AI in Venture Capital: Separating Signal from Noise

In both public and private markets, AI’s rise has been extraordinary: fewer than a dozen technology stocks now account for roughly 40% of the S&P 500, while AI-driven startups dominate venture inflows and valuations (see Figures 1 and 2).

Assessing fund quality now means distinguishing not only among managers but also among emerging technologies at varying stages of maturity. The central challenge remains: How can investors separate a signal from noise, and identify real, lasting value in AI-focused venture portfolios?

Figure 1

Figure 2

The following framework can help LPs and advisors cut through the noise and evaluate AI venture funds with greater precision.

A Simple Framework

LPs, advisors, and investors interested in AI-focused funds should start by asking themselves the following questions:

Am I just investing in generative pre-trained transformer (GPT) wrappers that will not withstand a new feature release from OpenAI?

How saturated are the verticals into which I would be deploying capital?

Is there value in reinventing legacy software-as-a-service (SaaS) with AI, even as incumbent enterprise SaaS companies (like ServiceNow) move fast to secure market share?

Once those initial questions are addressed, two additional factors can help investors assess the durability and scalability of AI-focused companies.

First, do these companies operate in areas with high barriers to entry, and are they well-positioned to take advantage of concurrent innovation waves? If so, they are more likely to have defensible staying power and deliver outsized returns as the market matures.

Startups with high barriers to entry have wider and longer lasting moats that provide some protection from the next OpenAI keynote or Google I/O event. The notetaking apps or coding assistants that emerge overnight will likely face challenges moving forward if they are not insulated from broader technological advancements.

In addition, one of the highest barriers to entry is, oftentimes, trust in the company. Trust is vital in product adoption and is built over time through relationships, expertise, and empathy. The best companies can harness trust and deepen relationships with targeted, rather than blanket, AI use. In these cases, AI acts as a supercharger for shorter development cycles to deliver in response to client feedback. AI augments, rather than replaces, and that augmentation builds client trust and supports the overall growth of the business. This is in contrast to “vibe coding,” where AI writes all the code in the interest of shipping with speed rather than focusing on delivering quality outputs or solving for real needs.    

Second, positioning around multiple innovative supercycles improves both the durability of a startup and its ability to scale its go-to-market strategy. Rather than investing exclusively in AI companies with AI-only use cases, expanding the aperture to include adjacent use cases raises the chances of building a competitive moat with multiple points of entry for customers.

Examples include a logistics startup using physical sensors alongside AI agents to manage shipyards autonomously, or a healthcare company leveraging AI for practice management functions such as scheduling, billing, and document sharing, delivering those capabilities seamlessly to patients via an app.

Wiz as a VC Case Study

A clear example of how these two factors come together is Wiz, a cloud-security startup founded in 2021, which Google intends to purchase for $32 billion.

Cloud security has significant barriers to entry. It is a segment built on a high degree of operational trust, given the sensitive nature of storing enterprise data and preventing leaks. Wiz grew its business with early proof-of-concepts, recruiting top engineering talent and embedding teams with clients to build trust.

Customers who initially adopted Wiz for early cloud migration faced new security challenges associated with enterprise AI development, and Wiz capitalized on that business as well. By building trust around their products and simultaneously selling into both the cloud and AI waves, Wiz attracted Google’s attention and delivered strong returns for investors.

Cutting Through the Noise

The proliferation of AI-focused VC funds demands sharper due diligence from investors and advisors. Applying this simple framework can help distinguish managers backing companies with real barriers to entry and long-term strategic positioning from those chasing hype. The investors who can tell the difference will be the ones who thrive in the years ahead.

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