Morgan Stanley
Wealth Management

AI Disruption Concerns Do Not Compute

Mar 11, 2026

Investors have been punishing software and data stocks on worries about AI disruption, but those fears don’t match the fundamentals.

Author
Dan Skelly
Author
Kevin Demers

Key Takeaways

  • Investor concerns about AI disruption have spurred heightened volatility in industries that sell software or data.
  • However, these fears may be outrunning evidence: “AI-disrupted” company profits are holding up and trusted, proprietary-data business models will likely be hard to replace.
  • A more probable outcome is widespread AI adoption, with companies paying for tools that make people work faster and more efficiently, rather than AI putting its major customers out of business.

It shouldn’t shock anyone that investors lately have been looking beyond some of the biggest tech stocks and into smaller companies, “cyclical” stocks that are sensitive to the performance of the economy, and markets outside the U.S. That’s often what happens as an economic and market cycle matures.

 

What has been stunning is the recent selling and elevated volatility of equities in industries viewed as vulnerable to disruption from generative AI. As recently as a few weeks ago, the market was acting as if entire swaths of software and data-driven businesses were about to be swept away. And even after a notable bounce in parts of software, trading has remained volatile, amid ongoing debate about what AI means for incumbents.

AI Market Dissonance

The odd part: Investors have been punishing many software and data stocks on the assumption that GenAI, with its rapidly improving capabilities, will soon replace those businesses. At the same time, investors are showing increasing skepticism about the AI build-out itself, especially around who will pay for it and whether massive capital investments will yield the hoped-for returns.

 

Put simply, the market is pricing AI as both an unstoppable trend and an uncertain one. Those two ideas don’t fit neatly together, and when markets try to hold both of them at once, prices can swing too far.

A Familiar Pattern

This type of market dissonance is not new. Textbooks talk about “efficient” markets, but real markets frequently wobble, especially early in a new innovation wave, when past data offers limited guidance.

 

Here’s a useful example: A few years ago, investors were simultaneously optimistic about both packaged food companies and the rise of GLP‑1 weight-loss drugs. But if weight-loss drugs really became mainstream, it stood to reason that selling lots of snack foods would become more difficult. Eventually, the market stopped trying to believe both things at once, and prices reflected the change: Since May 2023, when a large trial showed significant health benefits from GLP-1 drugs, the packaged food sector has declined 21%, while biopharma is up 50%.

Fear Outruns Fundamentals

We think something similar may be happening now in parts of the AI story, and that recent AI disruption worries may be overblown. Here’s why:

  1. 1
    Limited earnings impact so far:

    Some investors have been trading as if AI disruption is already hitting company results. But for many of the businesses labeled “AI-disrupted,” the financial damage isn’t obvious. Profits across the group have generally held up, and near-term earnings expectations haven’t collapsed. That doesn’t mean disruption can’t happen, but the market may be racing to a worst-case conclusion ahead of the evidence.
  2. 2
    Trust still matters:

    Another oversimplified fear is that AI will simply “scrape the internet” and make paid data businesses worthless. In reality, some of the most valuable information is private, regulated or shared only with permission. For businesses in areas like finance or health care, a quick but wrong answer can create real harm—and real legal risk. That changes how fast AI, with its “hallucinations” that can produce false or inaccurate results, can replace trusted services.

Base Case: Adoption Over Displacement

We think the more likely outcome is widespread AI adoption—companies paying to use AI to work faster—rather than AI putting major customers out of business. In that kind of world, investors may find opportunities in businesses with trust, distribution and hard-to-replace data, such as:

  1. 1
    Software:

    Some software companies will win and some will lose, but the blanket “software-is-doomed” view looks too extreme. One of the clearest uses of AI so far is helping people write and maintain code faster, which could actually improve efficiency inside software companies.
  2. 2
    Wealth management:

    These firms run on trust, relationships and regulated client data. AI may help financial advisors work faster, but it’s unlikely to replace the human and compliance layer that clients rely on.
  3. 3
    Health and financial data:

    In areas like clinical trials, insurance and other privacy-sensitive data businesses, accuracy and accountability matter. While large-language models are good at aggregating and analyzing publicly available data, companies that own the distribution around proprietary data are unlikely to give it away.

When uncertainty is high, markets can swing too far because investors react to compelling stories and fears, as they are doing now. Over time, however, equity prices usually move back toward what companies actually earn, rather than what’s in the news. Our base case remains that corporate America and AI tools will forge a collaborative relationship defined by chronic interdependence—not targeted obsolescence.

 

To learn more, ask your Morgan Stanley Financial Advisor for a copy of the March 2026 issue of On the Markets: Exploiting Rotation’s Excesses from the Global Investment Office.

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