Picture this: A junior developer sits at their desk, casually typing, “Build me a React component that shows user profiles with tabs for activity, friends, and settings.” Within seconds, code appears. The dev scans it quickly, clicks “Accept All,” and moves on. No debugging. No syntax checks. No reading the actual code.

Welcome to Vibe Coding.

Coined by Andrej Karpathy (former Tesla AI director and OpenAI co-founder) in early 2025, vibe coding describes a workflow where developers “fully give in to the vibes” and let AI handle the nitty-gritty of writing code. It’s the coding equivalent of telling a contractor, “Build me a house” without checking the blueprints.

Sound alarming? Maybe. Revolutionary? Possibly. Here to stay? Absolutely.

Let’s break down why vibe coding has taken off like wildfire, what it means for software quality, and how veteran engineers might want to approach this brave new world where reading your own code has become optional.

Historical Context: Nothing New Under the Sun

Remember CORBA? If you just said “Cor-what?” you’ve proved my point already.

Back in the ’90s, when building enterprise systems meant fighting with Common Object Request Broker Architecture, developers had to understand remote procedure calls at a painful level of detail. The code was verbose. Error-prone. Tedious.

Then came application servers. Suddenly, you didn’t need to know how network calls worked under the hood. JBoss and WebLogic handled it, and a whole generation of developers never learned what their predecessors had to master.

Fast forward to Struts and Spring. Another layer. More magic. More developers who could build complex systems without understanding how the older parts worked.

Each wave of abstraction follows the same pattern:

1. Old guard complains: “These kids don’t know what’s happening underneath!”
2. New developers ship products anyway.
3. The world keeps turning.

From machine code to assembly to high-level languages. From raw SQL to ORMs. From vanilla JavaScript to React. We’ve been hiding complexity for decades.

Now LLMs are doing it again, just more dramatically. The difference? Previous abstractions still required developers to learn a formal language. LLMs let you skip even that step.

But here’s the twist: despite all these changes, each new generation still built working software. Different, yes. Imperfect, sure. But functional. Those Java developers who never touched CORBA? They built the backbone of modern enterprise software.

So before we panic about vibe coding, let’s recognize it as part of this continuum. A more extreme abstraction, but abstraction nonetheless.

The Rise of Pure Vibe Coding

So what exactly is “pure” vibe coding? It’s when developers rely entirely on AI to generate solutions without reviewing the code line by line.

You describe what you want. The AI builds it. If it breaks, you tell the AI what’s wrong, and it fixes it. Lather, rinse, repeat. You never have to know what’s happening behind the scenes.

For prototyping or learning new tech, it’s magical. Imagine being a Java developer needing to build a quick React Native app. Rather than spending weeks learning a new framework, you can describe what you want and have functioning code in minutes.

“Decrease the padding on that sidebar,” you might say to your AI assistant. Why hunt through lines of CSS when you can just ask for what you want?

When Karpathy coined the term, he described it as “forgetting that code even exists.” That’s the heart of it. Code becomes a black box, and the focus shifts to outcomes rather than implementation.

But there’s a darker side to this story. While vibe coding works wonders for toy projects and prototypes, it can be a disaster for enterprise solutions.

The Rabbit Hole Problem

Here’s where vibe coding gets tricky. When LLMs hit a roadblock, they frequently dig themselves deeper instead of stepping back.

We’ve all been there. You’re working with an AI coding assistant and suddenly face an error. The model suggests a fix—maybe downgrading a library version. That creates a new error with a dependent library. The model adjusts again. And again. Before you know it, you’re five solutions deep and the original problem remains unsolved.

I’ve seen this repeatedly with Claude 3.7 Sonnet—arguably the best coding model available today. When Python code breaks, the model often blames library versions, shifting from one dependency to another in an endless cycle, never questioning its fundamental approach.

Unlike humans, who instinctively step back to reconsider their strategy, models double down. They lack the meta-awareness to say, “Wait, I’m approaching this problem all wrong.” They’re trained on solutions that worked historically, not on the process of problem-solving itself.

