Young investors are skipping over the fundamentals of capital structure and debt, a gap that Michael Buckley, who heads equities at Duquesne Family Office, says could prove costly as the artificial intelligence boom increasingly runs on borrowed money. Buckley argues that understanding how companies finance themselves, and where debt sits relative to equity, is an edge that early-career investors can build now and put to use.
The shift matters because the AI build-out is no longer funded only by cash flow and equity raises. Data centers and chips require large amounts of capital, and much of that is being financed through borrowing. Tech hyperscalers issued more than $100 billion in corporate bonds last year, according to a Bank for International Settlements figure cited in the reporting. Buckley says debt is now relevant not only to public markets but also to venture firms, and that interest rate and credit concerns are beginning to affect how investors value AI infrastructure. When a company's financing costs move, the assumptions behind its valuation move with them, which is why the mechanics of borrowing are no longer confined to bond desks.
Buckley has spent much of his career on the credit side of finance. He started at Sankaty Advisors, an arm of Bain Capital, where he worked in credit. He is a Harvard graduate who later spent two years as an analyst at CPMG. At age 27 he was recruited to Duquesne to work under Stanley Druckenmiller, the investor whose career includes a famous trade in which he helped Soros bet against the British pound in 1992. That background, which moved from the lending side of the market into a macro-oriented equity fund, shapes how Buckley thinks about the connection between credit conditions and stock prices.
Duquesne's recent investment focus includes AI, healthcare and biotech. The firm recently backed the pharmaceutical company Formation Bio. Buckley describes himself as a "non-dogmatic" investor and says his approach blends top-down macroeconomic views with bottom-up analysis of individual companies. That combination, he suggests, requires understanding both the broad rate environment and the specific debt obligations a company carries. An investor who can read a balance sheet's layers, from senior debt to equity, is better placed to judge which companies can absorb a higher-rate environment and which cannot.
For younger investors, the message is that a focus on growth stories and headline technology trends can leave gaps in understanding how those companies are financed. As borrowing becomes a larger part of the AI story, Buckley's argument is that knowing where a company's obligations sit, and how rising or falling rates change its position, is becoming part of basic investment judgment rather than a specialist skill. Skills that once belonged mainly to credit professionals are now relevant to anyone holding AI-linked shares, he suggests, and early-career investors who learn them first will be better prepared when the financing picture shifts.