Franklin Templeton CEO Warns AI Debt Is 'Very Complex' as She Favours Short-Term Bets

Franklin Templeton chief executive Jenny Johnson has urged caution over long-term investments in artificial intelligence financing, warning that increasingly complicated funding arrangements and rapid technological advances are making the sector harder to assess.

Speaking at the Milken Institute Asia Summit in Singapore on Friday, 9 October, Johnson highlighted the growing use of off-balance-sheet financing and supplier lending as technology companies expand their computing infrastructure. Although she sees opportunities in AI-related credit, she said she would favour shorter-term investments while the sector continues to evolve.

Her comments highlight a growing challenge for investors: assessing the financial risks behind the AI boom while determining whether companies can generate sufficient returns from their substantial infrastructure spending.

Why Johnson Favours Short-Term AI Debt

According to figures cited in the supplied report, investors provided approximately $500 billion in financing to AI-linked companies during 2026, including around $200 billion to major hyperscalers. These large technology companies operate extensive cloud computing infrastructure and require substantial capital to build data centres, acquire specialised chips and expand computing capacity.

Johnson described a complicated funding landscape involving conventional borrowing, off-balance-sheet financing guaranteed by hyperscalers, and suppliers increasingly acting as lenders. Such arrangements can make it harder to assess a company's overall financial exposure, particularly when certain obligations are not immediately visible on its balance sheet.

'Personally, I'd stay probably on the shorter end of that curve,' Johnson said, explaining her preference for shorter-term investments. Her caution reflects uncertainty over how quickly technological advances could change the value of existing infrastructure and financing commitments. Equipment and computing capacity purchased today could face different economic conditions as AI technology develops.

Johnson did not argue that all AI-related debt was unattractive. Asked whether hyperscalers' growing reliance on debt markets indicated financial stress, she emphasised the need to assess individual companies and their ability to meet their obligations. She also identified a potential opportunity for fixed-income investors. Widening credit spreads, which reflect the additional yield investors demand to hold riskier debt, could create attractive entry points if fewer market participants are willing to provide financing.

Johnson Says December Rate Hike Is Probable

Johnson also said an interest rate increase in December was probable. Her assessment was a forecast rather than a confirmed policy decision. The supplied report put the US 10-year Treasury yield at approximately 5.24% on Friday, with the 30-year yield near 5.62%. Johnson said economies could perform well with 10-year Treasury yields between 5% and 5.25%, provided other parts of the economy remained strong.

Higher interest rates can increase borrowing costs for businesses that depend on external financing. Companies with substantial cash reserves may be better placed to manage tighter financial conditions, although their exposure will also depend on their debt levels, investment plans, and cash flow. For investors assessing AI infrastructure, the cost of financing is an important consideration alongside the technology's potential to generate future revenue.

AI Productivity Gains May Still Lie Ahead

Despite her concerns about funding arrangements, Johnson remains optimistic about AI's potential to improve productivity across traditional industries. 'The AI story has not played out at all in sectors and traditional businesses,' she said, suggesting that the technology's wider economic impact could extend beyond the companies currently building AI infrastructure.

Johnson argued that current productivity gains of around 2% largely reflected technologies available over the past two decades rather than the latest advances in AI.

She pointed to healthcare as one area with potential for significant gains. AI could help expand pipelines of potential drug discoveries and improve the selection of clinical trial participants, potentially increasing treatment effectiveness and accelerating regulatory approvals. These outcomes remain uncertain and will depend on how successfully businesses apply the technology and translate advances into measurable results.

Johnson's outlook therefore combines optimism about AI's long-term economic potential with caution about the debt supporting its expansion. For investors, the challenge is to distinguish companies capable of managing their financial obligations from those exposed to uncertain technology returns and increasingly complicated funding structures.