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Research an AI company in four passes: Govern, Map, Measure, and Manage. First establish the legal entity and what actually generates revenue. Then map the model, data, infrastructure, and supplier dependencies; measure revenue quality, margins, cash needs, and adoption; and finally manage the risks that could destroy value. This approach separates a genuine AI business from ordinary software wrapped in AI language, while exposing weak disclosures, fragile economics, and unpriced liabilities.
Use primary filings and contracts wherever possible, write down your definition of “AI” before comparing companies, and label every figure as reported, estimated, audited, recurring, or one-time. The process works for public issuers and private startups, but private-company information is usually less complete and may be unaudited.
What are the four passes for researching an AI company?
The four passes create a repeatable order for diligence. Do not begin with a benchmark score or a dramatic product demo; begin with the business and its obligations.
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- Map: document the product, customers, revenue model, model stack, data rights, and external dependencies.
- Measure: test revenue quality, unit economics, operating cash flow, capital needs, adoption, and technical performance.
- Manage: compare alternatives, stress-test assumptions, price risks, and decide what evidence would change your view.
NIST’s AI Risk Management Framework (AI RMF) 1.0, published January 26, 2023, provides a useful organizing model. It is voluntary, rights-preserving, sector-agnostic, and intended for AI risks across design, development, deployment, use, and evaluation; using it is not certification and does not prove that a company or product is safe.
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NIST developed the framework with more than 240 contributing organizations. Its Generative AI Profile (NIST AI 600-1), published July 26, 2024, adds cross-sector questions for generative systems. Treat both as checklists for evidence rather than badges.
How do I define the company and decide whether it is really an AI business?
Before reading forecasts, establish exactly what you are buying and what “AI company” means in your analysis. The SEC Investor Advisory Committee’s December 4, 2025 recommendation notes that issuers use inconsistent AI statements and that no single accepted definition exists. Write an operational definition before comparing companies.
Verify the legal and financing identity
- Record the legal entity, ticker or financing status, incorporation jurisdiction, and all material subsidiaries.
- For a private company, identify each funding round, security type, liquidation preference, option-pool terms, and any debt or convertible instruments disclosed to you.
- Check whether the product is owned by the issuer or licensed from an affiliate, founder, university, or third party.
- List material acquisitions, joint ventures, exclusive distribution arrangements, and change-of-control provisions.
Describe the economic engine in one paragraph
Write a plain-language description covering the customer, problem, product, price, delivery method, and cost to serve. Then classify each offering as one of three categories:
- AI as the core product: the model or automated decision is the primary thing customers pay for.
- AI-enabled product: AI is a material feature inside a broader software or service offering.
- AI language attached to ordinary software: the product and economics would be substantially the same without the claim.
A company can have all three categories. Separate their revenue, gross margin, growth, and customer retention where management provides the data. If it does not, mark the mixture as an information risk instead of assuming that the highest-growth segment is AI revenue.
Identify customers and buying authority
Record the target customer, budget owner, procurement cycle, deployment environment, and whether the user and economic buyer are different. Enterprise sales may require security reviews, indemnities, data-processing terms, and lengthy pilots; consumer products may face high churn and expensive acquisition. Ask for customer references that can describe a measurable outcome, not only a product feature.
Which documents should I read first?
Primary documents are the anchor for every later claim. Marketing pages and demonstrations can generate questions, but they cannot substitute for audited statements, contracts, and risk disclosures.
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Public companies
- Read the latest annual report, then the latest quarterly filing, including the business description, risk factors, management’s discussion and analysis, financial-statement notes, and segment disclosures.
- Read registration statements and prospectuses for recent offerings, acquisitions, or spin-offs. They often contain customer concentration, use-of-proceeds, dilution, and risk details absent from presentations.
- Compare earnings releases, earnings-call transcripts, and investor presentations with the filed numbers. Note changes in definitions, metric labels, and the time period covered.
- Extract the company’s statements about AI dependencies, model ownership, data rights, supplier concentration, cybersecurity, regulation, and potential litigation.
