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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Assess AI lending concentration by looking through borrower names to the shared business drivers that could transmit AI-related pressure across a private credit portfolio. Measure direct and indirect exposures, compare their credit protections, and stress connected risks together. A portfolio can have many borrowers and still be concentrated if they depend on the same software markets, customers, sponsors, refinancing conditions, or enterprise valuations. There is no established universal AI concentration definition or regulatory limit for private-credit funds.
What “AI lending concentration” can mean
Before calculating exposure, decide which of three distinct risks you are measuring. Combining them in one number can obscure what the portfolio is actually vulnerable to.
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- Borrowers exposed to AI disruption: Loans to companies whose revenue, pricing power, or operating model could be affected by AI. This includes software companies whose products may be easier to substitute, as well as businesses that rely on AI-sensitive suppliers or customers.
- Lending to AI-related businesses: Loans to companies building AI products or infrastructure. Their risks may include customer adoption, technology dependence, and funding needs; they are not the same as loans to businesses threatened by AI.
- Use of AI by lenders: Risk arising from AI models used in underwriting, servicing, or other lending decisions. This is a model governance question, not a measure of borrower-sector concentration.
State the portfolio perimeter and measurement date, too. Specify whether the review covers a fund or selected sleeves, co-investments, warehoused loans, unfunded commitments, and relevant financing links. Separate drawn balances from commitments and from amounts under stress. Record unknown exposure rather than counting it as zero.
What the available sector evidence does—and does not—show
The Bank for International Settlements’ Bulletin 128, published 14 July 2026, reports around $115 billion in business development company (BDC) loans to software firms. That was about a fifth of BDC lending and more than 80% of BDCs’ fast-growing technology portfolios, according to the bulletin. These are sector-level observations, not estimates of losses at an individual fund or proof that every software borrower is equally exposed.
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The bulletin says that, at the time of publication, uncertainty about generative AI’s effects on borrower revenue had not affected these loans or led BDCs and their equity investors to price the software exposure differently. It also reports recently narrowed credit spreads, which can reduce loss-absorbing buffers, and shared borrower pools among some large BDCs. The authors note that low leverage and secured lending may limit spillovers. None of those observations establishes a probability or size of AI-driven losses for a particular portfolio.
A separate Federal Reserve staff note, published 23 May 2025, examines banks’ lending to private-credit vehicles—not the underlying borrower books of those funds. It defines the Herfindahl-Hirschman Index (HHI) on a 0-to-1 scale, where higher values indicate less diversification, and finds moderate concentration in bank commitments in its sample. In a hypothetical scenario in which vehicles fully drew unused bank lines, the note estimates $36 billion in increased drawdowns, about 2% of the sampled Y-14 banks’ CET1 capital, with roughly a 2-basis-point aggregate CET1 ratio impact and a 1-percentage-point liquidity coverage ratio impact. These are modeled outcomes for that scenario and sample, not AI-loss estimates. In its sample of 40 publicly traded BDCs, leverage rose from about 40% in 2017 to 53% in 2024.
How to measure concentration beyond borrower names
Build a look-through exposure map
Classify borrowers by sector and software sub-sector, revenue source, geography, customer and supplier dependencies, maturity, seniority, covenant package, collateral, and loan vehicle. Aggregate connected borrowers or sponsors where that reflects the underlying risk. Document why a borrower is tagged as AI-exposed—for example, product substitutability, customer adoption of AI, or dependence on a particular technology—instead of relying on a broad sector label alone.
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Calculate several concentration measures
Use more than one view of the portfolio. For each chosen unit, such as borrower, connected borrower group, sector, or sponsor, divide its exposure by total exposure to get its share. Report the largest-name and connected-group shares, sector and sub-sector weights, and top-N shares. You can also calculate HHI as the sum of the squared exposure shares for all units in the chosen grouping. If shares are expressed as fractions, HHI ranges from 0 to 1; the grouping and exposure basis must be stated so readers can interpret the result.
Calculate name-based measures separately from shared-factor measures. A software sub-sector might have many small borrowers but substantial common dependence on the same buyers, technology platforms, or market conditions. Useful overlap views include shared borrowers across funds, common sponsors, common end markets, and similar AI-sensitive revenue drivers. HHI summarizes concentration in the selected units; it does not capture every correlation or show, by itself, that a portfolio is safe.
Compare credit strength and loss protection
Exposure alone does not determine whether sector pressure becomes a credit loss. Compare AI-exposed and other portfolio segments on credit quality as well as size. Keep the sector tag distinct from the borrower’s risk grade.
- Business resilience: Recurring versus discretionary revenue, customer concentration, pricing power, liquidity runway, and dependence on continued growth.
- Debt and refinancing: Leverage, debt-service capacity, maturity timing, refinancing dependence, and sensitivity to financing costs or reduced credit availability.
- Contractual protections: Covenant headroom, lien priority, collateral coverage, and likely recovery if the borrower defaults.
- Valuation and sponsor reliance: Dependence on enterprise value to support repayment or recovery, plus the sponsor’s capacity to provide support.
These comparisons help distinguish a large but well-protected exposure from a smaller position that could be vulnerable to falling cash flow, tighter refinancing, or weaker recoveries.
Stress linked AI and credit risks together
Build scenarios around plausible changes in a borrower’s economics, then combine those changes with financing and recovery pressure. Consider product substitution, customer churn, weaker pricing, slower growth, margin pressure, and additional investment needs. Pair relevant operating shocks with higher financing costs, less refinancing availability, lower enterprise values, covenant breaches, weaker collateral recoveries, or correlated draws on bank lines.
Show the effects on defaults, recoveries, stressed losses, cash needs, and any relevant concentration limits. Make assumptions explicit and use ranges where appropriate: the cited publications do not provide AI-specific scenario probabilities. Avoid presenting a scenario as a forecast, or assigning precise odds that the available evidence does not support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn the assessment into limits and monitoring
Translate the portfolio’s risk appetite into limits or watch thresholds for individual names, sectors, sponsors, and shared risk factors. A low single-name share should not override a material common exposure. The appropriate thresholds depend on the portfolio and its ability to monitor exposures; the cited sources do not establish a universal AI concentration cap for private-credit funds.
Assign an owner, review frequency, independent challenge, and an escalation route to the investment committee or board. Monitor borrower data quality and changes in sector exposure alongside covenant pressure, spreads, valuations, and unknown exposures. Define in advance what happens when a limit is breached or a trigger is reached, including who reviews it and what decisions may need reconsideration.
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Interagency commercial real estate concentration guidance recommends supportable segmentation, limits and sublimits, portfolio-level oversight, correlation analysis, timely management information, stress testing, and contingency planning. It cautions against dividing segments simply to mask a concentration. Its scope is commercial real estate lending, so these are risk-management principles to apply by analogy—not rules that directly regulate private-credit funds. Interagency CRE concentration guidance and leveraged-lending guidance also support measurable underwriting standards, downside analysis, monitoring covenants and collateral, and attention to enterprise-value dependence.
Keep model and consumer-credit rules in their proper scope
The OCC’s 2026 revised model-risk guidance addresses model development and use, validation and monitoring, governance and controls, and vendor or third-party products. It says generative and agentic AI models are outside its scope, describes itself as non-prescriptive, and is most relevant to banking organizations. It is not an AI concentration rule for private-credit funds.
The CFPB’s 19 September 2023 guidance addresses a different issue: lenders using complex algorithms must provide accurate, specific reasons for consumer-credit adverse actions. It does not set a portfolio concentration standard for private credit.
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