Director of Equity Research explains how two AI narratives are now playing out on equity markets
When outlook season hits at the start of a new year one might be forgiven in thinking that a new calendar means a totally new dynamic for markets. This year many analysts and predictors declared that 2026 would be the year when AI investments are tested, when the hyperscalers need to show profits, and when AI adopting corporations in sectors beyond tech need to show ROI for their AI spending. The test for those companies, many outlooks declared, started in January and will define equity performance this year. Peter Hofstra takes a less delineated view.
Hofstra is SVP and Director of Equity Research at CI Global Asset Management. He argues that many of the AI narratives that held sway among investors last year will play out in 2026. He argues, too, that some of those tests will manifest this year. Investor sentiment, however, will not change entirely, even as the expectations for profits rises. He notes that two narratives will take hold this year: the ongoing investments in AI by the largest names on the market, and the implementation of AI processes in other sectors. Both stories, he says, are part of a revolutionary change that advisors will want to stay attuned to.
“The narrative around what's the ROI on the CapEx, that's kind of waxed and waned through all of this. It matters, then it doesn’t matter,” Hofstra says. “My own view is that the ROI doesn’t matter to the spend, in the sense that everyone has to be AI ready. If you’re Amazon, Alphabet, or Meta you cannot be left behind. Microsoft will continue to have a great return on equity, but it probably won’t come from the Capex spend on AI buildout right away… Then there’s those outside of that, those that are using AI to try and change their business, that’s where it needs to matter. If you’re going to spend a lot on AI as a retailer or a construction firm or a financial services company, there better be a benefit.”
Hofstra’s core view is that there are now two key AI narratives for investors to follow. The first is the ongoing role of the ‘magnificent seven’ or AI hyperscalers, those companies heavily involved in the development of AI infrastructure and software. Many of those companies are incredibly profitable beyond their AI lines of business, as Hofstra notes with the example of Microsoft. While their stock prices seem high, they are still not at the point of a bubble on a price-earnings basis.
The other narrative, that may hold more sway on markets overall in 2026, is how non-tech companies are using AI in their operations to drive productivity and efficiency. He notes that some early adopters have seen first-mover advantages for the development and deployment of AI. So far, he notes, the evidence has been largely anecdotal. However, in interviews with certain company leaders he is hearing about how AI implementation in supply chains and coding processes has made operations significantly more efficient for many.
Coding, Hofstra says, has been one of the first widespread applications where AI has driven greater productivity. Software companies are able to do more with less spending on payroll, driving margins higher. Call centers, too, have been made leaner and more efficient through the implementation of AI, as machine learning allows for customer feedback about the process to be implemented rapidly and effectively. Other sectors, such as health care and retail, are in earlier stages of AI implementation but Hofstra argues that as agentic AI becomes more widespread those sectors may see productivity benefits as a result. The challenge for investors may come in navigating between the tangible improvements already underway and the revolutionary promises that many speculate will take place.
For investors looking at these moves, Hofstra notes that there shouldn’t be too many instances of long-term outperformance. Even early movers in an industry ought to eventually be caught by their competitors and the excess profits will likely be depleted away in the process of competition. In the short-term, however, there could be opportunities for investors in those first movers.
Hofstra believes that there could be some bubble risk in the AI theme, but that this risk is largely isolated to many privately-held AI companies, like OpenAI, which have raised and are spending huge amounts of money while showing comparatively minimal revenue. The large-cap publicly listed tech hyperscalers, on the other hand, have more differentiated and robust business lines to support their AI spending.
In watching to assess who the AI winners and losers will be, Hofstra says that advisors need to watch the earnings calls of those AI end-users, the companies spending on AI to drive efficiency and profitability. Watching for use cases will inform where those first movers are and what kind of advantages they are getting. Right now, he says, the potential investment upside might not yet be priced in.
“The core message is that this is a great trend. This is real, this is vibrant. I mean, we all feel it probably in different ways,” Hofstra says. “But we know that a rising tide can lift everything, whether it's a piece of garbage or a beautiful yacht. So you do want to be careful. You do want to pick your spots.”
