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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUnderwrite an AI startup as a borrower first and an AI company second. The decisive question is whether operating cash flow or controlled collateral can repay the facility in a credible downside case—not whether the company has a high valuation, prominent investors, or hopes of raising equity. Start with repayment capacity, then test the business model, AI dependencies, liquidity, structure, and your portfolio’s correlated exposure.
The Office of the Comptroller of the Currency (OCC) says in Bulletin 2025-45 (December 5, 2025): “An uncommitted future equity raise is not a satisfactory primary source of repayment.” Treat future fundraising as uncertain and secondary unless a financing commitment is documented and its conditions are realistically achievable.
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What makes an AI startup a distinct lending risk?
AI is not a credit category by itself. A profitable software company that uses a small language-model API may present less repayment risk than a pre-revenue company training an expensive model, while an AI infrastructure borrower may have substantial equipment or contracted revenue. Stage, cash generation, collateral, customer evidence, and loan structure matter more than the label.
Venture borrowers can be pre-product, pre-revenue, pre-cash-flow, or pre-profit. They may have little collateral, high monthly burn, and a short operating history. The earlier the stage—and the farther the company is from free cash flow sufficient to service and repay debt—the more important structural protections and credit enhancements become. A lender should identify exactly which condition applies rather than treating “AI startup” as a homogeneous risk.
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AI adds diligence questions because value and operating continuity can depend on models, training or proprietary data, cloud capacity, application-programming interfaces, specialized hardware, and vendors. Regulators have also identified risks involving biased outputs, poor data quality, privacy, cyberattacks, explainability, common model weaknesses, and concentration among a small number of providers. Those categories require investigation; they do not prove that a particular borrower has a defect.
1. Identify the source that will repay the debt
Write the repayment case in one sentence before discussing valuation or growth. A satisfactory primary source is normally recurring operating cash flow that covers fixed charges and scheduled principal, or collateral that the lender can control and realize under the documents. Secondary sources can include a committed equity financing, refinancing, an asset sale, or an acquisition, but they should not be substituted for primary repayment capacity.
Separate primary and secondary sources
- Primary: cash generated by the borrower’s operations after normal expenses, working-capital needs, capital expenditure, and taxes, or appropriately controlled collateral.
- Secondary: a future equity round, refinancing, sale of assets, acquisition, sponsor support, or liquidation value that is not assured on the payment date.
- Weak evidence: an indicative term sheet, a recent valuation, investor reputation, or management’s expectation that markets will reopen.
The OCC’s repayment guidance says risk ratings should reflect expected performance and the borrower’s ability to service obligations on reasonable terms. Ask what happens if no new money arrives. If the answer is that the company must raise capital to make every scheduled payment, the facility is effectively dependent on an uncertain event and should be structured and priced accordingly—or declined.
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Obtain the executed commitment, conditions precedent, investor authority, closing timetable, minimum-performance tests, and termination rights. Determine whether the commitment is legally binding, whether the investor can withdraw, and whether the borrower can satisfy all conditions after a missed milestone. A signed document that remains contingent on an unproven product launch is not equivalent to cash in a controlled account.
2. Test the operating and financial case
Build an independent debt-service model from historical results and current contracts. Reconcile the borrower’s plan to bank statements, general-ledger data, invoices, bookings, recognized revenue, collections, gross margins, customer retention, and cash conversion. Record the date and definition for every metric; “annual recurring revenue,” for example, can mean contracted, invoiced, or collected revenue.
Compare actual performance with the plan
Prepare a month-by-month bridge from the original budget to actual results. Explain variances in customer additions, churn, usage, pricing, hiring, cloud costs, inference expense, research spending, and working capital. A plan that misses revenue while still claiming the same future growth rate needs a documented reason, not a cosmetic reforecast.
Challenge the assumptions that drive repayment
- Demand: What evidence supports the serviceable market, sales cycle, conversion rate, expansion, and renewal assumptions?
- Adoption: Are customers paying for production use or only running pilots? What proportion of pilots has converted, and over what period?
- Pricing and margin: Does the model include model calls, data licensing, cloud storage, bandwidth, human review, support, and customer-specific deployment?
- Timing: How long until positive free cash flow, and is that date supported by observed cohorts rather than a scale assumption?
- Expenses: Which research, compliance, security, sales, and infrastructure costs are unavoidable even if growth slows?
- Concentration: What share of revenue and receivables comes from the largest customers, channel partners, or one geography?
