Vibe coding: what it is, how it works, and its limits

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Vibe coding has entered the tech vocabulary as a different way to create software: describing what you want in words and letting an AI assistant write the code that makes it a reality. It's fast, effective, and, when used well, can drastically reduce the gap between an idea and a working app.

The concept emerged in early 2025, spearheaded by Andrej Karpathy, and proposes a paradigm shift: we move from typing line by line to directing AI work with clear objectives , validating and refining the results. For some, it's "programming without programming"; for others, a powerful complement to traditional professional practice.

What exactly is Vibe Coding?

In short, it's a workflow where a large language model (LLM) generates code from natural language instructions. You say what you want, and the system builds functions, components, or services. The focus is no longer on syntax, but on expressing the technical and business intent.

The key idea is that “the most popular new programming language is English” (or whatever language you use to communicate). This premise, popularized by Karpathy years ago, crystallizes into a practice where the human guides and the AI ​​translates the intention into implementation . In some cases, you even speak to the assistant by voice and “see and correct as you go.”

However, there's a key difference: in its most extreme form, the user may accept pieces of code without fully understanding them . This is the crux of the matter and the reason why many experts recommend a more responsible approach.

In practice, vibe coding manifests itself in everything from lightweight prototypes to small personal products, the so-called "software for one," where the goal is to solve a specific need with minimal friction . This terrain is ideal for experimenting at full speed.

From slogan to practice: origin and meaning

The term “vibe coding” entered the public conversation in early 2025. Karpathy described it as a conversational process in which you “look, speak, execute, and copy/paste” while the AI ​​writes the bulk of the code. It’s a colloquial description that captures the shift: the role is moving from operating to orchestrating.

Karpathy himself acknowledged limitations: there are bugs that users don't easily identify, requiring them to try variations until they find the solution. His verdict was clear: it's "quite fun" and useful for impromptu weekend projects , the kind where speed is more important than perfection.

The tech press picked up on it very quickly, and business media labeled it a buzzword in Silicon Valley. At the same time, voices within the technical community pointed to the need for caution and human review if the intention is to move beyond the prototype stage.

Two ways to practice it: “pure” vs. responsible assisted

We can distinguish two complementary approaches. On the one hand, there's "pure" vibe coding: it relies on AI to be accurate and progresses at full speed, almost "forgetting that the code exists." This is useful for brainstorming, testing concepts, and scrapping "disposable projects" where the important thing is to validate the idea as quickly as possible.

At the other end of the spectrum is responsible AI-assisted development. Here, AI acts as a "pair programmer," writing drafts, suggesting alternatives, and automating repetitive tasks, while the human team reviews, tests, understands, and assumes final authorship . This is the version compatible with professional environments.

The difference is significant. As researcher Simon Willison pointed out, if AI wrote every line but you reviewed, tested, and understood the whole thing, you're not "vibrating the code"; you're using AI as a technical writing assistant . Change the tool, not the responsibility.

Both approaches have their place. The key is choosing the right one for the context: for creating a prototype, the shortcut is valid; for a critical system, you need control, traceability, and quality rules.

Advantages that will entice anyone

Its greatest appeal is accessibility. Suddenly, someone without much experience can bring an idea to life, launch a prototype, and see if it's on the right track. This democratization can pave the way for more internal innovation and more autonomous multidisciplinary teams.

The second advantage is time. If the bulk of the boilerplate is written by AI, timelines are reduced, especially in the discovery phase. Even if rework or refactoring is needed later, this shortcut can accelerate learning and validation.

It also fosters creativity. Not having to worry about syntax or APIs and libraries like jQuery reduces the friction for testing approaches, comparing alternatives, and building functional sketches in a matter of hours . It's a clear boost for MVPs.

And even if you decide to code "for real," you can always use AI to generate skeletons, migrate between frameworks , create tests, or document. When properly channeled, it becomes a cross-functional accelerator of the development lifecycle.

Limitations and risks that should be on your radar

The biggest Achilles' heel is the quality and security of the generated code. Without manual review and proper testing, it's easy to slip in subtle errors, unstable dependencies, or questionable practices. Various industry players recommend systematically auditing AI output before deploying it to production.

Another risk is the "black box." If you accept code you don't understand, debugging and maintaining it will be difficult. When the system grows and something fails, it will feel like "debugging in the dark." This lack of understanding ultimately translates into technical debt and future costs.

There's also the degradation of skills. Relying too heavily on prompts without writing or reading real code diminishes your analytical ability over time. The result is a team that's faster initially but less autonomous and accurate when faced with complex problems.

You must also consider governance and compliance. With decisions based on a model, you need mechanisms to ensure the outcome complies with internal and regulatory standards. Without standards and version control , traceability is diluted and auditing becomes more complicated.

Finally, AI doesn't always know why something is wrong. It can propose patches that alleviate the symptom, not the cause, and prolong a phantom bug. Hence, the professional approach is based on guiding the AI, rigorous testing, and making informed decisions.

Tools that make Vibe Coding easier today

Although the term is recent, the ecosystem already offers several options for putting it into practice, from smart editors to complete platforms. They all share one idea: to shorten the distance between what you think and the code that runs.

repeat

Replit enables cloud-based development with built-in AI features for auto-completion, explanation, and generating complete blocks from descriptions. It's ideal for rapid prototyping and collaboration, emphasizing iteration with minimal setup and immediate results.

