The AI-Native Fund: What the Next Generation of Private Markets Looks Like

AI is rapidly becoming table stakes in private markets. But simply using AI is not enough to create an edge.

That was one of the central themes of our recent webinar with Private Equity Wire, featuring Nils Rode, Chief Investment Officer at Schroders Capital; Harry Vander Elst, Partner at Clipway; and Ali Dastjerdi, Co-Founder & CEO of Raylu.

Watch the full webinar recording below.

The discussion explored how leading private-market firms are moving beyond basic productivity use cases and embedding AI into sourcing, research, investment analysis, and decision-making.

AI should expand what investment teams can do

One of the clearest points from the discussion was that AI’s biggest opportunity is not simply reducing costs or making existing work faster.

It is allowing investment teams to analyze far more opportunities, go deeper earlier in the process, and make better decisions about where to focus their time.

Nils described the potential as moving from doing the same analysis more efficiently to conducting dramatically more and deeper analysis across the investment universe.

For smaller firms, that can significantly change the competitive landscape. Teams that historically lacked the headcount to cover an entire market can increasingly operate with a level of breadth once reserved for much larger platforms.

The real edge is proprietary judgment

As access to leading AI models becomes widespread, the technology itself becomes less differentiated.

The advantage comes from what firms build on top of it.

Harry described AI as increasingly becoming a necessary part of the operating system for investment firms. But the real moat comes from proprietary data, institutional knowledge, and the investment criteria unique to each firm.

Ali made a similar point about origination.

Historically, firms could gain an edge by purchasing differentiated datasets. Over time, competitors began buying the same data and identifying many of the same signals.

AI creates an opportunity to move beyond those common signals.

Investment firms can codify the nuances that define their strategy—characteristics of management teams, customer patterns, hiring behavior, market signals, or lessons from previous investments—and continuously search for those markers across thousands of companies.

The goal is not to replace the investment thesis. It is to make that thesis operate at previously impossible scale.

Human judgment remains central

Despite the increasing sophistication of AI, the speakers agreed that private markets will remain fundamentally human.

AI can retrieve and synthesize information, apply investment criteria consistently, and analyze enormous amounts of data. But investment teams still need to determine what matters.

Nils emphasized the importance of keeping humans in the loop and ensuring that AI operates within the investment philosophy, criteria, and processes defined by the firm.

The technology should support judgment, not outsource it.

That also makes transparency critical. Investors need to understand why a model reached a conclusion, verify its sources, and challenge its assumptions before trusting it in high-stakes workflows.

Build vs. buy

The discussion closed with a question many firms are currently wrestling with: what should be built internally, and what should be bought?

The answer depends on where a firm’s differentiation lies.

There is little reason to recreate foundation models or infrastructure that already exists and is improving rapidly. But proprietary data, investment methodology, institutional knowledge, and workflows that directly contribute to a firm’s edge are different.

The emerging model is likely a combination of both: firms own what makes their investment approach proprietary while relying on specialized technology to operate that intelligence at scale.

The next generation of private markets

The AI-native fund is not simply a traditional investment firm with a chatbot added to its workflows.

It is a firm that can turn its investment strategy, historical knowledge, and proprietary judgment into systems that operate continuously across the market.

As AI becomes ubiquitous, access to the technology will matter less.

What will matter is how effectively firms use it to scale what makes them different.