Back in 2013, Quantum UX described experiences generated on the fly by AI reading user behavior. A decade later, that is just how generative AI works.
Quantum UX (QUX) is a UX framework Fabio Devin created in 2013 that describes experiences generated on the fly by understanding user requests and behavior through AI and data analysis. It anticipated generative AI nearly a decade early, including how models would understand users through vectors (what QUX called XEL) and serve adaptive content. Its principles are now visible across ChatGPT, Gemini, Adobe, Figma and social feeds, whether or not those teams ever heard the name.
Key takeaways
- Quantum UX was created in 2013, years before generative AI went mainstream.
- It models experiences generated dynamically from user behavior using AI and data.
- Early prototypes used machine learning and big data; only compute was missing for full generative AI.
- Its predictions (adaptive, AI-generated content) now appear across major platforms.
- The author's view: UX will adapt, not die, alongside AI.
What Quantum UX is
QUX is a model that generates experiences on the fly by understanding user requests and behavior using artificial intelligence and data analysis. It described generative AI before the term existed: how a model would understand users, the vectors it would use (the XEL), how sites would serve content by preference and behavior, and how experiments would run and be measured on the fly. Early tests used machine learning and big data at small scale; the missing ingredient for today’s generative AI was simply computational power.
Quantum UX as a dominating force
The predictions held. QUX principles are visible in essentially every generative AI product, from ChatGPT and Gemini to Adobe, Figma and the generative feeds of social platforms. Whether those teams knew the framework is beside the point; the point is how early the direction was mapped.
The future of Quantum UX
QUX is complex, but its conceptual models are a down-to-earth way to reason about these new AI-driven scenarios. And despite the noise, UX will not die: it will adapt and become what it was meant to be. The author does not plan to extend QUX further, but hopes more people build new UX theories and experiments.
A UX framework from 2013 that describes experiences generated dynamically by understanding user behavior through AI and data analysis. It anticipated generative AI.
It described models understanding users through vectors, serving adaptive content by behavior, and running on-the-fly experiments, years before generative AI existed.
Its principles appear across generative AI products like ChatGPT, Gemini, Adobe and Figma, and in the adaptive content of social platforms, regardless of naming.
No. The framework’s view is that UX will adapt to the AI era rather than disappear, becoming what it was always meant to be.
XEL refers to the vector-like elements the framework described for how an AI would represent and generate experiences, conceptually close to the vectors modern models use.
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