Early Uber Eats designer Ron Goldin downloaded the driver app and did deliveries to understand user pain points firsthand. This direct immersion revealed a terrible onboarding process and built extreme empathy that drove crucial product improvements.
To transfer field research empathy, a designer recorded his chaotic delivery trips with a 360掳 camera and sent them to executives on Google Cardboard. This immersive storytelling was far more impactful than a traditional research report or memo in driving action.
When told an idea for an AI-powered checkout customizer "can't be done," a designer didn't argue. He used AI coding tools to build a functional prototype in minutes, demonstrating feasibility and immediately winning over skeptical VPs and engineers.
Modern orgs are becoming flatter, increasing the need for "player-coach" design leaders. These managers oversee a team while also contributing directly. AI tools enable this by drastically reducing prototyping time, allowing leaders to stay hands-on without sacrificing management duties.
The expectation for design leaders to be hands-on has reached the interview process. A director-level candidate recounts being watched by an engineer to confirm he could use Figma's auto-layout, signaling a major shift away from pure management roles.
AI tools have made building software incredibly fast, shifting the primary bottleneck for new products. The hard part is no longer the initial build, but the timeless challenge of marketing, distribution, and growing an audience. Technical barriers have fallen, but market barriers remain.
Instead of designing in Figma first, Ron Goldin used AI to generate a functional but ugly "build wireframe" for his product. This approach allows for rapid iteration on core flows and architecture, ensuring the product feels right before investing in high-fidelity design.
Get better results from AI coding tools by treating them like a new hire. Provide a clear strategy document or PRD as "long-term context" or "project memory." This initial onboarding helps the AI understand the project's goals, leading to more accurate and coherent builds.
Since AI can handle much of the executional design work, a designer's value shifts. Core skills are now product thinking, scrappy research, and brand taste to create products that are differentiated and emotionally resonant, not just functional.
In today's job market, building a product or company on the side is no longer just "extra credit." Hiring managers view these projects as direct evidence of initiative, product thinking, and technical skill, sometimes weighing them as much as traditional work experience.