The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Private-credit funds manage exposure to AI-dependent borrowers by examining how AI could change each borrower’s business and repayment capacity, structuring loans with appropriate protections, and monitoring risk against maturities and portfolio concentrations. “AI-dependent” is not a standardized borrower category: a company may be vulnerable to AI competition, benefit from using AI, rely on third-party AI or cloud services, or face several of these conditions at once.
How AI-related business change can become credit risk
AI matters to a lender when it could weaken a borrower’s ability to pay interest, repay principal, or refinance—not simply because the borrower sells software or uses AI. A potential disruption has to travel through the business and into cash flow, liquidity, or credit support before it becomes a loan impairment.
J.P. Morgan Asset Management identifies four possible routes: revenue erosion if customers leave or spend less; margin compression if a borrower must spend more to compete; valuation compression that reduces support for a refinancing; and a refinancing seizure if lenders or investors become unwilling to provide capital. These are risk channels, not evidence that AI has already caused defaults. The firm’s framework emphasizes that the nature of a software company’s exposure can matter more than a headline sector label.
What lenders examine in an individual loan
A practical review connects the borrower’s product and competitive position to its capacity to service debt. The following is an analytical checklist, not a universal regulatory scorecard or an industry-wide standard.
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Product, competition, and ability to adapt
- What the product does: Identify the work it performs for customers and how central that work is to their operations.
- How exposed it is to substitution: Consider whether customers could switch providers, build an alternative, or use AI to reduce their reliance on the product.
- Whether AI strengthens the offering: Assess if the borrower can use AI to improve its product or defend its position, as well as whether AI makes the product easier for competitors to replicate.
- How revenue is earned: Examine pricing, customer retention, recurring revenue, and customer concentration. These affect how quickly competitive pressure could show up in sales and cash receipts.
From operating performance to debt capacity
Managers can connect changes in the business to measures of repayment and recovery. Relevant indicators include gross margins and operating costs, cash generation, leverage, interest coverage, covenant headroom, collateral value, sponsor support, debt maturity, and access to refinancing. No single measure establishes whether an AI-exposed company can repay; the point is to evaluate how operating pressure could affect several of them together.
Time to maturity and refinancing
A borrower can remain current on its loan even as its valuation or refinancing prospects weaken. That distinction matters when a maturity is approaching: a business change that has not yet caused missed payments may still make refinancing harder. Managers therefore compare the time in which disruption might affect the business with the remaining loan term and the borrower’s likely refinancing path.
Oaktree Strategic Credit Fund’s March 31, 2026 shareholder update described pressure concentrated in older, pre-2022 vintages and ARR loans with 2027–2028 maturities. The update also described a business-resilience framework combining operating KPIs, financial metrics, and AI-related considerations. This is an account of one manager’s portfolio and approach, not evidence that all funds use the same framework or face the same exposures.
How funds monitor borrowers and portfolio concentrations
After a loan is made, managers can monitor operating results, covenant tests, liquidity, payment behavior, waivers, valuation changes, and borrower developments that could affect refinancing. They can also aggregate exposure by sector, product type, sponsor, vintage, maturity, borrower, and shared dependencies such as technology or financing providers.
Aggregation matters because risks can overlap. The Bank for International Settlements (BIS) found that some large business development companies (BDCs) had exposure to a shared pool of borrowers. The Financial Stability Board (FSB) says that technology-sector concentration, interconnected financing, valuation opacity, and limited loan-level information make system-wide exposure difficult to assess.
Loan protections can help manage, but not remove, risk
Seniority, collateral, covenant terms, reporting requirements, and limits on additional debt can affect recovery or give lenders ways to respond when performance weakens. Their value depends on the actual loan terms and circumstances; no single covenant can eliminate the business-model risk of AI disruption.
The Federal Reserve has cautioned that competition and pressure to deploy capital can weaken underwriting standards or contribute to more covenant-lite lending. It also notes that high leverage and floating-rate borrowing can leave borrowers more vulnerable to shocks. Those factors can compound operating pressure, so a manager’s assessment should consider both the borrower’s prospects and the loan’s protections.
Where AI tools fit in credit work
AI tools can assist with repeatable tasks when their output can be checked—for example, extracting terms from credit agreements, summarizing data rooms, comparing covenant definitions, flagging reporting exceptions, and organizing portfolio monitoring. They do not replace a judgment about whether customers will keep buying a product, a borrower can adapt, or a loan should be modified.
