In the AI Coding Era, Removal Beyond Automation: What Disappears with Vibe Coding

AI coding tools have begun not only automating code writing but eliminating entirely unnecessary steps from the development process. Vibe coding narrows the gap between planning and implementation, and boldly allows intermediate tasks like testing and documentation to be omitted. True productivity gains come not from speed but from eliminating complexity.

From Automation to Removal: A Paradigm Shift in Development

Past automation involved machines mimicking or handling tasks humans used to do. Typical examples include using code generators to churn out repetitive boilerplate or automating builds and deployments with CI/CD pipelines. However, such approaches merely increased mechanical efficiency without touching the underlying process. In contrast, removal changes the paradigm by eliminating the steps themselves. If an AI agent understands requirements and generates working code directly, then detailed design documents and much of repetitive code reviews become unnecessary.

This shift is already materializing in the global developer community. A July 2026 survey of 240 UK developers found that 83% had experienced vibe coding, and discussions on Reddit about 'what disappears and what remains' are lively. As the act of typing code directly loses significance, developers are rapidly moving toward a role of conveying goals to AI agents and managing the quality of the output.

Intermediate Steps That Vibe Coding Erases

The traditional software development life cycle goes through linear steps: requirements analysis, design, implementation, code review, testing, deployment, and monitoring. Each step incurs costs for documentation, approval, and communication, inevitably lengthening the feedback loop. Vibe coding fundamentally reshapes this pipeline. As AI and developers converse, many intermediate tasks are merged and boldly omitted.

A concrete example is test automation. In the past, after implementing a feature, developers wrote separate test cases and iterated on debugging when tests failed. Now, AI not only generates test code simultaneously with implementation but also proposes self-diagnosed fixes upon test failure. There have even been reports of open-source agents where merely changing the output format of code suggestions led to noticeably improved coding performance. This implies that the standalone task of 'writing test code' is disappearing. Boilerplate and repetitive CRUD tasks can also be handed off to AI, allowing developers to focus on business logic and architecture.

Changes Brought by Removal-Centric Thinking: DevOps, Testing, Documentation

The paradigm of removal extends beyond coding to surrounding areas. In DevOps, a natural language command like 'Deploy this service to AWS' completes everything from cloud resource provisioning to CI/CD pipeline configuration. The steps of manually writing and debugging YAML files are disappearing. In testing, unit tests and end-to-end test cases are auto-generated and auto-fixed, inevitably reducing the role of dedicated testers or shifting their focus toward strategic design.

The same goes for documentation. Updating wiki pages whenever code changes used to be cumbersome and often neglected, but now AI detects code changes and automatically generates or updates draft documentation. According to a 2026 Google study, coding AI has reached 80% of human developer performance and is rapidly absorbing ancillary tasks like documentation. This trend is strikingly evident in the acquisition of Cursor by SpaceX (approximately 60 trillion won). Massive capital flowing into AI developer tools hints at their strategic value in erasing fundamental steps of software development, beyond mere automation.

Productivity Comes from Eliminating Complexity, Not Speed

While one often hears 'adopting AI doubled my coding speed,' the real advantage lies in reducing the amount of code itself and dramatically shortening feedback loops by omitting intermediate steps. Less code means a lower probability of bugs and reduced maintenance costs. Moreover, faster feedback enables quicker validation of product-market fit, dramatically improving business agility.

In one Korean company case, the combination of a developer in their 50s with deep domain experience and AI resulted in productivity akin to a 'smart new employee.' Processing 2.48 million records of Seoul subway data, AI absorbed most of the complex preprocessing, allowing the experienced developer to focus solely on core business logic. Now, the survival motto of developers is converging on 'architecture and debugging.' That is why system design and problem-solving skills are becoming more critical than mere coding skills.

Conclusion: Redefining the Developer's Role in the Era of Removal

As AI erases intermediate tasks, developers can now focus on higher-level judgment and creativity. However, we cannot entrust everything to AI. A new form of human-in-the-loop collaboration becomes essential on top of the removed steps. For instance, tools like md-log allow humans to conveniently review AI-generated outputs, and each save creates an immutable version, building a collaboration history. While eliminating complexity, it's crucial to maintain a balance where humans lead critical decisions and accountability. In the future, successful development organizations will be those with the insight to decide 'what to erase.'

References

• Thoughts on AI Aggro and ERP: The Root Cause of Disputes Likely Unchanged
• 83% of 240 UK Developers Already Experienced Vibe Coding: Survey
• House Peo Ple (@airbnbhousepeople)
• If You Knew the AI Subscription Fee, You'd Faint, So Just Smile and...
• Experience in 50s + AI = Smart New Employee: 2.48 Million Seoul Subway Data...
• Cursor Start, Now Available in India for ₹649/Month
• Codeberg gives vibe-coded projects the toss, promotes human FLOSS