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An AI investment deserves the same disciplined investigation as any other security, plus tests for data rights, model performance, third-party dependencies and rapid obsolescence. Build your checklist in five gates: identify exactly what you are buying, verify the business and finances, test the AI evidence, examine resilience and legal exposure, and screen the pitch for fraud. Record the evidence for every answer, mark what cannot be verified, and do not treat an AI label, a checklist score or a model-generated analysis as proof of a good investment.
What an AI due-diligence checklist is—and what it cannot do
“AI investment” can describe very different exposures:
- An investment in a company that develops foundation models, software or hardware.
- An AI-enabled company whose products use machine learning as one part of a broader business.
- A company whose operations depend on an outside model, cloud provider or data supplier.
- A fund, note, private placement or other vehicle marketed with an AI theme.
Those structures have different revenue drivers, risks and disclosure standards. Start by naming the issuer, security and jurisdiction rather than accepting a promotional category. AI exposure alone does not establish material AI revenue, a durable technical advantage or a reasonable valuation.
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Gate 1: Establish exactly what you are being offered
Identify the issuer, security and route to purchase
Write down the legal name of the issuer, the security or contract, the offering jurisdiction, the promoter, the broker or platform, fees, lock-ups, liquidity limits and minimum investment. Save the prospectus, private-placement memorandum, subscription agreement, terms of service and every presentation or message that influenced you. A pitch that will not provide basic documents is not ready for investment.
For a public company, confirm the ticker, exchange, share class and latest regulatory filings. For a private company, determine whether you are buying shares, a convertible instrument, a special-purpose vehicle interest, a revenue share or something else. Your rights to information, voting, distributions and resale depend on that legal form.
Classify the AI exposure
Place the opportunity in one of three boxes: AI developer, AI-enabled operator or AI-dependent operator. Then quantify the connection. What percentage of revenue is generated by AI products? Which products are actually in production? Does management report AI revenue separately, or is the claim only a future strategy? If a business relies on a third-party model, identify the provider, contract term, price changes, usage limits and ability to migrate.
Verify people and intermediaries
Check the registration of the adviser, broker, promoter and platform through the relevant official regulator. Investor.gov advises investors to check registration and investigate unregistered professionals or platforms further; its Red Flags of Investment Fraud Checklist is a useful starting point. Navigate to the regulator independently instead of using a telephone number or link supplied in an unsolicited message. Compare the legal entity, domain, office address and disciplinary history. A real registration does not make an investment suitable, but an unverifiable intermediary is a reason to stop until identity and authority are established.
Record what is known and unknown
Create an evidence log with four columns: claim, document or test that supports it, date of the evidence, and unresolved question. For a private issuer, explicitly mark figures that are management estimates, unaudited, stale or unavailable. “Not disclosed” is a finding; it is not permission to fill the gap with an assumption.
Gate 2: Test the business and financial case
Define the paying customer and use case
Describe the product without marketing language: who pays, what task the system performs, what outcome the customer receives and why the customer would continue paying after an initial trial. Ask for examples that can be checked, such as customer contracts, renewal data, deployment counts or independently attributable case studies. Separate a pilot, a signed contract, recognized revenue and a customer’s verbal interest.
Examine revenue quality
- Source: subscription, usage, license, services, hardware, advertising or another stream.
- Concentration: the share of revenue from the largest customers and any dependence on one channel or government contract.
- Retention: renewal, churn, expansion and cohort behavior where disclosed.
- Terms: contract duration, termination rights, minimum commitments, refunds and service-level obligations.
- Economics: gross margin after inference, cloud and data costs; customer-acquisition cost; payback period; and contribution margin.
A fast-growing top line can conceal expensive inference, human review, discounts or one-time implementation work. Ask whether reported revenue would remain attractive if model prices fall, rise or become bundled by a larger platform.
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Review cash, funding and capitalization
Map cash on hand, monthly cash consumption, operating expenses, debt, upcoming maturities and the amount of additional capital required to reach the next stated milestone. For a private issuer, date every number and identify whether it came from audited statements, management accounts or a forecast. Inspect the capitalization table for multiple share classes, options, warrants, liquidation preferences, convertible notes and future dilution. A technically impressive company can still be a poor security if the price, senior claims or financing needs leave little value for the shares you would own.
Assess competition and durability
List direct competitors, open-source alternatives, internal customer development and the major platform vendors that could add a similar feature. Then test the proposed moat:
- Switching costs: Would a customer lose data, workflow integration or regulatory approvals by changing suppliers?
- Proprietary data: Is the data exclusive, legally usable and refreshed often enough to matter?
- Performance: Is the advantage large on the customer’s real task, not just on a selected benchmark?
- Intellectual property: Which software, model weights, patents, licenses and trade secrets are owned or merely licensed?
- Distribution: Can the company reach customers without paying an uneconomic share of revenue to a platform or reseller?
An SEC-filed description of one adviser’s 2026 process combines business and revenue analysis, competitive positioning, management, financial analysis and regulatory risk. Read it as an example of questions to ask, not as a universal formula: SEC filing excerpt on diligence for pre-IPO AI companies.
