Vibe Coding Isn’t Replacing Developers. It’s Replacing Waiting.

How AI is changing product development—and why humans are becoming more valuable, not less.

Every so often a new technology arrives that doesn’t just improve an existing process—it changes the sequence of how work gets done. I believe vibe coding is one of those. Not because it writes code better than experienced software engineers, and certainly not because it replaces them, but because it fundamentally changes how quickly an idea can become something tangible. After spending a week building an application almost entirely through AI-assisted development, I came away convinced that we’ve been looking at this technology through the wrong lens.

Like many people, I had followed the explosion of AI coding assistants with equal parts curiosity and skepticism. Depending on which article you read, AI was either about to replace every software engineer or it was an overhyped autocomplete destined to collapse under the weight of its own limitations.

Neither argument felt particularly satisfying because they focused almost entirely on the code itself. My own experience turned out to be far less about code generation and far more about product creation.

The project began with little more than an idea. Normally, that idea would have entered a familiar product development process. I’d sketch concepts, create wireframes, build a Figma prototype, review it with stakeholders, gather feedback, iterate for weeks, refine the interaction model, prioritize features, and eventually hand the work to engineering. Weeks and months would pass before anyone outside the product team could actually experience what we were trying to build. We’ve accepted this as normal because, until recently, there wasn’t another practical way to communicate software before it existed.

This time I decided to skip almost all of those steps. Instead of opening Figma, I opened Claude. Instead of spending days creating artifacts that represented the application, I started describing the application itself, much in a way I would define functional and technical requirements for engineering. The AI responded by writing code, generating interfaces and wiring together functionality almost as quickly as I could think through new ideas. We went back and forth refining the output, a task that would otherwise first surface as defects weeks or months after engineering finishes coding. Within a single afternoon I had moved from a loosely defined concept to a working prototype that I could click through, interact with and continuously refine. By the end of the week that prototype had evolved into something I was genuinely comfortable calling production-ready — integrated with APIs and ready for broader acceptance review. Applying branding, UX, UI, accessibility and implementing feedback from friendly users became an afternoon activity instead of next product release cycle. This capability is essential for teams to deliver shippable features in short timeframes, meeting the user demands faster, anticipating trends and testing in production, with very little to no overhead.

What surprised me most was not only the speed at which the software was created. It was how dramatically the nature of product conversations changed. Instead of presenting static wireframes or polished PowerPoint slides and asking people to imagine how the product might behave, I simply handed them a working application. The discussion immediately shifted from speculation to observation. Rather than debating whether a workflow would make sense, people could experience it themselves. Feedback became immediate, practical and infinitely more valuable because it was grounded in reality instead of imagination.

This, in my view, is where vibe coding delivers its greatest value. The biggest productivity gain is not writing code faster. It is eliminating the enormous amount of time we traditionally spend trying to explain ideas before anyone has the opportunity to experience them. Product development has always suffered from translation loss. Every wireframe, prototype and presentation is an attempt to communicate something that doesn’t yet exist. AI allows us to bypass much of that translation by creating software directly. The application becomes the prototype, the presentation and, increasingly, the conversation itself.

That does not mean AI replaces the need for experienced engineers or product professionals. In fact, I found the opposite to be true. As the application became more sophisticated, the importance of human judgment increased rather than decreased. Architecture still mattered. Data models still mattered. Security, accessibility, performance and maintainability all remained essential. The AI was remarkably capable of implementing ideas, but it was not responsible for deciding whether those ideas represented good engineering or good product design. Those responsibilities remained firmly human.

One realization became increasingly obvious throughout the week: the quality of the application depended less on the capability of the AI model than on the quality of the instructions I provided. Vague requests produced vague software. Poorly defined requirements produced equally poor implementations. Conversely, the more clearly I articulated user needs, business rules and design intent, the more impressive the results became. What many people refer to as prompt engineering felt remarkably similar to something product managers and solution architects have always done well—writing clear, structured requirements. The medium has changed, but the underlying skill has not.

There is, however, a danger that deserves more attention than it currently receives. AI has an uncanny ability to produce software that appears substantially more complete than it actually is. After only a few hours I found myself looking at an interface that felt polished and convincing, even though underneath it lacked many of the characteristics required of production software. Error handling was incomplete. Edge cases had not been considered. The visual design lacked consistency. Accessibility needed refinement. Security still required careful review. It became clear that AI is exceptionally good at creating the illusion of completeness, and that illusion can easily tempt inexperienced teams into believing a prototype is ready for production when it is not.

The same applies to user experience. While AI can generate attractive interfaces, it has little understanding of a company’s brand identity, design language or customer expectations unless those principles are explicitly taught. Great user experiences are rarely accidental. They emerge from empathy, iteration and countless small design decisions that reflect a deep understanding of users. Those qualities are still difficult to automate. If anything, they become more valuable because the mechanical act of producing software is becoming increasingly commoditized.

Much of the current public discussion around AI revolves around the question of whether it will replace software engineers. After this experience, I think that is the wrong question entirely. The more interesting question is how dramatically it increases the leverage of experienced teams. A skilled full-stack developer equipped with modern AI tools can now accomplish work that previously required several people. Product managers can validate ideas in hours rather than weeks. Designers can iterate against working software instead of static mockups. Entire organizations can shorten the distance between concept and customer feedback without compromising the need for experienced professionals to guide the outcome.

That distinction matters because AI is not replacing expertise—it is amplifying it. The people who benefit most are those who already understand software architecture, product thinking and user experience. AI removes much of the mechanical effort involved in implementation, allowing those individuals to spend more of their time making the decisions that genuinely require human judgment. The bottleneck is no longer typing code. It is deciding what should be built, why it matters and how it should evolve.

Looking back, the most significant change was not that I wrote less code. It was that I spent almost no time building artefacts whose sole purpose was to describe an idea. The software itself became the prototype, the specification and, ultimately, the product vision. That may prove to be the most profound impact of vibe coding. It doesn’t eliminate product strategy or software engineering; it accelerates both by allowing ideas to become tangible almost immediately.

Like every new technology, AI has been surrounded by unrealistic promises. It will not replace thoughtful engineers, experienced architects or talented designers, and organizations that expect it to do so are likely to be disappointed. However, dismissing it as little more than an autocomplete tool overlooks the genuinely transformative shift that is taking place. Vibe coding is not changing software because it writes better code than humans. It is changing software because it dramatically reduces the time between imagining an idea and learning whether that idea deserves to exist. In product development, that may be one of the most valuable accelerators we have seen in decades.