Most of the portfolios we review hold more artificial intelligence exposure than their owners think. Not because anyone set out to “bet on AI,” but because the broad-market funds many investors own have quietly done it for them. Here’s the number that often starts the conversation: as of September 30, 2026, the ten largest companies in the S&P 500 made up about 39% of the index, and the single largest company was more than 8% (S&P Dow Jones Indices). Three decades ago, the top-ten share was closer to the high teens (commonly cited in long-term market concentration studies).
That isn’t automatically bad news. Large companies can earn their way to the top, and investors who held diversified index funds through that climb were rewarded. But any portfolio that leans heavily in one direction deserves a deliberate look rather than an accidental one. Below is how we evaluate that “quiet AI bet” using three tools our advisory firms rely on—Morningstar, Nitrogen (formerly Riskalyze), and eMoney—because each answers a different question.
Two different risks that can look like one
When clients say, “Am I too concentrated in AI?” they’re usually describing two different risks:
1) Concentration risk (how much)
Concentration risk is straightforward math. If a large slice of your net worth ultimately traces back to a single company—or a small handful of companies—then a bad year for that company can become a bad year for you.
This is easy to miss when exposure is spread across multiple funds that look reasonable on their own.
2) Correlation risk (how things move together)
Correlation risk is about co-movement. You can own 20 different funds and still be making one big bet if they tend to react to the same headlines.
Today, chipmakers, mega-cap tech platforms, cloud providers, data-center builders, and even some utilities can share a common driver: the pace of AI infrastructure investment and the market’s expectations for AI-related growth.
These risks can compound. A portfolio might pass a “no single stock above 5%” check and still behave like one trade if the same theme dominates many holdings.
A common real-world example (especially in higher-income households):
- An S&P 500 index fund in the 401(k)
- A Nasdaq-100 fund in a brokerage account
- A large-cap growth fund in a Roth IRA
- A technology sector ETF bought “for growth”
- Employer stock or equity compensation tied to the tech sector
Five accounts. Five labels. Potentially the same few companies showing up again and again.
Step 1: Morningstar helps identify what you actually own
We often start with Morningstar’s portfolio look-through reporting (commonly via tools like Portfolio X-Ray) because it looks past fund names to the underlying holdings.
The key output many investors find eye-opening is overlap analysis (often described as “stock intersection” or “top holdings across accounts”). It aggregates the stocks inside your mutual funds and ETFs along with any individual shares you own, then ranks the largest positions by their share of your overall portfolio.
What we typically look for:
- Any single company that’s become “too big to ignore.” Not because a cutoff is universal, but because a position can quietly grow into a major driver of outcome.
- How much of the portfolio is tied to a cluster of AI-linked names. We often total the largest platform companies and semiconductor exposure as a group because they may move together.
- Sector tilts versus the broader market. If your technology weight is well above the market’s, you’re making an active bet—whether or not you intended to.
- Duplicate exposure. Two funds with heavy overlap can mean you’re paying two expense ratios for essentially the same “top 10.”
A practical limitation: look-through analysis is only as complete as the accounts included. If a spouse’s 401(k), outside brokerage account, or equity compensation isn’t part of the dataset, your “true” concentration may be understated.
Step 2: Nitrogen helps quantify how much risk you’re really taking
Morningstar is about what you own. Nitrogen is often used to evaluate how your portfolio behaves, especially in down markets—and to compare that behavior to your personal comfort level.
Many advisors begin with a risk-profile process (often expressed as a “risk number” concept) and then compare it to the portfolio’s modeled risk level. A common issue in concentrated markets: the portfolio risk drifts higher over time because the winners keep winning, even if contributions and allocations never changed.
For AI-related correlation risk, we often focus on:
- Correlation and diversification analytics. These can highlight when “different” holdings are effectively moving as one.
- Stress testing. We may replay historical tech-heavy drawdowns (like the early-2000s unwind) to see how a similarly structured portfolio could respond.
- “What-if” portfolio modeling. Before making changes, we can test rebalancing, reducing overlap, or broadening diversification and see how the risk profile shifts.
The goal isn’t to predict an AI downturn—no tool can do that reliably. The goal is more useful: if a sharp, tech-led drawdown happens, can you live with the experience and keep your plan intact?
Step 3: eMoney connects portfolio risk to real-life outcomes
A 30% decline means something very different to a 38-year-old high earner still accumulating than to a 63-year-old planning to retire in 18 months.
That’s why we use planning software (often eMoney) to connect portfolio behavior to personal goals.
Three planning takeaways matter most:
- The full household picture. Account aggregation can help ensure retirement plans, held-away accounts, and equity compensation are included in the analysis.
- Testing the plan, not just the portfolio. Monte Carlo-style analysis can help evaluate how a difficult early-retirement sequence might affect the probability of meeting goals.
- Comparing decisions side-by-side. It’s often helpful to model “keep the concentration” versus “trim gradually” and see how retirement timing, spending flexibility, or legacy goals may change.
This step keeps the analysis honest. A concentrated portfolio may look alarming on a holdings report yet still be financially “affordable” for a young, aggressive saver. That same exposure may represent a bigger threat when withdrawals are about to begin.
How the three tools fit together
- Morningstar: What do you really own (look-through holdings, overlaps, sector tilts)?
- Nitrogen: How does the portfolio behave under stress (risk level, correlation, scenario tests)?
- eMoney: Does your plan survive it (goal-based, retirement-focused modeling)?
What we typically discuss if AI exposure is higher than intended
When the analysis suggests the exposure is more than you want—or more than your plan can comfortably carry—the response is rarely all-or-nothing. Depending on taxes, timing, and account types, conversations often include:
- Rebalancing toward the intended target (often starting in tax-deferred accounts to reduce tax impact)
- Consolidating overlapping funds to avoid paying for the same exposure twice
- Broadening diversification (for example, adding exposure to smaller companies, international stocks, or value-oriented strategies)
- Gradually trimming highly appreciated positions over time rather than in one step
- If charitable giving is already part of the plan, considering gifts of appreciated shares (when appropriate)
And sometimes the best answer is: do nothing—intentionally. If the plan still holds up under stress scenarios and you understand the tradeoffs, choosing to maintain a meaningful AI tilt can be a deliberate decision. The goal is to avoid accidental concentration.
Find out what you’re really betting on
If you own multiple funds across multiple accounts, there’s a good chance your largest “real” position is a company you’ve never purchased directly. A clear look-through report, a risk-behavior check, and a plan-based stress test can turn a vague worry into a measurable decision.
This material is for educational purposes only and is not a recommendation to buy or sell any security. Diversification and asset allocation do not ensure a profit or protect against loss. Risk analysis, stress tests, and Monte Carlo simulations are hypothetical and do not guarantee future results. Morningstar, Nitrogen (Riskalyze), and eMoney are trademarks of their respective owners.