Vibe Coding Is Not A Sin
Lately, I’ve seen more stigma around using AI in software development. People react strongly, and sometimes emotions run high.
Developers who openly use AI tools in their workflow often face criticism, such as:
“You lost your touch.”
“You are not a real/hardcore programmer anymore.”
“AI writes your code now.”
“You are cheating.”
These comments aren’t unique to AI. Our industry has always seen skepticism about new tools. The idea is: if a machine helps you write code, your skills must be less valuable.
According to a 2026 developer survey by Boundev, 66% of developers are concerned that AI will replace human roles. These numbers reflect how often people resist new tools.
I hold a different perspective:
Using a better tool does not erase your skill. It exposes how you apply it.
This Is Not the First Time
This pattern has always existed in software development.
When frameworks appeared, teams hesitated in their first year, worrying about code quality and losing expertise.
According to Peymaan Abedinpour, when PHP frameworks gained popularity, some developers took pride in building applications from scratch and viewed writing raw PHP as a mark of a true programmer.
But frameworks like Laravel and FastAPI changed web development. Teams could release features in days instead of weeks, and bugs in things like routing and authentication became less common because frameworks handled them well. This lets developers focus on business logic, and projects move faster with more confidence in the code.
Routing, authentication, migrations, and queues were made standard.
Some defied. Many adapted. Eventually, frameworks became the norm.
Similar doubts arose with the adoption of graphical IDEs and version control like Git. Early on, some said real programmers didn’t need visual tools or automation, and that these would erode true craftsmanship. Even the move from manual memory management to languages with garbage collection faced criticism.
Yet today, those tools define contemporary software development.
AI-assisted coding is just the next step in this evolution.
The Judgment Around AI
Many developers think vibe coding (using AI to quickly create or modify code, frequently relying on AI-generated suggestions instead of writing everything yourself) means letting AI generate code without really understanding it.
The assumptions often sound like this:
The developer blindly copies and pastes code.
The developer no longer understands the architecture.
The developer stopped learning fundamentals.
The developer produces fragile software.
Sometimes these concerns are real. Some developers do misuse AI tools like that.
But this assumption ignores how experienced engineers actually work.
Senior developers use AI thoughtfully, shifting focus to high-impact problems.
Instead of repetitive typing, they focus on work where experience matters:
System architecture. Deciding how services communicate, how data flows, and how systems scale.
Security. Reviewing authentication flows, validating authorization logic, and preventing vulnerabilities.
Performance. Evaluating database queries, cache techniques, and latency.
Maintainability. Refactoring generated code into clear and understandable modules.
AI is an assistant, not a replacement.
Typing less code does not mean thinking less.
Yes, Vibe Coding Can Be Harmful
It is necessary to be honest about this aspect.
Careless vibe coding can harm both developers and businesses.
For example:
A developer generates a complex API endpoint and deploys it without reviewing the logic.
A team ships AI-generated features without writing or reviewing tests.
The authentication code is copied from the AI output without verifying the hashing methods or the token-handling logic.
Database queries are accepted without analyzing indexes or execution plans.
In these situations, AI itself isn’t the problem.
The real issue is not the tool, but a lack of discipline.
Poor engineering practices existed long before AI appeared.
AI does not create bad engineers. It exposes them faster.
Every Tool Has Two Sides
Technology always carries both constructive and destructive potential.
A knife helps a chef prepare food efficiently. The same knife harms someone when used with harmful intent.
A car helps people travel faster and connect cities. The same car becomes dangerous when driven recklessly.
People often say tools are neutral. But AI tools pick up patterns from the data they’re trained on. If that data contains biases or errors, the AI’s output can too. Knowing these limits is key to using AI responsibly.
The result depends on the user.
AI follows the same principle.
Vibe coding itself isn’t inherently good or bad. It’s the developer’s approach and discipline that determine the outcome.
What Vibe Coding Is Actually Meant For
To me, vibe coding is meant to make development smoother. For example, according to a McKinsey report, software developers using generative AI tools can complete programming tasks up to twice as fast, demonstrating that these tools genuinely boost team efficiency. However, it’s important to recognize that productivity gains may vary depending on the nature of the task, the codebase’s complexity, and how each team integrates AI into its workflow. In some cases, reviewing, adapting, or debugging AI-generated code can take extra effort. This nuance matters to developers who esteem both speed and high standards.