Private companies
Request a current capitalization table, monthly or quarterly management accounts, revenue by customer and product, cash-burn schedule, debt documents, major contracts, and the latest board or investor report. Ask which figures are audited, reviewed, or internally prepared. A private company may lawfully withhold sensitive information, but missing evidence should affect your valuation and position size rather than disappear from the analysis.
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Build a claim ledger
For every important statement, record the exact wording, document date, period, owner, and whether it is a reported fact, management estimate, analyst estimate, or your own assumption. This prevents a one-time pilot, an annualized run rate, or a non-GAAP metric from silently becoming a recurring fact in your model.
What financial metrics matter most for an AI company?
Start with cash generation and the cost of delivering each unit of intelligence, not headline revenue growth. AI economics can deteriorate when usage rises because inference, storage, support, and human review scale with activity.
Extract the core numbers
- Revenue growth by product, geography, and customer type.
- Gross margin and its trend, with cloud, model, chip, data, implementation, and support costs classified consistently.
- Operating cash flow, capital expenditure, free-cash-flow reconciliation, cash balance, debt maturities, and committed purchase obligations.
- Stock-based compensation, basic and diluted share counts, option overhang, and other sources of dilution.
- Customer concentration, largest-customer contract terms, renewal dates, and segment reporting.
- Research-and-development spending, sales and marketing efficiency, and the portion of costs tied to model training or infrastructure.
Reconcile management’s AI narrative to these numbers. A fast-growing AI segment can still destroy value if gross margin falls, cash conversion weakens, or the company must repeatedly issue shares to fund compute.
Separate recurring revenue from projects
Classify each contract as subscription, usage-based, consumption commitment, pilot, implementation, professional service, support, or one-time license. Ask when revenue is recognized and what remains to be delivered. A large contract announced during an earnings call is not equivalent to recognized, collectible, recurring revenue.
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Test retention and concentration
Request gross and net revenue retention, logo churn, renewal cohorts, expansion rates, average contract value, and sales-cycle length. Check whether the metrics include pilots, free users, acquired customers, or usage that has not yet converted to paid contracts. Compare the top-customer share with the renewal calendar: concentration is more dangerous when a few contracts expire in the same period.
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Model unit economics explicitly
For each customer or workload, estimate revenue less model inference, cloud and chip charges, data licensing, human review, implementation labor, support, and payment costs. Keep reported and estimated inputs in separate columns. If the company does not disclose a cost, use a range and show how the conclusion changes when the cost is higher.
How should I stress-test an AI company’s forecast?
Build a sensitivity case instead of accepting a single management forecast. At minimum, vary adoption speed, realized price, renewal rate, model cost, and funding timing.
Use five practical shocks
- Slower adoption: delay new customer starts and reduce conversion from pilots.
- Lower pricing: assume competition or open models force a lower price per task or token.
- Higher model costs: increase inference, hosting, storage, and data expenses, and delay expected hardware savings.
- Delayed renewals: push renewal cash receipts into a later quarter while keeping support costs running.
- Funding friction: reduce access to new debt or equity and test whether cash lasts through the next major infrastructure commitment.
Show the effect on gross margin, operating cash flow, runway, dilution, and covenant headroom. A high growth rate does not offset weak cash conversion, fragile supply access, or unsupported AI claims.
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Draw the complete model stack, from data entering the system to the output delivered to a customer. This reveals where intellectual-property disputes, outages, price increases, or a supplier’s strategic change could interrupt revenue.
Map the model stack
- Training and fine-tuning data: source, license, consent, geographic limits, retention, and deletion rights.
- Model ownership: internally trained weights, licensed models, open-source components, or a combination.
- Retrieval and application layer: indexing, prompting, guardrails, human review, and customer-specific data isolation.
- Inference infrastructure: cloud regions, chips, capacity reservations, latency, energy use, and fallback providers.
- Monitoring and release process: version control, rollback, evaluation gates, incident response, and customer notification.
