With AI accelerating idea generation, designers must be comfortable with their work being thrown away. John Bai's process involves extensive Figma sketches and prototypes that leadership never sees. The goal is rapid learning, not preserving every concept.

AI tools democratize prototyping, but their true power is in rapidly exploring multiple ideas (divergence) and then testing and refining them (convergence). This dramatically accelerates the creative and validation process before significant engineering resources are committed.

With AI, teams can create crude prototypes immediately after a customer call. This "build to learn" phase cheaply validates ideas. Only after confirming market need should teams shift to "build to earn," investing in scalable development. This strategy mitigates the risk of building unwanted products at high speed.

AI drastically lowers the cost of exploration. The best teams leverage this by building many prototypes and exploring multiple directions, knowing most will be discarded. This 'wasted work' is a sign of effective discovery, leading to better final products.

AI removes the dependency on engineering for prototyping. Designers can now build high-fidelity demos themselves, allowing them to visualize and sell an idea to stakeholders much faster without having to persuade a developer to join their journey first.

Traditional product development (PRD-first) was designed to protect scarce engineering resources. With AI making software creation as easy as writing a document, teams can shift to a prototype-first approach, where ideas are built and tested immediately without agonizing over ROI.

Years of focusing on MVPs has weakened the ability of product teams to imagine magical, delightful features. AI prototyping tools make ambitious ideas easier to build, helping teams reignite their creative muscles and aim for awesome products, not just viable ones.

AI prototyping allows product managers to build out every feature of an idea cheaply. This process of externalizing the full scope often reveals which features are unnecessary. This rapid iteration helps them 'detox' from initial over-scoping and arrive at the core value proposition before involving engineering.

Features like Anthropic's 'weatherman' concept—where a user's camera feed is overlaid on a design for feedback—would likely never be prototyped in a traditional workflow due to high effort. AI dramatically lowers the barrier to experimentation, allowing teams to explore unconventional ideas that can lead to unexpected value.