Vibe Coding After the Hype: What Actually Changed for Developers?
A year ago, everyone in tech seemed obsessed with “vibe coding.” Screenshots of full apps “written in English,” Twitter threads about AI building software from a single prompt, and bold claims that “coding is dead” dominated the timeline.
Fast-forward to now: vibe coding is still around, but the hype has cooled. The reality is more nuanced—and, in many ways, much more interesting.
In this article, I’ll share my perspective as a developer on what vibe coding really is, why expectations were unrealistic, and where it actually helps if you already know how to code.
What Is “Vibe Coding” Supposed to Be?
When Andrej Karpathy popularized the phrase “vibe coding,” he was describing a new way of working with AI tools: you describe what you want in natural language, iterate with the model, and let it generate large chunks of code while you supervise, correct, and refine.
In other words:
Coding by describing the vibe of what you want, then tightening it with edits.
I don’t think the original idea was: “Non-technical people will type English paragraphs and ship production-grade, scalable software.”
It was more: “Developers now have a supercharged autocomplete that can accelerate their work.”
And that distinction matters a lot.
Why Non-Technical Expectations Were Too High
Many non-technical people heard about vibe coding and thought:
“Great, I’ll just tell the AI: ‘Build me an app like Uber’ and I’m done.”
But every mid–senior developer knows how unrealistic that is.
Modern software is:
Complex – Multiple services, databases, APIs, state management, auth, caching, deployments.
Full of trade-offs – Performance vs. simplicity, flexibility vs. maintainability, time-to-market vs. robustness.
Decision-heavy – Architecture choices, naming, patterns, frameworks, library selection, data modeling, etc.
If you let AI assume most of those decisions for you, the probability of hidden mistakes grows very fast:
Wrong abstractions
Poorly chosen libraries
Security vulnerabilities
Performance bottlenecks
Code that’s hard to extend later
Coding is not just “telling the computer what you want.”
It’s making precise, logical changes at a very low level, in a way that remains understandable, testable, and maintainable over time.
The Precision Problem: Why English Isn’t a Programming Language
Even Larry Ellison (founder and CTO of Oracle) has pushed back on the idea of “programming in English.” His main concern: natural language lacks precision.
Programming languages are intentionally strict:
A missing bracket breaks the build.
A wrong type can be caught by the compiler.
A small change in syntax can completely change behavior.
Natural language, on the other hand, is vague and ambiguous:
“Make it fast” – where? For which users? What’s the budget?
“Handle large files” – how large? What’s the limit?
“Secure login” – OAuth? 2FA? Device trust? Rate limiting?
So while AI models are getting very good at translating from English to code, the source still needs to be precise. That’s why developer-supervised vibe coding is fundamentally different from non-developers trying to build production systems with prompts alone.
So… Did Anything Useful Actually Come Out of Vibe Coding?
In my opinion: yes—especially for experienced developers.
Vibe coding, when used correctly, has changed several parts of the development workflow:
1. Faster Prototyping
Need to quickly try a new idea, spike a feature, or validate an approach?
You can now:
Describe the feature.
Let AI generate starter code.
Refine, refactor, and harden it yourself.
You still need to understand what’s happening, but the time from idea → first working version is much shorter.
2. Boilerplate, Scaffold, and Glue Code
A lot of real-world development is:
Writing similar forms again and again
Setting up routes, DTOs, types, interfaces
Configuring linting, testing, and build pipelines
Repeating patterns across components, pages, or services
AI is great at generating repetitive or boilerplate code once you provide examples and constraints.
3. Faster Refactoring and Rewriting
AI can help with:
Converting class components to functional ones
Migrating from one library to another
Renaming, reorganizing, and extracting functions
Adding types to untyped codebases
Again, you need to review everything—but a task that would take hours can drop to 20–30 minutes.
4. Documentation, Tests, and Edge Cases
Vibe coding isn’t just about generating code. It also helps with:
Writing or improving docstrings and README files
Generating initial unit tests or integration tests
Suggesting edge cases you might have missed
You should never fully trust it, but it’s a good “second brain” in your workflow.
Vibe Coding for Non-Developers: A Narrow but Real Use Case
Did vibe coding produce something useful for non-developers?
Yes—in a narrower scope than the hype promised.
AI tools are already good at:
Building simple static websites (landing pages, personal sites, basic marketing pages).
Creating MVP-style demos that are not meant to scale, just to communicate an idea.
Generating content + layout together, especially for marketing experiments.
If the goal is:
“I want a simple one-page website for my idea.”
“I want a demo to show investors or teammates.”
Then AI-powered builders and vibe coding tools are genuinely helpful.
But that’s far away from:
“I built the next Airbnb entirely in English prompts.”
Developer-Supervised Vibe Coding: The Sweet Spot
For serious software, I believe the best model is:
Developer-supervised vibe coding.
That means:
A developer designs the architecture.
The developer writes precise prompts that encode their intent and constraints.
AI generates code or modifies existing code.
The developer:
Reviews the output line by line.
Runs tests and adds more.
Fixes mistakes.
Integrates the changes properly into the codebase.
Vibe coding is useful only when a developer stays in control.
It should feel like a powerful assistant, not an auto-pilot.
The Big Picture: Software Is Still Hard (But Now Faster)
Sam Altman has mentioned that software development used to be “slow” and is now getting faster because of AI. And that part is true:
Getting from idea to first version is faster.
Ship cycles can be shorter.
Individual developers can do more in the same amount of time.
But the fundamental nature of software did not change:
Complexity didn’t disappear.
Architecture still matters.
Good naming, structure, and testing are still essential.
Long-term maintainability is still a human responsibility.
AI is shrinking the gap between idea and implementation, but it’s not removing the need for engineering judgment.
Conclusion: Less Magic, More Leverage
Vibe coding wasn’t the magic wand some people hoped for—but it did give real developers new leverage.
If you’re a developer, the question is not:
“Will AI replace me?”
The better question is:
“How can I design my workflow so that AI handles the repetitive work, and I focus on architecture, decisions, and quality?”
Used this way, vibe coding is not a fad.
It’s just another powerful tool in the experienced developer’s toolbox.
And like every tool in software development, the value doesn’t come from the buzzword.
It comes from who is using it—and how.