AI tools accelerate development. Instead of using this new speed to add more features (increasing scope), designers should leverage it to deepen the craft and quality of the core, essential features, creating an experience users have never seen before.

True design intuition isn't innate; it's built through repetition. The fastest way to learn is to take many "shots on goal." Focus on generating a high quantity of rough, low-fidelity ideas and storyboards, rather than a few polished ones, to accelerate your learning and discovery process.

The focus on AI making work 'faster' misses its true value for designers. The real power lies in enabling them to push ideas 'further' into high-fidelity, interactive prototypes, allowing for deeper exploration and clearer communication of intent without engineering dependencies.

Karri Saarinen of Linear posits that design should be a "search" phase, free from coding constraints. Jumping directly into code introduces biases from the existing codebase, making designers more conservative and less idealistic, which ultimately hinders breakthrough product ideas.

Separate product development into two phases. The problem-finding and decision-making phase should remain slow and deliberate to ensure quality. However, once a decision is committed, AI tools should be leveraged to make the execution and feedback loops as fast as possible.

AI co-pilots have accelerated engineering velocity to the point where traditional design-led workflows are now the slowest part of product development. In response, some agile teams are flipping the process, having engineers build a functional prototype first and then creating formal Figma designs and UI polish later.

For creative work like design, AI's true value isn't just accelerating tasks. It's enabling designers to explore a much wider option space, test more possibilities, and apply more craft to the final choice. Since design is non-deterministic, AI serves creative exploration more than simple speed.

Figma's CEO argues that while agentic coding systems are powerful, they risk being too linear. True product innovation requires exploring a wide option space through design, using systems and components to ensure a cohesive user journey. Relying solely on code generation can lead to a suboptimal product, even if it's built quickly.

To build an effective AI product, founders should first perform the service manually. This direct interaction reveals nuanced user needs, providing an essential blueprint for designing AI that successfully replaces the human process and avoids building a tool that misses the mark.