When stakeholders interact with a feature built in actual code, it feels nearly finished. This creates an "aura of inevitability," shifting the decision from allocating resources for exploration to a simple "yes/no" on shipping the feature, which dramatically accelerates buy-in.

To keep pace with AI development, the barrier between design and engineering must fall. Intercom made it a non-negotiable job requirement for every product designer to ship code to production. This empowers them to fix UI bugs directly and accelerates the entire development cycle.

An interaction can look perfect in a static tool like Figma but feel terrible when built. Prototyping allows designers to experience the 'feel' of their work—a crucial step for validating ideas, developing intuition, and creating higher-quality products that you can't get from static mockups alone.

Karri Saarinen of Linear posits that design should be a "search" phase, free from coding constraints. Jumping directly into code introduces biases from the existing codebase, making designers more conservative and less idealistic, which ultimately hinders breakthrough product ideas.

At OpenAI, the development cycle is accelerated by a practice called "vibe coding." Designers and PMs build functional prototypes directly with AI tools like Codex. This visual, interactive method is often faster and more effective for communicating ideas than writing traditional product specifications.

High-fidelity, code-based prototypes are replacing static mockups as the primary artifact for design-to-engineering handoffs. At Stripe, engineers can use the prototype's code as a direct source of truth, minimizing translation errors and ambiguity from design to production.

In design thinking, early prototypes aren't for validating a near-finished product. They are rough, low-cost "artifacts" (like bedsheets for walls) designed to help stakeholders vividly pre-experience a new reality. This generates more accurate feedback and invites interaction before significant investment.

Resist the temptation to treat AI-generated prototype code as production-ready. Its purpose is discovery—validating ideas and user experiences. The code is not built to be scalable, maintainable, or robust. Let your engineering team translate the validated prototype into production-level code.

The primary value of PMs and designers coding isn't to increase feature velocity. It's to gain a deep, intuitive understanding of the material they are designing with, such as how an AI agent loop works. This mastery of the medium is more critical than direct code contributions.