Vibe Coding vs. Coding Today: The Difference Is Accountability

What vibe coding actually means

Andrej Karpathy coined “vibe coding” to describe a deliberately hands-off mode of building with an LLM: ask for a change, accept the output, run it, and keep moving without reading every diff or fully understanding the growing codebase. You see something, say something, run something, and it mostly works.

That is narrower than simply using AI to write code. A developer who asks an agent to add a feature, reads the changes, tests the result, and can explain how it works is not vibe coding in the original sense. They are doing AI-assisted software development. Simon Willison makes this distinction well: the defining feature is giving up close oversight of the code, not using an LLM.

There is no shame in that choice when the stakes are low. The problem starts when a useful label for fast experimentation is mistaken for a complete way to make software.

Why vibe coding is worth trying

Vibe coding lowers the cost of possibility. A person with an idea can make a small tool, a rough interface, a game, or a workflow prototype before they have the vocabulary to describe an architecture. An experienced developer can test a strange interaction or automate a one-off task without turning a weekend experiment into a planning exercise.

That makes it especially useful when the question is, “Would this be interesting or helpful at all?” A rough result can show whether a workflow makes sense, whether an interface is understandable, or whether an idea deserves more attention. That is close to the role of a prototype or proof of concept: learn something important without pretending you have already built the finished product.

The freedom is the point. You can try a dozen bad ideas quickly, keep the promising one, and learn where the tool is strong or unreliable. It is a wonderfully low-friction way to build intuition.

The difference is ownership, not AI

It is more useful to think of these as modes than opposing camps. The same person may vibe-code a rough interface in the morning, use an agent to make a reviewed change after lunch, and spend the afternoon investigating a production incident without AI touching the fix.

The rows are not a ranking of who is a “real” programmer. They describe how much responsibility the work carries. A prototype can begin with vibes and still become the seed of a serious product. The transition happens when the experiment starts making promises to someone else.

Coding today is more than typing

Writing code has never been the whole job. Before a change reaches a keyboard, someone needs to understand the person using the product, choose what belongs in scope, notice the systems it touches, and decide what “done” means. Afterwards, someone still needs to review, test, release, support, and improve it.

A 2024 Microsoft Research survey of 484 developers puts the point in perspective. In that sample, coding new features was only one part of a typical week; communication, debugging, design, review, tests, security, and the rest of the work filled most of it. It is one study, not a universal schedule, but it captures a familiar truth: typing source code is only one visible part of making software work.

AI changes that visible part quickly. It can draft a component, trace a call path, write a migration, explain unfamiliar code, or turn a specific bug report into a first patch. It does not remove the need to decide whether the patch solves the right problem or creates a new one.

That is why a short planning step remains valuable. In a calmer AI coding workflow, I describe using a plan to make a change legible before files move, then a measurable goal to keep the implementation honest. The tool can be fast after the direction is clear. It should not be the only thing providing direction.

Speed still needs a feedback loop

AI-assisted development is already ordinary practice. In Stack Overflow’s 2025 Developer Survey, 84% of respondents said they use or plan to use AI tools in development, and 51% of professional developers said they use them daily. Adoption is not the same as blind trust: 46% reported distrusting the accuracy of AI output, compared with 33% who trust it. The most common frustration was output that was almost right but not quite.

That is not evidence that AI is failing. It is evidence that “almost right” is a costly place to stop. A generated change may look plausible, pass a superficial demo, and still mishandle an empty state, an old account, a permission boundary, or the next developer’s understanding of the system.

The research on speed also resists a simple story. Google’s 2025 DORA report found that AI adoption can improve delivery throughput and product performance, while also relating negatively to delivery stability when teams lack strong testing, version control, and fast feedback loops. A METR randomized study found the opposite of the usual headline in one specific setting: 16 experienced open-source developers working in repositories they knew well took 19% longer with early-2025 AI tools available.

Neither finding settles the question for every team. Together, they make a more useful point: the benefit depends on the work, the codebase, the tool, and the quality of the feedback loop. Faster generation increases the value of clear constraints and good evidence; it does not make them optional.

When a vibe becomes software you own

The moment a project touches real customer data, handles money, controls access, integrates with another system, or needs to be maintained by someone else, it has crossed a boundary. You may still use an agent for much of the implementation. You should no longer treat the output as something that merely needs to look right on your screen.

At that point, the useful question changes from “Can the tool build this?” to “What could this change break, expose, or make harder to understand later?” The answer determines the level of review, testing, and operational care it deserves.

OWASP’s secure-coding guidance for AI is direct: every AI-assisted change needs a human owner responsible for its correctness, security, and maintenance. That does not mean manually rewriting generated code to prove a point. It means accepting the responsibility that the tool cannot accept for you.