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Research an AI company by checking whether its products are real and commercially used, whether customers and financial results support its claims, and whether the share price and risks make sense for the business. For a U.S. public company, begin with its SEC filings—not its AI branding, a promotional campaign, or an AI-generated stock summary. This process can help you assess evidence; it cannot establish that a stock is suitable for you or guarantee an outcome.
What does the company actually sell?
Start with the company’s own business description in its filings and investor disclosures. Work out what it sells, who pays for it, how it earns revenue, and how important AI is to that activity. A company that sells AI software or computing infrastructure is different from one that uses AI as a feature or to support an existing business.
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Separate what customers can buy and use now from research, pilots, partnerships, and plans for future products. A demonstration or announced partnership may be worth investigating, but neither alone establishes ongoing customer demand or revenue.
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- Role of AI: Decide whether AI is central to the product, one feature among many, an internal tool, or primarily a stated ambition.
- Evidence of demand: Look for disclosed commercial deployment, repeat purchases, customer concentration, and reported revenue—not just attention or launch announcements.
- Consistency: Compare management’s descriptions with the business and financial information in its filings.
FINRA’s general stock-research guidance recommends examining operations, demand, management, growth and profitability prospects, debt, industry conditions, and risks. It does not provide a formal classification system for AI companies; the categories above are a practical way to apply those broader questions.
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Which filings should you read?
For a U.S. public company, find its filings in the SEC’s EDGAR database. Read the latest annual Form 10-K and quarterly Form 10-Q, then compare them with earlier filings. FINRA describes the 10-K as an annual audited report and the 10-Q as a quarterly unaudited report; both provide business, risk, and financial information.
Start with the business and risk disclosures
Read the company description, risk factors, management’s discussion of results, financial statements, debt and liquidity information, and material legal proceedings. The goal is not to treat every disclosed risk as equally likely. Instead, ask how each risk connects to the company’s business and ability to deliver products, serve customers, and fund its operations.
Compare what changed over time
Check whether product descriptions, risk factors, revenue sources, costs, or management’s explanations have changed across reporting periods. For example, a growing emphasis on AI in promotional material is different from a corresponding change in reported business activity. A change in a filing is evidence to investigate, not proof by itself that the business has improved or deteriorated.
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How can you test the company’s AI claims?
The SEC’s investor alert advises investors to review company disclosures and promotional campaigns carefully, compare companies working on similar AI products when promotion dominates the message, and use EDGAR for public-company disclosures. It also warns that false claims about AI products or services can feature in pump-and-dump schemes.
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For each specific claim, ask what capability is being described, whether it is deployed and available to customers, and what the company says about its contribution to operations or revenue. Look for disclosed dependencies, limitations, and risks as well as claimed benefits. The SEC’s guidance is not a regulator-issued AI scorecard, and there is no single claim or metric that proves a durable competitive advantage.
- Do not treat the word “AI,” a demo, a partnership announcement, or a forecast as proof of adoption or commercial value.
- Check whether management gives specific, consistent descriptions of capabilities and customer use, and whether those descriptions are supported by filings or other independently checkable evidence.
- When a campaign makes strong claims but provides little company-specific evidence, compare the claim with disclosures and with other firms offering similar products.
In a statement last reviewed March 18, 2024, SEC Chair Gary Gensler said public companies should have a reasonable basis for claims they make about AI and should tell investors that basis, including particular risks they face from AI use. He also warned that “AI washing” by companies raising money from the public or by financial intermediaries may violate securities laws.
Be cautious with AI-generated investing summaries, too. The SEC’s January 25, 2024 investor alert says AI-generated investment information may be inaccurate, incomplete, outdated, or misleading, and may include faulty or fabricated output. Use such material, if at all, as a lead to check—not as a substitute for verifying the underlying sources.
How do you assess financial strength and valuation?
Track revenue, costs, earnings, cash generation, and financing needs across several reporting periods. Where the issuer reports them, examine spending or commitments related to computing capacity, data centers, research, and other infrastructure. Companies may present these items differently, so use the issuer’s actual statements and disclosures rather than assuming there is a universal AI accounting checklist.
