These Top Tech Stocks Can Stand Up to AI Risks

Key Concepts

• Economic Moat: A structural competitive advantage that allows a company to maintain excess returns over its cost of capital for an extended period.
• Weighted Average Cost of Capital (WACC): The average rate a company is expected to pay to all its security holders to finance its assets.
• Return on Invested Capital (ROIC): A measure of how well a company uses its capital to generate profits.
• Infrastructure vs. App Layer: A framework distinguishing between foundational technologies (infrastructure) and user-facing software (app layer), where the latter is deemed more susceptible to AI disruption.
• Network Effects: A phenomenon where a product or service gains additional value as more people use it, serving as a highly resilient competitive advantage.
• Enterprise SaaS (Software as a Service): Subscription-based software models often characterized by high switching costs.

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1. Morningstar’s Analytical Framework

Morningstar evaluates companies based on their "moats," categorized as None, Narrow, or Wide.

• Narrow Moat: High confidence in excess returns for 0–10 years.
• Wide Moat: High confidence in excess returns for 10–20 years.
• Methodology: Analysts conducted an expedited "moat committee" review of 132 companies identified as being at risk from AI. The process involved quantitative screening combined with qualitative analyst intuition to determine if AI would erode a company's competitive advantage.

2. Key Findings and Research Results

• Downgrades: Out of 132 companies reviewed, 40 received moat downgrades. Eric Compton noted this was a higher number than initially anticipated, reflecting a rigorous, healthy debate regarding the changing technological landscape.
• The "AI-Resilient" Sectors:
  • Cybersecurity: Viewed as part of the "infrastructure layer." AI is expected to increase demand for cybersecurity because nefarious actors will use AI agents to automate and scale cyberattacks.
  • Design Software: Resilient due to "workflow complexity." These tools require deep domain expertise and complex back-end integrations that are difficult for Large Language Models (LLMs) to replicate.
  • Financial Infrastructure/Data: Companies like credit rating agencies and payment networks rely on regulatory status and network effects rather than commoditized data. AI does not threaten the fundamental utility of these networks.

3. Real-World Applications and Examples

• Cybersecurity: Cloudflare and CrowdStrike were highlighted as top picks. Both were upgraded to "Wide Moat" status, as their services are essential infrastructure for the internet and business operations.
• Design Software: Bentley Systems and DALT were mentioned as potentially undervalued. Synopsys and Cadence (EDA firms) are expected to see increased demand as the need for specialized semiconductor chip design grows.
• Financials: Moody’s, S&P, and FICO were identified as resilient, undervalued picks. Their competitive advantage is rooted in their position within the financial regulatory apparatus, which AI cannot easily disrupt.

4. Strategic Perspectives on Enterprise SaaS

While Morningstar downgraded several Enterprise SaaS firms from "Wide" to "Narrow" moat, Compton argues that the market sell-off has been an overreaction.

• The Argument: The market is currently pricing these stocks as if they will face zero or negative growth.
• Evidence: High switching costs remain a barrier for customers. Most clients prefer that their current incumbents (e.g., Salesforce, Oracle) integrate AI functionality into existing platforms rather than migrating to unproven, new AI-native competitors.

5. Notable Quotes

• "AI is not a universal destroyer." — Ivana Hampton, summarizing the Morningstar analyst consensus.
• "The power of the network was never about the technology per se. It was about coordinating all the players in that network." — Eric Compton, regarding why financial networks are AI-resilient.
• "You don't need heroic assumptions for [Enterprise SaaS] valuations to do better from here." — Eric Compton, suggesting that current market pessimism is disconnected from the reality of customer retention and switching costs.

6. Synthesis and Conclusion

The primary takeaway for investors is to be selective rather than reactive. The market has treated many software and tech-adjacent stocks with a "correlation of one," selling them off indiscriminately. Morningstar suggests that investors look for companies with:

1. Infrastructure-level positioning (Cybersecurity).
2. High workflow complexity (Design software).
3. Strong network effects (Financial data/ratings).

While AI poses a legitimate threat to simple, app-layer software, many incumbents possess deep-rooted competitive advantages that remain intact. Investors should look for companies where the market has baked in "zero-growth" scenarios, as these often represent the best risk-adjusted opportunities.