What Vibe Coding Felt Like on a Real Product
I have been writing code professionally for more than five years. I have been on late-night production incidents, done large refactors that looked impossible at the start, and spent more time in architecture discussions than I ever expected when I started this career. So when "vibe coding" became mainstream, my first reaction was mixed. Part of me was curious. Another part thought this was probably hype with a short half-life.
Then I tried it on a real product.
Not a toy app. Not a weekend demo. A system that had to survive real data, awkward integrations, and users who do not care how elegant your code is as long as it works every day.
At first, it felt almost unfair.
I started the same way most people did: autocomplete, then snippets, then bigger chunks. At some point I noticed I was describing complete features in plain language and watching them appear in the editor. Tools like Claude Code and Cursor reduced the distance between "I want this behavior" and "there is code in the repo that does it."
For a while, that was addictive. Setup work became trivial. Boilerplate vanished as a problem. The first draft of almost anything arrived in seconds. I was shipping faster than usual, and not by a little. The product moved forward quickly enough that I started to wonder whether the part of my job I had trained for the longest was becoming optional.
That feeling lasted until I reached the parts of the system that matter most.
The first cracks were subtle. The code looked clean. It compiled. Basic tests passed. But in real scenarios, small assumptions started failing. A data format that looked standard was not actually standard in practice. An external integration failed silently in a way the generated code never anticipated. A retry path was missing because the model assumed failure was exceptional, while in production failure is often routine.
None of this was dramatic on day one. It was the slow accumulation that changed my mind. I kept seeing code that was correct in a textbook sense and wrong in an operational sense.
That difference is hard to explain if you have not lived through production systems for a while. AI can produce something that reads like a senior engineer wrote it. But it does not carry the memory of weird outages, legacy quirks, or those specific edge cases that only appear under real load at the worst possible moment. It recognizes patterns in code. It does not carry consequences in the same way humans do.
I also ran into the consistency problem. Even with a lot of context, the model would propose something that contradicted decisions made earlier in the project. Not because it is reckless, but because it does not truly understand why those decisions existed. It sees style and structure, not intent over time. If you are not strict in review, your architecture starts to drift file by file.
Testing exposed this even more clearly. The generated tests often looked polished and gave nice coverage numbers, but they mostly protected happy paths. The tests that actually saved me came from experience: "this exact thing broke once before," "this integration returns garbage in this scenario," "this timeout will happen in production even if it never happens locally." Those cases do not appear automatically just because a model can generate tests quickly.
Security had a similar pattern. If you ask directly, you can get decent security-related code. But proactive security thinking is still mostly on you. Trust boundaries, input validation at the right layers, failure behavior under malicious input, isolation choices that prevent data leaks - these things require intent and threat modeling, not just code completion.
The biggest shift was not technical. It was personal.
At some point I had to admit that most of the keystrokes were no longer mine. I was still responsible for architecture, tradeoffs, review, and final decisions, but the literal act of typing code had changed. That can feel uncomfortable if your professional identity is tied to writing every line yourself.
I think many developers are quietly dealing with that identity shift. We were trained to value output you can point to in a diff. Now a growing part of the value is upstream from the diff: framing the problem well, defining constraints, choosing what must never fail, and rejecting attractive solutions that will not survive production reality.
This is where domain knowledge becomes non-negotiable. If I ask for "a service that handles incoming data," that is too vague to be safe. But if I can specify exact failure modes, exact data quality rules, exact behavior for retries and persistence, then AI becomes useful in a very different way. The model is fast, but speed only helps if direction is accurate.
So my workflow changed. I spend more energy deciding what the system should do before I ask for implementation. I define boundaries first. I think through failure behavior earlier. I use AI heavily for drafts and repetitive work, then review with a much stricter lens than I used to. I still debug with the model, and it helps, but I trust it least in places where the bug is not syntax but assumption.
In other words, I now treat AI less like an oracle and more like a very fast collaborator that still needs senior supervision.
This has made me more productive, no question. I can move faster through the mechanical parts of development than I could even a year ago. But it has also made skill gaps more visible. If someone only knows how to produce code that looks clean, AI compresses that advantage quickly. If someone understands systems, constraints, and failure patterns in a specific domain, AI amplifies that advantage.
That is why I do not think this is a simple story about replacement. It is more like redistribution. Some parts of the job are becoming cheaper. Some are becoming more valuable. The typing part is less scarce than before. Judgment is more scarce than before.
I also do not think resistance helps. Refusing these tools now feels like refusing an IDE years ago. You can do it, but you are choosing friction without getting better outcomes in return. At the same time, blind trust is dangerous. Fast output is not the same thing as correct output, and confident language is not the same thing as real understanding.
For me, the honest summary is simple. Vibe coding is real. The productivity gain is real. The risk is also real. Both are true at the same time.
If I had to name what changed most, it is this: I spend less time asking "how do I write this code?" and more time asking "what exactly must this code guarantee when reality gets messy?" That second question always existed, but now it is the center of the job, not the side quest.
I am still figuring out what this means long term for our profession and for my own identity inside it. Maybe that uncertainty is normal during a transition like this. What I know today is that AI made me faster, but it also forced me to be more explicit about what only a human can contribute. That has been uncomfortable, useful, and strangely clarifying all at once.
If you have gone through a similar shift, I would genuinely like to hear how it felt for you - LinkedIn.