What Happens to Design After AI?
In this A16Z podcast, Microsoft VP of Design John Maeda and Impeccable founder Paul Backus discuss how AI is transforming design by automating routine work and raising the floor for average design quality, while arguing that human taste, craft, and conviction will become increasingly valuable for creating distinctive, high-end experiences that differentiate in a commoditized market.
Summary
The conversation explores the intersection of design, technology, and AI, beginning with John Maeda's observation that design became critical in the mobile era because high-frequency usage made poor UX painful. He introduces the concept of "auto design"—the vision of machines creating things humans are good at—tracing it back to Muriel Cooper's work at MIT in the 1980s predicting electronic publishing.
Paul Backus discusses Impeccable, an open-source design agent that addresses a critical gap: engineers using AI models get worse results than designers because they lack design vocabulary. Impeccable teaches both engineers and designers the language of design (vertical rhythm, negative space, making things "bolder" or "quieter") to better guide AI models. The tool includes three components: vocabulary, a quality layer that prevents "slop," and visual iteration modes that run in developers' codebases.
The speakers discuss the commoditization of design through AI automation. Paul notes that "slop" is a moving target—when purple gradients became ubiquitous (due to Tailwind's default theme), the models moved to beige backgrounds and serif fonts. Impeccable uses anti-attractors and randomization to create uniqueness rather than steering toward a different overused style. John predicts a shift from UX to AX (agentic experience)—designing for agents rather than humans, including API design, CLI design, and information architecture.
A central theme emerges around raising the floor versus raising the ceiling. The speakers argue that AI will make average-quality design accessible to everyone, but this creates opportunity for craftspeople to focus on the top 10-20% of distinctive, high-end work. John references the Arts and Crafts movement as a historical parallel, where mechanization sparked renewed human craft. Paul argues that human trust and accountability become more valuable when base-level quality is automated, creating smaller but more viable markets for bespoke, opinion-driven products.
The conversation addresses taste—how it emerges from scarcity and cultural maturity, and how LLMs don't truly understand taste because they're trained on humanity's output, not input (the decisions behind designs). John notes that taste is cultural and emerges through scarcity of materials; in an era of abundance, this concept requires rethinking.
Both speakers emphasize the importance of conviction in leadership—the ability to bet on a global maximum rather than a local maximum. Paul shares strategies for communicating design instinct versus deadline pressure: bringing leaders into the same emotional journey and mindset, helping them see the future you're envisioning together. The discussion concludes with excitement about GitHub's integration of Impeccable and the possibility of a new era of computational craft.
About this episode
Anish Acharya speaks with Microsoft VP of Design John Maeda and Impeccable founder and CEO Paul Bakaus about how AI is changing the practice of design. The conversation explores the relationship between design and technology, the rise of AI-powered creative tools, and whether automation raises the floor, the ceiling, or both. Maeda and Bakaus discuss software craftsmanship, taste, creative judgment, and why some aspects of design may become increasingly automated while others become more valuable. They also examine agentic workflows, the future of user experience, the role of designers in an AI-native world, and how new tools may reshape the relationship between designers, engineers, and software itself.
Key Insights
- Designers consistently get better results from AI models than engineers because designers use specialized vocabulary like 'vertical rhythm' and 'negative space' to guide the models, while engineers lack this design language.
- LLMs have been trained on humanity's output, not input, meaning they don't understand the reasoning behind design decisions and can only approximate taste for certain audiences and time periods.
- Taste emerges from scarcity and cultural maturity; as all materials become available to everyone, the traditional European concept of taste based on distinctive use of scarce materials no longer applies.
- AI-generated design 'slop' is a moving target—once purple gradients became ubiquitous, models naturally shifted to beige backgrounds and serif fonts, indicating the need for ongoing anti-attractors rather than one-time fixes.
- The design industry is shifting from raising the floor (automating routine, average-quality work) to raising the ceiling (enabling distinctive, high-end craft), creating smaller but more viable markets for bespoke, opinionated products.
- Human trust and accountability become more valuable as a competitive differentiator once base-level design quality is automated, making bespoke work worth premium prices.
- The next design frontier is agentic experience (AX)—designing for machine interactions including APIs, CLIs, and information architecture—rather than purely visual user experience.
- Design leaders must demonstrate conviction by bringing stakeholders into the same emotional journey about a future vision, rather than purely defending design nuance against deadline pressure and business metrics.
Topics
Transcript
Designers when using Claude, as opposed to engineers using Claude, would consistently get better results. And it's because of the language that they use. We have to remember that design in the European sense came from royalty and the desire to be distinctive because they were working with scarce materials. What's interesting about this era is that this idea of taste doesn't fit when all the materials are available to everyone. Right now everybody's trying to solve whether LLMs have taste. These models have millions of definitions of taste. LLMs have been trained on the output of humanity, not on the input. So what led to a design decision is not something that the LLMs know. Maybe advice for our…
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