Some of the fastest-growing startups today are leveraging AI and enabling experiences we only dreamed about a few years ago. LLMs unlock dramatic new capabilities: analyzing large datasets, interpreting abstract ideas, generating concise summaries, interacting in natural language, and shifting fluidly between formats like text, code, and structured data.
Incumbents are revamping their products to fend off the cohort of new businesses. "AI-native" is the new buzzword. But what does it really mean? Does adding a chatbot powered by an LLM make you AI-native? Or bolting on a high-leverage LLM feature? We don’t think so. An AI-native product needs more.
Designing Products Around LLM Gaps
AI-native design begins with a clear view of the shortcomings inherent in today’s LLMs. Put bluntly: LLMs make things up. They hallucinate. They're also inherently probabilistic. At runtime, you can't expect the same output every time.
Perhaps most perniciously, they seem just as confident when they’re wrong as when they’re right. Anyone who’s seen an AI model generate a hand with six fingers knows the stakes. It’s like the old adage: “50% of ad spend is wasted—we just don’t know which 50%.”
In a recent a16z podcast (link), the founder of Instabase put it well: “Enterprises don’t need 100% accuracy, they need predictability.” It's okay for the model to be wrong. What enterprises need is visibility into where it might be wrong. That’s where AI-native design comes into play.
What Makes a Product AI-Native?
An AI-native product acknowledges that the LLM is probabilistic, and sometimes confidently wrong, and provides product infrastructure to manage those inevitable errors. Here are a few of the frameworks we use at Doyen:
Verification architecture. We generate tests for the schemas and code we produce. We also benefit from working with structured financial data, which enables us to perform reconciliations at multiple levels to ensure accuracy.
Human-in-the-loop design. Our interfaces are built to make it easy for users to review, validate, and correct AI output. We surface uncertainty and flag items that need review.
Auditability and observability. We expose clear traces of what the model did, typically in the form of generated code or project plans—so users can inspect, rerun, and debug as needed.
AI-Native: What It Really Takes
An AI-native product isn’t defined by its use of a model. It’s defined by how it validates, supervises, and explains the model’s behavior.
You don’t build trust by pretending the model is perfect. You build it by making imperfections obvious—and manageable.
That’s what it means to be AI-native.