Evaluate management execution
Check whether leaders have delivered the milestones they previously announced, how they describe failures, and whether incentives reward durable cash generation rather than user counts or model size. Look for related-party transactions, unusual executive turnover, unexplained changes in accounting or contradictory statements across filings and presentations. Ask who owns model-risk decisions and who can halt a deployment when evidence is inadequate.
Gate 3: Verify that the AI claims are real and relevant
Pin down the task and success measure
Ask the company to state the exact task: classification, forecasting, recommendation, generation, fraud detection, robotics control or another function. Require the baseline it beats, the metric, the test population, the date and the operating threshold that matters to customers. A high benchmark score may not translate into lower costs, fewer errors or higher retention in production.
Inspect data rights and data quality
- What data trained, fine-tuned or retrieves into the system?
- Does the company own it, license it, obtain consent for it or rely on a public source with conditions?
- How are duplicates, leakage, stale records, missing values and biased samples detected?
- Can a customer or regulator require deletion, correction or provenance records?
- Could a supplier revoke access or change its license?
Request a data-governance policy, sample provenance records and the contractual language that permits commercial use. “Publicly available” does not automatically mean unrestricted, accurate or fit for a particular purpose.
Demand reproducible validation
Find out who selected the test set, whether it was held out from development, how often performance is retested and what happens after a model, prompt, retrieval source or data pipeline changes. Ask for confidence intervals or error rates where appropriate, not only a best-case accuracy figure. For consequential decisions, identify the human reviewer, escalation path and maximum tolerable error.
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Check controls when an adviser uses AI
If the investment professional uses AI for research, portfolio construction, client communications or surveillance, ask which tools are approved, what data may be entered, how outputs are validated, who reviews them and how inputs, outputs and communications are retained. SEC-filed policy examples discuss approved tools, data integrity, validation, human oversight, error handling and records; they illustrate control questions but do not make every detail a general legal requirement. See the SEC filing excerpt describing an adviser AI policy.
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Use independent checks
Reproduce a small, representative test with documented inputs, or have a qualified third party review the methodology. Compare the claim with customer evidence, audited metrics and filings. If the company refuses enough information to test a central claim, downgrade confidence rather than awarding the benefit of the doubt.
Gate 4: Examine risk, resilience and governance
Data and model risk
Inaccurate, incomplete, biased or stale inputs can produce plausible but wrong outputs. Ask how drift is detected, how incidents are logged, how models are rolled back and whether performance is monitored by customer segment. Determine whether a small error rate could create large financial, safety or legal losses.
Transparency and accountability
Identify what the company can explain about model inputs, limitations and decision paths. A black-box component may be acceptable for a low-consequence recommendation but unsuitable where a customer, employee or regulator requires a reasoned decision. Confirm who signs off on changes and who is accountable when an automated output causes harm.
Security and operational continuity
- Map access to training data, prompts, model weights, customer records and intellectual property.
- Review authentication, segregation of duties, logging, vulnerability management and incident notification.
- List critical third parties, including cloud, model, data, labeling and monitoring suppliers.
- Ask about service-level commitments, geographic redundancy, backups, recovery objectives and a manual fallback.
- Estimate the financial effect of an outage, a model withdrawal, a price increase or a supplier breach.
Legal and regulatory exposure
Assess privacy and data-protection duties, intellectual-property disputes, contract warranties, sector rules and restrictions in each market served. Review whether training data, generated outputs and customer content create ownership or indemnity obligations. A 2026 SEC filing discussing AI governance and risks provides examples of questions about competition, resource constraints, rapid obsolescence, privacy and data protection: SEC filing excerpt on AI risks and governance.
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Plan for rapid obsolescence
Model capability, hardware economics, open-source releases and platform pricing can change quickly. Stress-test the investment if a competitor matches performance, a cheaper model cuts prices, a key supplier limits access or a new regulation delays deployment. Ask how much of the product is portable across models and how much engineering would be required to migrate.
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Gate 5: Screen the pitch for fraud
Recognize classic warning signs
- Guaranteed or unusually high returns.
- Claims of little or no risk.
- Pressure to invest immediately or keep the opportunity secret.
- Credentials, partnerships or regulator endorsements that cannot be independently verified.
- Requests for payment to a personal account, cryptocurrency wallet or unrelated entity.
- AI-generated charts, testimonials or “signals” with no underlying source data.
“Claims of high guaranteed investment returns with little or no risk are classic warning signs of fraud.” — SEC Office of Investor Education and Advocacy, NASAA and FINRA, Artificial Intelligence (AI) and Investment Fraud: Investor Alert, January 25, 2024.
Read the full alert at Investor.gov’s Artificial Intelligence and Investment Fraud alert. Independently verify the person, firm, platform and any claimed regulator or partner. Do not rely on contact details in an unsolicited pitch.
Treat AI-generated analysis as unverified
An AI system can repeat inaccurate, incomplete, outdated or misleading information and can produce made-up citations or calculations. Open each underlying source, check dates and definitions, and compare more than one authoritative source before acting. Never let an automated summary replace reading the filing or contract that controls your rights.