This creates what I call the “rabbit hole problem.” A human would scrap the solution and start fresh. The model keeps tunneling, convinced the answer lies just a bit deeper.

Consider a recent project where I asked an LLM to build an API with specific authentication requirements. The code failed because the model misunderstood a key security concept. Instead of recognizing this fundamental error, it spent twenty iterations tweaking minor implementation details, moving further from a workable solution with each attempt.

This is precisely why pure vibe coding fails for enterprise solutions. The costs of these rabbit holes multiply across complex systems.

Finding the Balance: LLMs as Coding Assistants

The reality? LLMs make fantastic coding assistants but dangerous coding replacements.

When working with senior engineers who can verify output and identify conceptual errors, AI coding tools shine. A skilled engineer with AI assistance can build features 70-80% faster than coding from scratch. The AI handles boilerplate code, mundane tasks, and repetitive patterns, while the engineer focuses on architecture, edge cases, and business logic.

This partnership works because humans contribute what AI lacks:

1. Contextual understanding of business requirements
2. Ability to spot conceptual flaws
3. Meta-awareness to recognize when an approach isn’t working
4. Judgment about code maintainability and security

Simon Willison, a prominent developer exploring AI-assisted programming, makes this distinction clear. He writes that if you review, test, and understand AI-generated code before deploying it, that’s not vibe coding—it’s just using an AI as a typing assistant.

The key difference lies in comprehension. Engineers who understand what’s happening under the hood can harness AI’s productivity gains while avoiding its traps. Those who blindly trust AI-generated code soon find themselves with unmaintainable systems nobody understands.

This balanced approach lets us embrace the benefits of AI assistance without surrendering quality and reliability.

Best Practices: How to Effectively Use LLMs for Coding

To get the most from AI coding tools without falling into the vibe coding trap, follow these practical guidelines:

1. Keep features small and specific. Don’t ask for entire systems. Break work into clear, discrete components. The more specific your request, the better the result.
2. Request implementation plans first. Before generating any code, ask the AI to outline its approach. Review this plan carefully—it’s much easier to spot conceptual errors at this stage than in hundreds of lines of code.
3. Verify critical sections. While you might not need to review every line, understand critical components like authentication, data handling, and business logic.
4. Use implementation blueprints. Create templates that specify how your organization handles error logging, security, testing, and other cross-cutting concerns. Ask the AI to follow these patterns.
5. Treat AI code like junior developer code. Would you let a junior developer push code without review? Apply the same standard to AI-generated code.

Here’s a real example of this approach in action:

Instead of asking, “Build me a user authentication system,” try:

“Create a user authentication API with the following specifications:

• JWT-based authentication
• Password hashing using bcrypt
• Rate limiting for login attempts
• Following our error handling blueprint that logs all failures to our centralized logging system
• Unit tests for the login, registration, and password reset flows”

Then ask for an implementation plan before any code. Review the plan, make adjustments, and only then request the actual implementation.

This structured approach yields dramatically better results than vague requests.

Conclusion: The Future of Enterprise Development

Vibe coding isn’t going away. The productivity gains are too substantial, and each new model generation gets more capable. But like every abstraction that came before, it requires adaptation, not blind adoption.

Enterprise software development has always balanced innovation with reliability. The challenge now is finding where AI fits in this equation.

For toy projects, prototypes, and learning exercises, pure vibe coding works wonders. It democratizes programming and lowers barriers to entry.

For production systems handling sensitive data or critical operations, a balanced approach is essential. Use AI to accelerate development but maintain human oversight for architecture, security, and business logic.

The most successful organizations will be those that create clear guardrails—implementation blueprints that guide AI output toward company standards while leveraging its speed and versatility.

The next generation of developers won’t remember a time before AI coding assistants, just as today’s developers don’t remember programming without Stack Overflow. They’ll build differently, but they’ll still build amazing things.

The question isn’t whether to use AI coding tools—it’s how to use them responsibly. By understanding their limitations and establishing clear processes, we can harness their power while maintaining the quality and reliability our users expect.

Vibe coding or not, the fundamentals remain the same: good software solves real problems reliably. How we get there is just another layer of abstraction.