With the “Magnificent Seven” (Nvidia, Microsoft, Apple, Amazon, Meta, Google, and Tesla) now making up roughly 35% of the S&P 500, a few new considerations have entered our thinking.
Historically, we have not been overly concerned about concentration risk, as we view it as a natural byproduct of market growth. As stock pickers, we can diversify and sidestep risks as needed. However, the recent surge in capital expenditures from this group—particularly tied to artificial intelligence (AI)—is something we believe warrants close monitoring for potential systemic implications.
The Growing Scale of AI Spending
Our recent analysis on the Mag 7 revealed how deeply tied overall economic activity is becoming to AI-driven spending by these companies. This year alone, AI-related capital expenditures will represent around 1.2% of U.S. GDP, with projections rising to 2% or more by 2026. Looking longer term, the “Big Four” (Amazon, Google, Microsoft, Meta) are expected to spend nearly $1.7 trillion collectively by 2035. This does not even include the recent promises from Open AI to spend between $500B and $1T as it builds out a new data infrastructure footprint.
Looking forward, if the broader economy grows at a normal 4–5% nominal rate, this level of investment could represent nearly 4% of total GDP. While that may not sound overwhelming at first glance, the systemic nature of this spend is what concerns us. As these investments and promises of more investments grow at a projected 20%+ annual pace, the ripple effects extend far beyond the Magnificent Seven themselves—touching suppliers, builders, financial institutions, energy and service providers.
Risks of Overextension
Even today, we see reasons for caution. The real risk is not just a slowdown in AI demand, but rather the possibility that demand accelerates faster than it can be profitably monetized. Many of the foundational AI providers (such as OpenAI, Gemini, and others) remain unprofitable, which means the timeline for monetization is uncertain. Case and point; Open AI does not target any profitability until 2030 and is reliant on funding from other sources to keep them going until that time. If revenues fail to scale as quickly as capital spending, companies could face pressure to cut back.
This mismatch between expectations and reality could expose market cracks sooner than investors anticipate. On the other hand, cost breakthroughs or new business models could unlock value faster than expected. Both scenarios are possible—and that uncertainty is exactly why balance and diversification are key.
Our Strategy – Positioning Through Balance
In short, we are becoming more cautious on the AI trend. We have good exposure, have benefitted from our exposures and continue to think many wonderful businesses are directly tied to the AI narrative. While we recognize its transformative potential, we are deliberately searching for “old school” companies with proven business models, steady cash flows, and consistent returns of capital to shareholders. Many of these opportunities exist in spaces well outside the mag 7, where businesses are less tethered to AI-related flows. Our intent is to balance into these companies but not eliminate our AI exposure all together.
At a time when much of the market seems to be crowding into AI, we are leaning into diversification—balancing exposure to this powerful theme while allocating selectively to areas outside of it.
Risk of Balance
The current AI boom has pulled an extraordinary amount of capital into a narrow corner of the market. As a result, many high-quality businesses outside of AI now trade at attractive valuations that offer the potential for reasonable long-term returns at fair levels of risk. The challenge is that these returns may take time to materialize.
By allocating into areas that are less “in vogue,” we are, in effect, swimming against the tide. Diversification and balance require this posture: you never move entirely with the current, and at times—particularly when markets like the S&P 500 become extremely concentrated in a single trade—it can feel like you’re losing ground.
Because no one can predict when the tide will turn, our focus remains on owning great companies that are positioned to benefit once market currents shift from headwinds to tailwinds. This means we may lag AI-heavy benchmarks in the short run. But this is an intentional trade-off: we accept steadier, more sustainable returns to protect long-term financial plans, rather than chasing short-term outperformance.
The greater risk in moments like these is emotional. When overinflated assets appear to soar, it can create the impression that disciplined, steady returns are inadequate. Resisting this temptation is critical. Our objective is not to win a one- or two-year sprint against benchmarks—it is to build and sustain wealth that lasts a lifetime.
Closing Thought
AI will continue to be amazingly transformative, but as investors we must see that transformation result in bottom line cash flows. The longer it takes to turn this amazing technology into cash flows the larger the risk for investors. To combat this risk we are willing to give up some higher returns in favor of adding some ‘old fashioned’ companies and broadening our diversification. We will look to execute this strategy as opportunity affords the remainder of this year.
Disclosures
Evergreen Wealth Management, LLC is a registered investment adviser. This information is for educational purposes only and does not constitute an offer or solicitation to buy or sell any securities. Investments involve risk and are not guaranteed. Always consult with a qualified financial advisor or tax professional before implementing any strategy. Past performance does not guarantee future results.
The views expressed reflect the opinion of the firm as of the date indicated and are subject to change. Forward-looking statements are not guarantees and involve uncertainties that may cause actual results to differ.