Beyond the AI Trade: Sectors That May Offer Diversification in a Concentrated Market

Artificial intelligence (AI) has been the dominant theme in public markets for several years, but the names driving most of those returns are concentrated in a handful of mega-cap companies. For investors who want exposure to AI but feel the public market is already crowded, early-stage and private market investing may offer a different lens on the same broader theme. In this blog, learn more about how private market and early-stage investing could provide help provide exposure to AI beyond the largest publicly traded names.

Diversification Beyond the AI Trade Sector

Why Public AI Exposure May Be Narrower Than It Looks

The AI rally in public markets has been heavily concentrated. AI-related equities accounted for over 80% of the S&P 500’s gains in 2026, with the bulk of those returns flowing to a small set of companies.[i] For investors holding broad index funds, their AI exposure may be concentrated in specific mega-cap business models, including chip design, hyperscale cloud, and consumer-facing platforms.

That concentration isn’t necessarily a problem, but it can leave gaps. Many parts of the AI ecosystem, including specialized infrastructure providers, vertical applications, and emerging tooling companies, are still private. Investors who want broader thematic exposure may want to consider where in the AI stack they currently hold positions and what is missing.

How Private Markets May Offer Broader AI Exposure

Private market investing, including venture capital (VC) and private equity (PE), involves investing in companies that have not yet gone public. The same overall AI pattern has taken hold in private markets as well, with AI capturing roughly 65% of all venture deal value in 2025, up 46% from 2024, while total AI investment reached $339.4 billion[ii]

When investing in private AI startups, investment opportunities may expand further than public market exposure, including startups building foundation models, applied AI products, developer tools, and infrastructure layers that are less directly represented in public indexes. Private market exposure does typically come with longer time horizons and meaningful illiquidity, which is important for investors to understand, but it can also offer access to companies earlier in their lifecycle.

AI Infrastructure Outside the Mega-Caps

Public market investors looking for AI infrastructure exposure often end up in a small group of names focused on chips, the cloud, and networking infrastructure. The private side of the AI infrastructure layer, however, can be considerably broader. It may include companies working on alternative chip designs, model training and inference platforms, vector databases, AI-optimized data center components, and power and cooling solutions tied to data center demand.

These companies often operate as suppliers or enablers to the larger publicly traded companies in the AI ecosystem. Their growth may be tied to AI buildout, but their business models, customer bases, and stages of development can differ meaningfully from those of public mega-caps.

Vertical and Applied AI in the Private Market

A second category of private AI investing focuses on applied or vertical AI: companies building AI products for specific industries. Examples may include legal technology, healthcare diagnostics, financial services, defense and government, logistics, and cybersecurity.

Many of these companies are founded by industry specialists rather than general-purpose AI labs, and their growth often depends on domain-specific adoption rather than broad consumer trends. For investors thinking about AI as a horizontal capability with different vertical implementations, this category may be worth exploring.

Considerations for Private Market AI Investing

Private market AI investing carries significant risks that investors may want to weigh carefully.  Some of these apply to private investments broadly, while others are more specific to companies operating in the AI space.

Illiquidity

Illiquidity is a primary consideration for any private investment. Private holdings generally cannot be sold easily and may require investors to wait years for an exit, if one occurs at all.

Concentration Risk

Similar to the public markets, concentration risk can also be heightened in private AI markets, where a relatively small group of companies account for a large share of the capital and value. Because so much of the market opportunity may be concentrated in a handful of companies, investors may find it difficult to build broad exposure. For instance, in the first half of 2026, more than 50% of the $407 billion of capital raised by AI startups went to OpenAI and Anthropic.[iii] If the outlook for those companies were to shift, that concentration could have a disproportionate effect on a portfolio.

Key Talent Risk

Many AI startups heavily depend on a small group of researchers and engineers. In some cases, a company’s core intellectual property, product direction, and competitive position may be closely tied to a handful of individuals. The departure of one or more key people, whether to a competitor or to launch a new venture, could meaningfully affect a company’s prospects.

Technology Obsolescence

The pace of innovation in AI can be rapid, and a technological advantage may become outdated quickly. A new model, innovation, or entrant can reshape the competitive landscape in a short period, and a company’s products or business model could lose relevance faster than in slower-moving industries. Investors may want to consider how a company plans to maintain its position as the underlying technology continues to evolve.

Final Thoughts

Public market AI exposure tends to concentrate in a small group of large companies, while the broader AI ecosystem extends much further into infrastructure, vertical applications, and tooling. Early-stage and private market investing may offer access to a wider set of companies operating in the AI theme, though it carries meaningfully different risks, including illiquidity and key talent risk. Investors considering this type of exposure may want to evaluate their own goals, time frames, and risk tolerance.

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Want to learn more about investing in startups? Check out the following MicroVentures blogs to learn more:

• How to Evaluate a Startup’s Competitive Moat
• How Startups Can Prepare for an Acquisition
• AI-Powered Due Diligence
• The Rise of Agentic AI