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.
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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.
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.
In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.
The team obsesses over perfecting the BCI cursor, treating it as the key to user agency on a computer. However, the long-term vision is to eliminate the cursor entirely by reading user intent directly. This creates a fascinating tension of building a masterwork destined for obsolescence.
Unlike pure software, a product combining hardware, software, and content can't be validated with a "smaller, crappier version." The core user experience—the "fun"—only emerges when all components are polished and working together seamlessly, a moment that often arrives very late in the development cycle.
Unconventional AI operates as a "practical research lab" by explicitly deferring manufacturing constraints during initial innovation. The focus is purely on establishing "existence proofs" for new ideas, preventing premature optimization from killing potentially transformative but difficult-to-build concepts.
Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.
To truly validate their idea, Moonshot AI's founders deliberately sought negative feedback. This approach of "trying to get the no's" ensures honest market signals, helping them avoid the trap of false positive validation from contacts who are just being polite.
The misconception that discovery slows down delivery is dangerous. Like stretching before a race prevents injury, proper, time-boxed discovery prevents building the wrong thing. This avoids costly code rewrites and iterative launches that miss the mark, ultimately speeding up the delivery of a successful product.
