In the Era of Vibe Coding, True Competitiveness is 'Taste': AI Builds Prototypes, but Taste Remains

As we enter the era of vibe coding where AI generates code in an instant, many developers are pondering their role and competitiveness. The clear fact is that while AI tools excel at creating initial prototypes, what determines true completeness is the human’s taste and judgment that fill in the final 10%. Considering edge cases, ensuring maintainability, and meticulously refining user experience are areas that AI cannot yet replace. On the contrary, now that AI enables faster output, the ability to decide what to build and how to design it is emerging as a key differentiator.

AI Makes Prototypes, Humans Handle the Final 10%: Taste Is the Real Game

Addy Osmani recently emphasized on his blog, “If automation covers the easy 80–90% of software engineering, the last mile—edge cases, architecture, and taste—is everything.” The first draft generated by vibe coding is no longer of great value. It is merely a tool to quickly visualize ideas; turning it into a product ready for real-world deployment requires human developer intervention. For example, AI-written code may perform basic functions but often remains vulnerable in exceptional situations or fails to handle thousands of concurrent connections. Detecting and improving these aspects ultimately comes from the sharp insight cultivated through a developer’s experience.

As highlighted in the post “Taste Is All That’s Left,” good taste in software development leads to readability, conciseness, and maintainability. Beyond simply working code, writing code that one’s future self or team members can easily understand and modify is something that large language models (LLMs), adept at combining repetitive patterns, still struggle to accomplish. A developer’s taste shines in design decisions that make code more intuitive and robust.

Without a Clear Vision, AI Only Multiplies Rework

A common pitfall when using AI tools is that “if what you want isn’t clear, iterative corrections and rework can multiply.” As approaches like the ‘Project Design Starter Edition’ suggest, without design reviews and specifications, AI not only delivers results quickly but also races in the wrong direction. This leads to endless revisions and only inflates technical debt. In contrast, a developer with a deep understanding of the problem and a clear vision can guide AI in the right direction, effectively review the generated output, and elevate completeness.

Don’t Ask for Answers, Ask for ‘Approach’

Another important shift is the attitude of treating AI not as a ‘fixer’ but as an ‘exploration tool.’ Experts’ responses to the concern, “Why do some people build great apps with the same AI while I get shoddy results?” are consistent: “Don’t demand the right answer from AI; ask about the process and approach to solving the problem.” Instead of simply requesting code, asking “Why did you design it this way?” and “What are the trade-offs with other approaches?” can lead to deeper learning and better outcomes. The ability to lead the design process itself is a core competency that will become scarce in the AI era.

Human Intuition and Experience Make the Difference

As Arvind Narayanan said, “If AI is a technology that will soon greatly amplify our potential, now is the time to cultivate the ability to work with AI,” we must focus on the unique strengths of human developers. The intuitive understanding of physical world constraints and bodily experiences, such as the sense that a mobile app’s touch area should be designed to fit a user’s finger size, is insight that LLMs cannot easily acquire from text alone. Design infused with this real-world sense elevates a product from mere functionality to a truly loved experience.

Ultimately, the vibe coding era is one where the more AI does, the more human taste and judgment about ‘what to build and how to complete it’ become precious. In the process of reviewing, refining, and sometimes redirecting the AI-generated draft, human judgment is irreplaceable. Amid this trend, human-in-the-loop tools like md-log provide a collaboration method where humans review AI work and build immutable records of changes. Developers with good taste can leverage such tools to accumulate more refined outputs and exercise their own judgment.

References

• Move design reviews from human eyes to exit codes. ...
• "Why do some build great apps with the same AI while I get ...
• Software is the most powerful tool to change the world. Technology ...
• Taste Is All That's Left
• AddyOsmani.com - Earning taste and judgment
• What will be left for us to work on? - by Arvind Narayanan
• The tech industry doesn't talk about this enough. If you're ...
• Grudgingly accepting that AI isn't going away. Trying to figure out where that leaves me as a developer. : r/webdev
• Medium