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Vibe coding, the practice of using AI to generate code without understanding it, creates developers who can produce applications but cannot debug, maintain, or secure them. Research shows AI-assisted developers feel 20% faster but are actually 19% less productive due to time spent fixing AI-generated bugs. This approach leads to technical debt, security vulnerabilities, and career fragility. The article advocates for using AI as a tool to enhance existing skills rather than replace fundamental programming knowledge.

Vibe Coding is Creating a Generation of Unemployable Developers

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Tech in 2025 is in crisis. Over 94,000 tech workers were laid off in 2024 alone.

For junior developers, the game is borderline impossible. U.S. software job openings have dropped more than 70%. New grads can’t just apply and get lucky anymore.

Now comes the coping mechanism: vibe coding. A way to “build apps” without ever learning to code. Describe what you want in plain English, let the AI build it.

But it’s a trap. Vibe coding doesn’t create developers; it creates fragile intermediaries. People who can generate code but cannot read, debug, or maintain it.

If you’re a new developer, this isn’t a shortcut. It’s a cliff. And you’re being encouraged to run straight toward it.

Tech in 2025 is in crisis. Over 94,000 tech workers were laid off in 2024 alone.

For junior developers, the game is borderline impossible. U.S. software job openings have dropped more than 70%. New grads can’t just apply and get lucky anymore.

Now comes the coping mechanism: vibe coding. A way to “build apps” without ever learning to code. Describe what you want in plain English, let the AI build it.

But it’s a trap. Vibe coding doesn’t create developers; it creates fragile intermediaries. People who can generate code but cannot read, debug, or maintain it.

If you’re a new developer, this isn’t a shortcut. It’s a cliff. And you’re being encouraged to run straight toward it.

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It works for the one simple case, but breaks on every edge case.
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For me it is often the other way round. The LLM includes all the edge cases and then forgets the happy path.
Just last week, I needed a Python RAG app for testing an applicant. I told Jetbrains Junie that I need “a FastAPI app that answers questions with documents, stored in a local vector store, as context via RAG and connects to Azure OpenAI via langchain”. It build an app that checked for many misconfigurations and had a demo mode with fixed answers in case the API keys were missing. The function to call the real LLM endpoint contained a todo comment.
Follow up prompts didn’t fix the issue, so I did it myself. Resulted in some nice spaghetti code for the applicant to unravel.

“AI isn’t a shortcut; it’s a liability multiplier.” just true

%70 is misleading delete your post

It’s really awkward what’s happening, I just can’t understand how people can make these mistakes. We’ll see the results in few years. 😨