Artem Milinchuk, Founder and Head of Strategy of FarmTogether.
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The conversation around AI has evolved quickly over the past year. What began as a debate over whether AI-related equities had become overvalued has increasingly shifted toward a different concern: concentration risk.
Recent commentary from J.P. Morgan Asset Management, along with research from firms including RBC Wealth Management, Apollo and S&P Dow Jones Indices, reflects a growing focus on how AI has reshaped traditional equity portfolios. As a small number of companies have grown to represent an increasingly large share of major equity indices, the question has become whether portfolios remain as diversified as they appear.
AI Has Changed Portfolio Concentration
The numbers are well documented. RBC Wealth Management estimates that more than $40 of every $100 invested in a market-cap-weighted S&P 500 index fund is now allocated to just 10 companies, with NVIDIA alone representing nearly 8% of the index. At the same time, the cap-weighted S&P 500 trades at a meaningful premium to its equal-weighted counterpart, while hyperscaler AI capital expenditures have reached levels that some analysts compare to previous infrastructure investment cycles.
None of these observations establish that AI-related equities are overvalued. They do, however, illustrate how concentrated traditional equity portfolios have become.
The Industry’s Response: Equal Weighting
As concentration has increased, equal weighting has become a widely discussed approach for reducing exposure to the market’s largest companies.
Research from S&P Dow Jones Indices supports that approach (linked above). Following previous periods of elevated concentration—including the aftermath of the technology bubble—the S&P 500 Equal Weight Index has historically outperformed its market-cap-weighted counterpart. Their research also finds that concentration has historically tended to mean-revert across most sectors, supporting the case for reducing exposure to the market’s largest holdings.
Does Equal Weighting Solve Today’s Concentration Risk?
Equal weighting is a well-supported response to growing position concentration. But position concentration may not be the only form of concentration worth considering.
If today’s concentration is being driven not only by the growing size of a handful of companies, but also by a common investment theme—AI—does reducing position sizes fully diversify the underlying sources of portfolio returns?
The AI investment cycle extends beyond the companies at the center of today’s concentration debate. As investment in AI infrastructure accelerates, its effects are increasingly felt across a much broader set of businesses. That raises the possibility that reducing exposure to the market’s largest companies may not, on its own, meaningfully diversify exposure to the broader investment cycle.
An equal-weight portfolio remains invested entirely in U.S. large-cap equities. It continues to be influenced by the same macroeconomic environment, the same interest rate cycle, the same corporate earnings backdrop and, increasingly, the same AI-driven capital expenditure cycle. While exposure to individual companies is reduced, exposure to the broader economic forces shaping those companies may be less meaningfully changed.
That doesn’t diminish the case for equal weighting. It simply suggests that today’s discussion around concentration risk may need to extend beyond portfolio weights alone.
Beyond Position Concentration
Concentration risk extends beyond index construction. A portfolio can hold a large number of securities and still remain heavily exposed to the same broader market themes.
This points to a broader question worth asking of any diversification strategy: Does spreading exposure across more companies necessarily mean a portfolio is meaningfully less exposed to the forces affecting those companies?
One way to approach that question is to look outside public equities altogether. Real assets are one place this shows up. Farmland is a useful illustration, not because it is a solution to AI-driven concentration, but because it makes the distinction between position concentration and thematic concentration concrete.
Farmland returns are generated through a combination of crop revenue, cash rents and land appreciation. Those returns are influenced by factors such as global food demand, water availability, local growing conditions and commodity prices rather than the technology investment cycle currently shaping many AI-linked equities. The underlying drivers of return are simply different.
Historical performance provides one illustration of that distinction. U.S. farmland generated an average annual total return of about 10% over the period analyzed, similar to U.S. equities, while exhibiting substantially lower historical volatility.
That volatility comparison comes with an important caveat. Farmland’s lower historic volatility partly reflects its appraisal-based valuation methodology—properties are periodically appraised rather than priced continuously in a public market. That doesn’t invalidate the comparison—it just means the two numbers aren’t measuring risk in exactly the same way. Farmland also comes with its own considerations that don’t apply to public equities: it typically requires a longer holding period and higher minimum investment, and its returns depend in part on the operators managing the underlying land. This simply highlights that the potential diversification benefit comes with different characteristics, particularly around liquidity and transparency.
The broader point is not about farmland specifically. The same logic applies to other real assets and alternative strategies whose return drivers sit outside the AI investment cycle. Concentration risk, then, should be evaluated by considering what a portfolio is ultimately exposed to, not just how it is weighted.
The Broader Question
The discussion around AI has understandably focused on concentration risk. The growing dominance of a small number of companies has fundamentally changed the composition of many traditional equity portfolios.
Equal weighting offers one thoughtful response by reducing exposure to those companies. But today’s concentration risk may not be defined solely by portfolio weights. It may also reflect how much of a portfolio depends on the same underlying economic forces. That perspective doesn’t diminish the value of equal weighting or suggest that any single asset class is the answer. It simply broadens the conversation.
As the discussion around AI continues to evolve, the question may no longer be only how concentrated a portfolio is, but what is driving its returns. And that may mean thinking about diversification not only across securities, but across different sources of risk and return.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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