I’ve been an AI developer for a little over four years, but the last twelve months have been different from anything before it. Somewhere between late last year and now, my actual coding style changed completely. I stopped writing most of my logic line by line. I started describing what I wanted, watching an LLM generate it, tweaking the prompt, and shipping. People have started calling this “vibe coding” — you describe the vibe of what you want, the AI writes the code, and you steer rather than type.
I want to talk about what a full year of this actually looked like from the inside. Not the hype-reel version where AI writes your whole SaaS in a weekend and you retire to a beach. Not the doom version either, where AI code is unusable garbage that real engineers have to rewrite anyway. The truth, like most things in engineering, sits in the messy middle — and I think that middle is worth talking about honestly, because I don’t see enough of that conversation happening.
What Vibe Coding Actually Feels Like Day to Day
If you haven’t tried it seriously, here’s what the workflow looks like in practice. I open a chat or an agentic coding tool, describe a feature or a bug in plain English, and the model produces a chunk of working code, sometimes across multiple files at once. I read it, run it, and if something’s off, I don’t go hunting for the exact line to fix — I just describe what’s wrong and let the model take another pass. Entire features that used to take me a day of scaffolding, boilerplate, and Stack Overflow tabs now take twenty minutes of back-and-forth conversation.
The first few months of this felt genuinely intoxicating. I was shipping features faster than I ever had. Prototypes that would have taken a sprint were happening in an afternoon. I remember building an internal admin dashboard with authentication, role-based permissions, and a half-decent UI in about three hours — something that would have eaten two or three days of my life a year earlier. That speed is real, and I don’t want to undersell it. But somewhere around month four or five, the honeymoon phase wore off, and I started noticing a pattern: the faster I moved, the more small, sneaky problems accumulated underneath the surface. That tension — velocity versus hidden debt — has basically defined my entire year.
The Pros: Where Vibe Coding Genuinely Earned Its Place in My Workflow
I want to be fair here, because I think a lot of skeptical developers dismiss this whole approach too quickly, and I get it — I was skeptical too. But after a year of daily use, here’s where it’s actually earned a permanent seat in my toolkit.
Boilerplate and scaffolding disappeared as a time sink. Setting up a new API route, a CRUD component, a form with validation, a config file for a new service — this used to be the tedious 20% of every project that nobody enjoys. Now I describe the shape of what I need and get a working first draft in seconds. This alone probably saved me hundreds of hours over the year.
It’s an incredible rubber duck that talks back with actual solutions. When I’m stuck on an architecture decision, I can describe the problem, get three different approaches with tradeoffs, and pressure-test my own thinking against them. I don’t always take the AI’s suggestion, but the act of articulating the problem clearly enough for a model to respond usefully often surfaces the answer on its own.
Learning unfamiliar languages and frameworks got dramatically faster. I picked up enough Rust this year to ship a small internal tool, something I would have avoided entirely before, because the AI could translate my mental model from languages I already knew into idiomatic Rust, and I learned by reading and correcting its output rather than starting from a blank tutorial.
Refactors that used to feel too risky to attempt became routine. Renaming a pattern across forty files, migrating from one state management library to another, updating a deprecated API across a whole codebase — these used to be the kind of work you scheduled reluctantly because they were tedious and error-prone. Now they’re often a single well-scoped conversation.
It genuinely reduced the emotional friction of starting. There’s a specific kind of dread every developer knows, staring at an empty file before a new feature. Having something to react to, even an imperfect first draft, removes a surprising amount of that friction. I ship more simply because starting is easier.
Those are real, durable benefits. I’m not walking any of that back. But the cons are where the actual story of this year lives, and I think they matter more than most vibe-coding evangelists want to admit.
The Cons: What Nobody Tells You Until You’ve Shipped It
Confidence without correctness. The single biggest danger of AI-generated code is how convincing it looks. It’s clean, it’s well-formatted, it often includes comments explaining its own logic — and none of that has any correlation with whether it’s actually correct. I’ve caught myself skimming code that looked so professional I assumed it had to be right, only to find a subtle logic error three weeks later in production. Confidence and correctness are two completely different axes, and AI-generated code is very good at the first one and inconsistent at the second.
Context loss on large codebases. The bigger and older my codebase got, the worse the AI got at understanding it holistically. It’s excellent at reasoning about the file directly in front of it, and much weaker at understanding how that file interacts with twelve other files it hasn’t been shown, or business logic that lives only in a teammate’s head. I lost real time this year to AI-generated changes that were locally correct and globally wrong.
A quiet erosion of my own hands-on skill. This one is uncomfortable to admit, but I noticed my own instinct for certain syntax and idioms getting rustier over the months I leaned hardest on generation. When I did need to write something by hand — during an outage, on a plane with no connection, in a code review where I needed to catch something subtle — I was slightly slower than I used to be. I’ve since course-corrected by deliberately writing things by hand on a regular basis, but it’s a real cost that doesn’t get talked about enough.
Debt hides in code you didn’t struggle to write. Normally, the pain of writing something hard teaches you where the fragile parts of your system are. When the AI does the hard part invisibly, you lose that instinct. I’ve shipped modules I technically “wrote” in the sense that I approved every line, but that I couldn’t have debugged from memory a week later, because I never actually struggled through the logic myself.
Overconfidence in unfamiliar domains. When I’m using AI to write code in a domain I understand deeply, I catch its mistakes quickly. When I’m in a domain I understand less well — security-sensitive code, concurrency, cryptography — I’m far more likely to accept something that sounds authoritative but is subtly wrong, because I don’t have the intuition to know what “wrong” looks like there. This is genuinely the riskiest part of vibe coding, and it’s the part I’m most careful about now.
