Prelude
I recently sat in a meeting where a product manager told me he had "vibe coded" a prototype over the weekend. He was ecstatic. He had spoken to a computer, told it his dreams, and the computer had spat out a working React application. He felt like a wizard. He felt like the future had finally arrived to liberate him from the tyranny of engineers like me.
Then I looked at the code.
It ran. I will give him that. It rendered pixels on the screen. But beneath the shiny interface lay a subterranean horror show of hard-coded secrets, duplicated state logic, and security vulnerabilities so gaping you could drive a truck through them. It was not software. It was a facade.
We are currently living through a mass delusion. The industry has latched onto a new term. Vibe Coding. The idea is simple. You don't need to know how to code. You just need to know the "vibe" of what you want. You supply the vision. The AI supplies the implementation.
It sounds magical. It sounds like the democratization of creation.
It is actually a catastrophe in waiting.
I have spent the last decade building production systems. I have seen frameworks come and go. I have seen the rise and fall of "low-code" and "no-code" promises. This is different only in its mechanism of delivery. The fundamental flaw remains the same. You cannot abstract away complexity by ignoring it. When you generate high-level vision without low-level understanding, you do not create software. You create technical debt. You create "slop".
And eventually, someone has to clean it up.
The Orthodoxy
The narrative selling this dream is seductive. It tells us that the barrier to entry for software engineering has been artificially high. It argues that syntax is a gatekeeper preventing brilliant "idea guys" from building the next unicorn.
The proponents of Vibe Coding believe that natural language is the ultimate programming interface. They argue that we are moving from a deterministic era of explicit instruction to a probabilistic era of intent. In this worldview, the "how" is irrelevant. Only the "what" matters.
Platforms like Lovable and a thousand other "text-to-app" wrappers have sprung up to service this belief. They promise a world where you simply describe your application and it manifests. No debugging. No architectural diagrams. No understanding of memory management or API latency. Just pure creation.
The orthodoxy states that traditional coding skills are becoming obsolete. Why learn to invert a binary tree or understand the difference between TCP and UDP when an LLM can do it for you in seconds? The argument follows that the future belongs to the generalist. The prompt engineer. The "Vibe Manager" who curates the output of the machine.
This narrative is backed by an explosion of tools. GitHub Copilot, ChatGPT, Claude, and specialized agents all offer to write our boilerplate. And they do it fast. The productivity metrics, at first glance, seem to support the hype. Lines of code produced per hour are skyrocketing.
The orthodoxy says: "Speed is everything. Detail is for the machine."
The Cracks
If this utopia were real, we would see a golden age of software stability and innovation. We are seeing the opposite.
The data is starting to pour in, and it paints a grim picture of the "Vibe Coding" reality. We are not building better software. We are building worse software faster than ever before.
Let's look at the evidence.
GitClear analyzed over 150 million lines of code changed between 2020 and 2024. Their findings are damning. They found a massive increase in "code churn"—code that is written and then almost immediately deleted or rewritten. Even more worrying, they found an eight-fold increase in duplicated code blocks. This is the hallmark of copy-paste programming. Whats Wrong With Generative Ai Driven Development Right Now
This is not efficiency. This is thrashing.
The Google 2025 DORA report reinforces this. They found that a 90% increase in AI adoption correlated with a 9% climb in bug rates and a massive 91% increase in code review time. State Of Ai Code Quality
Think about that for a second. We are spending double the time reviewing code because the code we are generating is suspect. The machine generates a solution that looks correct. It compiles. It passes the happy-path unit test. But it fails in the edge cases. It introduces subtle race conditions. It hallucinates APIs that don't exist.
The "Context Gap" is the killer here. A recent survey showed that 65% of developers cite "missing context" as the top issue with AI code. State Of Ai Code Quality
The AI sees the file you are working on. It might see the file next to it. It does not see the legacy database schema from 2018 that you have to interface with. It does not understand the regulatory compliance requirement that forces you to encrypt that specific field. It does not "know" your architecture. It guesses.
We are already seeing the user base for platforms like Lovable churn. People sign up. They build a demo. It looks great. They show their investors. Then they try to add a complex feature. They try to integrate a legacy payment gateway. They try to scale it to 10,000 users.
The system breaks. The code is a tangled mess of hallucinated logic and spaghetti dependencies. The user cannot fix it because they never understood it. They leave.
The market knows this. We are seeing a correction. The job market is not hiring "Vibe Coders". It is aggressively hiring senior engineers who can fix the mess. Software Engineer Job Market August 2025
The Deeper Truth
Vibe Coding is a lie because it relies on a fundamental misunderstanding of what software engineering is.
Coding is not the act of typing syntax. That is typing. Coding is the act of rigorous specification. It is the process of taking a vague, fuzzy human requirement and constraining it until it can be executed deterministically by a machine.
When you use a Vibe Coding platform, you are not skipping the hard part. You are ignoring it.
The Abstraction Leak
We have been here before. Every ten years, the industry invents a new way to "hide" the code.
