What Is Vibe Coding? Where It Works and Where It Breaks

Vibe coding is a style of software creation where you describe what you want in plain language and let AI generate most of the code. The term was coined by Andrej Karpathy in early 2025, and the core idea is simple: instead of writing every line yourself, you guide the model, test what it outputs, and keep iterating.

My take: vibe coding is great for starting software and often bad for owning software. It works best when speed matters more than precision, and it breaks when security, maintainability, compliance, and operational accountability start to matter. That conclusion lines up with Google Cloud’s split between exploratory “pure” vibe coding and reviewed AI-assisted development, Simon Willison’s warning that reviewed code is not the same thing as vibe coding, and recent NCSC guidance on the security tradeoffs.

Why this matters now

This topic is moving fast. Google Cloud has a freshly updated explainer on vibe coding, major vendors like IBM and Cloudflare have published their own definitions, and the UK’s National Cyber Security Centre has already stepped in to warn that AI-generated code can create real organizational risk if it is adopted carelessly. In other words, vibe coding is no longer a niche joke on developer Twitter; it is now part of the mainstream software conversation.

That makes this the right moment for a more practical article. Searchers do not just want a definition. They want to know whether vibe coding is a shortcut, a superpower, or a future maintenance disaster.

What vibe coding actually means

At the broadest level, vibe coding means using an AI model to generate code from natural-language prompts instead of manually writing everything line by line. Google Cloud describes it as a software development practice that makes app building more accessible, especially for people with limited programming experience. Replit similarly frames it as instructing AI agents to write code from natural-language prompts. IBM and Cloudflare use nearly the same core definition.

But the narrow definition matters more than the broad one. Karpathy’s original framing was not “AI helps me code faster.” It was closer to: I accept the output, I stop caring about the code itself, and I optimize for momentum. Simon Willison’s key distinction is useful here: once you review, test, and understand what the AI produced, you are no longer really vibe coding in the original sense; you are doing software development with AI assistance.

That distinction is important because a lot of content on this topic blurs the line between AI-assisted engineering and unchecked AI-generated software. They are not the same thing, and founders should not treat them as the same thing.

What vibe coding is not

Vibe coding is not the same as using GitHub Copilot, Claude, ChatGPT, Gemini, or Cursor carefully inside a disciplined engineering workflow. If your team reviews diffs, writes tests, checks security, understands data flow, and can explain how the system works, you are doing AI-assisted development, not surrendering the app to “the vibes.”

That is why the phrase is useful and misleading at the same time. Useful, because it captures a real shift toward prompt-led app creation. Misleading, because some people now use it to describe almost any coding with AI, and that erases the accountability question that made the term interesting in the first place.

Where vibe coding works

1. Fast MVP experiments

Vibe coding is strongest when the goal is speed, not perfection. If a founder wants to validate a workflow, show a clickable concept to investors, or test whether users even care about an idea, prompt-led building can compress days or weeks of work into hours. Google Cloud explicitly says the exploratory form is best suited to rapid ideation, and Cloudflare highlights near-instant prototyping as one of the biggest benefits.

2. Internal tools and one-off workflows

It also works well for internal dashboards, lightweight admin tools, temporary ops workflows, and “software for one team” use cases where the blast radius is limited. In those cases, the business value often comes from removing friction quickly, not from crafting a perfect codebase. That is exactly the kind of environment where imperfect but useful software can still win.

3. Non-technical founders exploring product direction

Replit, Google Cloud, and IBM all emphasize that vibe coding lowers the barrier for people who are not traditional developers. That does not mean non-technical builders suddenly become software engineers. It means they can get much closer to a real product concept before they need a full development team. For discovery, ideation, and early shaping, that is a big deal.

4. Boilerplate-heavy first drafts

Vibe coding is especially useful when the first version of the work is repetitive: scaffolding a UI, wiring simple CRUD flows, producing admin pages, or getting a rough app shell live. Cloudflare points to boilerplate reduction and faster feature creation as core upsides, and Google Cloud’s docs go a step further by showing how AI-generated apps can then be deployed and hardened on infrastructure like Cloud Run.

