Vercel's Pranati Perry explains that tools like V0 occupy a new space between static design (Figma) and development. They enable designers and PMs to create interactive prototypes that better communicate intent, supplement PRDs, and explore dynamic states without requiring full engineering resources.

Prototyping directly in the production environment makes high-quality interactions achievable without extensive resources. This dissolves the traditional design dilemma of sacrificing quality for speed, allowing teams to build better products faster.

AI-powered "vibe coding" is reversing the design workflow. Instead of starting in Figma, designers now build functional prototypes directly with code-generating tools. Figma has shifted from being the first step (exploration) to the last step (fine-tuning the final 20% of pixel-perfect details).

Contrary to claims that "handoff is dead," designers at top companies use AI-generated prototypes as highly detailed specs. These interactive prototypes provide more information than static designs but are still handed off to developers for implementation, rather than being merged directly into production.

AI coding agents enable "vibe coding," where non-engineers like designers can build functional prototypes without deep technical expertise. This accelerates iteration by allowing designers to translate ideas directly into interactive surfaces for testing.

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.

AI co-pilots have accelerated engineering velocity to the point where traditional design-led workflows are now the slowest part of product development. In response, some agile teams are flipping the process, having engineers build a functional prototype first and then creating formal Figma designs and UI polish later.

Building a true AI product starts by defining its core capabilities in an AI playground to understand what's possible. This exploration informs the AI architecture and user interface, a reverse process from traditional software where UI design often comes first.

Designers need to get into code faster not just for prototyping, but because the AI model is an active participant in the user experience. You cannot fully design the user's interaction without directly understanding how this non-human "third party" behaves, responds, and affects the outcome.