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Common ratios can help frame questions, but none determines whether a stock is attractive. FINRA cautions that ratios should be compared with the broader market and relevant industry because typical levels vary substantially among industries.
| Measure | What it compares | How to use it cautiously |
|---|---|---|
| Earnings per share (EPS) | Earnings divided by shares. | One indication of current financial strength; interpret it alongside the company’s earnings trend and share count. |
| Price-to-earnings (P/E) | Share price relative to EPS. | Shows what investors pay per dollar of earnings. It may be difficult to use meaningfully when earnings are negative or unusually volatile. |
| Price-to-sales (P/S) | Market capitalization relative to revenue. | Can help compare companies that are not yet profitable, but it does not account for profit or show whether sales are generating cash. |
| Debt-to-equity (D/E) | FINRA describes this measure as total liabilities divided by shareholder equity. | Use it to consider leverage and debt financing. Interpret it in context, since business models and balance sheets differ. |
When comparing ratios, use the same reporting date and a relevant peer group. Explain what went into the calculation and avoid treating firms with substantially different business models as interchangeable. The reviewed guidance supplies ratio definitions and cautions, not an AI-specific valuation threshold or a market-wide AI return figure.
Which AI-related risks and oversight issues matter?
Read risk disclosures in the context of how the company operates. Depending on its products, data, suppliers, and customers, issues to investigate may include privacy, bias, cybersecurity, legal compliance, operational dependence, or reliance on third-party providers. A risk that matters to one business may be immaterial or inapplicable to another.
For public companies covered by the SEC cybersecurity rule, the SEC describes requirements for current disclosure of material cybersecurity incidents and periodic reporting about processes to assess, identify, and manage material cybersecurity risks, management’s role, and board oversight. The SEC rule page states that the rule became effective September 5, 2023. Applicability and compliance details depend on the issuer and should be checked against current SEC materials. Disclosure does not guarantee that a company is secure, and not every AI-related incident is necessarily material.
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Treasury’s discussion of AI risks in financial services specifically identifies privacy, bias, and third-party-provider risks in that sector, and recommends that financial firms review AI use cases for compliance before deployment and reevaluate them periodically. That sector-specific guidance should not be treated as proof that every company faces the same exposure or that the recommendations govern all AI businesses.
How should you compare AI companies?
Use the same evidence-based questions for each company. This comparison framework draws on general SEC and FINRA investor guidance and the sector-specific risk material above; it is not an official regulator scorecard.
| Comparison dimension | Evidence to examine | Question to answer |
|---|---|---|
| Business model and AI’s role | Products, services, revenue sources, and descriptions across filings. | How does the company make money, and how essential is AI to the product or service? |
| Customer demand and deployment | Disclosed customers, commercial use, repeat purchasing, and revenue. | Is there evidence of use and demand beyond demonstrations, pilots, or plans? |
| Financial position | Revenue growth, costs, earnings, cash generation, debt, and financing needs. | Can the company fund its operations and growth on terms it can sustain? |
| Valuation | Relevant financial measures and a defensible peer set. | What assumptions are reflected in the price, and are the comparisons like-for-like? |
| Dependencies | Disclosures about suppliers, platforms, and third-party providers. | Could a supplier, platform, or provider materially affect delivery or operations? |
| Risk and oversight | Operational, cybersecurity, privacy, regulatory, and legal disclosures. | Which risks are specific to this business, and how does the company describe its oversight? |
| Management’s AI claims | Specificity and consistency of descriptions across disclosures and other evidence. | Can you connect the claims to a product, customer use, or reported business result? |
What should you verify before acting on a stock promotion?
Check the security and the person or firm offering it rather than relying on a polished website, celebrity endorsement, or purported executive announcement. The SEC, NASAA, and FINRA investor alert points U.S. investors to Investor.gov registration checks and SEC resources for registered exchanges and alternative trading systems. Confirm that the security trades where the issuer says it does and that any intermediary is registered for the relevant activity and jurisdiction.
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Guaranteed returns, pressure to act quickly, unregistered sellers, and promotion-heavy campaigns are warning signs, not by themselves a definitive finding of fraud. Independently authenticate communications: the alert describes deepfake audio and video and impersonation scams, including material that may appear to come from an executive.
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What is different about a private or pre-IPO AI investment?
Public-company filing research does not transfer completely to private offerings. Public information may be limited, and access to an offer or its documents does not eliminate the need to examine the seller and the terms independently. The SEC warns that pre-IPO shares may be difficult or impossible to resell, the company may never go public, and an investor may lose the entire investment.
The SEC also warns that broad public solicitation may be unlawful depending on registration or an applicable exemption, and that emerging-technology themes such as AI are used to pitch offerings. Before considering an offer, verify:
- the seller’s registration and authority to offer the investment;
- the offering documents, fees, and markups;
- whether the seller actually owns the shares being offered;
- resale restrictions and the practical limits on finding a buyer; and
- any claimed IPO timetable through independent sources.
These checks do not determine whether a specific offer is fraudulent. Securities rules and access to private offerings vary by jurisdiction and issuer type, so U.S. guidance may not apply elsewhere.
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