Google's strategy involves the core AI model progressively absorbing the surrounding tooling and infrastructure (the "scaffolding"). This creates a standardized, extensible "harness" that accelerates development and ensures a consistent, high-quality agentic experience across Google's vast and diverse product landscape, from Search to consumer apps.

The current user experience for AI tools is too complex, forcing users to make choices like which model or mode to use. The next major step is a unified, consolidated interface where the AI intelligently handles resource allocation behind the scenes, simply delivering 'intelligence'.

Giving AI freedom to generate custom layouts dynamically risks disorienting users if interface patterns shift with every response. Rhiannon Bell explains that while AI can assemble components from design systems, designers must enforce human-defined rules. Predictability in where answers, interactive widgets, and links appear is critical so users do not have to learn a brand-new interface on each query.

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.

The future of AI interaction won't be a multitude of specialized apps. Instead, it will likely converge into a smaller number of powerful, generalized input boxes that intelligently route user intent, much like the Chrome address bar or Google's main search page.

For generative UI, designers move from defining exact layouts to creating a "skill" that guides the AI. This skill defines a design language, references tokens, and suggests best practices, steering the model's output without over-constraining it.

To use AI effectively in design, organizations need a mature, machine-readable design system. AI tools rely on this system's well-documented components and rules to assemble experiences consistently. Without it, AI-generated designs risk being off-brand and low-quality. Investing in the design system is a key prerequisite for mitigating AI risks at scale.

AI models are poor at "last-mile" visual design. However, if a human designer invests heavily in creating a perfect set of primitives (e.g., buttons, cards), AI becomes incredibly effective at reusing and intelligently extrapolating from that foundation for new contexts. Human effort on the core system pays off exponentially.

At Eleven Labs, product design isn't about applying existing patterns. When the research team develops a new model with unprecedented capabilities, the designer's job is to invent a completely new interface. This requires the ability to discard classical UI conventions and imagine interactions from scratch.