Top Bugs I’ve Actually Hit in AI-Generated Code
This is the part I really want to dig into, because after a year of this, I’ve started keeping a mental (and now literal) list of the bug patterns that show up again and again. If you’re vibe coding seriously, these are the ones to watch for specifically, because they don’t look like bugs at first glance — they look like normal code.
1. Hallucinated APIs and functions that don’t exist. This is the classic one, and it hasn’t fully gone away even with newer models. The AI writes a call to a library method that sounds completely plausible, matches the naming conventions of the real library perfectly, and simply doesn’t exist. It’s especially common with less popular libraries or slightly older training data versus a library’s current API surface. The fix is always the same: run it before you trust it, every time.
2. Off-by-one and boundary errors in loops and pagination. AI-generated code is surprisingly prone to subtle boundary mistakes — an inclusive range that should be exclusive, a pagination offset that skips or duplicates the last record, a slice that’s one index short. These bugs are dangerous specifically because they only surface at the edges of your data, so they sail through casual testing and show up later with edge-case inputs in production.
3. Silent error swallowing. I’ve lost count of how many times generated code wraps something in a try/catch block, catches the exception, and does… nothing. Or logs it quietly to a console that nobody watches. It looks defensive and careful. It’s actually the opposite — it hides failures instead of surfacing them, and I’ve had entire features fail silently in production because an error was caught and never handled properly.
4. Race conditions in async and concurrent code. This is the category that scares me the most. AI-generated async code often looks syntactically correct and passes every straightforward test, but breaks down under real concurrent load — two requests updating shared state at the same time, a missing await that lets a promise resolve out of order, a lock that isn’t actually enforced across the code path it needs to cover. These bugs are hard for humans to spot too, but AI models seem to underweight them specifically because they’re invisible in a quick read-through.
5. Security shortcuts baked in by default. Unless explicitly prompted otherwise, generated code has a tendency to reach for the easiest secure-adjacent pattern rather than the actually secure one — string-concatenated SQL instead of parameterized queries, permissive CORS settings, secrets read from a hardcoded string instead of environment variables, weak or missing input validation on user-facing endpoints. None of this is malicious; it’s just optimizing for “this will run” over “this is safe,” and it’s on the developer to catch it every time.
6. Stale or mismatched dependency versions. Because training data has a cutoff and package ecosystems move fast, generated code sometimes assumes an API shape from an older version of a library than the one actually installed in your project — a function signature that changed, a default behavior that flipped, an import path that moved. It compiles fine in isolation and breaks the moment it touches your actual installed version.
7. Overfitting to the example instead of the general case. When I give the AI a specific example to work from, it sometimes writes code that handles that exact example beautifully and nothing else — hardcoded assumptions about data shape, a schema that only works for the sample I pasted in, a validation rule that’s actually just my test case in disguise. It’s a subtle form of overfitting, and it’s easy to miss because the demo always works.
8. Inconsistent state management across a file. In longer generated files, I’ve noticed the AI occasionally lose track of its own earlier decisions — initializing a variable one way at the top of a function and treating it as a different shape further down, or reintroducing a piece of state that was already handled elsewhere in the file. It reads like two different people wrote two halves of the same function, because in a sense, two different generation passes did.
9. Tests that assert the code does what it does, not what it should do. When I ask for tests alongside the implementation, the AI sometimes writes tests that simply mirror the implementation’s actual behavior rather than the intended behavior — meaning a bug and its test get generated together, and the test passes happily around a genuine defect. Green tests started meaning a lot less to me than they used to, unless I write the critical assertions myself.
10. Overly clever abstractions that don’t match the codebase’s actual patterns. Occasionally the AI introduces a design pattern that’s technically elegant but inconsistent with how the rest of the codebase does things — a new abstraction layer, a different error-handling convention, a state management approach that doesn’t match what’s used two files over. It’s not wrong exactly, but it adds cognitive load and inconsistency that a human reviewer has to catch and reconcile.
If there’s one lesson across all ten of these, it’s that AI-generated bugs rarely look like bugs. They look like reasonable code, written in a reasonable style, that fails in specific and often unglamorous circumstances. That’s precisely why they’re more dangerous than the bugs I used to write myself — my own mistakes had a personal signature I’d learned to recognize. These have a much more generic, plausible-sounding signature, which means my old instinct for “this looks off” doesn’t fire as reliably.
How I Actually Work Now, One Year In
I haven’t abandoned vibe coding, and I don’t think I ever will at this point — the productivity gains are too real to walk away from. But my process has changed a lot from month one to now. I treat AI-generated code the way I’d treat a pull request from a very fast, very confident junior developer who has read every textbook but has zero production scars. I review everything before it merges. I write my own tests for anything security-sensitive, concurrent, or business-critical, rather than trusting generated tests for those paths. I still write code by hand regularly, specifically to keep my own instincts sharp. And I’ve gotten much more deliberate about scoping what I hand to the AI — small, well-defined chunks with clear context produce dramatically better results than “build me this whole feature” in one shot.
Is Vibe Coding Worth It?
A year in, my honest answer is yes, with real caveats. It has made me faster in ways I don’t want to give up, and it has made me more careless in ways I’ve had to actively correct for. The developers who are going to get the most out of this next era aren’t the ones who trust it blindly or the ones who reject it out of stubbornness — they’re the ones who treat it like exactly what it is: an extremely fast, occasionally brilliant, occasionally wrong collaborator that still needs a human who actually understands the system to catch what it misses. That’s the discipline I’ve had to build over the last twelve months, and I suspect it’s the discipline that separates the developers who’ll thrive with this shift from the ones who’ll get burned by it.