Medium: Six product principles from building an AI startup

What the article is about

David Garnitz outlines six product principles drawn from his experience building Yapify, an email voice agent, over three years, as well as earlier work on Atrium, an AI-powered search tool for investment firms. The article is not theoretical — each principle addresses a specific mistake or trap he observed firsthand, and most of them contradict advice commonly repeated in startup culture.

Context

Garnitz published in April 2026. The AI startup environment he describes is one where tooling has made it faster than ever to ship, but where the ease of building has not resolved the harder question of what is worth building. His experience spans a B2B SaaS tool (Atrium) and a consumer-adjacent workflow product (Yapify), giving the principles some coverage across different product contexts.

Key takeaways and methods

The first principle — building one thing excellently rather than many things adequately — runs against the instinct to ship features in response to every user request. Garnitz argues that unnecessary features make it harder to isolate whether the core value proposition is working, which matters most in early stages when a product’s fundamental viability is still unknown.

The second principle, embedding within existing tools, is specific to AI products: adoption friction is lower when the product meets users inside tools they already open daily. Garnitz points to Cursor IDE as an example of this working well. He draws a contrast with GitHub Copilot, which underperformed expectations relative to the underlying technology, as an illustration of what can happen when integration is not prioritized.

The remaining four principles cover problem-first thinking over technology-first building, avoiding differentiation that generates unnecessary complexity, the gap between product-market fit and growth readiness, and the time required to do genuinely excellent work rather than fast-but-sufficient work.

Who it is useful for

Product managers and founders at early-stage AI startups, particularly those deciding which features to build or evaluating whether their current product has enough focus to be meaningfully tested with users.