I used to be pretty skeptical of vibe coding. Watching people ship apps without understanding a single line of what was generated always felt like a disaster waiting to happen, especially when beginners hit the first real bug and have no idea where to start. And the idea that you can build successful software or SaaS without learning software development? I never bought that for a second.
That said, my opinion has shifted a bit. The models are better, I've been using Claude Code, GPT-5.5, and OpenClaw in a much more serious way, and I've seen firsthand where AI can actually carry real weight. The catch is that it only works when you already understand what you're doing.
Vibe coding is the extreme end of the AI coding spectrum
There's a real spectrum when it comes to coding with AI. On one end, you have one-shot prompting with platforms like Lovable. On the other, you have autocomplete in VS Code. Somewhere in the middle is the sweet spot: let the agent write the code, but you still make the architectural decisions, write the specs, and test the result.
That middle ground is what I actually teach in my AI course, and it's a lot different from what I mean by vibe coding.
Vibe coding, to me, is when you're barely looking at the code at all. You're going off the vibes instead of the syntax, and for a long time I was totally against that. I still think a lot of people confuse "using AI" with "understanding software," and those are not the same thing.
Better models changed the equation, not the rules
A big part of why I've softened is simple: the models are just better now. Claude Opus 4.7 with Claude Code, GPT-5.5 with Codex, and even OpenClaw with GPT-5.5 are all good enough that, if you know how to direct them, they can do a lot of the heavy lifting.
I'm seeing less hallucination, more first-try success, and a lot less need to babysit every line the way I would have with something like GPT-5.3. That matters.
But the model getting better is only half the story. The other half is that I've learned how to work with these systems more effectively. I know how to manage context and memory, how to map out documentation and spec files, and how to steer the model toward the outcome I want.
That's exactly why I'm not willing to pretend the tool alone is enough. Better output does not erase the need for actual judgment.
Foundation still decides who can use AI well
I'm not budging on this part: vibe coding is not okay under any circumstance if you do not have a foundation in software development and architecture. If you do not understand the basics, AI does not magically create them for you.
A few things make up that foundation:
- How the web actually works
You need to understand requests, responses, status codes, and what happens between the browser and the server. Without that, you're just guessing at what the app is doing.
- How data is structured
You need to understand how to model data, how relationships work, and what an index is. If the data model is fuzzy, the app will be fuzzy too.
- How to read code, not just write it
You need enough hand-written code under your belt that you can read code and actually see what it's doing, instead of skimming past it like noise.
- How debugging feels in real life
You need to have been burned by enough bugs to recognize bad patterns. That kind of pattern recognition does not come from prompting.
You do not need a CS degree. You do not need to memorize sorting algorithms. You do not need to be a genius. But you do need to have built things from scratch, broken them, fixed them, and learned what failure looks like.
That confidence is the dangerous part.
Beginners should use AI to learn faster, not skip learning
I am not saying beginners should stop using AI tools. That ship has sailed, and fighting it is the wrong move. The better move is to change what you use them for.
Use AI to accelerate learning, not to bypass it.
There's a huge difference between those two things, and that difference is where most people go wrong. If you're new, let the model explain the code it generates. Type it out yourself. Break it on purpose. Then figure out why it broke.
That process is where the actual growth happens, and it's also why the developers who will do well over the next few years are not just the ones who can prompt well. They're the ones who can prompt well, read code, design systems, and debug under pressure.
The AI handles the typing. You still have to handle the thinking.
Vibe coding works best after you've earned the right to trust it
Once you're past the learning stage, vibe away. Seriously. If you already have the foundation, AI can be an incredible force multiplier, and I've been using it that way in my own workflow and in my home lab with eight machines managed by OpenClaw.
But it should not become your entire workflow for every project. If you rely on it for everything, you will forget too much and become too dependent on it. That's where the trouble starts.
My position now is simpler than my old one. I am not against vibe coding anymore. I just think it should be a tool, not the whole system.
The AI handles the typing. You still have to handle the thinking.
And that is the whole line between useful and dangerous: let Claude, GPT-5.5, and the rest speed you up, but never let them replace the part of the job that actually makes you a developer.
This article was adapted from I've Changed My Opinion On Vibe Coding