The OCC cautions about projections in which free cash flow remains negative for a prolonged period, positive cash flow is heavily back-loaded, or assumptions lack lender analysis. Require management to show the path from current gross profit to fixed-charge coverage rather than accepting a terminal-year margin.
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Run explicit downside cases
At minimum, model a base case and separate downside cases for delayed adoption, lower growth, higher operating and AI-inference costs, slower collections, delayed fundraising, and constrained refinancing or exit markets. These are stress scenarios, not predictions. Show revenue, gross margin, operating expense, cash balance, debt service, covenant headroom, and the date on which liquidity is exhausted in each case.
Test whether the borrower can reduce expenses without damaging the revenue engine. Identify expenditures that are contractually fixed, personnel reductions that require severance, and cloud commitments that cannot be cancelled. A downside that assumes every cost falls immediately is not credible.
3. Understand the product and its AI economics
Describe what the customer buys without using the word “AI.” Is the borrower selling a workflow, a regulated decision, an API, a model, data, compute capacity, or professional services wrapped around software? Then identify precisely where AI contributes to customer value and where it contributes cost or liability.
Measure unit economics under realistic usage
Calculate contribution margin by product and customer cohort after inference, hosting, data, implementation, support, human quality review, and payment costs. Model high-usage customers separately: a contract priced on seats can become unprofitable when users generate large volumes of model calls. If a third-party provider changes pricing or limits throughput, show the effect on gross margin and required working capital.
Test differentiation and substitution
Ask what prevents a customer from switching to another model, building an internal tool, or buying a broader platform. Review proprietary data rights, workflow integration, evaluation datasets, switching costs, distribution advantages, and evidence that performance remains superior on the customer’s actual tasks. A temporary benchmark lead is not the same as durable pricing power.
Examine customer obligations
Review service-level commitments, accuracy representations, indemnities, data-use restrictions, audit rights, and termination clauses. Determine whether the borrower must reimburse customers for erroneous outputs or a security incident. For consequential uses, establish how outputs are validated, who approves them, and what records are retained.
4. Map model, data, cloud, and vendor dependencies
Create a dependency map covering foundation models, fine-tuning services, cloud regions, databases, vector stores, data suppliers, deployment tools, monitoring systems, and specialized hardware. For every critical component, record the contract owner, renewal date, pricing formula, service-level terms, data rights, outage history available to the lender, and a practical substitute.
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Assess provider concentration and migration
The Bank of England’s Financial Policy Committee report, Financial stability in focus: Artificial intelligence in the financial system (April 2025), warns that growing concentration in AI-service suppliers can increase financial-system risk. It notes that disruption is more serious when firms cannot migrate quickly to an alternative. Ask for a tested exit plan, portable data and model artifacts, compatibility with another provider, estimated migration time, engineering capacity, and the cash cost of operating two environments during a transition.
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Do not treat a contractual right to terminate as operational resilience. A borrower may have the right to leave but lack the engineers, data format, capacity reservation, or cash to complete the move before customers terminate their contracts.
Investigate data integrity and rights
Document the origin, license, consent status, retention period, and permitted uses of training and customer data. Check whether a vendor can use prompts or outputs for its own training, whether the borrower can delete data on request, and whether corrupted or biased data can be detected. Poor data quality can degrade model performance, increase support costs, and trigger customer disputes.
Confirm ownership and enforceability of model weights, fine-tuning files, prompts, evaluation sets, and generated content. The available regulatory sources describe data and explainability concerns but do not provide a universal legal checklist; obtain jurisdiction-specific advice for privacy, intellectual property, consumer protection, and sector rules.
5. Evaluate model performance, bias, privacy, and cyber risk
The U.S. Government Accountability Office report Artificial Intelligence: Use and Oversight in Financial Services (GAO-25-107197, May 19, 2025) identifies potential biased lending decisions, data-quality problems, privacy concerns, and new cyber threats. These issues matter to a lender when they can cause customer losses, regulatory action, remediation expense, or a sudden loss of revenue.
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- Define the intended use, prohibited use, and acceptable error rates for each model.
- Maintain validation records showing performance on representative, current data rather than only vendor benchmarks.
- Monitor drift, hallucination or error rates, disparate outcomes, and material changes after a model update.
- Provide a human review and appeal path when an output affects credit, employment, insurance, health, or another high-impact decision.
- Keep logs that allow the borrower and its customers to reconstruct a material decision.