Thread 1: The 4GL (Fourth Generation Languages) era. "Just tell the database what you want!" Thread 2: The CASE tools of the 90s. "Draw the diagram and we build the code!" Thread 3: The Low-Code/No-Code boom. "Drag and drop your business logic!"
They all fail for the same reason. The Law of Leaky Abstractions.
Eventually, the abstraction leaks. The drag-and-drop interface doesn't have a component for the specific weird API you need to call. The generated SQL query is unoptimized and brings the database to its knees.
GenAI is just another layer of abstraction. But it is a uniquely dangerous one. Traditional compilers are deterministic. If you feed them garbage, they scream at you. They refuse to build.
GenAI is probabilistic. If you feed it garbage, it smiles. It nods. It says "Certainly! Here is the code."
It generates code that looks right. It adopts the "vibe" of correctness. This is where the term "slop" comes from. It is code that occupies space but provides no nutritional value to the system.
The Pseudo-Code of Failure
Let's look at what happens inside the "Vibe" engine. This is speculation based on observation, but it aligns with the behavior of every LLM I've tested.
INPUT: "Make a dashboard for user metrics."
VIBE_LAYER:
- Retrieves 'React' context.
- Retrieves 'Dashboard' context.
- Ignores 'Security' context (not in prompt).
- Ignores 'Scale' context (not in prompt).
GENERATION:
- Create Component UserMetrics.
- Fetch data from /api/metrics (Endpoint doesn't exist yet).
- Store data in local state (Ignoring global store architecture).
- Render list.
The output is a perfect prototype. It is also a production nightmare.
It has introduced technical debt instantly. It has created a frontend dependency on an API that hasn't been designed. It has managed state locally in a way that will conflict with the rest of the application.
The "Vibe Coder" looks at this and sees success. The engineer looks at this and sees a rewrite.
The Necessity of Detail
The irony of the AI age is that detail matters more, not less.
When you write code by hand, you are forced to confront the details line by line. You have to think about the variable types. You have to think about the error handling. The friction of typing is a quality control mechanism. It forces your brain to engage with the logic.
When you generate code, you bypass that friction. You can generate a thousand lines of garbage in a second.
This means the role of the engineer shifts from "writer" to "auditor". And auditing is harder than writing. To audit code effectively, you need a deeper understanding of the system than the person (or machine) who wrote it. You need to spot the subtle bug that the AI introduced because it didn't understand the thread-safety model of your specific language version.
You cannot audit what you do not understand.
This is why "Prompt Engineering" is a misnomer. Real prompt engineering isn't about finding the right magic words to vibe with the bot. It is about specification.
To get good code out of an AI, you have to provide it with:
- Architectural constraints
- Error handling strategies
- State management patterns
- Security boundaries
In other words, you have to know how to code. You are just doing it via a different interface. Why Ai Based Code Generation Falls Short
Implications
The industry is waking up to the hangover. The party is over.
The Return of the Expert
We are seeing a massive flight to quality in the job market. The demand for junior developers who can only do what ChatGPT does is collapsing. But the demand for senior engineers is robust. Programming Expertise Whats In Demand In 2025
Companies are realizing that if they fill their teams with people who rely entirely on AI, they create a "lazy mentality". This is a documented phenomenon. Over-reliance on AI leads to reduced vigilance. We stop checking the output. We assume the machine is right. 10 Pitfalls To Keep In Mind With Ai Software Development
The engineers who will thrive in 2025 and beyond are the ones who treat AI as a subordinate, not a savior. They are the ones who can look at a generated function and say, "That's clever, but it's going to deadlock under load."
The Debt Bomb
We are sitting on a time bomb of AI-generated technical debt. In two years, we will see a wave of high-profile failures in companies that went all-in on "Vibe Coding".
We will see security breaches caused by hallucinations. We will see startups fail because they cannot pivot their codebase—it's too brittle, too duplicated, too misunderstood.
The cost of maintenance is about to skyrocket. We used to say that code is read ten times more than it is written. With AI, code is written once (instantly) and then never read until it breaks. Then it takes ten times longer to fix because nobody knows how it works. Ai Generated Code Impacts
Security as a Casualty
The security implications are terrifying. AI models are trained on public repositories. They learn patterns. They also learn mistakes. They learn insecure configurations.
If you ask an AI to "connect to a database", there is a non-zero chance it will suggest a connection string pattern that is vulnerable to injection or exposes credentials. If you don't know enough to spot it, you ship it.
We are automating the injection of vulnerabilities into our systems. Challenges With Ai Generated Code
Conclusion
I am not a Luddite. I use AI every single day. I have Copilot in my IDE. I use Claude to brainstorm architecture. I use ChatGPT to generate regex (because life is too short to write regex).
But I do not "vibe code".
I verify. I architect. I understand every line that goes into my commit.
The dream of the "idea guy" who builds an empire without understanding the technology is a fantasy. It sells subscriptions to tools like Lovable, but it does not build enduring value.
If you want to build software, you have to respect the craft. You have to understand the machine. You have to accept that complexity cannot be wished away with a vibe.
The market is correcting. The tools are hitting their limits. The "slop" is piling up.
Now if you will excuse me, I have some code to review. And I'm going to read every single line.