Compare multiple candidates without pretending to have a magic score
Use the same evidence fields for every candidate, then write a decision memo explaining material differences. The source material identifies the following comparison axes but supplies no universal weighting system:
| Axis | Questions to answer | Evidence to retain |
|---|---|---|
| Business and revenue quality | Who pays, for what, under which terms, and how concentrated is revenue? | Filings, contracts, cohort or retention data, margin bridge |
| Customer evidence | Are deployments real, renewed and expanding? | Referenceable customers, renewal data, usage records |
| Unit economics and resources | Does each sale create value after compute, data and support costs? | Cost model, cash balance, burn, financing plan |
| Management | Has the team met commitments and assigned accountability? | Track record, governance documents, incentive plan |
| Technology capability | Does performance hold on the customer’s task and after updates? | Test protocol, baselines, error analysis, monitoring reports |
| Data rights and quality | Can the company legally use reliable, representative data? | Licenses, provenance, consent and data-quality controls |
| Competitive durability and IP | What prevents substitution or copying? | Switching-cost analysis, IP ownership and competitor map |
| Security and operations | Can the service withstand attack, outage or supplier loss? | Security reviews, continuity plans, incident history |
| Legal, privacy and regulatory exposure | Which rules, claims or jurisdictions could impair the business? | Legal assessments, disclosures, contracts and insurance |
A numerical score can make missing information look precise. If you score items, publish the definitions, evidence date and treatment of unknowns; do not use the total as a substitute for judgment or independent research.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical workflow for completing the checklist
- Freeze the offer. Save the exact presentation, term sheet, URL, price, fees and deadline. Record who contacted you and how.
- Verify identity. Confirm issuer, intermediary, registration, jurisdiction and payment destination using official sources reached independently.
- Build the evidence log. For each central claim, attach a filing, contract, test result, customer confirmation or other checkable record. Date every item.
- Map the economics. Reconcile revenue, margins, customer concentration, cash use, financing needs and dilution. Mark estimates separately from historical results.
- Test the AI. Define the task, baseline, data rights, validation method, monitoring cadence and human review. Reproduce a representative result where feasible.
- Stress the risks. Model supplier loss, price changes, outage, breach, regulatory delay, competitor parity and rapid model obsolescence.
- Challenge the pitch. Look for guarantees, urgency, unverifiable credentials and unsupported AI-generated material. Pause if a promoter resists independent verification.
- Write a decision memo. State the thesis, disconfirming evidence, unknowns, position size rationale, liquidity needs and conditions that would trigger a sale or no-invest decision.
- Set a review date. Recheck filings, cash runway, customer retention, model performance, major suppliers, legal developments and ownership changes on a defined schedule.
Questions to send management or an adviser
- Which product generated recognized AI-related revenue in the latest reporting period, and how is it defined?
- What percentage of revenue and gross profit depends on one customer, model provider, cloud provider or data license?
- What benchmark or production test supports the principal performance claim, and what is the baseline?
- What data was used, what rights permit that use, and how are bias, drift and deletion requests handled?
- What happens when the model is wrong, unavailable or changed by a supplier?
- Which controls require human review before a consequential output reaches a customer?
- How much cash remains, what is the monthly burn, and what financing or dilution is expected?
- Which intellectual-property claims, privacy complaints, regulatory inquiries or material incidents exist?
- What would make management withdraw or redesign the product?
- For an adviser using AI, which tools are approved and how are prompts, outputs, client data and communications retained?
Common checklist failures and how to correct them
Confusing a product demo with a business
Failure: treating an impressive demonstration as evidence of recurring revenue. Correction: require paying-customer, renewal, margin and cash evidence tied to the specific product.
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Accepting a benchmark without context
Failure: comparing a selective benchmark number with a competitor’s real-world result. Correction: request the test population, baseline, error costs, date and post-deployment monitoring.
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Ignoring dependency risk
Failure: valuing an application as if its model, cloud and data suppliers were permanent. Correction: identify termination rights, portability, migration cost and a fallback architecture.
Scoring unknowns as neutral
Failure: awarding an average score when evidence is missing. Correction: mark the item “unverified,” define what document would resolve it and set a decision rule for unresolved central claims.
Assuming disclosure equals protection
Failure: reading a risk factor as proof that management has controlled the risk. Correction: seek operating controls, test results, incident history, insurance and accountable owners.
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Failure: sending money because an allocation, token or pre-IPO window supposedly closes today. Correction: pause, independently verify identity and documents, and accept that a legitimate opportunity can be declined when evidence is inadequate.
When the evidence is insufficient
Stop or defer the investment when you cannot establish what security you would own, who is authorized to sell it, how the central AI claim is measured, or whether the issuer has enough resources to operate. A missing document may be tolerable for a minor detail; it is not tolerable for ownership, valuation, cash runway, data rights, customer revenue or a material legal exposure. Record the unresolved item and the evidence required to reopen the decision.
Revisit the file after a defined event—such as audited statements, a financing close, a production deployment, a security incident or a regulatory change—rather than allowing a persuasive update to reset the entire investigation. The objective is not to prove that an AI story will succeed. It is to know what you are buying, what evidence supports the thesis, what can break it and what information would change your decision.
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