Ask for production evidence
Request evaluation results that match the actual use case, including error rates, reliability limits, latency, abstention behavior, and performance by relevant customer segment. Ask how tests were designed, whether they were independent, and when they were last run. Red-team findings, security controls, incident history, and corrective actions are more informative than a single benchmark score.
A benchmark can show capability under a particular test but cannot prove production economics, legal data rights, customer value, or resilience under changing inputs. Record the test population, version, date, and threshold before comparing results.
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Check exit and fallback options
Determine whether the company can switch models or infrastructure without rewriting the product, retraining customers, or violating licenses. Review minimum-spend commitments, exclusivity, termination rights, portability of fine-tuned weights, and the time required to qualify a substitute. A technically impressive product with no practical fallback may carry more supplier risk than its marketing suggests.
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Use the trustworthiness characteristics in NIST’s AI RMF as an evidence checklist: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed.
Governance questions
- Which board committee and executive own AI risk, and how often do they receive documented reports?
- Are model approvals, change controls, access rights, and incident escalation written down and tested?
- Can the company show evaluation results, exceptions, remediation owners, and deadlines rather than policy statements alone?
- Are customers told when a model changes, when human review is required, and what service levels apply?
NIST adoption is not a certification. A company can claim alignment while operating weak controls, so verify implementation and testing.
Legal and regulatory exposure
- Data-protection obligations, cross-border transfers, retention, deletion, and customer instructions.
- Intellectual-property ownership, training-data licenses, open-source obligations, and indemnities for generated output.
- Sector rules for health, finance, employment, education, critical infrastructure, or public services.
- Cybersecurity, model misuse, prompt injection, data leakage, fraud, and business-continuity exposure.
- Workforce effects, contractor practices, and who bears liability in customer contracts.
The U.S. Treasury’s December 19, 2024 report on AI in financial services highlights privacy, bias, and third-party-provider risks. Even outside finance, those categories are useful prompts for contract and control reviews.
How can I spot AI-washing and inconsistent disclosure?
Treat every marketing claim as a hypothesis that must survive comparison across documents. The SEC Investor Advisory Committee’s December 4, 2025 recommendation describes uneven issuer practices and difficulty comparing AI statements.
Run a consistency check
- Copy the company’s definition of AI from filings, product documentation, customer materials, and earnings calls.
- Mark claims that lack a quantified outcome, reasonable basis, named customer evidence, or a defined measurement period.
- Compare claimed benefits with gross-margin trends, support costs, implementation labor, and renewal data.
- Look for risk discussions that are absent from marketing but appear in legal filings, or promises that change wording between quarters.
- Ask management to reconcile contradictions in writing and preserve the response in your claim ledger.
AI disclosure prevalence is not proof of quality. A 2024 Skadden analysis reported that more than 40% of S&P 500 companies mentioned AI in fiscal-2023 annual reports and more than 40% cited AI on second-quarter 2024 earnings calls. Those figures describe how often companies discussed AI, not whether the businesses were attractive or the statements accurate.
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How should I compare two AI companies?
Compare like-for-like business models using the same definitions, periods, and evidence standards. Do not compare a usage-based infrastructure supplier with a pilot-heavy application startup using only revenue growth.
| Comparison axis | Questions to answer |
|---|---|
| Growth quality and durability | Is growth recurring, expanding within existing accounts, and supported by renewals rather than pilots or acquisitions? |
| Gross-margin trajectory | Are inference, cloud, chip, data, implementation, and support costs captured consistently, and is margin improving or deteriorating? |
| Cash runway and funding | How long does cash last under base and stress cases, and what infrastructure commitments arrive before break-even? |
| Dilution | How do stock-based compensation, options, convertibles, and new financing affect ownership? |
| Customer concentration | What share of revenue comes from the largest customers, and when do those contracts renew? |
| Compute and supplier dependence | Can the company obtain capacity, switch providers, and absorb price or availability shocks? |
| Model and data rights | Who owns weights and training data, and what licenses, indemnities, or restrictions apply? |
| Evaluation evidence | Are use-case tests current, reproducible, and tied to customer outcomes rather than headline benchmarks? |
| Security and privacy controls | Are controls tested, incidents disclosed, and customer data isolated and deleted as promised? |
| Regulatory and litigation exposure | Which rules, claims, contracts, or jurisdictions could materially restrict deployment or raise costs? |
| Competitive moat | Does the company have proprietary data rights, workflow integration, distribution, switching costs, or another defensible advantage? |
| Valuation sensitivity | What happens to value if adoption slows, prices fall, model costs rise, or renewals slip? |
For a private company, label unaudited or incomplete data in every comparison. If one company refuses a metric that the other reports, treat the asymmetry as a diligence finding rather than filling the gap with a guess.
