Investing in an AI or autonomous-vehicle (AV) startup can produce a total loss, and you may never be able to resell the investment. The technology may fail, regulation may delay deployment, customers may not pay enough to support the business, and the company may need repeated financing that dilutes earlier investors. A credible decision therefore depends less on an impressive demo than on verifiable performance, safety evidence, legal access to the offering, commercial economics, and enough cash to reach the next milestone.
AI and AV are related but not interchangeable risks. An AI software company may fail because its model is inaccurate, expensive to run, dependent on another provider, or marketed with unsupported claims. An AV company faces those issues plus vehicle engineering, physical safety, insurance, operational constraints, public-road permissions, and long development cycles. The checks below are designed for U.S.-focused investor education; securities, liability, insurance, and deployment rules differ elsewhere.
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Why the technology label is not an investment thesis
“AI” describes a capability, not a durable business advantage. Ask exactly what the model does inside the product, which performance measure matters to the customer, and what happens when the system is wrong. A company that merely calls an ordinary software feature “AI” may have no technical moat, while a genuinely capable model can still be unprofitable if inference, data, support, or compliance costs exceed revenue.
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Do not treat a pitch deck, a short demonstration, or a famous customer logo as independent proof. Request reproducible metrics, the conditions under which they were measured, known failure cases, and evidence that the product is deployed rather than merely promised.
First risk: early-stage securities may be impossible to exit
The SEC’s June 7, 2024 pre-IPO investor alert warns that an early-stage company may never succeed or go public, and that a resale market may never develop. You could be unable to sell even if the company continues operating. Read the alert at Investor.gov’s pre-IPO guidance.
Before evaluating the startup, identify what you are actually buying:
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- Security type: common stock, preferred stock, a convertible note, a SAFE, a limited-partnership interest, or another instrument has different rights and risks.
- Offering status: determine whether the offering is registered or relies on an exemption, and obtain the governing documents rather than relying on a salesperson’s description.
- Transfer restrictions: find out whether resale requires company consent, an exemption, a broker, or a future liquidity event.
- Fees and pricing: ask about upfront charges, commissions, markups, carried interest, conversion discounts, valuation caps, liquidation preferences, and other economic terms.
- Intermediary identity: verify the people and platform involved through appropriate regulator records and investigate disciplinary histories.
For offerings under Regulation D, eligibility and verification requirements depend on the specific rule and offering. The SEC’s March 21, 2025 explainer notes that a self-certification checkbox alone is not sufficient in situations requiring verification; review the current requirements at the SEC’s Regulation D guidance. An exemption does not make an investment safe, liquid, or suitable.
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AI-specific diligence: test the claim, not the slogan
Map the model to the product
Ask for a plain-language architecture diagram. Which functions use a model? Is it trained in-house, fine-tuned from a third-party model, or accessed through an application programming interface? What data can the company legally use? Which parts of the result are automated and which require human review? A startup that cannot answer these questions may not control the capability it is selling.
Demand measurable and reproducible evidence
Request the evaluation set, baseline comparison, date of testing, error rates, confidence intervals where relevant, and examples of failures. Separate laboratory results from production performance. Ask how the metric changes with new users, unusual inputs, language or geographic shifts, adversarial prompts, outages, and model updates. “More accurate” is incomplete unless the company defines the task, benchmark, and comparison.
Check dependencies and rights
External model providers, cloud platforms, specialized chips, licensed datasets, and open-source components can create concentration, pricing, continuity, and intellectual-property risk. Determine whether a supplier can change its terms, restrict a use case, or suspend access. Review ownership of training data, customer data, model outputs, source code, patents, and employee-created inventions. A technical demonstration is not evidence that the startup owns the inputs or can continue operating at the quoted cost.
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For high-impact uses, ask about human escalation, audit logs, access controls, privacy, cybersecurity, incident response, and the process for correcting harmful outputs. A company should be able to explain who bears the cost when its system makes a consequential error and whether contracts limit or transfer that liability.
