The traditional design-to-engineering handoff is plagued by tedious pixel-pushing. As AI coding tools empower designers to make visual code changes themselves, they will reject this inefficient back-and-forth, fundamentally changing team workflows.

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.

The creative process with AI involves exploring many options, most of which are imperfect. This makes the collaboration a version control problem. Users need tools to easily branch, suggest, review, and merge ideas, much like developers use Git, to manage the AI's prolific but often flawed output.

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.

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.

The idea that design systems stifle creativity stems from the high cost of re-coding components after a design change. In a world with a single source of truth, where design changes automatically update the code, this cost disappears, allowing systems to be radically changed without engineering overhead.

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.

AI coding tools dramatically accelerate development, but this speed amplifies technical debt creation exponentially. A small team can now generate a massive, fragile codebase with inconsistent patterns and sparse documentation, creating maintenance burdens previously seen only in large, legacy organizations.

AI tooling is creating a 'fluid model' where any employee, regardless of role, can potentially ship code. This dramatically expands the design system team's responsibility, which must now create tooling and guardrails to support a much broader and less technical user base across the entire organization.