Equities: AI profitability doubts grow – Nordea

Nordea analysts Kirsti Sunde Midttun and Ole Håkon Eek-Nielsen argue that AI profitability faces structural pressure from high inference costs, rapid model depreciation and growing competition from free and open alternatives. They question the durability of current business models and point to rising investor scepticism toward AI-related equities, alongside a rotation from technology stocks into cyclical, defensive and value-oriented sectors.

Nordea questions AI margin durability

"With the AI buildout now driving a meaningful share of US growth, we examine the sustainability of the underlying business models and whether the recent market scepticism is warranted."

"Despite AI's rapid growth, we see several challenges to profitability and present a more sceptical view of the industry's prospects."

"The net effect is that inference costs remain the central economic challenge for AI developers, and a key reason why the leading model companies are, for now, not profitable."

"Frontier models are, in short, best understood as infrastructure with an unusually short useful life: the value must be extracted before the technology is obsolete."

"Publishing capable models free of charge suppresses willingness to pay across the market and undercuts the business models of developers who charge for access."

"Taken together, the picture is this: frontier models are expensive to build, they depreciate within months, and they face growing competition not just from each other but from free, open alternatives."

"Over the summer, we have also seen some scepticism towards AI-related equities. This has led to a notable rotation out of tech stocks and into cyclical, defensive, and value-oriented sectors."

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