How AI is Shaping the Small-Cap Space
Although mega-cap tech companies dominate today’s AI narrative, we believe the next generation of leaders may be emerging in the small-cap space. In five questions, we explain why semiconductors, software, industrials, and biotechnology are areas to watch.
1. What are some potential ways to play the AI trade in small caps?
The hype around AI can be exciting, but it is also volatile. We are focused on two dimensions of this evolving landscape: picks-and-shovels behind the buildout and monetization across sectors. Among the picks-and-shovels, semiconductor cap equipment stands out. This space was largely overlooked just two years ago, and it is now a leading market segment, especially in essential tools that support GPU and other AI chips manufacturing. An underappreciated driver here is memory, which has become a major cost input as large language models require massive storage both locally and in the data center, creating opportunities in both memory providers and the semiconductor cap companies that enable scaling.
The second dimension is monetization, where software and infrastructure companies are turning AI into new revenue streams through offerings like model security or predictive analytics. This may allow active managers to capture opportunities as different layers of the technology stack develop at uneven paces.
2. Where are the primary opportunities for AI monetization?
We seek companies leveraging AI to unlock entirely new revenue streams rather than merely optimizing internal processes. This opportunity is often visible in supply chain platforms benefiting from the flood of raw data—the surge in machine-driven decisions, exceptions, and execution paths triggered by AI-generated code. By addressing the resulting complexity and risk, these platforms are launching high-margin products centered on AI model security and governance.
3. What impact is AI's evolution having on the industrial sector?
We are witnessing one of the most disruptive periods in recent history across defense and the broader industrial sector. What we find interesting is how all of these themes are starting to overlap. On the defense side, demand is expanding beyond traditional programs into AI-driven capabilities, space-based intelligence, and real-time data. This shift is accelerating growth in areas like satellite imaging, radar, and emerging space enterprises that are supporting both government and commercial end markets.
AI is also driving a surge in power demand, catalyzing investment into grid upgrades, behind-the-meter power, and cooling infrastructure to support data centers and broader electrification. Alongside these converging structural shifts, a cyclical recovery is taking hold, with strengthening activity broadening beyond aerospace and defense into areas like equipment rental, industrial services, and other end markets.
4. In what ways is AI infrastructure influencing the biotech space?
What is compelling, in our view, is the convergence we are seeing between AI infrastructure and biotech innovation. The same high performance computing capabilities that power AI workloads are increasingly essential for computational biology, clinical trial design, and drug discovery. As AI moves closer to the core of how drugs are developed and executed, we see biotech becoming a natural downstream beneficiary and a high value end market for these investments. At the same time, biotech is entering a new phase of innovation. After a major boom in 2020 and 2021, the space has been relatively quiet, but renewed M&A activity is helping catalyze what we view as the early stages of a new IPO cycle, often led by experienced management teams who have done this before.
5. How do you navigate risks in biotech and capture alpha?
We believe our investment edge in biotech comes from a disciplined, long term approach that evaluates the sector through a risk modeling lens, rather than a purely scientific one. Instead of underwriting early stage outcomes, or binary ones, we focus on areas where the market misprices risk—particularly following material clinical or regulatory de-risking events. Idea generation is bottom-up and catalyst-driven.
We use a host of methods, technological and analytical, to actively track clinical and regulatory milestones, engage across the biotech ecosystem, and maintain a deep bench of ideas so we can be selective as inflection points emerge. We look for asymmetrical opportunities where improving data or execution can materially shift outcomes, including differentiated therapies or scalable platforms like late stage royalty models. Overall, our goal is a repeatable process that seeks to generate alpha by buying resilience and upside, taking a long term view where value compounds through progress rather than single events.
We treat M&A as validation, not the reason to own something. The focus is on late-stage, de-risked assets with differentiated science that can stand on their own, so even if a deal never comes, the investment still works. Many of these companies are under-researched, domestically focused, and operating at the intersection of compute-driven innovation and healthcare, which dovetails naturally with our broader small cap focus.
If you want to explore the next generation of small-cap leaders, our Public Equity team is ready to explore them with you.
