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AI stocks are a theme, not a universally defined market sector. They overlap heavily with technology stocks, but an AI-focused fund may concentrate on companies tied to chips, data centers, or AI applications, while a broad technology fund can include many businesses with less direct AI exposure. To compare them, look past the label: check how exposure is defined, what the fund actually owns, how concentrated it is, and whether the underlying companies can turn AI investment into profitable business.
What counts as an AI stock?
There is no single market-wide definition of an “AI stock.” A company may develop AI models or applications, supply the chips and data-center infrastructure they require, or use AI in its operations. Those are different kinds of exposure, and a company’s involvement in AI does not by itself show how much revenue it earns from AI or whether that business is profitable.
Fund and index providers set their own criteria. For example, the March 30, 2026 SEC-filed VistaShares Artificial Intelligence Supercycle ETF summary prospectus defines an AI company as one that derives at least 50% of revenue from, or has at least 50% of assets invested in or devoted to, specified AI-related high-performance semiconductors, AI data centers, or AI-enabled applications. That threshold applies to this fund’s definition; it is not a standard used by all funds or investors.
By contrast, a broad technology fund or index generally follows its stated sector or index methodology. It can include large technology companies investing heavily in AI alongside businesses whose products and services are not primarily AI-related. The precise boundary depends on the product’s rules.
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How AI exposure differs from broad technology exposure
| What to compare | AI-themed stocks or funds | Broader technology exposure |
|---|---|---|
| Definition | Varies by company, index, or fund; may focus on AI developers, infrastructure suppliers, or adopters. | Follows a sector or index methodology and may include AI leaders as well as businesses with less direct AI exposure. |
| Business exposure | Can span semiconductors, data centers, and AI-enabled applications. | Can cover a wider range of technology products and services. |
| Concentration | May be concentrated in information technology, semiconductors, or a small number of companies. | Can also be top-heavy when weighted by market capitalization; “broad” does not necessarily mean evenly diversified. |
| Risks | Includes AI adoption, spending, competition, obsolescence, intellectual property, and regulatory risks, in addition to broader technology risks. | Includes competition, product cycles, regulation, and technology change; may also be affected by the same large AI-linked companies. |
| Valuation and performance | Must be compared using named securities or indexes, the same metric, and the same observation date and period. | Must be assessed on the same basis for a meaningful comparison; the available sources do not establish a current relative valuation or performance winner. |
| Fund construction | Check the index rules, holdings, fees, rebalancing schedule, and any active discretion. | Check those same details; a broad label alone does not describe a fund’s actual exposure. |
AI funds can be heavily weighted toward technology and chips
The VistaShares prospectus provides a dated example of how an AI-themed index can overlap with technology. As of March 13, 2026, the VistaShares Artificial Intelligence Supercycle Index had 89% exposure to information technology and 49% exposure to semiconductors and semiconductor equipment, according to the fund’s March 30, 2026 summary prospectus. These are sector and industry figures for that particular index on that date—not current figures for every AI fund.
Broad market-capitalization-weighted indexes can have meaningful AI-linked exposure too. A separate SEC-filed 2026 prospectus says a small group of mega-cap information-technology companies, many investing heavily in AI, had been a primary driver of broad stock-market gains in recent years and represented significant portions of some market-cap-weighted indexes. This means an investor may already own some AI-linked companies through a broad-market holding; the amount depends on the particular index or fund and its holdings.
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How to compare a stock or fund’s actual AI exposure
- Read the definition. For an ETF, find its prospectus and index methodology. Identify what qualifies as AI-related activity and whether the rule measures revenue, assets, or another factor.
- Inspect the holdings and weights. Review the current holdings list, the largest positions, and any sector or industry breakdown. Compare overlapping holdings across products rather than assuming different labels mean different investments.
- Trace the business exposure. For an individual company, look at reported revenue sources, customer demand, AI-related spending, and disclosures about the role AI plays in its business. AI use or investment is not the same as AI-derived revenue.
- Assess the economics and risks. Consider profitability, capital requirements, competition, and whether the company can defend its position as products and technology change. For funds, also check fees, rebalancing, and whether management has discretion to change holdings.
- Make valuation or performance comparisons on a like-for-like basis. Name the companies or indexes, choose a comparable metric, and use the same date and measurement period. A label alone cannot show that one group is cheaper or more likely to outperform.
AI adoption does not guarantee returns for companies or investors
AI-related businesses may face substantial research and capital spending, uncertain profitability, competition, rapid product obsolescence, intellectual-property exposure, and legal, regulatory, or political changes. The VistaShares prospectus also warns that a failure or safety concern involving a prominent product could materially harm an issuer. These risks can affect companies at different points in the AI supply chain, not only developers of AI software.
Adoption and return figures also depend on what a study measures. In a recommendation approved December 4, 2025, the SEC Investor Advisory Committee cited a Boston Consulting Group report from October 2024 that found 22% of companies had moved beyond proof of concept toward integrating AI into core business functions or creating new revenue lines. The committee also cited MIT NANDA’s July 2025 report, which said 95% of organizations in its study were getting zero return on their GenAI investment. These are findings from different studies with different scopes; neither should be treated as a forecast for all AI uses, companies, or investments.
The same SEC committee recommendation cited a Deloitte and USC Marshall School of Business report from October 2024 stating that 60% of S&P 500 companies viewed AI as a material risk across areas including cybersecurity, competition, regulation, intellectual property, ethics, and reputation. That is a cited study result, not an SEC finding.
Technology and AI exposures can also move together. The SEC-filed 2026 prospectus on broad-market risk warns: “Significant downturns in the information technology sector, which includes companies that are investing heavily in AI research, development and infrastructure, could rapidly lead to widespread market weakness.” A portfolio that holds both a broad technology fund and an AI-themed fund may therefore have more overlap than the fund names suggest.
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Check claims before investing
AI-related investment promotion can itself be a risk. A joint SEC, NASAA, and FINRA investor alert, issued January 25, 2024, warns about false claims concerning public companies’ AI products and deepfake impersonation scams. The agencies advise investors to review company disclosures rather than relying on promotional claims. They also caution: “Be cautious about using AI-generated information to make investment decisions or to attempt to predict changes in the stock market’s direction or in the price of a security.”
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