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.
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A key reason Figma won was its cloud-based, real-time collaboration. The trend of using local AI dev tools (like Cursor) is a step backward in this regard, reintroducing friction around sharing work and getting feedback, the very problems that led designers away from local files in the first place.
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.
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.
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.
The emerging paradigm is a central coding agent with multiple specialized input tools. A canvas tool (like Paper) will be for visual prompting, an IDE (like Cursor) will be for code refinement, and a text prompt will be for direct commands, all interoperating with the same agent to build software.
Companies like Shopify and Atlassian now require designers to use AI tools like Cursor and Claude in their work, enforced through performance reviews. This top-down mandate aims to accelerate exploration of new workflows, such as stateful prototyping, and overcome the friction of adopting new tools amidst tight deadlines.
Use the Claude chat application for deep research on technical architecture and best practices *before* coding. It can research topics for over 10 minutes, providing a well-summarized plan that you can then feed into a dedicated coding tool like Cursor or Claude Code for implementation.
Instead of creating static mockups in Figma, Cursor's design team prototypes directly in their AI code editor. This allows them to interact with the "life states of the app" and get a more realistic feel for the product, bridging the gap between design and engineering.
A seasoned CTO finds negligible performance differences between major AI coding tools (Claude, CodeX, Cursor) for rapid prototyping. The primary value is speed, not marginal accuracy. Subscribing to multiple services is more for staying current with market trends than for a specific tool's superiority.
For over a decade, software development fragmented into siloed roles (PM, Design, Eng) with their own tools. AI code editors are collapsing these boundaries by creating a unified workspace where a single "maker" or a streamlined team can build, iterate, and ship, much like in the early days of computing.
