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Holley Miller

Venture capital is in the middle of a quiet power shift.

Over the past few years, some of the largest and most consequential deals in tech, healthcare and life sciences have not been led by traditional venture firms at all. Instead, family offices and sovereign wealth funds are backing bigger bets, longer timelines and platforms that stretch across borders, often outside the constraints of the traditional 10-year fund model. 

These investors aren’t just deploying more capital; they’re deploying it differently.

At the same time, artificial intelligence is rapidly reshaping how venture decisions are made. Large language models and agent-based tools are now capable of scanning tens of thousands of companies, parsing unstructured data and filtering opportunities at a speed no associate team could match. For many firms, this feels like an edge.

However, the reality’s more complicated. In an AI-saturated capital market, who deploys capital is changing faster than many founders and investors realize.

What AI is changing in venture, and what it isn’t
AI is already transforming the front end of venture investing. LLM-based agents can now scan pitch decks, websites and research papers in minutes, then sort opportunities based on priorities. They handle the early screening work that used to sit with junior teams, and they do it faster and at a greater scale.

And, of course, that brings real advantages. Firms can see more of the market, move with less friction and process far more information without adding headcount. In some cases, these tools are even better than humans at spotting patterns in historical data.

However, there are limitations. AI can only evaluate what’s already been written down. It mainly sees companies that have already made it into the funnel. And because it is trained on investor-defined strategies, it tends to reinforce existing assumptions about what a “good” opportunity looks like, rather than question them.

AI is powerful at optimizing within a frame, but it’s far less capable of questioning the frame itself.

Why AI makes traditional VC more vulnerable, not stronger
Venture funds struggle with differentiation. Many firms chase similar sectors, follow overlapping signals, and compete for the same types of founders. AI does not fix that structural issue, and in many cases, it accelerates it.

As more VC firms adopt similar LLM-driven screening tools, trained on similar strategies and market narratives, their sourcing and selection methods begin to converge. Capital crowds into obvious categories faster. Pattern recognition improves, but originality declines.

This creates a paradox. While better tools lead to faster decisions, they don’t necessarily make better ones. 

Novel categories, disruptive innovations and unconventional science often fall outside what AI models are trained to surface. The result is a market that becomes more efficient at funding what already makes sense and worse at discovering what does not yet fit.

The structural edge of family offices and sovereign wealth funds
This is where family offices and sovereign wealth funds quietly gain an advantage.

Unlike traditional VC funds, these investors are not bound by rigid fund clocks or pressure for quick exits. They can tolerate longer paths to market and invest in deep science that requires patience. Many can invest across an entire stack, spanning hardware, software, data layers and even molecular innovation, rather than making isolated bets.

In sectors like healthcare, climate and advanced technology, this matters. These aren’t markets where success is driven solely by speed or technology iteration. They’re systems that require coordination, integration and long-term capital alignment.

Family offices and sovereign wealth funds also have greater freedom to back unconventional ideas that may not look attractive through standard AI-driven screening. That freedom, paired with the right use of AI, becomes a powerful combination.

Human expertise as the real differentiator
AI struggles most when venture outcomes depend on how institutions and decision-makers actually behave.

Behavior change among industry decision-makers is difficult to predict. In the life sciences space, there is a persistent assumption that guidelines drive compliance; evidence leads to adoption; and mandates create urgency. In practice, however, behavior rarely shifts based on information alone. 

Change occurs primarily when consequences begin affecting a business's roles, budgets, reputation or risk. Education may raise awareness, but consequences drive the action. For example, changing the standard of care requires trust, alignment and sustained adoption over time. These are factors machines struggle to assess.

Senior operators and system-level thinkers bring something different. They can determine whether a product will change real-world behavior, whether an ecosystem can form around it, and whether a new category can be shaped rather than simply entered.

The most effective approach, then, is not choosing between AI and human judgment; instead, it’s designing a system that uses both deliberately. So how would that look?

To begin, LLM agents can map the venture landscape and create a wide, efficient funnel aligned with an explicit mandate. This is where AI excels.

From there, senior partners and domain experts step in to review AI-derived clusters, challenge assumptions, and identify contrarian opportunities that models may overlook. This is where pattern breaking happens.

Finally, capital is deployed with a systems mindset. Instead of backing isolated companies, investors place complementary bets across layers, intentionally building ecosystems rather than portfolios of one-off positions. Portfolio construction becomes a strategic design choice, not a series of opportunistic decisions.

Putting this into practice does not require more technology – it requires clarity. Investment fund strategies must be explicit enough to guide AI without reinforcing consensus thinking. 

AI should be used to automate busywork, not replace judgment, freeing senior leaders to focus on higher-order analysis, partnerships and category design. Human oversight should also be formalized, with standing councils of operators, clinicians, behavioral scientists and system thinkers who can pressure-test decisions and support long-horizon bets.

The real edge isn’t AI. It’s how you’re allowed to invest.

AI-driven screening is quickly becoming expected, but it won’t be a lasting source of advantage.

Family offices and sovereign wealth funds are better positioned in this environment not because they have superior technology, but because they operate with more freedom. When that freedom is paired with patient capital, a cross-stack perspective and senior human expertise, it creates an edge that traditional VC structures struggle to match.

The opportunity is not to imitate Sand Hill Road with better software, but rather design a fundamentally different approach to investing – one built for an AI-saturated world.