How do I turn the research into an investment decision?
Convert evidence into explicit conditions, not a vague impression that the company is “leading in AI.” Set a base case, downside case, and evidence threshold for changing your mind.
Write the base and downside cases
- Base case: specify customer additions, renewal rate, realized price, gross margin, infrastructure spending, cash burn, and dilution assumptions.
- Downside case: use slower adoption, lower pricing, higher model costs, delayed renewals, and reduced financing access.
- Upside case: include only improvements supported by a documented product release, signed contract, measured retention change, or cost reduction.
Attach a source and date to each assumption. Keep estimates separate from reported figures and update the model when filings, contracts, model versions, or regulations change.
Use a decision checklist
- I can explain what customers buy and why AI is economically necessary.
- Revenue is separated into recurring, usage-based, pilot, implementation, and one-time components.
- Gross margin includes the material costs of inference, data, infrastructure, labor, and support.
- Cash runway survives the downside case without assuming effortless financing.
- Model, data, and supplier rights are documented, with a practical fallback.
- Evaluation, security, privacy, and incident evidence is current and relevant to the use case.
- Board and management oversight is documented and controls are tested.
- AI claims are consistent across filings, customer evidence, technical material, and calls.
- Valuation still offers an acceptable return after slower adoption and higher costs.
If several boxes remain unchecked, the appropriate conclusion may be “insufficient evidence,” not an automatic buy or sell. Position size, price paid, and willingness to wait for disclosure are part of the decision.
What mistakes should investors avoid?
- Counting mentions: the presence of “AI” in a filing does not establish product quality, revenue, or a moat.
- Using one benchmark: benchmark scores do not establish production reliability, cost, legal rights, or customer value.
- Annualizing a pilot: an announced contract or run rate may not be recurring, collectible, or profitable.
- Ignoring dilution: stock-based compensation and repeated equity raises can overwhelm operating growth.
- Assuming framework alignment is certification: voluntary risk frameworks organize questions; they do not guarantee safety.
- Mixing incomparable models: compare subscription, usage, services, infrastructure, and consumer businesses on appropriate metrics.
- Hiding uncertainty: missing private-company data, unaudited figures, and changing metric definitions should be visible in the valuation.
- Repeating stale facts: products, suppliers, regulations, prices, and valuations change; date every company-specific number.
Frequently Asked Questions
What should I ask an AI startup’s founders in the first meeting?
Ask which customer problem would remain if the AI feature were removed, what percentage of revenue is already paid and recurring, and which costs rise with usage. Request the latest renewal cohort, largest customer contract, model and data licenses, and the most serious production incident, including the fix and date.
Can an open-source model create a defensible AI business?
Open-source availability alone is not a moat. Defensibility may instead come from legally usable proprietary data, workflow integration, distribution, switching costs, fine-tuning rights, or reliable operations that customers cannot easily reproduce.
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How often should I update an AI-company investment thesis?
Review it whenever the company files new financial statements, changes its model or infrastructure supplier, signs a material contract, reports an incident, or faces a significant regulatory development. At minimum, refresh public-company assumptions each reporting period and re-date every forecast input.
What evidence would disprove an AI-washing claim?
A credible rebuttal would connect a specific AI capability to measured customer outcomes, recurring revenue, consistent definitions across filings and product materials, and disclosed costs that support the margin story. Independent or reproducible evaluations and named customer references strengthen that evidence.
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