Ask management to explain how it detects a silent provider change. A vendor can alter a model version, safety filter, context window, or data-retention policy without the borrower changing application code. The lender should know which changes require customer notice, revalidation, or a contract amendment.
Review security as a repayment issue
Map privileged accounts, secrets, model endpoints, customer data stores, and administrative access. Review incident response, backup restoration, vulnerability management, employee access, and notification obligations. Determine whether a breach could halt service, create an indemnity claim, or force a costly rebuild. Require prompt notice of material incidents and evidence that critical backups can be restored, not merely that they exist.
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6. Analyze liquidity and collateral correctly
Calculate unrestricted cash, restricted cash, committed but undrawn facilities, monthly net burn, and remaining months of liquidity using a consistent definition. Reconcile the calculation to bank accounts and the financial model. Show the result under each downside case and after scheduled debt service.
Runway is a monitoring measure, not a repayment guarantee. OCC guidance notes that unrestricted liquidity and a remaining-months-of-liquidity covenant may not ensure full repayment. Controlled or pledged cash has a different credit role because the lender can limit withdrawals or apply it to obligations under agreed conditions.
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Determine what collateral is actually realizable
Inventory intellectual property, receivables, cash, equipment, and securities. For each asset, establish ownership, liens, perfection steps, valuation method, liquidation discount, and time to realization. Software code or model weights may be valuable to an acquirer but difficult to sell separately. Customer contracts may prohibit assignment. Data may be unusable outside the original purpose or jurisdiction.
Borrowing-base formulas should exclude receivables that are disputed, concentrated, aged, subject to set-off, or dependent on continued performance by the startup. If collateral value depends on a functioning team, preserve access to source code, documentation, credentials, and transition assistance through the loan documents.
7. Match the facility structure to the risk
Set tenor, draw conditions, amortization, collateral, covenants, reporting frequency, and remedies to the borrower’s stage and credible repayment date. A short-dated bullet can create a refinancing cliff for a company whose cash flow is back-loaded; a long interest-only period can increase exposure while providing no evidence of repayment.
Structural protections to consider
- Milestone-based draws tied to verified cash, revenue, or customer events rather than optimistic forecasts.
- Minimum unrestricted or controlled-cash requirements calibrated to the actual burn profile.
- Limits on additional debt, liens, asset sales, acquisitions, dividends, and related-party payments.
- Reporting covenants covering monthly financials, cash, burn, bookings, collections, customer concentration, and material model or vendor changes.
- Collateral accounts, cash dominion, intellectual-property protections, and access to records where appropriate.
- Events of default or escalation triggers for missed payments, material security incidents, loss of a critical provider, or a material adverse change in customer contracts.
Terms should preserve an operating company’s ability to function. Overly rigid controls can cause a technical default or prevent the borrower from responding to a provider outage. Document cure periods, information rights, and the steps required before a draw is suspended.
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8. Build a monitoring system before closing
Monitoring should test the assumptions that justified the credit, not simply collect quarterly compliance certificates. Establish an owner, data source, frequency, threshold, and action for every key indicator.
| Indicator | What to track | Escalation question |
|---|---|---|
| Cash and burn | Actual cash, net burn, controlled cash, and months of liquidity versus plan | What expense or draw restriction is available before liquidity falls below the agreed level? |
| Debt service | Interest, principal, fixed-charge coverage, and covenant headroom | Can operations repay without a new financing? |
| Revenue quality | Bookings, recognized revenue, collections, churn, renewals, and customer concentration | Are contracts converting to cash on the modeled schedule? |
| AI gross economics | Inference, hosting, data, support, and human-review cost per customer or transaction | What happens if usage doubles or a provider reprices? |
| Model and vendor resilience | Material model changes, outages, service levels, incidents, and migration readiness | How long can the borrower operate if its primary provider is unavailable? |
| Funding plan | Committed capital, closing conditions, investor communications, and runway impact | Is the financing legally committed or merely expected? |
Require updated projections after a material variance, financing delay, major customer loss, provider change, or security incident. Define escalation in advance: information request, enhanced reporting, draw suspension, cash-control step, waiver negotiation, or workout review. The response should be proportionate to the cause and the remaining recovery value.
9. Manage concentration across the portfolio
A loan can be acceptable alone and still be dangerous when combined with similar exposures. Review common customers, investors, cloud regions, model families, semiconductor suppliers, energy providers, data centers, and exit markets across the portfolio. Also consider whether several borrowers depend on the same venture-funding cycle.