Cursor

Cursor focuses on a minimalist experience with AI closely tied to the repository context. Suggestions, refactoring, and complete file generation coexist in an environment that prioritizes fluidity between prompt, code, and preview.

Claude

Claude excels at long conversations, maintaining context and style, which is useful for explaining designs, reviewing features, or proposing migrations. His strength lies in reasoning with the user about changes and alternatives , rather than simply writing isolated lines.

GPT-4 applied to the code

ChatGPT (GPT-4) generates functions, tests, documentation, and scripts on demand. It is flexible and accessible to both technical and business profiles, provided there is a clear validation and results control process.

Google Firebase Studio

In line with accelerating delivery, there are proposals focused on deploying components and backends with minimal friction. The promise is to streamline the creation of apps and services with integrated tools and well-defined prompts.

Cases, voices, and the debate surrounding it

Journalist Kevin Roose experimented with small AI-generated apps to solve personal tasks, which he dubbed “software for one.” One charming example was a tool that looked at the contents of his refrigerator to suggest lunches. The lesson was clear: the approach allows non-programmers to build useful things, but the results can be limited and prone to errors.

Simon Willison hit the nail on the head: if you understand, test, and validate the code, then you're not doing "pure" vibe coding; you're using AI as a copilot. The difference is crucial for separating playful prototyping from responsible and maintainable development.

On the business front, Business Insider picked up the term as the new buzzword in Silicon Valley, and accelerators like Y Combinator noted that around a quarter of their Winter 2025 startups had codebases with majority AI generation . It's a symptom of a real shift in workflows.

There are also anecdotes that illustrate the tensions: an assistant refused to "do the programmer's job" when asked to simply generate an entire solution. Beyond the joke, it encapsulates the dilemma: the engineer's value lies not in typing, but in thinking, deciding, and taking responsibility for the outcome.

Impact on business, marketing, and hybrid teams

For marketing or product managers, vibe coding opens a shortcut to move from a hypothesis to a working demo without relying entirely on IT. This changes the pace of proof-of-concept testing and allows for exploring more ideas with less upfront cost.

Accessibility doesn't mean abandoning engineering; it means collaboration. Non-technical profiles can drive automation, dashboards, or internal tools, while an expert team sets standards, reviews code, and ensures quality.

This creates a healthy division of roles: the business defines needs and validates value, AI produces drafts, and the technical team transforms that into robust, secure, and maintainable software . That's the middle ground that works best.

Best practices for using AI without losing control

Adopting a smart coding vibe requires simple yet firm rules. These are recommendations that many organizations are already institutionalizing to achieve speed without incurring technical debt or taking risks. The goal is to maintain productivity with guaranteed results.

• Define the intent precisely: objectives, constraints, examples, and acceptance criteria before requesting code.
• Use isolated environments and specific branches for AI-generated content, with mandatory pull requests and human reviews.
• Automate testing (unit, integration, and security) and require that any change passes the entire test suite.
• Add observability: logs, metrics, and traces that allow you to understand behaviors and diagnose quickly.
• Document architectural decisions and dependencies; traceability is key to auditing and regulatory compliance.
• Reserve the “pure coding vibe” for prototypes and internal testing; in production, prioritize the responsible assisted approach.

For training and upskilling, it's advisable to strengthen your fundamentals in programming, testing, security, and architecture. AI amplifies what you already know: if your foundation is solid, you'll be much more effective. Investing in structured learning and real-world practice remains essential.

Do you need to know how to program if AI already programs?

The short answer: yes, and perhaps more than ever. AI writes lines of code, but it doesn't understand the business context, non-functional requirements, or the trade-offs between performance, cost, and maintainability. That's where human judgment makes all the difference and prevents surprises.

A good developer today guides AI with precise prompts, chooses technologies and architecture, such as Python , validates security and quality, and plans for system evolution. It's no longer just about "writing code": it's about managing a fleet of intelligent tools with technical expertise and responsibility.

In fact, several real-world examples show that the collaborative model (human + AI) produces better results in the medium term than blind delegation. You gain in speed, yes, but also in control, understanding, and resilience.

What about agencies and specialized training?

Many companies prefer to rely on expert teams that combine digital strategy, marketing, and AI-assisted development to transform ideas into products. This support accelerates timelines and reduces risks because metrics, standards, and quality assurance processes are applied.

In parallel, foundational training—from patterns and architecture to testing and security—allows users to leverage code assistants without losing expertise. Reference programs help transform AI into a multiplier, not a crutch, through real-world practice and mentorship.

With this approach, AI doesn't replace professionals; it multiplies their reach. Effective collaboration is what allows an organization to build reliable and competitive solutions in less time.

The idea behind vibe coding is powerful: think big and build fast, without getting bogged down in the mechanics of syntax. If we understand its limitations, apply rigorous human review, and use tools like Replit, Cursor, Claude, or GPT-4 with clear standards, we get the best of both worlds: speed for prototyping and discipline for production deployment . The future of development isn't about abandoning programming, but about guiding AI with purpose to create software that works, maintains, and is secure.