The artificial intelligence (AI) investment cycle has entered a more selective phase, with broad-based gains giving way to a sharper focus on individual company performance, according to Ben Arnold, investment director, global equities at Schroders.
As a result, returns have diverged and AI-related stocks are no longer moving in tandem, with performance increasingly reflecting views on who will ultimately win.
Arnold said the shift marks a turning point for investors.
“AI has entered a new phase, where broad exposure is no longer enough and returns are becoming far more selective,” said Arnold.
“Markets are now moving to a stock-by-stock assessment of who is best positioned to deliver sustainable returns, rather than rewarding the theme as a whole.”
Arnold identifies three critical factors shaping the next stage of the AI investment cycle; deployment of capital, debt, and demand, which together determine where value will emerge.
On capital deployment, Arnold noted that while technology companies continue to invest heavily in AI infrastructure, investors are becoming more discerning about how effectively that capital is being used.
“A year ago, rising capital expenditure was seen as a sign of confidence and leadership,” said Arnold.
“Tech businesses are committing vast sums to AI infrastructure; across chips, networking, data centres and cloud capacity. However, the market is increasingly questioning whether all this investment will generate sufficient returns.”
At the same time, increased use of debt across the AI ecosystem is adding complexity and risk, with markets beginning to differentiate between companies based on their balance sheet strength and ability to sustain investment.
“Take Oracle, issuing almost as much debt since January 2025 than in the previous seven years combined in a bid to accelerate its data centre build out. The perceived risk of its debt rose quickly in Q4 2025, signalling investor scepticism around its ability to catch up in the race for AI leadership and generate sufficient returns to justify both the investment, and the leverage used to fund it.
“The growing use of leverage is amplifying both opportunities and risks. But not all leverage is being treated equally, with markets more comfortable with the credit profiles and capital structures of other hyperscalers.”
Demand remains the most difficult factor to assess, with strong AI adoption not always translating into immediate revenue, particularly as companies balance short-term monetisation with long-term strategic investment.
“Understanding where real, durable demand sits requires much deeper analysis, as usage, pricing power and revenue can diverge significantly across the AI value chain,” Arnold said.
“Even at the individual company level, the link between demand and revenue can be unclear. This was evident in the market’s reaction to Microsoft’s recent earnings. Slower-than-expected growth in its cloud computing platform was initially seen as a leading indicator of weakening demand. However, management clarified this reflected a deliberate decision to redirect capacity towards internal AI development (such as Copilot), aimed at driving long-term monetisation.”
Schroders believes the next phase of the AI cycle is underway, with markets now treating companies in the space very differently.
“Broad exposure to the theme has worked up until recently, but it’s becoming clear that stock selection, not general thematic exposure, will drive the next leg of returns,” said Arnold.
“Diversification remains critical, but the focus now is on identifying the companies that can execute and deliver sustainable returns through the cycle,” he added.
