Vibe Coding on a Real Product — An Honest Developer's Take
I've been writing code professionally for over five years. I've debugged production incidents at 2 AM, mass-refactored legacy systems, and sat through enough architecture meetings to fill a lifetime. So when "vibe coding" became a thing — the idea that you could just describe what you want and let AI write the code — my first reaction was somewhere between amusement and mild existential dread.
Then I tried it. On a real product. Not a toy project or a weekend demo — an actual system that needed to handle real data, real edge cases, and real users.
Here's what actually happened.
How It Started
Like most developers, I eased in gradually. First it was autocomplete suggestions. Then generating boilerplate. Then, at some point, I realized I was describing entire features in natural language and watching them materialize in my editor. Claude Code, Cursor — the tooling had gotten good enough that the friction between "I want this" and "this exists" had nearly disappeared.
So I decided to push it. I had a side project — a production-grade system in a domain I knew deeply from years of professional work. The kind of project where I understood every edge case, every integration point, every way things could break. I figured: if vibe coding works anywhere, it should work here.
Where AI Was Genuinely Impressive
Let me give credit where it's due. For certain categories of work, AI coding tools are borderline magical.
Boilerplate and scaffolding. Setting up project structure, configuration files, Docker setups, CI pipelines — all the stuff that takes hours and requires zero creativity. AI handles this in minutes, and honestly does it better than I would because it doesn't cut corners out of impatience.
Well-documented technologies. If you're working with popular frameworks, standard REST APIs, or common database patterns, AI produces remarkably solid code. It has essentially memorized every Stack Overflow answer and every documentation page. For conventional tasks, it's faster and more consistent than I am.
Translating between formats. Data transformation, protocol conversion, parsing — anything that's essentially "take this shape and make it that shape." AI excels at this because it's pattern matching at scale, which is literally what these models do.
First drafts. Need a React component? A database migration? A utility function? AI gives you a 70-80% complete first draft in seconds. You refine from there. This alone changed my velocity dramatically.
For the first week or two, I was euphoric. I was shipping features at 3-4x my normal speed. The product was taking shape faster than anything I'd built before. I started wondering if my five years of experience were about to become irrelevant.
Then the real work started.
Where It Fell Apart
The problems didn't show up in the easy parts. They showed up exactly where you'd expect them to — at the edges, in the details, in the places where domain knowledge matters more than syntax.
AI doesn't understand your domain. It can write a perfectly valid HTTP handler. But it doesn't know that in your specific field, certain data formats sometimes arrive malformed because legacy equipment generates non-standard identifiers. It doesn't know that a particular integration will silently fail under specific conditions that only someone with years of operational experience would recognize. I kept finding myself fixing subtle bugs that were invisible to someone who hadn't lived through the pain of production systems in this space.
It optimizes for "looks correct" over "is correct." AI-generated code compiles, passes basic tests, and reads well. But it often makes architectural decisions that are subtly wrong — choosing synchronous processing where you need queues, using in-memory storage where you need persistence, skipping retry logic where failures are expected. The code works on your laptop. It breaks in production.
Context window is not understanding. You can paste your entire codebase into the context window and AI will still make suggestions that contradict decisions you made three files ago. It doesn't understand why you chose a specific pattern — it just sees the pattern. So it'll happily introduce a different pattern in a new file, creating inconsistency that compounds over time.
Testing is where the illusion shatters. AI writes beautiful tests — that test the happy path. The tests that matter, the ones that catch the weird edge cases you've seen in production, still need to come from human experience. I can't tell you how many times AI gave me a test suite with 100% coverage that would have caught exactly zero of the actual bugs I've seen in similar systems.
Security is an afterthought. AI treats security the way a junior developer does — it adds it when you ask for it, but it doesn't think about it proactively. Authentication, data isolation, input validation at trust boundaries — these things need to be designed in, not bolted on. And they require understanding threat models specific to your domain.
