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Two startups, described as simple "LLM wrappers," are seeing huge growth not in Silicon Valley but by targeting Middle America. Their moat isn't technology; it's affiliate programs and accessible customer service. They provide human support that tech giants can't or won't, winning over a massive, underserved user base.
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The rapid growth of AI products isn't due to a sudden market desire for AI technology itself. Rather, AI enables superior solutions for long-standing customer problems that were previously addressed with inadequate options. The demand existed long before the AI-powered supply arrived to meet it.
While Silicon Valley is saturated with AI discourse, the real, untapped market is small businesses in places like Iowa. These businesses are desperate for labor automation and efficiency gains, representing a massive last-mile distribution challenge and opportunity for AI.
While early AI companies built moats on user data by being first-to-market with API wrappers, today's startups cannot. A defensible AI product now requires a true moat beyond a simple wrapper, as model providers can easily replicate basic functionality, making such businesses non-defensible.
Assuming technology is increasingly commoditized, a startup's defensibility comes from relentless execution. This includes providing a seamless onboarding experience and superior customer service—areas where large, impersonal frontier labs are unlikely to compete effectively.
Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.
As AI commoditizes technology, traditional moats are eroding. The only sustainable advantage is "relationship capital"—being defined by *who* you serve, not *what* you do. This is built through depth (feeling seen), density (community belonging), and durability (permission to offer more products).
To build a moat against large language models like ChatGPT, focus on features they will never prioritize. Build multiplayer functionality, a strong user community, and human-in-the-loop support services around the core AI. These layers create defensibility that a generic interface cannot replicate.
Perplexity's CEO argues that building foundational models is not necessary for success. By focusing on the end-to-end consumer experience and leveraging increasingly commoditized models, startups can build a highly valuable business without needing billions in funding for model training.
With foundation models making technical features easy to copy, the sustainable advantage for AI companies lies in deep customer understanding. Serval's CEO stays in over 100 customer Slack channels daily to build this "customer insight" moat, which is harder to replicate than any product feature.
An AI application can be a powerful business, even as a 'wrapper,' if it serves a niche audience that is unlikely to use a frontier model like GPT directly. The defensibility comes not from unique technology but from a deeply tailored user experience for a specific market, such as language-learning for children.