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PwC’s 2026 survey page reports that 53% of respondents were more frequently implementing technology in private-credit investment processes, 54% were most likely to use AI in underwriting, and 16% viewed AI-enabled portfolio management as a current priority. These are survey responses, not adoption rates for the entire private-credit market. PwC says data quality, integrated workflows, and governance matter, and that final economic judgment remains human. US Partner Erich Butters described a possible next stage as “the combination of better data, better monitoring, and more active asset management underpinned by agentic AI to increase efficiency and control across the end-to-end process.”
A CRISIL-authored case study describes a US fund using an LLM-based tool to review loan agreements and covenant data across about 100 active deals, identify exceptions, and support borrower engagement. It illustrates one possible workflow; as a vendor-authored case study, it is not independent evidence of performance or proof of common industry practice.
What public figures show—and what they do not
Available estimates indicate meaningful software exposure and market repricing, but they describe different populations and should not be read as a single, directly comparable measure. Public-market share-price changes are not private-loan default rates.
| Reported evidence | What it measures | How to interpret it |
|---|---|---|
| About $115 billion | BIS reported in 2026 that this was BDC lending to software firms—about one fifth of BDC lending and more than 80% of BDC technology portfolios. | This describes BDC exposures, not all private-credit lending or AI-caused losses. |
| More than $500 billion | BIS reported that outstanding private-credit loans to SaaS firms exceeded this amount at end-2025, equal to 19% of total direct loans; it also reported that one third of private-credit funds had extended loans to the SaaS sector. | This is a broader private-credit SaaS exposure estimate and is not directly interchangeable with the BDC software figure. |
| Almost 30%; about 10%; around 5 percentage points | BIS reported that software-company stock prices declined almost 30% between October 2025 and February 2026. BDC stocks fell about 10% on average over that period, and BDCs with high software exposure underperformed those with low exposure by around 5 percentage points. | These are market-price movements, not measures of loan defaults or proof that AI caused impairment. |
| 53%; 54%; 16% | PwC’s 2026 survey page reported that 53% of respondents were more frequently implementing technology in private-credit investment processes, 54% were most likely to use AI in underwriting, and 16% treated AI-enabled portfolio management as a current priority. | These figures describe survey responses, not the share of all funds using those practices. |
| Around $220 billion; commercial estimates of $270 billion to $500 billion | The FSB reported around $220 billion in drawn and undrawn bank credit lines to private-credit funds captured in available data across FSB members; some commercial estimates ranged from $270 billion to $500 billion. | These figures indicate links between banks and private-credit funds, not AI-specific borrower exposure. |
What remains uncertain about AI-related loan impairment
BIS reported in July 2026 that AI-related revenue uncertainty had not yet affected the BDC software loans it studied or changed how BDCs and their equity investors priced those exposures. Separately, BIS documented substantial software-market repricing and weaker share-price performance among BDCs with higher SaaS exposure. Taken together, those findings support monitoring the risk; they do not establish sector-wide AI-caused defaults.
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Private-credit loan data are less transparent than public-market data. The FSB identifies limited fund- and loan-level information, inconsistent definitions, valuation opacity, and concentration as obstacles to assessing exposures and transmission channels. No published statistic in the cited sources directly measures the share of all private-credit borrowers whose repayment capacity has already deteriorated specifically because of AI. Nor is there an independently validated, industry-wide AI-disruption scorecard established by the cited sources.
How to compare a fund’s approach or two AI-exposed loans
There is no universal weighting for these factors; their importance depends on the loan and fund mandate. A useful comparison asks:
- How exposed is the borrower to AI disruption, and how able is it to adapt?
- How resilient are recurring revenue, customer retention, and pricing power?
- How sensitive are margins and cash flow to competitive or operating pressure?
- What are the borrower’s leverage, interest coverage, and covenant headroom?
- What seniority, collateral, and other lender protections apply?
- How does the time to maturity compare with plausible disruption and refinancing timelines?
- How concentrated is the fund’s exposure across related borrowers and shared dependencies?
The borrower-level factors help connect AI-related change to debt capacity; portfolio-level aggregation can reveal whether similar risks cluster across loans. A sector label alone cannot answer either question.
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