Unlike pure software, a product combining hardware, software, and content can't be validated with a "smaller, crappier version." The core user experience鈥攖he "fun"鈥攐nly emerges when all components are polished and working together seamlessly, a moment that often arrives very late in the development cycle.
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Founders often get stuck endlessly perfecting a product, believing it must be flawless before launch. This is a fallacy, as "perfection" is subjective. The correct approach is to launch early and iterate based on real market feedback, as there is no perfect time to start.
For AI products, the quality of the model's response is paramount. Before building a full feature (MVP), first validate that you can achieve a 'Minimum Viable Output' (MVO). If the core AI output isn't reliable and desirable, don't waste time productizing the feature around it.
Product teams often use placeholder text and duplicate UI components, but users don't provide good feedback on unrealistic designs. A prototype with authentic, varied content鈥攅ven if the UI is simpler鈥攚ill elicit far more valuable user feedback because it feels real.
While the goal is to build a platform (second-order thinking), initial single-purpose app ideas (first-order) are critical. They serve as your "golden evaluation set"鈥攁 collection of core use cases that validate your platform is solving real user problems and is truly useful.
To create a truly unique value proposition, the "Bored" team prioritized game mechanics that leveraged the combination of physical pieces and a digital surface. For example, one game uses the height (Z-axis) of stackable pieces, an interaction that cannot be replicated on a standard tablet.
The obsession with lean methodology has created a market of low-quality, uninspiring software. In this environment, building a polished, considered, and beautiful end-to-end product is no longer a luxury but a true competitive advantage that stands out and inspires users.
Historically, resource-intensive prototyping (requiring designers and tools like Figma) was reserved for major features. AI tools reduce prototype creation time to minutes, allowing PMs to de-risk even minor features with user testing and solution discovery, improving the entire product's success rate.
For frontier technologies like BCIs, a Minimum Viable Product can be self-defeating because a "mid" signal from a hacky prototype is uninformative. Neuralink invests significant polish into experiments, ensuring that if an idea fails, it's because the concept is wrong, not because the execution was poor.
To cut through MVP debates, apply a simple test: What is the problem? What is its cause? What solution addresses it? If you can remove a feature component and the core problem is still solved, it is not part of the MVP. If not, it is essential.
