By Anshul Sharma

A relatively small group of companies tied to artificial intelligence has driven a disproportionate share of global equity returns in recent years, helping propel indexes like the S&P 500 to record highs. Their rapid ascent has generated both excitement and uncertainty among investors, leaving many wondering whether they are witnessing the early stages of a long-term transformation or a thematic surge that has already run its course.

Whether AI valuations have run too far is a legitimate debate, and one this piece won't settle. But for those who believe that shift has legs, the question isn't whether to have exposure, but how to build it wisely. What often gets overlooked is that AI-driven growth isn't confined to a handful of technology giants. It is observable across infrastructure, energy, industrials, and real assets, creating opportunities beyond the big names.

The key for financial advisors is to recognize that there are multiple ways to gain exposure to AI depending on their client's risk tolerance, liquidity needs, and holistic wealth profile. Understanding these different approaches can help clients participate in the long-term structural growth of AI, while managing risk appropriately.

Pick and shovel play. The hyperscalers, chip makers, and memory producers responsible for building and powering big platforms sit at the center of the AI narrative, and have delivered impressive performance as demand for computing power has surged. But their very prominence makes them particularly sensitive to investor sentiment and earnings expectations. In such situations, modest disappointments can trigger meaningful volatility.

A risk-aware approach recognizes that AI depends on a vast supporting ecosystem. Every AI model requires electricity, networking infrastructure, cooling systems and physical data centers. By focusing on the broader ecosystem, investors can participate in AI's growth, while reducing exposure to the concentrated risks associated with a small number of companies.

This dynamic mirrors the " pick and shovel" play during the California Gold Rush, when those supplying tools and infrastructure often generated more consistent returns than those companies searching for gold itself.

ETFs and SMAs. AI exposure should never exist in isolation from a client's broader financial picture. Goals, time horizon and existing portfolio exposures influence how -- and whether -- AI fits into their overall strategy.

For many investors, exchange-traded funds provide a practical starting point. ETFs spread exposure across multiple companies and sectors, reducing reliance on individual stock selection while capturing broader industry growth. Note that this approach, which offers simplicity and built-in diversification, assumes the client isn't already overexposed to AI in other investment vehicles like 401(k)s

As portfolios grow more complex, separately managed accounts allow for greater customization. Advisors can adjust sector exposure, emphasize infrastructure-oriented investments, and manage tax implications more precisely.

Ultra-high-net-worth investors may also have access to private markets, including venture capital, private equity, and real asset investments such as data centers. Such investments provide exposure to the physical backbone of AI while offering diversification beyond stocks. They do, however, require careful consideration due to their illiquidity, high investment minimums, and greater risk profile, as well as longer time horizons and reduced transparency.

A range of AI risks. The excitement surrounding AI can make it easy to ignore subtle concentration risk. Since a small number of AI-related companies account for an outsized share of market indexes, investors may have significant AI exposure without realizing it. When performance is strong, this concentration can amplify gains. But when sentiment shifts, the same dynamic can magnify declines.

Another challenge lies in separating innovation from durable profitability, introducing the potential for monetization risk. As companies invest billions in AI buildouts, markets will increasingly ask which firms can turn proof-of-concept into sustainable revenue streams. In the end, there may be only a handful of winners in the AI race, at least in terms of the large, direct plays.

Regulatory uncertainty adds another shade of risk. Governments and policymakers are still defining how AI should be supervised and accounted for in legal frameworks, particularly as its influence expands. Future regulations could reshape competitive dynamics, introduce new compliance costs or alter how companies deploy AI capabilities. Narrative and societal risks also exist if AI meaningfully disrupts labor markets, or public perception shifts against AI.

Finally, valuation itself can become a source of vulnerability. Strong growth attracts capital quickly, but elevated expectations leave little room for disappointment. Even fundamentally strong companies can experience sharp price adjustments if investor expectations reset.

These risks don't diminish AI's long-term potential, but they do highlight the importance of diversification and disciplined portfolio construction.

Anshul Sharma is CIO of Savvy Wealth. He is a wealth management veteran with more than 25 years of experience building multibillion-dollar investment platforms. Prior to Savvy, he held senior roles at Bank of America, U.S. Trust and Merrill Lynch, where he drove investment strategy across multiple asset classes, partnering with advisors to strengthen portfolio construction.

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May 04, 2026 16:07 ET (20:07 GMT)

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