The Hammer and the Piano
Here's the analogy I keep coming back to: the tools are the same, but one person uses a hammer to nail boards together and another uses it to build a piano. Vibe coding with domain expertise and vibe coding without it produce fundamentally different outcomes.
When I described what I needed, I wasn't saying "build me a server." I was saying "build me a service that accepts incoming connections on this protocol, routes them based on specific metadata fields, validates data integrity against these particular standards, handles these specific failure modes, and archives to cloud storage with this organizational structure." That's not a prompt — that's a technical specification that took years of experience to formulate.
Someone without that experience might get AI to build something that looks similar. It would handle the happy path. It would demo well. But it wouldn't survive the first week in production.
The Part Nobody Wants to Admit
Here's the uncomfortable truth that I've been wrestling with:
About 80% of the code in my project was written by AI. I directed it, reviewed it, corrected it, and made the architectural decisions. But the actual keystrokes? Mostly the machine.
This creates a weird identity crisis for developers. We've spent years building our identity around writing code. Not designing systems, not understanding domains, not making architectural decisions — but literally writing code. And that specific skill is being commoditized at an alarming rate.
The developer who prides themselves on writing clean React components in 15 minutes should be worried, because AI does it in 15 seconds. But the developer who prides themselves on knowing which components to build, how they should interact, and what will break in production — that person is more valuable than ever. They're just not doing the typing anymore.
What Actually Changed in My Workflow
After a few months of this, here's what my process actually looks like:
Architecture and design: 100% human. I decide the overall structure, the technology choices, the data flow, the failure modes. AI is terrible at this because it requires understanding constraints that don't exist in code — business requirements, team capabilities, operational realities, compliance needs.
Implementation: 80% AI, 20% human. AI writes the first draft of almost everything. I review, correct, and refine. The 20% I write myself is typically the most critical and domain-specific code — the parts where being wrong has real consequences.
Debugging: 50/50. AI is surprisingly good at finding syntax errors and obvious logic bugs. It's useless at finding the subtle issues that come from misunderstanding how systems behave under real-world conditions. "The code is correct but the assumption is wrong" — that's still a human problem.
Testing: 30% AI, 70% human. AI writes the scaffolding and basic assertions. The meaningful test cases come from me, because they come from experience.
My Honest Assessment
Is vibe coding real? Yes. It's a genuine productivity multiplier.
Is it going to replace experienced developers? No. But it's going to replace the parts of their job that they've been doing on autopilot. And for many developers, that's a larger percentage than they'd like to admit.
What worries me isn't that AI can write code. What worries me is how many developers have been getting by on code-writing ability alone, without developing the deeper skills — system design, domain expertise, operational intuition — that AI can't replicate. For those developers, the window is narrowing fast.
The developer job isn't disappearing. But it's transforming from "person who writes code" to "person who knows what code should do and can verify that it does it correctly." If you've been building domain expertise, operational experience, and architectural judgment — congratulations, you're more productive than ever. If you've been coasting on your ability to write clean for-loops... it might be time to evolve.
What I'd Tell My Past Self
If I could go back to before I started this experiment, here's what I'd say:
Use it. Don't resist. The productivity gains are real and they compound. Refusing to use AI tools in 2025 is like refusing to use an IDE in 2010 — technically possible, but you're handicapping yourself for no good reason.
Don't trust it blindly. Review everything. Test everything. AI is a very confident junior developer who has read every textbook but has never been paged at 3 AM. Treat its output accordingly.
Double down on what AI can't do. Understand your domain more deeply. Learn how systems fail in production. Develop the judgment to make architectural trade-offs. These skills are appreciating in value precisely because the mechanical parts of coding are depreciating.
Be honest about what you're doing. There's no shame in using AI tools. There is shame in pretending you're not. The industry needs honest conversations about how we're actually building software now, because the mythology of the lone genius developer hand-crafting every line of code is already fiction.
This is my honest experience. Your mileage may vary depending on your domain, your tools, and how many production outages have shaped your architectural instincts. I'm interested in hearing from other developers who've gone through similar experiments — reach out on LinkedIn.