For roughly a decade, the standard digital product pipeline remained virtually unchanged: craft detailed vector screens in Figma, string together a clickable simulation, assemble component guidelines, and pass everything over to engineers to translate into working software. This handoff ritual was consistently the most friction-laden phase of production. That dynamic is now unraveling as a new development pattern—vibe coding—fundamentally transforms the industry.
Vibe coding refers to the practice of building functional digital products through conversational prompts, instant code generation, and fast feedback loops. Instead of manually typing syntax, creators articulate the logic, behavior, and visual tone of an interface, allowing artificial intelligence to draft, launch, and refine the underlying code. Ayomide Olukoga, a Lead Product Designer with a focus on interface systems and digital product strategy, views this evolution not as an existential threat to his profession, but as an elevation of it. He notes that once code creation becomes instant and universal, raw syntax turns into a commodity—making refined design discernment the truly scarce asset.
This insight could separate the designers who flourish from those who struggle as generative coding takes hold. Having navigated conventional design-to-engineering handoffs for years, Olukoga argues that static canvases are fundamentally limited. A mockup can showcase hover effects, yet it cannot realistically replicate database calls or live edge cases. Vibe coding eliminates that barrier: by simply describing complex behaviors—such as responsive filtering bars with dynamic fluid animations—an AI engine produces executable code on the spot. Designers are no longer merely drafting blueprints of interfaces; they are generating live, responsive software architectures.
Olukoga likens this shift to the craft of filmmaking: the designer acts as the film director, while the AI functions as the crew handling lighting and building the sets. The machine follows commands, but the creative and strategic direction rests entirely with human insight. He stresses that artificial intelligence does not grasp human psychology, emotional subtleties, or intuitive aesthetics on its own.
He highlights this gap using a personal project: a video game prototype built with an AI assistant. While the tool produced clean, stable code, the overall user experience felt sterile and lacked regional character. Resolving the issue required engaging a Nigerian illustrator to craft visuals that captured local nuances—proving that automated code generation still requires cultural awareness and human empathy to build compelling products.
In professional settings, this dynamic plays out similarly. While leading the architecture overhaul of an extensive enterprise website for a Middle Eastern telecom client, Olukoga leveraged AI to dissect the existing ecosystem and surface user journey choke points. However, picking the most effective user funnels required analyzing distinct regional behaviors and running user interviews—nuances the model could not resolve independently. The tool mapped the terrain rapidly, but the strategic navigation remained human-driven.
Addressing broad industry fears that automation could erase creative jobs—such as warnings from global economic institutions regarding wide-scale workforce disruption—Olukoga offers a different perspective. Rather than eliminating 40% of creators, technology is stripping away 40% of repetitive, execution-heavy chores. Routine pixel-pushing is automated, freeing designers to concentrate on framing core problems, evaluating trade-offs, and optimizing visual balance and spatial harmony.
This transition also reframes the function of primary design tools like Figma. Rather than acting as a factory for endless static specs and high-fidelity mockups, these platforms will increasingly serve as spaces for conceptual alignment and high-level structural strategy. Simultaneously, the low barrier of vibe coding transforms startup economics: early-stage founders can rapidly launch production-grade MVPs with conversational interfaces, postponing the need for dedicated frontend hires until scale demands it.
