The Friction Test: What the market gets wrong about AI disruption

It has been a difficult period for the strategy. At the time of writing, a number of our holdings are being challenged by an increasingly narrow view of where durable value creation will sit in an artificial intelligence (AI)-driven world.

On one side sit the obvious beneficiaries of the current AI build-out – semiconductors, memory companies, selected hard assets tied to data-centre investment, parts of defence and other businesses linked to physical capacity.

On the other side sit the digital and capital-light companies we tend to prefer, many of which have been treated as though disruption is not only coming, it is likely to impair their long-term economics in a meaningful way. In most of these businesses, underlying earnings have continued to grow, buybacks have generally increased, and competitive positions appear intact – in some cases, stronger. Yet share prices have de-rated materially.

In our view, part of the problem is that investors are applying a broad “AI discount” to businesses that may look similar at a distance, but whose economics, competitive positions and exposure to disruption differ markedly. We continue to see this as an area where a long-term investor can add value: not by denying the importance of AI, but by distinguishing between businesses where AI may genuinely alter long-term industry structure and cash-flow durability, and those where we believe the effect is being extrapolated too broadly.

In a companion piece, we discuss the key drivers of performance and current positioning across the portfolio. But here, we want to dive deeper into AI, to assess where it is already changing the economics of an industry, where we think the effect is being overstated, and what that means – specifically – for the businesses we own.

We believe the real question is not whether earnings are holding up today, but whether AI is likely to weaken a business in a lasting way. In many cases, we think investors are conflating what large language models (LLMs) are already good at with a much broader set of capabilities that they do not reliably possess.

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