AI and the Circle of Competence

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  Stocks and Investing on Wed, Oct 28th, 2026 by The Motley Fool
      Locale: UNITED STATES

The Circle of Competence and the AI Gap

A central tenet of Buffett's philosophy is the "circle of competence"—the idea that an investor should only allocate capital to businesses they fundamentally understand. For many value investors, AI presents a significant challenge because the technology evolves faster than traditional business models can be analyzed. The complexity of Large Language Models (LLMs), neural networks, and semiconductor architectures often falls outside the traditional scope of value investing, which typically prioritizes predictable cash flows over disruptive potential.

However, the focus is not necessarily on the technology itself, but on the economic outcome of that technology. The critical question is not how a specific AI algorithm works, but whether the implementation of that AI provides a company with a durable competitive advantage—a "moat"—that allows it to maintain pricing power over the long term.

The Distinction Between AI Tools and AI Products

An analysis of the current AI landscape suggests a necessary distinction between companies that produce AI as a standalone product and companies that use AI as a tool to enhance existing operations.

1. AI as a Productivity Tool: For established enterprises, AI can be a powerful catalyst for margin expansion. By automating routine tasks and optimizing supply chains, a company can lower its cost structure. From a value perspective, this is a positive development provided the cost of implementing the AI does not outweigh the efficiency gains.

1. AI as a Disruptive Product: Companies whose entire value proposition is based on AI are subject to higher volatility. In a field where a new breakthrough can render a previous model obsolete in a matter of months, the "moat" is fragile. The risk of rapid obsolescence makes it difficult to calculate a reliable terminal value for these stocks.

The Peril of Overvaluation

One of the primary concerns for value-driven investors in the current market is the gap between price and value. The AI boom has led to a surge in Price-to-Earnings (P/E) ratios that often assume perfect execution and exponential growth for decades.

Buffett's historical caution regarding bubbles stems from the understanding that even a great company can be a poor investment if paid too high a price. The extrapolation of current AI growth rates into the future often ignores the inevitable plateau of adoption or the emergence of unforeseen regulatory hurdles. For the disciplined investor, the focus remains on the "margin of safety"—buying an asset at a significant discount to its intrinsic value to protect against downside risk.

Seeking the Infrastructure Moat

While many avoid the volatility of software-as-a-service (SaaS) AI companies, there is a logical argument for focusing on the infrastructure that enables AI. This mirrors the strategy of investing in the "toll bridges" of the economy. Companies that control the essential hardware, data centers, or energy sources required to power AI may possess more tangible moats than those building the applications themselves. These infrastructure providers often benefit from high barriers to entry and long-term contracts, providing the predictability that value investors crave.

Conclusion

Integrating AI into a value-based portfolio requires a shift in perspective. Rather than chasing the latest trend, the objective is to identify businesses where AI strengthens an existing competitive advantage without compromising the company's financial stability. The intersection of AI and value investing is found not in the excitement of the new, but in the disciplined analysis of how the new creates lasting, sustainable value.