Jenny Xie, writing for Figma, collects a set of community sketches about what AI-native software might feel like. The useful part is how concrete the sketches get: controls that appear only when they’re needed, systems that change pace when the user is overwhelmed, and interfaces that treat intent as the starting point instead of the final prompt.
Figma Weave marketing director Itay Schiff starts with creative tools:
When you’re working with a creative tool, select an element—a frame, a sentence, a scene—and state what you need to do. The right controls appear around it, surfacing only what’s relevant to that moment. No hunting through menus, no memorizing where things live. When you’re done, they disappear. Someone editing a video that feels too slow selects a clip and sees options for timing, pacing, alternate cuts, and sound bridges. You’re not losing touch with your craft, but accessing the right controls more directly, through context. This bridges the gap between how people think and how tools are structured. Creative work starts with intent—make this clearer, more nuanced, more intense—but traditional software is organized around persistent menus, panels, and modes. When users can focus on decisions rather than mechanics, the conversation shifts from what buttons we pressed to what we changed, and why.
This extends the generative UI debate from generated content into the surrounding interface: what controls appear, when they appear, and when they disappear.
Anna Oh, Head of Product and Design at Norbert Health, pushes the same idea into domains where software has to adapt to the person using it:
An intelligent system continuously adjusts how it communicates and acts based on how you respond. It offers structured guidance when you seem lost, steps back when you don’t need it, and shifts between voice, text, and visuals based on the context. It controls pacing, simplifies language, or breaks down information into smaller steps to reduce overwhelm. For decades, humans have had to adapt to machines. The mismatch between system capability and human readiness has only grown more visible as software becomes more powerful, and in domains like healthcare and education, especially with an aging global population, that gap has real consequences. As AI becomes the intelligence layer, this reverses: Systems meet people where they are, instead of the other way around.
For design teams, that means pacing, language, modality, and restraint become interface decisions, not polish applied after the workflow is done.
Tech anthropologist Jésabel DC shifts from adaptive behavior to the sensory cues that help people understand where they are in a product:
Sound, motion, and pacing are reintroduced as signals that orient you inside an experience. In the early 2000s, digital interfaces were full of these situational cues: the dial-up sound as you went online, the progress bar acting as a passage to the world you were entering, the voice announcing “You’ve got mail.” As technology became faster and more seamless, these cues disappeared, replaced by notifications engineered to grab attention rather than provide orientation. Because our users’ overall tech experience is already so dysregulating, it’s not enough to design something that isn’t overwhelming. We have to actively design experiences that counterbalance the rest of our users’ tech stack—otherwise, we risk losing users not because our product is bad, but because their nervous systems are already maxed out before they arrive. Think: always visible progress bars, sounds that mark transitions, and consistent visual language.
