How can investors diversify away from AI?

Artificial intelligence remains one of the most promising investment themes in equity markets. But after several years of extraordinary gains, the outlook now warrants a bit more caution.

Hyperscaler free cash flow is declining, more AI spending is being funded with debt, and competition is increasing as cheaper open-source models become more viable. As a result, investors may want to make sure their portfolios are not overly dependent on AI.

Within the U.S., large-cap growth is the clearest area of concentration.

We recently did an analysis using ChatGPT and FactSet to find the approximate AI exposure in various indices. Following the recent Russell reconstitution, AI infrastructure beneficiaries now account for approximately 36% of the Russell 1000 Growth Index. These include semiconductor, memory, networking, electronic design automation, and electrical infrastructure companies.

The reconstitution reduced exposure to the hyperscalers—the mega-cap companies funding much of this investment—but they still account for another 22%. Altogether, nearly 60% of the Russell 1000 Growth Index is highly levered to AI.

U.S. large-cap value has considerably less exposure, but AI is still meaningful. Approximately 12% of the Russell 1000 Value Index consists of hyperscalers, while another 7% is exposed to AI infrastructure.

For investors seeking diversification within the U.S., small caps stand out.

We found that only about 5% of the S&P 600 Small Cap Index is directly exposed to AI infrastructure, and there are no hyperscalers in the index.

There is a logical reason for this. Many of the companies benefiting most from the AI buildout have seen their market capitalizations rise dramatically, eventually becoming too large to remain in the small-cap universe.

Outside the U.S., the picture varies considerably.

Emerging markets have substantial AI exposure. Approximately 36% of the MSCI Emerging Markets Index is tied to AI infrastructure. This exposure is driven primarily by memory stocks, but also includes semiconductor foundries, advanced packaging, server assembly, power management, and other hardware.

The index also includes exposure to Chinese hyperscalers such as Alibaba, Tencent, and Baidu.

Developed markets outside the U.S. look very different.

AI infrastructure exposure is much lower, and there are no hyperscalers. This is particularly true for value stocks. The MSCI EAFE Value Index has almost no direct exposure to AI infrastructure, making it one of the clearest diversification options among major equity indexes.

Importantly, areas with limited AI exposure have still produced strong returns.

Over the year ended August 24, 2026, the MSCI EAFE Value Index returned approximately 29%, while the S&P 600 returned roughly 27%.

Those returns are unlikely to be repeated indefinitely, but both areas continue to have strong fundamental support.

U.S. small-cap earnings are currently expected to grow approximately 19% over the next 12 months, while the S&P 600 price is only about 15 times forward earnings.

EAFE Value earnings growth expectations are more modest, at roughly 9%. But the index price is at an even more attractive valuation of less than 13 times forward earnings.

The bottom line is that AI remains a promising investment theme, and investors should be careful about moving too far away from it.

But rising capital intensity, increasing leverage, and a more competitive landscape suggest that investors also should make sure their portfolios are adequately diversified.

U.S. small-caps and developed-market value stocks are two particularly attractive options. Based on our analysis, both have limited AI exposure while still offering healthy fundamentals and reasonable valuations.

Reflecting this view, our Asset Allocation Committee currently maintains overweight positions in both U.S. small-cap stocks and non-U.S. value equities.

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