What comes after the chips trade for AI investors?

Reassessing quality to find AI opportunity

Livingstone argues that AI has already upended our idea of what constitutes a quality company. Before the rise of AI software was a sector that dominated quality metrics. Software companies had, and in many cases still have, strong profitability, high margins, and tended to have strong protective moats around their business models. AI’s capacity to code and create new software has, in the minds of investors, filled in that moat causing the software sector to correct significantly downwards. AI has the potential to radically change the competitive landscape for companies, especially those seen as higher quality.

Now Livingstone believes that AI’s value can be best realized by companies with two defining traits: the capacity to add AI to improve margin and competitive moats that AI cannot bridge. Those companies tend to be value names, and in some cases were viewed as a value trap for decades.

Livingstone’s prime example of a sector with these traits is the Canadian banks. The banks, he says, have huge amounts of data and can use AI to streamline the labour-intensive processes required to digest, analyze, and use that data. That should result in cost savings and bottom-line improvements. Moreover, the Canadian banks’ competitive moats are based on scale, brand presence, and regulation rather than technology, making them better insulated from AI disruption. He notes that some of these characteristics have already been price in to Canadian banks, with the S&P/TSX Composite Index Banks (industry group) up over 60 per cent in the past 12 months and up nearly 30 per cent in 2026 so far.

Finding, and explaining, long-term AI ROI

While sectors like Canadian banks may be seen as new beneficiaries of the AI trend, there are ongoing questions about how much AI services will eventually cost and how ROI in AI services will be measured over the longer-term. Livingstone says that markets have largely shifted from the view that the promises of future profitability from AI justifies investment now. That view initially buoyed the hyperscalers, but has been replaced by the consensus that companies must demonstrate how AI is improving margin or driving growth for them now. Livingstone, also notes that the rise of cheaper AI models out of China as well as efficiency improvements from US AI service providers may keep costs down and allow for AI profitability improvements to play out over the longer-term.

For advisors, there can be an element of narrative whiplash that comes with the AI theme. The shift from hyperscalers to chip manufacturers to non-tech value names may be a lot to take on, let alone explain to clients in the context of a 30-minute meeting. These rotations have been violent, too, introducing volatility into portfolios that need to be managed. Livingstone says that while he wants to manage opportunities related to AI, he needs to manage risks as well.