Design Systems at Scale

Most design systems die. They launch with fanfare, live in Figma for six months, and then quietly become a graveyard of outdated components that nobody trusts.

Building a design system that survives requires understanding what actually kills them—and designing against those failure modes.

What Kills Design Systems

1. Governance failure. Nobody owns it. Or too many people own it. Updates happen randomly. Quality varies wildly.

2. Reality drift. The system in Figma diverges from the system in code. Designers design components that don't exist. Engineers build components that aren't documented.

3. Adoption friction. It's easier to build from scratch than to find and use the "right" component. The system adds work instead of removing it.

4. Scope creep. The system tries to solve everything. It becomes a bloated monster that nobody understands.

What Makes Systems Survive

1. Clear ownership with distributed contribution. One team owns the system. Everyone can contribute. There's a clear process for proposing, reviewing, and merging changes.

2. Design-code parity. The Figma component and the React component are always in sync. Breaking this parity is treated as a bug.

3. Documentation that's actually useful. Not just "here's the component." But: "Here's when to use it. Here's when NOT to use it. Here's an example in context."

4. Ruthless scope management. The system does a few things perfectly. If something doesn't need to be shared, it doesn't go in the system.

AI-Native Design Systems

In an AI-native world, design systems need new components:

• Uncertainty banners and confidence signals
• Loading states that show work, not just spinners
• Kill switches and undo patterns
• Citation and provenance components
• Human-in-the-loop approval flows

Your design system should make it easy to build trustworthy AI features. If every team is reinventing "how do we show the user what the AI is doing," your system has failed.

Measuring System Health

Track:

• Adoption rate: What percentage of new features use system components?
• Contribution rate: How many people outside the core team are contributing?
• Drift incidents: How often do design and code diverge?
• Time to component: How long does it take to add a new component to the system?

A healthy system has high adoption, distributed contribution, low drift, and a fast path to adding new components.

An unhealthy system is a tax. A healthy system is leverage. Build leverage.