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AI-washing and investment fraud
A joint January 25, 2024 alert from the SEC, NASAA, and FINRA warns about unregistered platforms, guaranteed or unusually high returns with little risk, false claims about public-company AI products, and deepfake audio or video used to impersonate executives. The alert is investor education, not a Commission rule; read it at Investor.gov’s artificial-intelligence fraud alert.
Gary Gensler, SEC Chair at the time, said in remarks dated March 18, 2024: “Public companies should make sure they have a reasonable basis for the claims they make and yes, the particular risks they face about their AI use, and investors should be told that basis.” He also said, “AI washing, whether it’s by financial intermediaries such as investment advisers and broker dealers, or by companies raising money from the public, that AI washing may violate the securities laws.” Those comments are a warning to test claims, not a finding that every private startup or AI investment is unlawful. The remarks are available at the SEC transcript.
Investigate rather than automatically dismiss an offer when you see these signs:
- cold calls or social-media messages pressuring you to act immediately;
- an “imminent IPO” claim without verifiable filings or a credible timetable;
- comparisons to successful companies without explaining differences in product, stage, or valuation;
- unclear share ownership, missing cap-table information, or unexplained fees;
- promoters whose names, registration status, or disciplinary records are difficult to verify;
- guaranteed returns, claims of no downside, or requests to send funds to an unrelated account.
AV adds safety, regulation, and deployment risk
Know what “autonomous” means
NHTSA states that no vehicle currently available for sale in the United States is fully automated or “self-driving.” States allow limited public-road testing, research, and pilot programs, while NHTSA monitors safety through its Standing General Order. Driver-assistance features, supervised testing, restricted pilots, and consumer-available fully automated operation are different stages; do not treat one as proof of another. See NHTSA’s automated-vehicle safety overview.
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NHTSA reports 39,254 people killed in U.S. motor-vehicle crashes in 2024. That is an all-vehicle roadway fatality figure, not a count of deaths caused by autonomous vehicles, and it cannot be used as an AV safety rate.
Examine the operational design domain
Ask where the system is intended to operate: roads, weather, speeds, lighting, construction conditions, vehicle types, and geographic boundaries. Request disengagement and intervention data, incident reports, simulation methodology, edge-case testing, remote-assistance assumptions, and the safety case used to justify deployment. A system that works on mapped highways in favorable weather has not demonstrated general-purpose autonomy.
Separate current permissions from proposals
Regulatory authority is distributed among federal, state, and local bodies. Confirm the exact permit, exemption, reporting obligation, and operating geography. On December 20, 2024, NHTSA announced a proposed voluntary ADS-equipped Vehicle Safety, Transparency, and Evaluation Program (AV STEP). It was a proposal at that time, not a blanket authorization; check the agency’s current rulemaking status before relying on it. NHTSA Chief Counsel Adam Raviv said, “Safe, transparent, and responsible development is critical for this technology to be trusted by the public and reach its full potential.” The announcement is at NHTSA’s AV STEP release.
Price the consequences of failure
Review recall exposure, crash investigations, litigation, insurance availability, indemnities, and who pays for vehicle damage, injury, downtime, remote support, and maintenance. A commercial customer may require safety certification, contractual remedies, or proof of insurance before deployment. Public distrust after an incident can delay sales even when regulators permit continued testing.
A separate SEC-filed commercial-vehicle issuer has described evolving rules, state restrictions, possible liability and negative publicity after accidents, government scrutiny, and uncertainty about obtaining third-party hardware and software for higher autonomy as business risks. Those statements are that issuer’s disclosures, not a market-wide forecast; read the filing at the SEC-filed annual report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Runway, dilution, and unit economics
Technology risk becomes investment risk when the company runs out of cash before reaching a commercial milestone. Build a cash timeline from the latest financial statements: cash and equivalents, quarterly operating burn, debt maturities, committed funding, milestone payments, and the date management expects to raise again. Treat forecasts as management estimates and test what happens if certification, customer deployment, or revenue arrives later.
Aurora Innovation’s 2024 annual report illustrates the scale and duration issue for one AV issuer. It reported net losses of $748 million in fiscal 2024 and $796 million in fiscal 2023, and said losses could continue until driverless commercial operations scale, which may take longer than expected or may never occur. The report also cites large funding requirements, long vehicle-development lead times, the challenge of achieving better-than-human driving performance, evolving regulation, and the need to build public trust. These are Aurora-specific figures and disclosures, not averages for AI or AV startups. See Aurora Innovation’s 2024 annual report.
