AI Risk Could Trigger $75B–$120B in Leveraged-Loan & Private-Credit Defaults

Executive summary

Modeling indicates a baseline increase of $75 billion–$120 billion in additional defaults across leveraged loans and private credit if rapid AI-driven disruption accelerates through late 2026. This baseline applies modeled default-rate increases of up to 2.5% for leveraged loans and up to 4.0% for private credit, and uses market-size assumptions of roughly $1.5 trillion (leveraged loans) and $2.0 trillion (private credit).

"$75B–$120B in fresh defaults by year-end is the baseline stress range under a rapid-disruption scenario."

"A tail-risk scenario could roughly double those default estimates and trigger a broader credit repricing."

These outcomes imply concentrated stress on below-investment-grade borrowers, with private-equity-owned software and data-service companies—often highly leveraged—most at risk. Monitor large software and AI-equity moves (ticker: AI) and major enterprise software names (CRM, ADBE) as early signals of disruption velocity.

Key, quotable datapoints

• Baseline incremental defaults: $75 billion–$120 billion across leveraged loans and private credit.
• Modeled default-rate shocks: up to +2.5 percentage points for leveraged loans; up to +4.0 percentage points for private credit by late 2026.
• Market sizes applied: leveraged loans ≈ $1.5 trillion; private credit ≈ $2.0 trillion.
• Tail-risk: modeled doubling of baseline defaults in an extreme outcome, producing broader repricing and liquidity stress.

How rapid AI disruption transmits to credit stress

AI-driven productivity and capability gains can create a winner-take-all dynamic across software and data-service markets. Transmission channels to credit markets include:

• Revenue and margin compression: incumbents that fail to integrate new AI capabilities can lose market share and pricing power, reducing cash flow coverage for debt service.
• Balance-sheet strain at PE-owned firms: private-equity-owned software and data-service companies with elevated leverage and short refinancing windows face truncated flexibility.
• Funding withdrawal and liquidity squeeze: a sudden reappraisal of credit risk can reduce demand for leveraged-loan and private-credit paper, tightening refinancing options and accelerating defaults.

The incremental default exposure concentrates in below-investment-grade borrowers and mid-market software/data-service franchises with high leverage and limited covenant protection.

Market mechanics: why leveraged loans and private credit matter

• Core financing channel: leveraged loans and private credit are principal funding sources for mid-market, below-investment-grade companies and sponsor-backed deals.
• Heterogenous, illiquid market structure: these markets are less liquid than public investment-grade credit; mark-to-market repricing and forced selling can amplify initial losses.
• Amplification via lenders and funds: mark-to-market losses and capital pressure at CLOs, BDCs, private-credit funds, and banks can trigger selling and tighter underwriting standards, widening spreads on new issuance.

If defaults rise by the modeled amounts, expect measurable losses at middle-market lenders and private-credit funds, wider bid-ask spreads, and higher yields on new issuance.

Tail risk and contagion: what doubling defaults would mean

A tail-risk scenario that roughly doubles baseline defaults would likely:

• Cut funding to many mid-market software and data firms, driving quick operational distress and cash-flow shortfalls.
• Trigger a broad repricing of leveraged credit, raising borrowing costs and reducing refinancing options for a wide set of issuers.
• Produce knock-on effects across sectors that rely on mid-market suppliers and service providers, increasing counterparty and operational risk for leveraged lenders.

If liquidity providers retreat simultaneously, these dynamics can evolve into a broader credit crunch in loan markets with system-level amplification.

Timing, uncertainty, and key variables

Realized outcomes hinge on four variables:

1. Pace of enterprise AI adoption and integration.

2. Rate of improvement and deployment speed of leading AI models.

3. Incumbent firms' ability to monetize AI and defend margins.

4. Lender risk tolerance, covenants, and available liquidity.

Because these variables are uncertain, scenario planning—rather than point forecasts—provides a practical framework for risk management.

Practical implications and recommended actions for traders and institutional investors

• Reassess sector exposures: increase due diligence on private-equity-owned software and data-service credits with elevated leverage and short refinancing windows.
• Stress testing: implement absolute default-rate shocks of +2.5 percentage points for leveraged loans and +4.0 percentage points for private credit by late 2026; apply a $75B–$120B baseline shock and evaluate a doubled-tail scenario.
• Liquidity monitoring: track available liquidity in leveraged-loan and private-credit funds; tightening liquidity is a leading accelerator of realizations and forced selling.
• Early indicators: monitor enterprise adoption signals and investment pace at investment-grade software names (CRM, ADBE) and AI equities (AI). Also track risk-sentiment flows from major banking names and market commentary (UBS, CNBC) as market-readiness signals.
• Portfolio construction: increase convexity management, tighten covenant scrutiny, extend maturities where feasible, and consider hedges that protect against widening spreads in below-investment-grade credit.

Risk-management checklist (operational)

• Run portfolio-level scenario analyses with baseline and doubled-default cases.
• Reprice covenants and test recovery assumptions at varying liquidation multiples.
• Quantify liquidity buffers across funds and warehouses; stress counterparty exposures.
• Revisit hedging programs for spread widening and downgrades, including CDS and index protection where eligible.

Concluding assessment

Rapid AI progress has moved from a long-term productivity narrative to a plausible near-term disruption that can materially affect credit markets. The modeled $75B–$120B baseline in additional defaults provides a concrete benchmark for credit-risk frameworks focused on leveraged loans and private credit. Preparing for both the baseline disruption and a potential tail event is essential for risk managers, portfolio allocators, and lenders active in these markets.

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Excerpt: Modeling shows a $75B–$120B baseline rise in leveraged-loan and private-credit defaults if rapid AI disruption accelerates, with a tail risk that could double losses and prompt broad repricing.