The AI gold rush is real — but great companies don’t need to mine it

Artificial intelligence now dominates the investment conversation. It is front and center in headlines, company narratives, and — most visibly — in capital flows. In 2025, AI and machine-learning deals accounted for nearly two-thirds of all U.S. venture capital dollars — up from roughly 10% a decade earlier.

That level of concentration reflects a real and powerful shift. AI represents a profound technological transformation, one likely to reshape productivity, cost structures, and competitive dynamics across the global economy. Many of the most compelling growth companies today are directly enabling — or benefiting from — this transition, and several may emerge as category-defining public companies of the next decade.

But the intensity of the market’s focus raises a more subtle question for investors: does a company need to be an AI company to be a great company?

Public markets offer a clear answer. Some of the strongest, most valuable companies in the world are explicitly not AI businesses. Their success is driven by durable competitive advantages, attractive unit economics, disciplined execution, and the ability to compound through cycles — not by proximity to a single technology narrative.

Private markets, however, do not always price this distinction cleanly. As attention concentrates around AI, valuation dispersion has widened. Perceived AI category leaders can raise multiple rounds in rapid succession, often at successively higher prices, reinforcing momentum and further concentrating capital.

At the same time, many high-quality non-AI businesses face a very different funding environment. Despite strong fundamentals and large addressable markets, they may attract less investor demand simply because they lack an explicit AI story.

For disciplined investors, this divergence creates both risk and opportunity.

The case is not to be skeptical of AI — quite the opposite. Investors should consider opportunities in derisked AI businesses where valuations align with long-term underwriting assumptions. Equal weight should be given to non-AI companies where fundamentals remain strong and market dynamics have become more favorable as capital concentrates elsewhere.

This pattern is familiar. Periods of technological transformation often coincide with capital over-concentration, valuation compression outside the favored theme, and eventual normalization. The lesson is not that transformative technologies fail to deliver value — it is that technology alone is never sufficient.

AI adoption is moving faster than any prior platform shift, and we remain early in the cycle. Some eventual category leaders may not yet exist, while others will face competition, commoditization, or changing economics over time.

In that environment, selectivity matters more than enthusiasm.

For long-term investors, the goal is not to build an “AI portfolio” or a “non-AI portfolio,” but to allocate capital where fundamentals, valuation, and durability intersect. That means leaning into AI where risk is appropriately priced — while recognizing that many of tomorrow’s great public companies will emerge from sectors and business models that attract far less attention today.

AI is reshaping the investment landscape. But seeing the full picture requires remembering that great companies have always been defined by more than a single technology wave.

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