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For any startup, calculate how each new financing could affect you. A low share price may not be attractive if the company issues many more shares, converts notes at a discount, grants large employee options, or gives new investors senior liquidation preferences. For AV businesses, also model fleet utilization, vehicle purchase or retrofit costs, maintenance, mapping, remote operations, insurance, and the revenue retained after these costs. For AI software, examine inference and cloud costs, support labor, data acquisition, customer churn, and gross margin at realistic usage levels.
A practical due-diligence sequence
- Verify the offer. Obtain the private-placement memorandum or equivalent documents, subscription agreement, capitalization table, financial statements, fee schedule, and risk factors. Confirm the issuer and intermediary independently, not through links in an unsolicited message.
- Define the product. Write down the customer, use case, required inputs, output, deployment environment, and failure consequence. If the description cannot be made precise, pause the investment review.
- Validate technical evidence. Request dated test results, baselines, production metrics, limitations, update policy, third-party dependencies, and an explanation of what is proprietary.
- For AV, validate safety and operations. Map the operational design domain, test coverage, intervention assumptions, incident history, permits, insurance, maintenance plan, and responsibility for remote assistance.
- Test commercial economics. Confirm paying customers, contract duration, renewals or usage, implementation time, deployment cost, gross-margin path, and who pays when the system fails.
- Stress the financing plan. Model delayed milestones, lower pricing, higher compute or hardware costs, and another financing round. Read conversion, dilution, liquidation, and investor-rights provisions.
- Review governance and legal exposure. Check IP ownership, data rights, related-party transactions, executive history, cybersecurity controls, privacy obligations, litigation, regulatory correspondence, and consistency between promotional claims and formal disclosures.
- Decide the loss limit. Treat the investment as illiquid and capable of losing all value. Do not commit money needed for near-term expenses or assume a future IPO will provide an exit.
AI and AV risks compared
| Question | AI startup | AV startup |
|---|---|---|
| Core proof | Task-specific accuracy, reliability, cost, and production performance | Safety performance across a defined operating domain, plus vehicle and system integration |
| Main dependencies | Models, cloud, chips, data, licenses, and customer integrations | Those dependencies plus vehicles, sensors, maps, maintenance, remote operations, and insurance |
| Regulatory exposure | Varies by sector, data use, privacy, consumer-protection, and securities claims | Vehicle safety, state and local permissions, reporting, recalls, liability, and insurance |
| Typical commercialization constraint | Customer adoption, accuracy, retention, and compute economics | Long development cycles, safety validation, fleet deployment, public trust, and capital intensity |
| Evidence to request | Benchmarks, production telemetry, failure analysis, data rights, and dependency contracts | Operational design domain, disengagement and incident data, permits, safety case, fleet economics, and maintenance assumptions |
Questions that should have clear answers before you invest
- What exact security am I buying, under which registration or exemption, and when could I legally resell it?
- What independent evidence supports the company’s most important technical claim?
- What happens in the documented failure cases, and who pays for the consequence?
- Which supplier, license, dataset, customer, permit, or financing event could stop the business?
- How much cash remains, what is the current burn, and what milestone must be reached before the next raise?
- How much dilution, seniority, or fee burden could a future financing impose on my position?
- Can I verify the promoter, issuer, financial statements, permits, and customer claims from primary records?
Geography and evidence limits
The regulatory examples here are U.S.-focused. Securities exemptions, accredited-investor tests, AV operating authority, insurance rules, privacy law, and liability standards can differ by country, state, or municipality. A filed risk factor is an issuer’s description of what could affect its business, not proof that the event occurred or an independent probability estimate. Obtain current primary documents and qualified local advice for a specific transaction.
No regulator cited here provides a success rate, average capital requirement, typical valuation, or expected return for AI or AV startups as a group. Any decision should therefore rest on the particular offering’s documents and evidence, not on a sector-wide statistic or a technology buzzword.
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