The debate about whether artificial intelligence will replace programmers has long since moved beyond the realm of theory—with the advent of "vibe coding," it has become a reality. While AI previously merely suggested code fragments, it now generates entire functions and modules based on simple text descriptions, transforming the developer from an executor into a process manager. The article describes How this is changing the profession, raising the bar for engineering culture, and what programmers should do in the AI era.
Why "vibe," and what does code have to do with it?
The term "vibe coding" instantly spread among developers and was even chosen as the "Word of the Year 2025" by Collins English Dictionary. What's behind this buzzword?
Imagine a common task: a programmer needs to create a website button that smoothly changes color when hovered over. Previously, they would open an editor and write CSS and JavaScript code. Now, they can simply tell the AI, "Create a button with a smooth background change when hovered over." And the neural network will produce a ready-made working fragment. This is vibe coding in action.
Simply put, this is an approach where the developer formulates the task in ordinary words, and the AI turns these instructions into code. The programmer ceases to be a mere typist and becomes an architect, task setter, and chief controller of the AI. Their key task is to clearly explain what needs to be done, thoroughly check how the AI has done it, and make improvements.
The term became popular after a casual tweet by AI researcher Andrej Karpathy in early 2025. But conversational programming technology had been developing for the previous 5-10 years—in parallel with the growth of large language models and the emergence of the first intelligent assistants for developers, such as GitHub Copilot.
Vibe coding is a logical continuation of the old idea of code "style" (vibe). Good code has always been valued for its clarity, logic, and beautiful structure. Now it's born in dialogue: the quality and style of the resulting code directly depend on how clearly, competently, and thoroughly the programmer defines the task for the AI.
The Main Signs of "Good Karma" in Code
When code can be created in seconds, questions arise about its quality and reliability. Because even AI can write bad, confusing code. Furthermore, it needs to be corrected, expanded, and maintained. It becomes technical debt that consumes the team's resources for years.
What does this "karma" consist of? It consists of two complementary parts: clear, verifiable rules and unspoken principles of team culture.
Clear rules are the technical foundation. They can and should be automated.
Code with "good karma":
- Easy to read at first glance, like clear instructions. This means variables and functions have meaningful names (calculateTotalDiscount, not calc1), a logical structure, and no mysterious numbers scattered throughout the logic.
- Strictly adheres to a consistent style. Indentation, brackets, naming conventions—everything should be consistent so the code looks neat and predictable.
- Solves the problem in the simplest and most direct way. Unnecessary complexity, unnecessary abstractions, or detailed one-liners that take half an hour to decipher are a surefire way to errors.
Unspoken principles are about culture, experience, and responsibility. Commands:
- Respect for the future reader of the code—whether colleagues or the developer themselves in six months—who should quickly understand the logic without having to decipher the intentions.
- Consciously avoid questionable "hacks" and premature optimizations if they make the code fragile and non-obvious. Reliability and clarity are almost always more important than microscopic speed gains.
- A sense of the context of your project. There is no such thing as perfect code. What's great for a quick startup prototype may be unacceptable for a high-load banking server, where every millisecond and maximum security matter.
The main paradox of the AI era is that assistants do not automatically create code that adheres to these principles. A vague request like "write a function for email" will yield code that works, but is primitive and potentially vulnerable.
Conversely, a professional, detailed prompt willis a clear specification: "Write a Python function called validate_user_email." It should:
- check for the presence of the "@" symbol and domain;
- use the re library;
- return the result and error text;
- have documentation in Google Style format."
This approach leads to a qualitatively different, ready-to-use result.
AI doesn't replace engineering culture, but rather makes it more important than ever.
Technology vs. Vibe: How Tools Help You Code
Behind a simple AI query lies a whole range of tools, and it's important to understand which one is needed for what. Choosing the wrong tool leads to frustration: you can waste hours chatting with an AI that's simply not designed for the task.
AI assistants in the editor (GitHub Copilot, CodeWhisperer).
- What it is: Advanced autocompletion built right into the development environment (VS Code, PyCharm).
- How it helps: As you type code, the AI suggests completing a line or function. If you write a comment, you get a ready-made fragment. It's ideal for speeding up routine tasks when the logic is clear and you just need to jot it down quickly.
- Main advantage: Huge time savings on boilerplate operations.
- Limitation: These tools only see the current file or a few lines. They don't understand the overall project architecture and can't plan complex changes.
Cloud prototyping platforms (Replit, Bolt.new).
- What it is: Online services where a programmer describes an entire idea in a chat, and AI creates an application framework.
