How should value investors approach AI?
Value investors traditionally place greater weight on the present and past, favouring tangible evidence over more speculative views of the future.
AI challenges this instinct. Past success is no longer a reliable guide to future durability, and value investors must explicitly accept that business model disruption risk has increased. It also challenges a second core assumption: mean reversion. In many technology markets, supernormal returns can persist longer than expected because software economics and network effects can deepen moats over time. Meanwhile, losers can fade into irrelevance rather than reverting back to ’fair’ returns.
Avoiding losers is the primary discipline. Crucially, losers are rarely obvious ex-ante. Many disrupted companies are screening as cheap, stable and cash-generative. This reinforces the central role of value trap analysis within our investment process. By systematically assessing reinvestment rates, management’s ability to adapt, pricing power, customer switching costs and balance-sheet flexibility, we seek to distinguish temporary mispricing from structural decline. History shows that assets with eroding strategic relevance tend not to recover – remaining cheap or becoming cheaper still.
Clearly though, opportunities do exist. AI-driven uncertainty has created cases where bad news is over-discounted and optionality ignored. Applying a large margin of safety remains critical. One area we find interesting is software companies that support industry verticals. Here, SaaS (software as a service) applications are industry specific, bespoke and deeply embedded. This software is routinely used in regulated or operationally complex industries where switching costs are high due to proprietary datasets and deeply integrated customer relationships. Where these businesses can incorporate AI as an enhancement rather than a replacement, we see scope for durable moats alongside valuation underwriting that is currently overlooked by narrative-driven markets.
Additionally, value investors can uncover hidden value in businesses with undiscovered or latent AI exposure. This logic also extends to parts of the ’picks and shovels’ layer of AI. While much investor attention has gravitated toward highly visible infrastructure winners, crowded positioning means these stocks are trading at very high multiples in many cases. By contrast, we have focused on less obvious enabling segments – for example, specialized semiconductors tied to mobile and Internet of Things devices.
Overall, valuation nuance matters: a company on a headline ’high’ multiple can still be value if it has sustainably high returns on capital, strong cash conversion and reinvestment runway – whereas a low multiple can be expensive if durability is deteriorating.
