Artist's co-founder warns that the biggest mistake founders make is building technology too early. Her team validated their text-based learning concept by manually texting early users, confirming the core hypothesis and user engagement before committing significant engineering resources.

To de-risk innovation, teams must avoid the trap of building easy foundational parts (the "pedestal") first. Drawing on Alphabet X's model, they should instead tackle the hardest, most uncertain challenge (the "monkey"). If the core problem is unsolvable, the pedestal is worthless.

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.

A startup's success depends on many factors working in concert. Founders often default to their strengths (e.g., an engineer building the product). The correct, de-risking approach is to first tackle the biggest uncertainty or personal weakness, such as customer acquisition.

Instead of creating a massive risk register, identify the core assumptions your product relies on. Prioritize testing the one that, if proven wrong, would cause your product to fail the fastest. This focuses effort on existential threats over minor issues.

When facing a major technical unknown or skill gap, don't just push forward. Give the engineering team a dedicated timebox, like a full sprint, to research, prototype, and recommend a path forward. This empowers the team, improves the solution, and provides clear data for build-vs-buy decisions.

The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.

Validate startup ideas by building the simplest possible front end—what the customer sees—while handling all back-end logistics manually. This allows founders to prove customers will pay for a concept before over-investing in expensive technology, operations, or infrastructure.

Drawing from Verkada's decision to build its own hardware, the strategy is to intentionally tackle difficult, foundational challenges early on. While this requires more upfront investment and delays initial traction, it creates an immense competitive barrier that latecomers will struggle to overcome.