We’re shipping faster than ever, but under the surface, the structure is rotting. AI code can pass tests, but can it pass time?
This article includes:
- Why vibe coding feels amazing and why that’s the problem
- The hidden architectural debt AI tools are quietly creating
- Actionable ways to use AI tools without turning your system into spaghetti
1. When the code “just works,” but the system doesn’t
The CI passed.
The PR was approved with a quick “LGTM.”
The feature worked perfectly on staging.
And yet… something felt wrong.
The logs were noisy.
The service boundaries were blurry.
The data flow diagram looked like spaghetti thrown at a microservices wall.
But hey, it shipped. So who cares?
Welcome to vibe coding. 😎
If you’ve spent any time around modern AI coding tools like Copilot, Cursor, ChatGPT, Claude then you know the feeling. You type half a thought, and boom, an entire function appears. Feels like magic. You didn’t architect it. You barely planned it. But it works.
Until it doesn’t (at least for now).
This article is not about the AI-generated code being bad. It’s about the invisible damage it’s doing to system architecture. The kind of damage that doesn’t crash the app today, but makes it impossible to change tomorrow.
We’re building systems that feel fast, light, and productive.
But under the surface? It’s structural rot. And no linter can save you.
This is the dark side of vibe coding: the part where the code compiles, the tests pass, but the entire architecture is being held together by duct tape, vibes, and an AI’s best guess.
Let’s talk about what happens when we let autocomplete steer the ship and why we need to take the wheel back before the hull splits in half.
2. What is vibe coding (and why it feels amazing)
Let’s be honest. You’ve done it. I’ve done it.
We’ve all vibe-coded our way through a feature.
You open your editor, start typing a vague function signature, and your AI pair programmer finishes your thought before you even knew what it was. You accept the suggestion. A few more prompts, a few more completions and suddenly the feature works. You didn’t design anything. You didn’t even really think. But it’s green on CI and that dopamine hits hard.
That’s vibe coding.
And it feels incredible.
It’s like jazz improvisation… except your jazz partner is a language model trained on every GitHub repo from the past 10 years. You’re not building from first principles. You’re riffing in real time, trusting the suggestions, feeling your way through the feature.
And the AI?
It’s so helpful. Too helpful.
It won’t ask you if you’ve mapped your domain.
It won’t tell you that you’re leaking abstractions.
It won’t nudge you to separate concerns or question your architecture.
It’ll just make the code appear.
And when you’re on a deadline or chasing that Friday release buzz, that’s hard to say no to.
But here’s the catch: AI is optimizing for local correctness, not systemic soundness. It gives you functions. Not frameworks. Snippets. Not structure.
It can autocomplete your method, but it can’t reason about whether that method even belongs in this class, this module, or this service.
And if you’ve never seen real architecture before, how would you even know?
3. The illusion of speed hides structural rot
Here’s the thing about vibe coding:
It tricks you into thinking you’re crushing it.
You build faster than ever.
Merge PRs before lunch.
Demo a working feature by 2 p.m.
By the end of the sprint, it looks like you’ve been blessed by the gods of productivity.
But under the hood? It’s all driftwood.
Architecture doesn’t break loudly.
It breaks slowly.
It starts with a weird dependency in a place it shouldn’t be.
Then a second one. Then an async callback where a queue should’ve been.
And before you know it, you’re debugging a memory leak across three services that somehow all use the same data model… differently.
The real danger of vibe coding is that it produces working code that hides systemic dysfunction. Your feature might function in isolation, but the interactions, the flow, the lifecycle of your system? That’s where the rot starts to spread.
And AI doesn’t see the forest. It only sees the branch you’re typing on.
From recent experience:
We shipped a complete AI-generated logging system in two days. It worked fine, until we tried to scale to multi-region. Why? Because AI had added region-specific assumptions into every logging call. Now decoupling the architecture meant rewriting everywhere. 😕
It was fast. It was impressive. It was architectural debt disguised as acceleration. So yeah, you might be sprinting. But you’re sprinting through a house of cards.
4. The collapse of shared standards and mental models
Before vibe coding, we used to argue. No, seriously.
Architects, leads, seniors, we’d huddle around whiteboards and fight over REST vs GraphQL, hexagonal vs layered, whether the frontend really needed a separate service.
It was annoying. It was slow. It was also how shared mental models were formed. Now?
One dev vibes out a feature using Copilot.
Another glues on a new endpoint via ChatGPT.
A third rewires the database layer “just to get it working.”
No one talks. No one diagrams.
No one even knows what’s under the hood, because AI built it in silence.
What used to be design discussions are now autocomplete sessions.
And when there’s no shared architecture, there’s no shared ownership.
Instead of standards, we get spaghetti.
Instead of conventions, we get chaos.
Instead of context, we get “uhhh I think Copilot did that?”
Remember Stack Overflow?
When you pasted a solution from there, it came with context. A question. An explanation. Someone admonishes you to avoid such actions in production. Now the code just… appears. No warnings. No rationale. No tradeoffs.
It’s code without culture.