- How it helps: It's a tool for quickly testing hypotheses. Just write "create a website with a menu for a coffee shop" and in 10-15 minutes you'll have a working prototype ready to show to the client.
- Main advantage: incredibly fast demo creation.
- Limitation: the generated code is often crude, redundant, and requires significant refinement before using it in a real project.
Advanced AI-powered development environments (Cursor, Windsurf).
- What it is: These are no longer just editors, but intelligent environments that "understand" the entire codebase.
- How it helps: You can instruct the agent: "Replace the outdated library X with Y throughout the project, following our style." It will analyze dozens of files, create a plan, and accurately implement changes.
- This is a tool for experienced professionals, helping them solve complex refactoring and architectural problems.
The key limitation of all these tools is the AI's lack of a true understanding of the business context and the intricacies of the team's work. It doesn't understand internal terms and unspoken rules. It can "hallucinate," confidently suggesting non-existent libraries or error-filled code. And if a non-standard problem arises, only human experience and intuition can pinpoint its root cause and give the AI the precise command to fix it.
Therefore, AI tools do not relieve the developer of responsibility.
Veib Coding Under Control: How Engineering Culture Manages AI
Engineering culture determines how a team uses AI, what tasks to assign it, how to review its code, and how to avoid common mistakes.
A Proven Foundation: Quality Automation
These classic tools have become a critical safety net:
- Linters and formatters (ESLint, Prettier) automatically enforce a consistent style, turning comma disputes into objective rules.
- Type systems (TypeScript) check logic at the data type level, catching many errors before the code is run.
- CI/CD Pipelines (GitHub) Actions are automated assembly lines. Every change, even from AI, goes through them: tests, security checks, and builds. This ensures that only verified code makes it into the main branch.
New methods: managing dialogue with AI
- A new layer has been added to the classic approach—control over the neural network itself:
- Rule setting. Developers create instruction files for the agent, where they describe the project's architectural patterns, prohibited libraries, and business context.
- Out-loud planning. Before making changes, the AI is forced to describe in detail the steps it will take.plans to execute. This allows us to see the logic in advance and prevent errors.
- Privacy restrictions. AI agents are configured so that they cannot perform dangerous operations (deleting files, accessing production databases).
The main tool is a vibrant team culture.
No technology works without a shared understanding. Therefore, the following remain key:
- Joint coding sessions using AI, where the team develops a unified approach.
- Clear documentation explaining not only the "how" but also the "why" of certain decisions.
- Constantly updated internal guidelines that align the entire team's understanding of good code.
This approach demonstrates that AI-assisted coding is an evolution of engineering culture, not a rejection of it.
Vibe Coding in Business
The proper implementation of AI in the programming process directly impacts business metrics. But, like any powerful tool, the effect is twofold.
Obvious benefits:
- Speed.Creating a prototype or the first version of a product is reduced from weeks to days or hours. This allows for faster testing of hypotheses in the market.
- Team engagement.Developers spend less time on routine tasks and more time on complex, interesting tasks: architecture, optimization, and solving non-standard problems.
- Talent retention. Programmers are more comfortable and productive working on projects with a clean, understandable codebase.
- Quick onboarding for newcomers. AI acts as an "omniscient mentor" for the codebase, answering new employees' questions and reducing onboarding time.
However, these advantages conceal serious and costly risks. Modern information security research reveals an alarming picture. On average, 45% of the code generated by leading AI models contains at least one vulnerability from the list of top security threats.
Furthermore, the security quality of the code does not improve automatically; rather, it can deteriorate during the dialogue process. Some studies indicate that each subsequent AI code edit to a new request increases the average number of newly introduced critical vulnerabilities by 37% after just five iterations. This happens because AI focuses on superficial changes without understanding the security implications.
What does this mean for business? Blindly trusting AI as a "black box" leads to:
- the formation of "toxic technical debt": hidden vulnerabilities are quickly built into the code, becoming time bombs;
- direct threats to security and reputation: data leaks, financial losses, and brand damage.
The desire to save time can result in multi-million dollar bug fixes, completely offsetting all initial benefits.
Brief Summary
Vibe coding isn't a story about the disappearance of a profession, but rather a story of its profound transformation. AI is taking on the role of a fast implementer, turning ideas into code. But it is the human developer who remains the indispensable strategist, architect, and responsible for results.
The future lies in close symbiosis. AI tools free up developers' time for higher-level tasks. An engineer's value is now determined not by typing speed, but by their ability to think systematically and engage in meaningful dialogue with artificial intelligence.
So can AI replace programmers? Not yet. It will change tools, require new skills, and redistribute responsibilities, but it will not eliminate the need for human expertise.
