Conditioning AI on design system tokens for consistent, on-brand output

What two months of working inside Claude Code taught me about design systems, technical negotiation, and the future of IC work

As we entered 2026, my weekly feed has been saturated with forecasts from top-tier practitioners regarding the future of work. These predictions trigger a unique blend of anxiety and excitement — anxiety from the fear of falling behind, but excitement because the barriers to building products that once required an entire team are being systematically lowered.

Based on the insights I’ve gathered, I believe the ‘New IC’ (Individual Contributor) is on the rise. These are not passive freelancers forced into independence by downsizing; they are professionals who actively choose to oversee the entire lifecycle of planning, research, design, and delivery. Their scarcity stems from their T-shaped knowledge structure:

A deep, specialized core, a broad, cross-disciplinary vision, and crucially, the ability to translate their expertise into a language that AI Agents can execute.

This article is a documentation of my journey over the past two months — a transition from ‘using AI tools to speed up tasks’ to ‘co-architecting systems with AI Agents.’ It all boils down to one fundamental question:

Can a designer, empowered by AI Agents, independently build a maintainable and scalable product system?

Everyone is talking about AI replacing designers. I think we need to have an honest conversation.

I am not anti-AI. In fact, I think tools like Claude and MCP services have genuinely changed how designers work, automating repetitive tasks, speeding up component generation, and helping early-stage teams move faster. That's real value, and I won't dismiss it.

But let's stop pretending there are no trade-offs.


a𝗧𝗵𝗲 𝗰𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 nobody talks about
Every serious AI workflow runs on credits. Token costs add up fast, especially when you're iterating, generating variants, or running MCP pipelines at scale. Anyone telling you AI is free either hasn't used it extensively or doesn't understand how the credit system works.


𝗔𝗜 𝗻𝗲𝗲𝗱𝘀 𝘆𝗼𝘂𝗿 𝗱𝗲𝘀𝗶𝗴𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗼 𝗯𝗲 𝘂𝘀𝗲𝗳𝘂𝗹
AI doesn't generate consistency on its own. To get reliable, on-brand output you need to feed it your design tokens, component logic, and spacing rules first. Ironically, you need a designer to build the foundation that makes AI useful for design. For early-stage products, this workflow can be powerful. For large, distributed teams with an established brand identity, the gaps become harder to ignore.


𝗧𝗵𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗰𝗲𝗶𝗹𝗶𝗻𝗴 𝗶𝘀 𝗿𝗲𝗮𝗹
AI learns from what exists on the web. It recombines, it optimises, it executes, but it doesn't invent. If you want your product to look like every other modern SaaS tool in the market, AI will serve you well. But if differentiated visual identity and brand depth matter to you, AI output will feel generic without significant human direction.

Image and video generation? Impressive. Building a product that feels genuinely personalised and distinct? We are still far from that.


𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲
AI is a powerful tool in the right hands. The designer who understands how to direct it will always outperform the one who blindly follows the hype — or the one who refuses to engage with it at all.

The future isn't AI replacing designers. It's designers who understand AI, replacing those who don't.

What's your experience been working with AI in your design workflow?