AI's debt-fueled boom is boosting growth - and building systemic risk, BIS warns
The surge in AI spending is propping up growth, but it's being financed increasingly with debt - especially private credit. A new staff bulletin from the Bank for International Settlements (BIS) flags rising vulnerabilities if expected AI returns don't show up.
Capital needs are climbing fast: chips, hardware, software, and data centers. Analyst estimates point to an additional US$100 billion-US$225 billion per year in data center spending over the next five years.
Debt is becoming the default - with private credit taking the lead
AI firms are leaning harder on external financing, from corporate bonds to private credit. The private credit share of loans to AI-related companies has jumped from under 1% of outstanding volumes to almost 8%.
On current trends, outstanding private credit to AI firms could reach US$300 billion-US$600 billion by 2030. That concentration of leverage in a less transparent market should be on every risk committee agenda.
The pricing gap: equity euphoria vs. credit discipline
Private credit spreads imply AI loans are being priced like plain-vanilla corporate risk. Equity markets, meanwhile, are assigning valuations that assume outsized future cash flows.
That split leaves two possibilities: lenders are underestimating risk as exposures ramp, or equity investors are overestimating what AI can deliver. Either way, the reconciliation won't be painless if cash flows lag the narrative.
Where fragility shows up
- Leverage and spillovers: Heavier balance sheets can amplify shocks across lenders and funds if AI returns disappoint.
- Opacity in private credit: Less disclosure, valuation discretion, and fund-level leverage reduce visibility when conditions turn.
- Circular financing: Interconnected funding within the AI ecosystem can transmit stress quickly.
- Concentration risk: Big bets on data centers and semiconductors tie outcomes to a narrow set of cash flow drivers.
- Terms risk: Covenant-lite structures can delay recognition of stress and widen ultimate loss severity.
What finance teams should monitor now
- Debt service capacity: Track DSCR, interest coverage, and sensitivity to higher rates or delayed revenues.
- Underwriting drift: Watch covenant quality, leverage multiples, and add-backs in sponsor models.
- Fund plumbing: Map warehouse lines, NAV facilities, and redemption terms that can force asset sales.
- Cross-exposure: Identify overlap across banks, BDCs, CLOs, and private credit funds to understand correlation.
- Utilization and unit economics: For data centers, monitor capacity utilization, energy costs, and AI workload mix.
- Scenario tests: Model a slowdown in AI capex plus a 25%-40% equity drawdown and widening credit spreads.
- Liquidity buffers: Pre-fund near-term maturities; diversify funding; confirm backstop lines are durable.
Practical steps for CFOs, lenders, and risk leaders
- Map exposures end-to-end: Borrower, sponsor, supplier, and fund-level links across the AI stack.
- Tighten pricing and terms: Align coupons and covenants to realistic ramp timelines and downside cases.
- Stage-gate financing: Release capital against operational milestones (utilization, bookings, margin).
- Limit circularity: Cap related-party revenues and intra-ecosystem financing dependencies.
- Upgrade disclosure: Standardize performance reporting for AI projects and data center assets.
- Board oversight: Treat AI exposure as a thematic risk with explicit limits and early-warning triggers.
Base case vs. downside
If productivity gains materialize, today's leverage gets absorbed and spreads hold. If not, expect sharper corrections in both equity and debt - with a hit to GDP as AI capex cools.
The takeaway: growth today is real, but so are the balance-sheet risks behind it. Price it, structure it, and monitor it like the cycle depends on it - because it probably does.
Further reading
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