AI’s biggest infrastructure builders (Alphabet, Amazon, Meta, Microsoft, and Oracle) are in the middle of one of the largest borrowing sprees in corporate history. They’re raising enormous sums to fund data centre buildouts, and they’re doing it in the public bond markets.

The obvious question is: can the AI-related borrowing binge actually break the bond market?

So far, the answer looks like “no,” but the early deals are starting to show where the pressure points might be.

For many investors, emerging markets still conjure a reductive caricature: low-income economies, recurring political crises, heavy-handed governments, and returns that haven’t lived up to the hype. While that picture isn’t entirely wrong, it’s materially incomplete. Over the past three decades, the emerging world has shifted from the periphery of the global economy to its fabric: supplying critical components to the AI supply chain, incubating globally competitive consumer platforms, and hosting a growing share of the world’s middle class.

In investing, some ideas age gracefully. The notion that data can be a source of competitive advantage is one of them. But as artificial intelligence (AI) and large language models (LLMs) reshape the landscape, and, in particular, as market sentiment has shifted violently over the past few weeks against asset-light, software-based businesses, it’s worth asking: what does a true “data moat” look like today…and is it still as defensible as it once was?