AI-Driven “Vibe Coding”: A New Era of Development 2025-Future?

Author(s): Ben Looi

Originally published on Towards AI.

Is AI developer a “Vibe Coder”?

The term “vibe coding” has become shorthand for a new style of programming in the age of generative AI. Instead of painstakingly writing code line by line, a developer describes intent in natural language, and an AI system such as GitHub Copilot, Cursor, or ChatGPT generates the corresponding implementation. The developer, rather than obsessing over syntax, instead iterates through prompts and execution results until the system works as desired.

This has raised a serious debate: is an AI-assisted developer simply a “vibe coder,” coasting on machine intelligence, or are they a legitimate engineer, wielding a new set of tools? To answer this, we need to look deeply at what vibe coding means, how AI alters workflows, the risks and opportunities it presents, and whether this marks an evolution or erosion of software engineering.

Lowering the Barrier: AI as Supercharged Autocomplete

AI tools have lowered the barrier to entry for software creation in ways that were unimaginable a decade ago. Systems trained on billions of lines of code can generate functions, boilerplate, and even full applications from a few natural language instructions. What once required careful reading of documentation and weeks of effort can now often be prototyped in an afternoon.

The essence of vibe coding is that code is not crafted in the traditional sense but summoned. A request like “build a REST API with endpoints for user authentication and profile management” can yield an entire scaffolded service. The developer’s job shifts from writing syntax to orchestrating intent, testing, and re-prompting. The coding interface looks less like an IDE and more like a conversation window.

This approach democratizes development. Someone with no computer science background can build a working prototype. What once required the specialized literacy of programming languages is now accessible through natural conversation. In effect, AI turns English into the new API surface.

Traditional Engineering vs. Vibe Workflows

Traditional software engineering emphasizes a cycle: requirements gathering, system design, coding, testing, and iteration. Code is seen as the central artifact that developers must deeply understand and manage. Every line is written and reviewed, forming a body of knowledge about the system.

Vibe coding replaces much of this with prompting and reviewing execution results. Instead of opening a text editor to write functions, the developer opens a chat interface to describe features. Debugging is not reading stack traces line by line, but asking the AI to fix or reframe the approach. Architecture diagrams are sketched in plain language, then fleshed out by the machine.

This shift is radical. For seasoned developers, it can feel reckless — as if the very craft of programming is being abandoned. For newcomers, it feels liberating: a chance to build without the barrier of years of learning. The truth lies somewhere in between.

The Engineering Dilemma

The heart of the debate is whether vibe coding undermines the discipline of engineering. Engineering demands accountability: knowing why code works, ensuring reliability, guarding against security flaws, and maintaining systems over time. AI-generated code can produce correct-looking but subtly flawed implementations. Without understanding, those flaws may go unnoticed until they cause failures.

A pure vibe coder, who accepts machine output without review, risks shipping systems that are brittle, insecure, or unmaintainable. A professional AI-assisted developer, however, treats AI like a powerful autocomplete. They prompt, but also review, test, and refine. They do not abdicate responsibility; they accelerate delivery.

This distinction is crucial. If a developer cannot explain the code their system is running, they are not truly in control. Vibe coding in its extreme form — code without comprehension — is therefore unsuitable for production systems. But AI-assisted development with human oversight is a pragmatic and often highly effective new workflow.

Prompt Engineering as the New Literacy

In this paradigm, the skill of prompt engineering becomes the new technical literacy. Where once developers needed to master syntax and language idioms, they now need to master instructing AI systems.

Good prompts are specific, contextual, and constraint-driven. Instead of asking “write me a Python function”, an effective prompt is “write a Python function using the requests library to call an external REST API, handle retries with exponential backoff, and return JSON parsed as a dictionary.” The difference between vague and precise instructions can mean the difference between usable output and wasted cycles.

Iterative refinement is also part of the process. Developers may not get the perfect code on the first try, but through conversation — adjusting requirements, clarifying constraints — they shape the AI’s output. This is, in effect, a new kind of pair programming where the machine is the junior partner generating drafts at lightning speed.

The Risk of Fragile Systems

The real danger of vibe coding is not in prototypes, but in long-lived, complex systems. Production systems demand maintainability. They are extended, debugged, and audited over years. If a system is composed of large blocks of AI-generated code with little human understanding, it becomes extremely fragile.

Subtle bugs can lurk undetected. Security vulnerabilities may be introduced through unsafe patterns. Efficiency may be sacrificed for expedience. And when something goes wrong, the lack of comprehension makes debugging nightmarish.

Engineering discipline is what prevents this. Code reviews, test suites, security audits, and documentation remain as important as ever. AI can generate the code, but humans must still apply rigor. Otherwise, vibe coding becomes a shortcut that piles up hidden technical debt until systems collapse under their own weight.

When Vibe Coding Works

Despite these risks, vibe coding has real value when used appropriately. Rapid prototyping, hackathons, and exploratory projects benefit immensely. Instead of spending days setting up scaffolding, developers can jump straight to iterating on functionality. For greenfield projects where speed matters more than long-term robustness, this can be a game-changer.

Educationally, vibe coding can also accelerate learning. Beginners can build and run working systems, then dissect them to understand how they function. This offers a gentler on-ramp than the old model of slogging through textbooks and tutorials before producing anything tangible.

And even in professional contexts, vibe coding accelerates boilerplate-heavy tasks. Writing tests, generating type definitions, scaffolding microservices, or drafting configuration files can all be offloaded to AI. Developers remain in the loop, but freed from the tedium of repetitive work.

The Professional Standard

For professional developers, the standard must remain clear: AI is a tool, not a substitute for comprehension. Using AI to draft code is valid. Using AI to brainstorm architectures is valid. Using AI to accelerate development is valid.

But a professional cannot blindly accept AI output. They must review, understand, test, and refine it. The AI is not accountable for bugs, outages, or breaches — the human developer is. Thus, vibe coding in its extreme, uncritical sense is not professional engineering. But AI-assisted development with discipline and oversight is absolutely part of the modern engineer’s toolkit.

The Future: Developers as Orchestrators

The rise of vibe coding foreshadows a broader shift in the nature of software work. Developers may increasingly become orchestrators, guiding AI systems, reviewing their output, and focusing on higher-level design and problem solving. The day-to-day work of typing syntax may fade, replaced by a blend of natural language prompting, architectural reasoning, and system oversight.

This mirrors shifts in other industries. Pilots no longer manually control every aspect of a flight; they supervise sophisticated autopilots. Financial analysts no longer hand-calculate forecasts; they guide powerful statistical engines. In each case, professionals moved up the stack of abstraction. Software developers are undergoing the same transition.

But autopilots fail, models mispredict, and AI-generated code can misfire. Professionals must remain vigilant. The engineer’s role is not erased — it evolves. Responsibility and accountability remain human.

Conclusion

So, is an AI developer a vibe coder? The answer depends on how we define the term. If we mean someone who accepts AI-generated code without understanding or oversight, then no — that is not engineering, but a risky shortcut. If we mean someone who leverages AI tools heavily, but still applies engineering discipline, then yes — they may code by vibe, but they remain developers.

Vibe coding is a powerful new approach that democratizes software creation and accelerates prototyping. But without comprehension and rigor, it becomes a liability. Professional developers must embrace AI as a partner while never abandoning their responsibility to ensure correctness, security, and maintainability.

The future of software development will not be vibe coding alone, but a synthesis: engineers who can both harness the vibes and uphold the discipline.

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Published via Towards AI