Why Guardian Capital has put AI inside its new management strategy

Each month at WP, we offer a slate of articles and content pieces that go deep on a particular topic. This month, we're focusing on AI in wealth management.

The team at Guardian Capital is now using AI to capture AI. More than a tautology, the new Guardian Active AI Technology and Innovation Fund managed by the firm’s i³ Investments team, is meant to upend the way investment themes and sectors are accessed. The fund uses multiple layers of artificial intelligence, from numerate machine learning models they’ve used since 2018 to brand-new AI agents, to inform its core team of human managers. Srikanth Iyer, Managing Director and Head of i³ Investments believes that this approach can both control for bias and cost in asset management.

Despite acknowledging that the timing of a new AI fund launch on the last week of July may not be immediately ideal, as AI chip stocks pull back significantly, Iyer argues that the approach should represent a longer-term shift in the way asset managers operate more broadly. He explained what he expects other asset managers and even advisors to try and roll out as AI-assisted strategies become more popular and outlined how advisors can try to assess management quality in a fund where the team of humans is backed by a team of machines.

“We’re a little bit late to the party with this ETF launch, probably about two to three years, in my opinion… We are not momentum managers, like every other fundamental manager is buying memory compute six months ago. And believe it or not, they’re blowing up in front of us right now. I know it. On the long only side. So not only are the growth managers blowing up, but even dividend managers who bought all these memory compute stocks are giving up a lot of alpha for us,” Iyer says. “The evolution here is we use AI to manage money. But obviously we have a deep insight into the supply chain and the evolution cycle of AI companies. And so for us, the question was, how do we create an opportunity set for ourselves and our clients? Because we are well known for dividend growth and earnings growth. And the new growth is now what we call the AI economy.”

How AI agents inform AI investing

The new Guardian ETF outlines two core groups of companies related to AI in its strategy documents: AI enablers and AI adopters. On the AI enabler side are the vast array of AI compute, memory, semiconductor, manufacturing, and equipment companies. That also includes some of the AI provider companies, many of which fell into the so-called ‘hyperscaler’ category. Among AI adopters are the companies now emphasizing AI in their corporate filings, communications, media opportunities, and operations. Given the incredibly far-reaching nature of this theme, one of the core jobs of the AI tools backing up the investment team for this ETF is in determining how central AI is becoming to a company.

Making that determination, Iyer says, is where the probabilistic mode of thinking that AI uses can shine. It’s also a prime use for AI as a collator of information. Determining the centrality of AI in a business requires discussions with management, reading regulatory filings, reading news, and comparing all that information to the businesses’ competitors. While AI may not be able to directly speak with managers, it can collect and synthesize information from those meetings, filings, and press reports. From there, Iyer says, his team built a framework of AI agents to categorize that information and use it as a base layer of evidence to protect against hallucinations. Then another layer of agents reads that information and tells the management team how central AI is to that business. The human managers then test the AI’s thesis, forcing it to check itself and, if necessary, tracing the path of its data inputs to the premise of its argument.

Once the AI has built a database of AI-centric companies, the management team uses yet more AI agents to identify the names most likely to grow their earnings. Rather than the deterministic Gaussian mathematics that certain quant models use, Iyer says that the AI agents’ Bayesian model allows for more variance because it focuses on probability. Because there is no certainty in investing, the thought process that informs an approach should be built on probability rather than certainty.

AI agents as the future of investment management

While this layered multi-agent process is being used to capture AI as an investment theme that spills over traditional boundaries of sector and asset class, Iyer believes the approach can be applied to a host of different investable areas. He believes that this approach, or versions of it, will become more commonplace.

While Iyer is quick to emphasize his own team’s experience building machine learning models as well as using large language models and AI agents, he acknowledges that even an advisor with enough money to access all the regulatory filings and Claude code can build something like the model his firm has put together. He expects other asset management firms to follow suit more quickly.

It’s one thing to use AI, though, and another to use it successfully. Iyer says three factors will determine that success: data, infrastructure, and talent. In addition, liability and regulatory controls all need to be factored in. For advisors looking at AI-driven investment strategies, Iyer insists that they need to look past labels and hype to the actual uses and advantages of AI within any particular strategy.

“[Advisors] have to do their due diligence and they have to make sure that the communication between the asset manager and the advisor is open. The asset managers have to have the ability to communicate exactly what is being done by artificial intelligence,” Iyer says. “It’s not a black box, it’s like a glass box in a sense. It shows you what artificial intelligence is being used to do. It also has to be disclosed what artificial intelligence cannot do.”