And when every developer on the team is soloing with a different AI model, what you get isn’t a product. It’s a quilt of half-compatible assumptions stitched together with YAML and hope.
5. Architectural debt is the new tech debt
Tech debt is easy to spot. A hacky if-statement. A hardcoded config.
A TODO you left in five sprints ago and now pretend you didn’t see.
But architectural debt? That’s sneakier. It’s when:
- Your services don’t scale independently, even though they’re “micro.”
- A change to one domain model breaks five features you didn’t know were connected.
- You add caching because performance tanks… but the real issue is your data flow is a mess.
AI won’t tell you that. AI can’t see that. AI doesn’t even care.
It’ll just keep helping you ship code that works locally, individually while your system becomes increasingly unchangeable. Like a house where the wiring works but is all buried behind concrete walls… and no one remembers where the pipes are.
And because it’s AI-assisted, it looks impressive. Until it isn’t.
6. AI can write your code, but it can’t design your system
Let’s get one thing clear: AI doesn’t know what your system is.
It doesn’t know your domain logic.
It doesn’t know what your users are doing.
It doesn’t know what broke in prod last week, or what you promised your product team last quarter.
It knows patterns. It knows syntax.
It knows how 10,000 people on GitHub solved similar problems, badly, beautifully, or somewhere in between.
But it doesn’t know you.
So when you lean on it to build your architecture, what you’re actually doing is handing off the hardest, most context-dependent part of engineering… to something that has zero context.
And that’s where things go wrong.
Real architecture isn’t in the code
It’s in:
- How services talk to each other
- How state moves through your system
- Where boundaries are drawn — and why
- What you agree to not do when designing a feature
None of that lives in autocomplete. It lives in conversations, diagrams, versioned contracts, interface definitions, shared mental models.
Architecture is intentional.
AI-generated code is opportunistic.
And when you build opportunistically at scale, you get chaos that runs… until it can’t.
So how do you protect the architecture?
Not with vibes. Not with magic. With discipline:
- Design first, code second Yes, even if the AI can write it for you. You should know what you’re asking it to do.
- Document your decisions Not just what the code does, but why it’s shaped that way.
- Use AI like a junior dev Let it suggest. Let it scaffold. But don’t let it steer. You’re the architect. You sign off.
- Review for systems, not just syntax In code review, ask: “Does this align with our model?” Not just: “Does it work?”
- Ruthlessly refactor bad patterns early If AI introduces a leaky abstraction, don’t leave it. Fix it before it becomes gospel.
Conclusion: The code will run, but will the system survive?
This isn’t a rant against AI.
It’s not a nostalgia trip for the days of hand-rolled YAML and three-hour whiteboard battles. It’s a warning.
A lot of modern code “just works.” And that’s the problem.
We’ve trained ourselves, and our tools, to chase features, not form.
We celebrate commits that close tickets, not ones that preserve the integrity of the system five months from now.
But systems don’t fail loudly. They rot quietly. And by the time the logs are screaming and the on-call engineer is crying, it’s already too late.
So vibe out if you must. Use the tools. Enjoy the magic.
But don’t forget:
Your system has to survive beyond the merge.
Architecture is the only thing standing between “this works now” and “this still works in six months.”
Be the one who sees the system. Be the one who keeps it alive.
Architecture sanity checklist (for vibe coders who still care)
Before you vibe your way through the next sprint, ask yourself:
✔️ Do I know where this logic belongs in the system?
If you’re not sure, diagram it before coding.
✔️ Does this feature reinforce or break existing boundaries?
Sometimes the cleanest code lives in the wrong place.
✔️ Would another dev understand why I did this?
Leave comments. Write commit messages. Tell future-you what the heck is going on.
✔️ Is this coupling accidental or intentional?
If a new module magically knows too much — pause.
✔️ Did I design this first, or just prompt until it worked?
Be honest. If you don’t have a plan, AI will happily build chaos for you.
Real resources for building systems in the AI era
🔹 AI code generation ≠ software engineering
A thought-provoking piece by JeanYang on why shipping code isn’t the same as building maintainable systems.
https://world.hey.com/jean/ai-code-generation-is-not-software-engineering-7c8cc5cd
🔹 Martin Fowler’s “Design Stamina Hypothesis”
Short, legendary blog post on why good architecture feels slow now but wins big later, especially true when AI accelerates the wrong things.
https://martinfowler.com/bliki/DesignStaminaHypothesis.html
🔹 “The Fallacy of Fast AI Code” by Alex Volkov
Why we need to stop assuming fast AI code = good code. Especially powerful for teams starting to rely on Copilot.
https://www.alexvolkov.io/why-fast-ai-code-will-break-your-product/
🔹 OpenTofu contributors’ blog
Yes, it’s about infra, but their reasons for forking Terraform also reflect deeper values about transparency, maintainability, and trust in automated tooling. https://opentofu.org/blog/
🔹 The “Cognitive Load of Modern Systems” talk by Honeycomb
A brilliant conference talk explaining how complexity sneaks in, and how AI tools can accelerate that without checks.
Watch on YouTube → https://www.youtube.com/watch?v=4cGJzjTqN1g
Top comments (0)