Where vibe coding breaks

1. Production systems you need to trust

The biggest failure point is simple: software gets riskier the moment people depend on it. NCSC warns that insecure software produced without human review can propagate vulnerabilities, while Simon Willison argues that production-quality software still requires code that can be explained, maintained, and trusted. Once an app matters to customers, revenue, compliance, or operations, “it mostly works” stops being good enough.

2. Security, privacy, and compliance-heavy apps

Cloudflare explicitly calls out the risk of vulnerabilities reaching production faster and the possibility of compliance issues when teams lose track of how apps process personal data. NCSC’s current guidance makes the same basic point from a security-policy angle: the opportunity is real, but the risk is real too. If the app touches sensitive data, permissions, regulated workflows, or critical business logic, vibe coding alone is not a governance model.

3. Complex product logic

LLMs are good at generating common patterns. They are less reliable when product rules become unusual, state gets messy, integrations multiply, and edge cases matter. IBM flags technical complexity as a core limitation, and Google Cloud’s own framing suggests that the responsible path is guided generation plus review, not blind acceptance. Complex apps punish shallow understanding.

4. Long-term maintainability

The hidden cost of vibe coding is not usually launch speed. It is future ownership. Cloudflare notes that teams can lose understanding of their own codebases, making bug fixes and vulnerability remediation harder. Simon Willison makes the same point from a developer perspective: if you cannot explain the code, you should not ship it as if you own it.

5. Scaling from demo to company asset

Google Cloud’s Cloud Run guidance for AI-assisted developers is revealing here. It does not stop at “generate the app.” It immediately moves into deployment, containers, permissions, secrets, resource limits, VPC controls, scaling, and least-privilege service accounts. That is the real story: software gets harder after the demo. The second half of the journey is operations.

A simple framework: use it for speed, not for denial

A good founder heuristic is this:

Situation	Vibe coding fit	Why
Landing-page experiment or rough MVP	High	Speed matters more than elegance
Internal ops tool with low risk	High	Fast utility beats long build cycles
Investor demo or concept validation	High	You need momentum and proof
Customer-facing app with payments, auth, or sensitive data	Low	Security and correctness matter too much
Regulated workflow or compliance-heavy product	Low	You need traceability and control
Long-lived product with a roadmap and team handoff	Medium to low	Ownership and maintainability become the real work

This table is a synthesis of the current official explainers and security guidance: rapid ideation is a strong fit, while production-critical and high-risk environments demand review, testing, and stronger engineering discipline.

So should founders use vibe coding?

Yes, but with the right expectation.

Use vibe coding to get from idea to artifact. Do not rely on it by itself to get from artifact to reliable product. That is the cleanest way to think about it. Vibe coding is a strong tool for discovery, prototyping, and early MVP movement. It is a weak substitute for software engineering when the app must be secure, maintainable, and accountable.

For founders, that means the smartest move is often hybrid: use AI to collapse the first draft, then bring in experienced builders to structure the app, clean up architecture, secure the flows, tighten the UX, and prepare it for real users. That is not anti-AI. It is how you get the upside of AI without inheriting all of its hidden debt. This is an inference from the current vendor and security guidance, not a direct quote.

Final takeaway

Vibe coding is real, useful, and easy to oversell. It works best when the goal is to learn fast, ship a rough first version, and reduce the friction between idea and software. It breaks when teams confuse generated output with engineered reliability.

The strongest position is not “never use it” and not “AI will replace engineering.” It is this: vibe coding is a great front door to software, but a poor substitute for product-grade development discipline. 

CTA: Talk to Maveristic about your app idea if you want to turn an AI-generated concept into a real MVP, customer-ready product, or production-grade app through AI App Development and MVP Development.