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 barrier to building AI products has collapsed. Aspiring builders should create a one-hour prototype to focus on the truly hard part: validating that they're solving a problem people actually want fixed. The bottleneck has shifted from technical execution to user validation.

Elix founder Lulu Ge launched a beta test called "#periodpainfree" with basic packaging. This allowed her to gauge real-world demand from strangers online before committing resources to a full brand launch, proving the concept's viability cheaply and effectively.

Validate business ideas by creating a fake prototype or wireframe and selling it to customers first. This confirms demand and secures revenue before you invest time and money into development, which the speaker identifies as the hardest part of validation.

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"—a collection of core use cases that validate your platform is solving real user problems and is truly useful.

To avoid over-engineering, validate an AI chatbot using a simple spreadsheet as its knowledge base. This MVP approach quickly tests user adoption and commercial value. The subsequent pain of manually updating the sheet is the best justification for investing engineering resources into a proper data pipeline.

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.

Replace speculative feedback from discovery calls with a process that would be "weird if it didn't work." First, get strangers to pre-pay for a solution. Then, deliver it manually. This confirms real demand (payment) and validates the solution's value (retention) before writing code.

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.