The Federal Reserve Bank of Chicago article Tail Risk for Banks Posed by Investments in Generative Artificial Intelligence (2026) reports that large-bank commitments to selected AI-adjacent industries were around 13% of total commitments, or about $450 billion, in late 2025, compared with around 9%, or about $250 billion, in 2015. These figures cover a selected grouping of AI-adjacent industries at large banks; they are not default rates, recovery rates, or measurements of AI-startup lending.
The same analysis discusses possible spillovers among AI-linked software, infrastructure, semiconductor, energy, and data-center activity through direct and indirect credit channels. Set concentration limits that reflect your own capital, risk appetite, correlated providers, and ability to manage a simultaneous stress event. Aggregate exposures by economic dependency, not only by industry-code label.
How to compare two AI lending opportunities
Use the same decision axes for every borrower so enthusiasm for a novel product does not replace credit analysis.
| Comparison axis | Questions to answer | Stronger evidence |
|---|---|---|
| Primary repayment | What pays principal if no new equity is raised? | Demonstrated operating cash flow or controlled, realizable collateral |
| Runway and downside liquidity | How long does cash last after delayed adoption, higher costs, and debt service? | Verified cash, credible cost actions, and headroom in multiple cases |
| Product adoption | Are customers paying for repeat production use? | Collected revenue, renewals, low concentration, and documented cohorts |
| Gross economics | Does each incremental customer improve cash generation? | Unit margins including model, cloud, data, support, and review costs |
| Model and vendor dependence | Can the borrower substitute providers without losing customers? | Portable data, tested alternatives, contractual rights, and migration budget |
| Collateral and controls | Can assets be perfected and realized in a default? | Clean liens, controlled cash, eligible receivables, and documented IP access |
| Terms and monitoring | Do covenants detect deterioration early? | Frequent reporting, milestone draws, usable triggers, and workable remedies |
| Portfolio correlation | Would another exposure fail for the same reason? | Limits by provider, funding source, customer, infrastructure, and sector |
A practical pre-approval checklist
- State the primary repayment source and demonstrate it with historical cash data.
- Obtain actual-versus-plan results and explain every material variance.
- Build base and downside cases for adoption, pricing, costs, collections, fundraising, and refinancing.
- Reconcile runway to bank statements and distinguish unrestricted from controlled cash.
- Map model, data, cloud, hardware, and deployment dependencies.
- Review provider contracts, portability, substitution, outage response, and migration cost.
- Test unit economics at realistic and high-usage volumes.
- Review validation, bias monitoring, privacy, explainability, logging, and cyber controls for consequential uses.
- Identify collateral, lien perfection, exclusions, liquidation value, and access requirements.
- Set covenants, reporting, draw conditions, escalation triggers, and cure periods.
- Aggregate correlated exposures across the portfolio and apply concentration limits.
- Document which facts are verified, which are management estimates, and which remain contingent.
Warning signs that require a different credit decision
- Repayment depends entirely on an uncommitted future equity round.
- Management cannot reconcile bookings, revenue, collections, and cash.
- Positive cash flow appears only after an extended period of unproven scale.
- Gross margin excludes material inference, hosting, data, support, or human-review costs.
- A single model or cloud provider is critical and no tested substitute exists.
- Customer contracts impose broad accuracy, privacy, or indemnity obligations that are not modeled.
- Collateral value depends on confidential data or a key team that the lender cannot access or preserve.
- Runway covenants count cash that is restricted, already committed, or not under effective control.
- The lender’s existing portfolio has substantial exposure to the same provider, funding cycle, or AI-adjacent infrastructure.
Evidence limits and regulatory context
The OCC bulletin supplies general venture-lending guidance, while the GAO and Bank of England reports describe AI oversight and financial-stability risk categories. They do not provide a dedicated default or recovery dataset for AI-startup loans, and they do not establish that AI companies are inherently more or less creditworthy than other startups. The Bank of England’s finding that around half of respondents to its 2024 AI Survey reported only a “partial understanding” of vendor-provided AI technologies describes UK financial-services respondents, not AI borrowers or lenders generally.
Confirm current lending, privacy, consumer-protection, intellectual-property, and sector-specific requirements for the lender’s jurisdiction and the borrower’s customers. Use qualified legal, technical, and model-risk specialists where the product or collateral requires expertise beyond ordinary financial underwriting.
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