AI doesn't replace creative experts; it elevates their role. Their craft shifts from manually creating individual assets to designing and building robust, reusable AI systems that empower the entire organization to generate on-brand content.

AI models are proficient at the busywork of turning concepts into tangible formats like mockups or code, but they still lack genuine design thinking. This elevates the designer's role, freeing them to focus on deep thinking and strategy, as their primary value now lies in the quality and originality of their ideas.

At a research-led company like OpenAI, a designer's role expands beyond packaging existing technology. They must envision what the technology *should* do to solve user problems, thereby setting a vision that helps direct future research and engineering efforts.

The role of a designer on a developer-focused AI product has fundamentally changed. Ed Bays from OpenAI reports spending 70-80% of his time coding, indicating that design execution at the frontier is now primarily a software engineering discipline.

The role of an expert designer in an AI-powered organization splits in two. They must build systems to harness the influx of competent work from non-designers, and also use AI to explore and create entirely new, previously impossible user experiences.

At OpenAI, the first question is "Can we solve this with the model (tokens) instead of pixels?" This treats the AI as the primary design material, pushing designers to think about interaction and behavior before creating bespoke user interfaces.

For non-deterministic AI systems where every output can't be controlled, designers' roles must evolve to creating "evals"—systems that define and test for quality. Figma's CDO argues this is crucial for ensuring experiences generated by algorithms or LLMs consistently meet the desired standard of "good."

As AI models become proficient at generating high-quality UI from prompts, the value of manual design execution will diminish. A professional designer's key differentiator will become their ability to build the underlying, unique component libraries and design systems that AI will use to create those UIs.

As AI masters content generation, it will handle the "blank page" problem. The crucial human task will then shift from creation to evaluation: defining what 'good' looks like, identifying AI failure modes, and building better verification systems to ensure outputs are trustworthy and useful.