AI Interfaces of the Future: A Design Review

As AI technology evolves, we're witnessing a revolution in user interfaces that transcends traditional designs. This evolution poses a fascinating question: how will our interactions with technology change in the years to come?

“Much of the design of what AI does is more verbs—Auto Complete, Auto suggest, go out and gather some information for me. We don’t really have the tooling yet to draw verbs on the screen.” — Rafael Shad

The Shift from Nouns to Verbs in Design

Traditional software interfaces center on static elements—buttons, text fields, menus—essentially “nouns” on the screen. In the AI era, the focus shifts to “verbs,” or actions the system can take autonomously on behalf of the user. Instead of clicking “Search” or filling a form manually, users can ask AI to fetch, summarize, schedule, or even negotiate. This verb-driven model transforms software from reactive to proactive, anticipating user needs and streamlining workflows.

Designers now face the challenge of creating visual metaphors for actions: how do you represent “summarize this document” or “optimize my schedule” as intuitive UI components? We’ve seen early prototypes using animated icons, live previews, and conversational chips. As AI interfaces mature, we expect richer multi-modal feedback—combining voice, text, and graphics—to signal that an intelligent process is underway. This fundamental shift will reframe how we teach new users to interact with software in general.

Exploring Cutting-Edge Examples

To illustrate this shift, Rafael Shad reviewed several AI interface prototypes from the Y Combinator community. Two voice-based tools stood out:

Vapy lets developers build, test, and deploy voice agents in minutes. A built-in latency meter shows real-time response times in milliseconds, helping gauge conversational naturalness. While powerful, Vapy’s demo could be more intuitive with on-screen cues—such as a waveform or indicator light—to confirm voice input and output. Even so, enabling developers to toggle between “Dev mode” (with raw metrics) and a polished end-user interface highlights how AI empowers both technical and non-technical teams.

Retail AI focuses on call-center automation. Users receive live calls from AI agents that handle tasks like appointment scheduling or debt collection. The AI’s ability to adjust when a caller corrects their identity—shifting from “Aaron” to “Steve” mid-call—demonstrates adaptive learning in real time. Latency remains a limitation: slight delays can break the illusion of human interaction. Future iterations should aim for seamless handoffs between AI and human operators, with a unified UI that presents transcripts, sentiment analysis, and next-step suggestions.

Revolutionizing Workflow with Autonomous AI Agents

gumloop introduces a visual canvas for no-code automation. Users drag and drop nodes to define autonomous agents that scrape websites, combine data, or trigger emails. By representing decision trees and loops visually, gumloop reimagines flowcharts as living workflows. Color-coded blocks indicate inputs, actions, and outputs, but a legend or collapsible nodes at different zoom levels would improve readability. As AI agents proliferate, such visual monitoring tools become essential to maintain trust and control over background processes.

Aggregating Information with AnswerGrid

AnswerGrid transforms free-form prompts into structured spreadsheets. Pre-filled example buttons help users get started quickly—no blank-screen panic. When querying “AI companies in San Francisco,” AnswerGrid returns a table with company names, headquarters, and industries. Adding a custom column like “funding raised” spawns parallel AI agents, each populating cells with line-item data and source citations. This inline footnote pattern—click to verify—builds confidence in AI-aggregated data. For enterprise software, such trust mechanisms will be a cornerstone of user validation.

Tweaking Design with Polyat

Polyat acts as an AI product designer. Users describe interfaces in plain language—“create a dashboard with a collapsible glass-morphic sidebar and dark orange gradient”—and receive production-ready HTML/CSS code. Dynamic feedback loops trace which prompt terms were honored and which were ambiguous, guiding further refinement. Interactive term suggestions (e.g., “flat design,” “neumorphism”) would help less expert users craft effective prompts. Iterative sub-prompts on individual modules could maintain consistency across revisions and minimize full-page re-generation.

Adaptive Interfaces: A Step Towards Personalization

Zuni is an AI-powered email assistant that surfaces suggested responses based on inbox context. Instead of generic templates, it offers three tailored options—“A: Confirm call time,” “B: Ask for more details,” etc.—mapped to single-key shortcuts. This adaptive UI shows only relevant actions, reducing cognitive load compared to traditional toolbars. Designers must balance dynamism with predictability: users need clear focus indicators to avoid hot-key mishaps and consistent shortcut mapping, even as the buttons themselves change per email.

Creating Dynamic Video Production with Argil

Argil leverages deepfake and text-to-video pipelines to generate realistic videos within minutes. Early blurry previews let users confirm script and framing before committing to a 10–15 minute high-fidelity render. This staged workflow prioritizes rapid iteration and human oversight. Ethical safeguards—such as watermarking, consent verification, and tamper-evident markers—will be critical as AI-generated video becomes mainstream. In design software, editors may soon routinely animate prototype walkthroughs or personalized marketing clips without manual shooting.

Conclusion

AI is redefining interfaces from static, noun-driven layouts to dynamic, verb-centered experiences. Across voice, workflow, data aggregation, design, and video, these tools emphasize proactive assistance, real-time adaptation, and user control. As designers, product managers, and developers, we have a unique opportunity to craft the next generation of software and user experiences.

• Actionable takeaway: Focus on verbs and adaptive elements in your next UI to anticipate user needs and create more intuitive, efficient workflows.

What AI-enhanced interactions could transform your daily tasks? Share your ideas and let’s design the future together.