After 8 years designing at Meta, George Kedenburg III pulled a 180 and joined Humane as a design lead. So this conversation is a deep dive into designing AI products and how the role of product designer evolves in an AI-native company:
- How to become a creative problem solver
- How George navigates ambiguity at Humane
- Why there’s no such thing as an edge case with AI
- What George learned while using AI to learn Python
- How AI is reshaping the landscape for software design
- Why George created a Slackbot to prototype his ideas
- Why designing AI products is a bit like designing a kitchen
- a lot more
Pushing past the pixels
The real value of design is being able to look at an ambiguous situation and understand what you should explore.
Rectangles so happen to be the most common way to express that value. But the real skill is creative problem solving.
Working at a company like Humane forces designers to contribute design thinking beyond the pixels.
Prompt design > prompt engineering
If the AI model is a chef, then you’re responsible for designing the kitchen.
You don’t know what the user will order, so it’s a lot of trial and error to ensure you have the right data on hand at the right moments.
It’s no different than thinking through drop-off in an onboarding flow. Which is why George views working with these models as “prompt design” rather than “prompt engineering”
There are no AI edge cases
When you’re prototyping AI products, your prototypes don’t “break” or “fall over” like they do in Figma. That’s because the boundaries of what exists in the prototype become much blurrier.
Instead of designing contained flows, you’re laying a foundation and allowing the model to extrapolate out from there. There are no more hard edges.
George mentions Claude Artifacts as an example of someone putting the pieces together in the right order
