Every founder who has watched an AI tool spit out a working app in ten minutes eventually asks the same thing: if it can write the code, why can’t it just fix the code? And the next version will be better, so won’t the bugs sort themselves out soon enough?
It’s a fair question, and it gets at the real issue with the future of vibe coding. The honest answer is: partly yes, partly no, and the gap between the two is where you need to pay attention.
We spend our days inside AI-generated codebases, cleaning up the mess and shipping the fixes. So we have watched these tools improve in real time, month by month, across hundreds of projects. Here is where things actually stand, and where we think they are going.
Where AI coding actually is right now
Today’s tools are very good at producing code that runs. Point Cursor, Bolt, Lovable, or v0 at a clear prompt and you’ll get something that compiles, looks the part, and demos well. For a prototype, that is genuinely useful, and it wasn’t possible three years ago.
The problem is the distance between “runs” and “holds up.” An app that works when you click through it yourself can fall over the moment two people use it at once, or a user types something you didn’t expect, or the free-tier database hits its limit. We wrote about why vibe-coded apps break in production precisely because this gap catches so many founders by surprise.
So the current state is a strange one. The code looks more professional than ever, and underneath it the same categories of problem keep showing up: missing error handling, auth that guards the front door but not the back, logic quietly duplicated in five places. The surface got polished faster than the foundations did.
What’s genuinely getting better
I don’t want to be the person insisting nothing has changed. Plenty has, and some of it matters.
Context windows have grown enormously, so a tool can now hold much more of your codebase in its head at once. That directly reduces one of the classic failures, where the AI rewrote a function from scratch because it forgot the one it had already written. Multi-file editing has come a long way too. The better tools can now change three files that depend on each other and keep them roughly consistent, which used to be a reliable source of breakage.
Test generation is improving as well. Ask for tests and you’ll get something reasonable, where a year ago you’d get placeholder nonsense. The models have also grown noticeably better at spotting their own obvious slips when you point them at an error message and ask them to look again.
Add these up and the floor has risen. The dumb, mechanical mistakes are getting rarer. That’s real progress and worth saying plainly.
What stays hard no matter how good the models get
Here’s the part that doesn’t get fixed by a bigger model, and it’s the part that decides whether your app survives contact with real users.
The first is architecture. Deciding how your app should be structured, where data should live, how the pieces should fit so the thing is still workable after your tenth feature, that’s a judgement call rooted in where your product is going. An AI tool doesn’t know where your product is going. It knows what code usually looks like. Those are not the same, and the difference compounds with every feature you add on top of a shaky base. Our guide to taking vibe code to production is really a guide to undoing architecture decisions that were never decisions at all.
The second is business logic. An AI can write a flawless permission check and attach it to the wrong route. It can build a discount system where the maths is perfectly valid and also lets someone stack coupons into a negative total. The syntax is clean, the logic is wrong, and no model catches it because being wrong here depends entirely on what your specific business is supposed to do. This is the deepest reason AI tools keep making the same mistakes: they optimise for plausible code, not for correct behaviour, and plausible is exactly what a business-logic bug looks like.
The third is security thinking, which is really just paranoia applied to code. A good developer asks, “what happens if someone malicious does the opposite of what I expect?” An AI tool, by default, builds the happy path and moves on. It will secure things when you ask, but it won’t spontaneously worry on your behalf, and worry is most of the job. That’s why so many AI-built apps ship with the same security holes.
None of these are bugs to be patched in the next release. They come from the tool not knowing your intent, and no amount of extra training data hands it your intent.
The job that’s appearing: reviewing the AI, not replacing the human
The interesting shift isn’t AI replacing developers. It’s what the developer’s job turns into.
When the machine writes most of the first draft, the valuable human skill stops being “can you type code fast” and becomes “can you tell whether this code is actually right.” Reading critically, spotting the plausible-but-wrong, knowing which twenty lines out of two thousand will hurt you, that’s the work now. The AI writes; the human interrogates.
This isn’t a far-off prediction, it’s already how the good teams operate. It’s also, frankly, most of what we do all day. And it’s why we keep saying that automated tools and human review catch different things. The tools will keep getting better at the objective, rule-shaped problems. The judgement calls stay human, because judgement is the one thing the model can’t borrow from its training set.
Our bet for the next two years
Predictions are cheap, so here’s a concrete one you can hold me to.
Vibe coding tools will keep getting better at the mechanical layer. Fewer syntax slips, tidier multi-file changes, more of the boring stuff handled automatically. Building the first version of an app will get even faster and even more accessible, and that’s genuinely good news for anyone with an idea and no engineering team.
What won’t change is the ceiling. The tools will get you to a convincing prototype faster than ever, and the distance from that prototype to something you can safely put real users and real money through will stay roughly where it is. That last stretch is architecture, business logic, and security thinking, and those are human problems wearing a code costume.
So no, AI tools won’t fully fix their own bugs, because the bugs that matter aren’t the kind you fix by being a better autocomplete. They’re the kind you fix by understanding what the app is for. Use the tools, and use them boldly. Just don’t confuse “the AI built it” with “the AI is responsible for it,” because when it breaks in front of a customer, you are.
Whatever the future holds, your app needs to work today. If you built something with an AI tool and want to know whether it’s ready for real users, get a free code review and we’ll tell you exactly what’s solid and what isn’t.