Vibe Coding: Hype, Reality, and What It Actually Means
For years, software development required training, experience, and patience. Building a tool inside a company meant months of meetings, documentation, requirements, planning, approvals, testing, and more meetings. Creating a proof of concept (POC) could take months.
Now, with universal access to AI, almost anyone with decent prompting skills can build an app: Welcome to vibe coding.
What Is Vibe Coding?
Vibe coding is a development approach that relies on large language models (LLMs) to generate working code from natural language descriptions. Instead of writing every line yourself, you describe what you want:
“Build a dashboard that tracks API usage and exports a CSV.”
The AI generates the code.
1. You refine it
2. You tweak it
3. You ship something fast.
Some people are selling this as the future where “everyone can code.” Others talk about it like it’s opening the gates of chaos. So, what is the truth? Vibe coding is here to stay, but it’s not a magical system that turns anyone into a production-ready engineer overnight. However, vibe coding can help in multiple aspects to move fast.
Speeding Up Proofs of Concept (POC)
Before AI, building a POC often required:
Project documentation
Headcount discussions
Planning resources
Timelines
Internal alignment
And then developer bandwidth.
But Now? You can build a working prototype in an afternoon and then test it internally. Followed by showing developers what you want instead of explaining it abstractly and create a starting point for them. For internal tools, small automations, or experimental ideas, vibe coding dramatically lowers friction. It doesn’t eliminate developers instead, it gives them something concrete to evolve and scale properly. The pros are real:
Vibe coding lowers the barrier to entry, allowing more people to experiment and build without years of setup.
2. Speeds up prototyping, turning ideas into working demos in hours instead of weeks.
3. It can also enhance creativity, as AI suggests patterns, structures, or features you might not have considered on your own.
4. IF you are interested on coding, a prompt-driven coding can accelerate learning, helping you understand new approaches as you build.
The Criticism
Some of the pushback has a point:
If you rely entirely on AI, you risk losing depth and stop thinking through architecture. This can make you over time more dependent on a tool that generates generic solutions
Writing good software takes planning, trade-offs, and experience. AI gives fast answers but fast isn’t always thoughtful. Until AI can write clean, scalable, secure, and bug-free systems on its own, humans are still responsible for quality. Operating purely on “vibes” without understanding your system is risky. Especially at scale.
Software isn’t just about writing code; it’s also about maintaining it. Spaghetti code, over-engineering, and messy abstractions become expensive later. AI can generate code, true, but it doesn’t own the long-term consequences. Humans do.
Will Vibe Coding Replace Developers?
Short answer: No.
Long answer: It will absolutely change their role.
Developers won’t disappear, but their focus will shift toward:
• Architecture
• UX flow
• Data logic
• Security
• Performance and optimization
This transition will create friction. Not everyone will love it. But that’s true of every major shift in technology.
My approach to Vibe coding
Risk Management
Vibe coding is like building a plane while flying it. Developers traditionally optimize for clean code. Vibe coders optimize for shipping fast and fixing later. But remember, AI won’t:
Warn you about security holes
Optimize for scalability
Understand your business constraints
Stop you from deploying something broken
Using vibe coding to unleash creativity? Great. Shipping it to production without review? Dangerous. If You’re Going to Use It, Do It Smart and always review, test and keep human in the loop.
My Recommendation
Use AI to build faster.
Learn enough to fix AI’s mistakes.
Stay paranoid about security.
Vibe coding isn’t the end of software engineering. But it is the end of pretending that code generation is the hard part.
The hard part has always been and will remain thinking.