Figma's success as a general-purpose design tool (useful for posters, floor plans, etc.) is precisely what makes it suboptimal for software development. Its WebGL-based canvas is fundamentally disconnected from the DOM, creating a "pretty picture" that requires a separate, costly engineering effort to translate into code.
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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.
At Perplexity, the design system lives in the codebase, not Figma. Designers contribute directly to the frontend, creating a single source of truth that eliminates drift between design files and production code, forcing a highly practical and collaborative process.
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).
While tools like Miro serve many use cases adequately, they are a "bad fit for all of them." The future of canvas tools lies in vertical-specific applications that go deeper on a single use case (e.g., pharmaceutical workflows, remote onboarding), offering a more powerful, tailored feature set that a generic tool cannot match.
Early versions of Figma failed to gain traction because designers, its target users, fundamentally didn't trust the tool's own subpar visual design. This meta-problem highlights that for a tool to be credible to its expert users, its own execution must embody the principles it espouses. A redesign was the key to unlocking user trust and adoption.
Contrary to conventional startup advice, Figma's founders began with a fascination for a technology (WebGL) and then searched for a problem to solve. This technology-first approach, a hammer looking for a nail, led them to explore various failed ideas like face-swapping before eventually landing on collaborative design tools.
For individuals who both design and code, finishing a visual design isn't a moment of triumph but one of dread, as they know the lengthy process of coding it from scratch has just begun. This specific emotional pain point is a core motivator for building next-generation tools that eliminate this redundant step.
The current model of separate design files and codebases is inefficient. Future tools will enable designers to directly manipulate production code through a visual canvas, eliminating the handoff process and creating a single, shared source of truth for the entire team.
Traditionally, designers needed to understand code limitations to create feasible UIs. With tools that render a live DOM on the canvas, this is no longer necessary. If a design can be created in the tool, it is, by definition, valid and buildable code.
AI coding tools generate functional but often generic designs. The key to creating a beautiful, personalized application is for the human to act as a creative director. This involves rejecting default outputs, finding specific aesthetic inspirations, and guiding the AI to implement a curated human vision.
