Indexing as an Evidence‑Based Strategy

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AI has moved from buzzword to investment obsession almost overnight. From semiconductors and data centres to software platforms and critical minerals, “the AI trade” has become shorthand for backing the companies expected to benefit most from this technological shift.

But before assuming today’s obvious winners will still look obvious in a decade, it’s worth revisiting the last time a world-changing technology captivated markets.

In this episode, Stuart unpacks what really happened during the dot-com bubble and where investors went wrong. The internet thesis was correct. The valuations were not. Many of the most celebrated companies of 2000 ultimately destroyed long-term shareholder value, despite the technology itself reshaping the world; only a handful adapted and endured.

He explores the parallels with AI today: sky-high expectations, capital flooding into perceived winners, and the growing belief that “this time is different.” We also examine why many of the true long-term winners may not yet exist, and why broad market exposure may already capture much of AI’s eventual impact.

Most importantly, Stuart explains why you don’t need to predict the winners to benefit. History suggests that trying to identify and then time the next dominant technology companies is far harder than it looks. Instead, a rules-based, diversified approach allows markets to sort winners from losers over time.

AI may well be the most significant technological advancement of our generation. But that doesn’t mean your investment strategy needs to change.

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