Five Eras of UX and the Design Principles to Carry into AI
1. Human factors and the web: start with people and information architecture
The first two eras of UX focused on the whole systems people use and the organization of information.
- The human factors era (1940s–1988)
Bell Labs studied telephone keypad layouts around hands, memory, and how people read numbers.
- Principle to keep: include physical interaction and guidance in the experience, alongside screens.
- Habit to drop: narrow UX to the screens and pixels designers can most easily control.
- The web era (1994–2007)
As websites multiplied, organizing information and making it findable became essential.
- Principle to keep: treat naming, classification, and hierarchy as foundations of product design.
- Habit to drop: treat information architecture as a sitemap you create once and file away.
2. Mobile: priorities still matter when constraints disappear
Small screens forced choices about what to show and what to remove.
- The mobile era (2007–2014) made one-handed use on the move a reference point for design.
- Primary actions came first, with additional information revealed when needed.
- Bigger screens and easier production still require criteria for choosing.
- Principle to keep: remove features and information according to the user’s purpose.
- Habit to drop: prioritize only when devices or technology impose constraints.
3. Design systems: reduce repetition and make time for judgment
Standardization creates room to focus on important problems.
- The design systems era (2014–2022) improved production speed and consistency through tokens and reusable components.
- Teams had less need to recreate the same button or revisit settled decisions.
- Changing production costs also invite a review of approval and handoff processes.
- Principle to keep: systematize recurring decisions.
- Habit to drop: retain procedures from a time when execution was slow and expensive.
4. AI: decide what to build and how it should work
The AI era (2022–present) highlights the role of designing system rules and behavior.
- The information a model consults, the actions it takes, and the scope of its involvement shape the user experience.
- A whole-system human factors perspective and clear information architecture become important again.
- The author explains earlier technological shifts through Jevons paradox: lower production costs can increase use and demand.
- He suggests demand could expand with AI, but this does not guarantee growth for every design role.
- Principle to keep: exercise judgment about how a system should work.
- Habit to drop: assess design’s value solely by the number of screens generated and the speed of production.
XploreD Perspective
A journey through five eras that helps distinguish principles to retain from practices worth reconsidering in today’s work.
When to read
- When defining the designer’s role and scope in AI products.
- When reviewing whether your team’s production and approval processes still serve a purpose.
What to take away
- Include the information AI can access, the actions it can take, and the features to exclude in product reviews.
- Consider whether time saved on repetitive work is going toward problem definition and validation of the user experience.
