These Industry ETFs Could Be Vulnerable to AI Disruption

iShares Expanded Tech-Software Sector ETF

IGV

+0.44%

One of the most obvious candidates when thinking about AI disruption is IGV. When you look at the portfolio, it reads like a who’s who of enterprise software.

Some of the more notable names include Salesforce, Oracle, Palo Alto Networks, Intuit, Adobe, CrowdStrike, ServiceNow, Autodesk, and Fortinet, among many others.

Several of these companies were explicitly mentioned in the Citrini memo, and a number of them have already seen meaningful price pressure as investors reassess the long-term outlook for the software-as-a-service (SaaS) model.

What makes this group uniquely exposed is the nature of the products they sell. A large portion of enterprise software revolves around organizing data, managing workflows, and providing analytical insights. Those are exactly the kinds of tasks that modern AI systems are designed to automate or compress dramatically.

That does not mean these companies are going bankrupt. Far from it. Most have enormous installed customer bases and deeply embedded products. The issue is valuation.

Many software-as-a-service businesses have been priced for years on the assumption that they can maintain exceptionally high margins and strong free cash flow growth well into the future. If AI tools begin to compress pricing power or reduce the need for multiple software layers, investors may no longer be willing to pay the same premium multiples.

That said, IGV does have some built-in resilience. The fund’s two largest holdings are Microsoft and Palantir, which account for roughly 9.4% and 8.7% of the portfolio respectively. Both companies sit at the center of the AI ecosystem rather than being disrupted by it.

Microsoft’s position is reinforced by its partnership and investment in OpenAI as well as its integration of AI tools across Azure and its productivity suite. Palantir, meanwhile, has leaned heavily into AI-enabled data analytics platforms that are already being deployed across both government clients.

Even with those anchors, sentiment toward the software sector has clearly deteriorated. As of March 4, IGV is down 16.25% year to date. Over the same period, the S&P 500 has essentially gone nowhere, posting a price return of just 0.35%.

iShares U.S. Broker-Dealers & Securities Exchanges ETF

IAI

-1.1%

IAI is another interesting case, although the name is somewhat misleading. If you were just going by the label, you might assume the ETF mainly consists of brokerage firms such as Charles Schwab or Interactive Brokers, along with exchange operators like CME Group, Intercontinental Exchange, Nasdaq, and Cboe.

In reality, the ETF is much broader than that. Calling this a capital markets ETF would probably be more accurate. The two largest holdings are major U.S. investment banks, Goldman Sachs and Morgan Stanley.

I do not see those two being meaningfully disrupted by AI. If anything, the next wave of major AI companies going public will likely rely on these banks to underwrite their IPOs, generating substantial advisory and underwriting fees.

Where I do see potential risk is among some of the other holdings. The ETF has significant exposure to ratings agencies such as Moody’s and S&P Global, as well as index and benchmark providers like MSCI. S&P Global also plays a major role on the index side of the business.

Smaller holdings include FactSet and Morningstar, both of which provide financial data, analytics platforms, and research tools used by institutional investors, asset managers, and advisors.

These businesses sit directly within the kind of rent-seeking structure that the Citrini memo highlights. Their models rely on controlling access to financial information, benchmarks, and analytics.

Firms pay extremely high subscription fees for market data terminals, index licensing, ratings reports, and proprietary analytics platforms. That has historically been a very profitable business with strong margins. AI has the potential to challenge that structure.

Large language models and open-source data pipelines can already process vast amounts of financial filings, earnings transcripts, and market data. In theory, firms could begin building internal tools that replicate parts of what these data providers offer today.

Even open-source alternatives could chip away at the value of proprietary datasets. When you consider how expensive many of these data licenses are, even modest competition could lead to meaningful margin compression.

None of this means these companies disappear overnight. They have long-standing institutional relationships, regulatory recognition, and decades of accumulated data.

But no moat is permanent. If AI tools make it easier for firms to build their own analytics, credit models, or index frameworks, parts of the traditional financial data ecosystem could start to erode.

Recent performance hints at that dynamic. While brokerages, exchanges, and investment banks have generally held up well, several of the data and analytics providers have struggled. As of March 4, IAI is down 4.68% year to date, even as the S&P 500 sits essentially flat over the same period.

Please note this article is for information purposes only and does not in any way constitute investment advice. It is essential that you seek advice from a registered financial professional prior to making any investment decision.