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.