Vibe coding is cool — but it won’t build your enterprise app (yet)

Thanks to vibe coding, we’re witnessing a wave of weekend apps. Clean UIs, functional features, even a few smart edge-case workarounds. Impressive, no doubt.

But here’s what I’d ask every budding developer vibing with AI:

Can your app handle a million users? Can it survive a security audit? Would you bet your startup on it?

That’s the promise — and the paradox — of vibe coding.

What is vibe coding?

Vibe coding is the shiny new frontier in the AI-meets-software space. It uses large language models (LLMs) like ChatGPT or GitHub Copilot to turn natural language into working code. Just type what you want, and you’ll have a starting point.

Unlike traditional no-code or low code platforms, vibe coding doesn’t box you into rigid interfaces or proprietary runtimes. It generates real, editable code. And yes, it follows many modern best practices.

This is powerful.

This means that non-programmers can finally interact with the code, prototyping is faster than ever, and more people are getting to build.

But here’s the catch: Vibe coding is like running a demo server, not production-ready

Why everyone's vibing

It’s not just students. Early-stage startups, indie hackers, and even senior devs are playing around with popular tools as Tempo Labs, Bolt. diy Lovable.dev and AI coding tools like Aider, Cursor, Windsurf and more. 

These tools are game-changers for learning, prototyping, and side projects. A study by GitHub showed that developers using Copilot completed tasks up to 55% faster. That’s not marginal—it’s seismic.

And yet, when it comes to building the systems that power banks, hospitals and global platforms, speed isn’t the only metric.

What vibe coding doesn’t tell you

Building enterprise-grade software is like designing a skyscraper. The scaffolding is quick to throw up, but the real work lies in the beams, the foundation, the plumbing and the fire exits.

Enterprise products demand:

• Robust architecture – layered, modular and future-ready.

• Security – encrypted, compliant, airtight.

• Scalability – ready for 10 users or 10 million.

• Maintainability – clean, documented and debugged.

These aren’t just “nice-to-haves.” They’re non-negotiables.

And here’s the problem: AI doesn’t understand your business logic. It doesn’t know your compliance obligations. It won’t think about that edge case that breaks everything during Black Friday. It can’t sit in a design review and explain tradeoffs. You still need humans with experience to handle these.

Vibe coding meets composability: A reality check

In the world of composable architecture, building blocks are everything. APIs, microservices, and headless services must communicate clearly, consistently, and reliably with each other.

Vibe coding can help you generate the first draft of a block. But it rarely helps you understand how that block fits into a system of systems.

Here’s why:

• The generated code may not follow your organisation's architectural patterns.

• API integrations are often handled in a shallow, surface-level manner.

• There's little awareness of performance trade-offs, scalability concerns or long-term maintenance.

• Versioning, observability, and compliance are crucial for composable products but are not part of the AI’s worldview.

You might get a working page or workflow, but you won’t get a resilient, composable unit that can be easily integrated into your digital experience platform.

And if your team doesn’t understand what the code is doing or why, it becomes harder to maintain, extend, or debug it later.

Why technical understanding still matters

When we build composable enterprise applications, we aren't just creating code; we're designing ecosystems. Decisions about data flow, service boundaries, caching strategies, user permissions, performance budgets and more shape these ecosystems.

An LLM can assist, generate, suggest, or even refactor, but it doesn’t know your business logic, customer personas, or how your backend APIs throttle under load. These nuances are essential, and they require human understanding.

You can’t design for resilience if you don’t know where failure points exist.

You can’t debug edge cases if you don’t know how the system behaves under pressure.

You can’t ensure security if you don’t understand the threat model.

These are not constraints; they are the foundations of real-world software.

Vibe coding can assist with all of this, but it’s your responsibility to ensure the AI takes these critical aspects into account.

So, should you stop vibe coding?

Absolutely not.

Use it, play with it, learn from it, let it accelerate your ideas, push beyond boilerplate templates, and teach you things. AI is an incredible collaborator.

But don’t rely on it completely to do your job, not yet.

Because, at the end of the day, AI doesn’t own the responsibility. You do.

Maybe a few years from now, I’ll look back and be humbled by how far AI has come. Maybe I’ll be proven wrong.

But until then, trust your knowledge. Build it. And make sure you tell the AI what to do—not the other way around.

Vasu Kothamasu is the General Manager of India & Global Engineering Leader at Contentstack. Follow him on LinkedIn.