To enable AI-powered prototyping without production risks, large tech companies are creating separate, forked repositories for designers. This "designer playground" approach avoids the friction of production environments (e.g., linting, deploys) while providing a real-world starting point for stateful design exploration.
In large companies, designers overwhelmingly use local AI coding tools (Cursor, Claude) over cloud-based ones (Replit, V0). The key advantage is using the company's real production app as a "starting place," which eliminates the need to recreate screens or components from scratch for every prototype.
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.
The biggest hurdle for enterprise AI adoption is uncertainty. A dedicated "lab" environment allows brands to experiment safely with partners like Microsoft. This lets them pressure-test AI applications, fine-tune models on their data, and build confidence before deploying at scale, addressing fears of losing control over data and brand voice.
The biggest barrier for designers entering the codebase isn't writing code, but the complex, brittle setup of a local development environment. Tools that abstract this away into one-click, sandboxed environments are critical for unlocking designer participation.
Connecting to a design system is insufficient. AI design tools gain true power by using the entire production codebase as context. This leverages years of embedded decisions, patterns, and "tribal knowledge" that design systems alone cannot capture.
While forked codebases empower designers with AI tools, they create a new operational cost. Teams must now maintain two copies of the app—the real one and the designer one—which risks falling out of date. This mirrors the long-standing problem of syncing Figma design systems with production code.
While traditionally creating cultural friction, separate innovation teams are now more viable thanks to AI. The ability to go from idea to prototype extremely fast and leanly allows a small team to explore the "next frontier" without derailing the core product org, provided clear handoff rules exist.
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.
A prototype-first culture, accelerated by AI tools, allows teams to surface and resolve design and workflow conflicts early. At Webflow, designers were asked to 'harmonize' their separate prototypes, preventing a costly integration problem that would have been much harder to fix later in the development cycle.