As AI agents become the primary 'users' of software, design priorities must change. Optimization will move away from visual hierarchy for human eyes and toward structured, machine-legible systems that agents can reliably interpret and operate, making function more important than form.

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.

A major focus for OpenAI's design team is the growing gap between what their models are capable of and what users actually know they can do. The design team's job is to create interfaces and tools that expose the model's full potential to the user.

Contrary to assumption, the design process at OpenAI isn't about planning for a distant future. It's a fast-paced environment where designers work in close concert with the latest research advancements, adapting to new capabilities as they emerge.

At OpenAI, the development cycle is accelerated by a practice called "vibe coding." Designers and PMs build functional prototypes directly with AI tools like Codex. This visual, interactive method is often faster and more effective for communicating ideas than writing traditional product specifications.

OpenAI is developing a "dynamic user interface library" designed so the AI model can interpret and compose UI elements itself. This forward-thinking approach anticipates a future where the model assembles bespoke interfaces for users on the fly.

With AI, designers are no longer just guessing user intent to build static interfaces. Their new primary role is to facilitate the interaction between a user and the AI model, helping users communicate their intent, understand the model's response, and build a trusted relationship with the system.

Despite comparable model capabilities, OpenAI's thoughtful UX, like providing trending templates in a TikTok-style feed for image generation, successfully guides users. In contrast, Google's blank-slate interfaces can intimidate users, proving that small product details are crucial for adoption.

Building a true AI product starts by defining its core capabilities in an AI playground to understand what's possible. This exploration informs the AI architecture and user interface, a reverse process from traditional software where UI design often comes first.