Over the past few months, we’ve spoken with dozens of design leaders. The design-to-code gap remains top of mind. In fact, the frustration is alive and well: designers build one thing, only to find it ships as something else.
A decade-old problem
This isn’t a new pain point. We've seen waves of attempted fixes:
- Better spec tools like Zeplin, InVision’s Inspect (RIP), and Figma’s Dev Mode.
- Smarter design tools with features like reusable components, design tokens, and auto-layout, designed to embed logic deeper into visual artifacts.
- And operational fixes: mapping design system components to code counterparts, in the hope that naming conventions alone would ensure fidelity.
But the reality? These fixes haven’t scaled. Keeping design and code in sync is operationally heavy, and most organizations don't commit the resources needed to maintain that alignment.
Why the problem might actually go away
Something feels different this time. Emily Campbell (VP of design at HackerRank, and a Chordio advisor and creator of shapeof.ai) recently mentioned she'd be surprised if this problem still existed a few years from now. We're seeing signs that AI might finally make this problem go away—not by managing the handoff better, but by removing the need for a handoff altogether.
Here’s why:
1. AI translates Figma to code automatically.
A great write-up from Waqar Ali at Typeform shows how they taught AI their design system and got it to build UI from Figma with remarkable accuracy.
2. The design medium is becoming code.
Tools like Bolt.new, Lovable, and Figma Make allow teams to generate production-like apps from prompts. These tools are still limited in enterprise settings because they don’t yet integrate cleanly into company tech stacks, but they prove that the medium is shifting.
3. From prototype to production, without rewriting.
Tools like Cursor and Claude Code allow teams to prototype using natural language, directly within their company’s tech stack and design system. When paired with well-configured sandbox environments, this setup gives designers access to real company code in a safe, isolated space. The prototypes they design are grounded in the actual infrastructure and can transition into production with minimal friction.
4. UI is becoming generative and embedded in workflows.
Platforms such as Vercel 's AI SDK, Airtable 's Omni, and Microsoft 's Power Apps are making it possible to assemble interfaces inside domain-aware tools. In many cases, the need for traditional UI design disappears altogether.
The real last mile to bridge the design-code gap: adoption
AI-native generation, the convergence of design and code, and natural-language tooling will solve 80–90% of fidelity issues in the next few years. The real blocker won’t be tech. It’ll be how fast teams are willing to change how they work.
Design-to-code is solved. Judgment isn’t.
As AI helps solve one problem, it introduces another. In a world where design seamlessly translates to code, the questions still remain: Is this design good? Does it meet our standards?
The bad news? With AI accelerating the pace of design generation, it becomes impossible to answer these questions at human speed. The good news? AI can help answer these questions at scale. The next bottleneck in design-to-code will not be solved with better inspection tools, but with automated design QA.