The “AI trade” has delivered massive gains for those who rode the Magnificent 7 to market riches. But it’s also caused a great deal of concentration risk for advisory clients.
While a concentrated winning position is a high-quality problem, it is still a portfolio management problem that advisors need to address.
Even though the source of the concentrated position may be new to investors, the traditional techniques for diversification still hold true, according to Kurt Nye, chief investment officer at MAI Capital Management. The most common solutions he employs are options strategies, tax-loss harvesting, and exchange funds.
“We are seeing an increase in use cases for 351 exchanges, in kind transfers to seed a new ETF, and combining long-short direct indexing with variable prepaid forwards. Each solution comes with trade-offs and requires thoughtful planning to achieve the desired outcome. Further, you can combine multiple solutions to potentially greater effect,” Nye said.
Elsewhere, Anshul Sharma, chief investment officer at Savvy Wealth, is managing concentration risk through “disciplined portfolio construction” and diversification, not market timing. Within their discretionary portfolios, they have broadened exposure beyond the largest AI beneficiaries to include companies supporting the broader ecosystem in areas like infrastructure, utilities, and industrial automation.
“For highly concentrated positions, we also consider tax-aware rebalancing and hedging approaches, including selective use of options strategies, to manage downside risk while maintaining long-term participation,” Sharma said.
Stephen Tuckwood, director of investments at Modern Wealth Management, says he’s tilting the equity sleeve of his active models away from pure market-cap into areas of the market that exhibit quality, value, and free cash flow generation. At the margin, he believes this helps to reduce the concentration of big tech, which has grown to become a dominant portion of the market.
“By many measures, concentration in the S&P 500 Index is at record high levels whether looking at the share of the market-cap index, the earnings growth contribution, or the proportion of total Capex spending,” Tuckwood said.
The transformative potential of AI is seemingly being reflected in the valuations of many of the AI stocks. While there will be some long-term winners, Nye believes the odds are that many will fail to live up to investors' expectations.
“One of the many interesting things about current AI stocks is that cohort includes mega-cap highly profitable companies such as NVIDIA and Microsoft while also including newer, much less profitable firms. For example, ancillary names in the energy space have rallied significantly based on AI’s projected power demand,” Nye said.
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Tuckwood agrees, saying the market has a “rich history of taking a great investment story and becoming too enthusiastic too quickly.” He cites the dot-com bust in the early 2000s as being the prime example of this.
“The internet did end up changing the world, as AI is likely to do, but that doesn’t mean between here and there that all investments will work out in investors’ favor. The current and planned capex spend on AI infrastructure can only be described as astronomical and there is risk of over-investment, especially if a new chip design can do more with less,” Tuckwood said.
He sees the sell-off in Meta shares last week on the announcement of “significantly higher spend” in 2026 as a good sign that market participants are “still rational” and that profitability is still a key consideration.
“Spending and investment are necessary for companies to remain competitive in the AI development race, but at some point, it must result in profit generation,” Tuckwood said.
Savvy Wealth’s Sharma has been finding opportunities among the second-order effects of the AI buildout, or the “picks and shovels” powering the ecosystem. That includes industrials, power, and utilities companies enabling data-center expansion.
“Allocating across these areas provides a more balanced way to participate in AI’s structural growth while avoiding excessive single-stock risk,” Sharma said.
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