Software engineering involves a lot of repetitive work. AI tools save time on these tasks, allowing developers to focus on design and problem-solving.
Some examples include:
Generating the initial structure of an API endpoint, including request validation and response schemas.
Creating database migrations and seed data for new features.
Drafting unit test structures that developers later improve and extend.
Producing boilerplate CRUD operations so developers can focus on business logic.
Suggesting refactoring approaches for complex functions.
In this process, AI creates the first draft. The developer then reviews, verifies, and improves the code. This resembles how teams approach pull requests. The first version is rarely the final one.
AI writes the first draft. Engineers write the final system.
The Factory Analogy
Consider how modern factories operate.
Factories no longer rely entirely on manual labor. Machines carry out repetitive tasks such as assembly, packaging, and sorting. Humans supervise the system, maintain the machines, and optimize production processes.
This approach boosted productivity while keeping human expertise in the loop.
Software development is moving in a similar direction.
AI handles repetitive patterns and boilerplate structures. Developers oversee the system, evaluate the output, and ensure quality.
This change doesn’t make skilled engineers less valuable. On the contrary, it highlights the importance of careful, responsible engineers.
The Developer of the Future
As AI tools get better, the skills that make a great developer will change. Picture debugging in 2030: instead of spending hours on confusing logs, you’ll work with AI advisors that spot performance issues right away, predict problems, and suggest fixes for your situation. Developers will need to use good judgment, explain what they want clearly, and guide the AI just as much as they use their technical skills.
As a developer, these are the concrete steps that I intend to follow to prepare and build skills for the AI-driven future:
Practice prompt engineering through experimenting with various ways of asking questions or giving instructions to AI tools. Notice how the results change, and learn to craft clear, effective prompts.
Develop strong habits for critically reviewing AI-generated code, just as you would with a teammate’s pull request. Look for edge cases, security flaws, and performance bottlenecks.
Take online courses or attend workshops centered on AI and machine learning fundamentals to better understand the strengths and limitations of these tools.
Work together with peers to share tips and review each other’s AI-assisted work, building collective experience.
Keep up to date on AI tool updates and best practices, and experiment with integrating new capabilities into your workflow.
Syntax memorization will matter less. Judgment will matter more.
The strongest developers will combine several capabilities:
Strong fundamentals in programming, algorithms, and system design.
Extensive understanding of performance, scalability, and database behavior.
Awareness of security risks and safe programming practices.
The ability to guide AI tools effectively through unambiguous instructions.
The discipline to review and refine generated code.
Developers who rely entirely on AI without understanding the output will struggle, while those who treat AI as an assistant will move faster while maintaining quality.
AI will not replace engineers. It will reveal which engineers truly understand their craft.
Ethical Concerns
It’s not only about productivity. Incorporating AI and vibe coding into software development also brings ethical responsibilities. Developers must remain accountable for their work, including any harm, bias, or security risks resulting from AI-generated code. By communicating these responsibilities from the outset, we demonstrate our dedication to progress and the well-being of users, teams, and the community.
To put these principles into action, we can follow practical steps to uphold high ethical standards:
Always review and test AI-generated code before it goes into production. Peer code reviews help spot hidden issues or errors that the AI may introduce.
Consistently check for bias by evaluating both the data used to generate code and the code’s impact on different users.
Use security tools and practices to assess AI-generated code for vulnerabilities, like unsafe authentication logic or poor data validation.
Document decisions involving AI-generated code, including any changes you make during review, so that the reasoning behind implementation is transparent for your team.
Stay informed on best practices and the latest guidance regarding responsible AI use.
Vibe Coding Is Not a Sin
Using AI in development does not mean a programmer loses their skills or cheats. It means adapting to better tools. Technology is constantly evolving, and developers need to keep up. Just as factories brought in machines to boost production, engineers now use AI to improve development.
The purpose of vibe coding is simple. It helps good developers become better developers.
Here are a few effective steps that helped me get started:
Experiment with a new AI development tool and reflect on where it helps or hinders your work.
Set up a code review process specifically for AI-generated code to encourage your team to catch errors and share best practices.
Pick one routine task and use AI to automate it, then document what you learned for others.
Join an online community or forum focused on responsible AI-assisted development to exchange experiences.
Take a short workshop or online course to sharpen your prompt engineering skills.
By taking action, we can build real experience and confidence with these advancing tools, ensuring our teams make the most of AI safely and responsibly.
