as the data and analytics of that, are going to become increasingly important. Our index strategy has evolved to find more winners in these areas.
How can investors evaluate the competitive landscape and a company’s market position?
We closely assess technical strength, leadership, investment focus and how new technologies change supply, demand and friction. We also examine fundamentals, margins and product mix, then weigh risks and opportunities from open‑source innovations. Understanding how each business fits within the wider AI ecosystem is essential.
Nothing happens in isolation, so investors need to grasp the greater body of AI, not just the brain. The reality is that things are moving faster. There is importance in understanding the impact of the actual adoption of AI and what that means for society as well as which companies are actually enabling it and are part of that infrastructure.
What are the impacts of mergers and acquisitions on the AI industry?
M&A is heating up. You have seen M&A from the mega-cap names trying to acquire talent and technical teams and data. Meta has spent heavily in both human capital and technical acquisitions.
A lot of the small, mid-sized and even smaller large cap names are seeing a tailwind from their acquisition potential, as they could enhance and accelerate the hyperscaler names. Given THNQ’s more equal-weighted, non-market cap driven approach, this has been a positive tailwind over time with 16 takeouts from THNQ since inception.
M&A is also useful to expand the addressable market of the acquirers. As a buyer, it enables you to broaden and accelerate your market reach and go-to-market strategy. We have been seeing a lot of that within the AI space, especially in the last five years, but it is now picking up.
What are the most significant recent AI breakthroughs and news?
Lately, we have seen some incredible breakthroughs in AI-based physics simulations as well as vision language models that are simplifying training for digital and physical operations, including robotics.
The next top-tier models are increasingly powerful. They are capable of being run on smaller devices, which somewhat goes against the grain of "one company rules them all" in the sense that personal AI will become the standard in five years or less. You will have a super-intelligent physician– all of the elements and frontier domains of human knowledge are going to be available, but not necessarily through the bias of a company selling you advertisements and also uniquely yours. It will still be algorithmic but more tailored.
That is a paradigm shift in how technology and the internet have evolved since the dawn of adoption and deployment going back to the early 2000s. It is a big game changer that open source is actually viable and powerful and can accomplish things.
Additionally, a lot of vision-language models are seeing massive technical leaps. Being able to observe a scene is actually showcasing a robot's physics and dexterity. This is where you start to see the convergence of robotics and AI, and the potential for a super cycle.
As you start to see more use cases of robotics, you will see more deployment of that enabling ecosystem I have been discussing. That ties into the connectivity, cybersecurity, edge device and the actuation of robots. That is going to create more data, which will create more training data leading to creating better robots. Again, you have this super cycle that is starting to emerge and for investors, there are a lot of players that benefit from this.
THNQ is the underlying index for the ROBO Global Artificial Intelligence ETF (THNQ).
VettaFi is the index provider for THNQ ETF for which it receives an index licensing fee. However, THNQ ETF is not issued, sponsored, endorsed, or sold by VettaFi. VettaFi has no obligation or liability in connection with the issuance, administration, marketing, or trading of THNQ ETF.
Originally published on ETF Stream.
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