What Is Vibe Coding and Why Senior Developers Warn Against It
Problem
A junior developer on my team committed code last week that he couldn’t explain. When I asked why he used a particular caching strategy, he said: “The AI suggested it, and it looked right.”
That’s vibe coding. And it’s destroying developer skills.
The term comes from a Reddit thread where senior developers sounded the alarm: “vibe coders struggle with understanding the code they write and the gap in problem solving skills will only widen with time.” One team lead even said: “I forbid the usage of vibe coding for juniors in my team.”
What Is Vibe Coding?
Vibe coding is accepting AI-generated code based on whether it “feels right” rather than verified correctness. You ask an AI assistant to write code, it generates something that looks plausible, and you commit it without fully understanding how it works.
┌─────────────────────────────────────────────────────────────┐│ VIBE CODING CYCLE │├─────────────────────────────────────────────────────────────┤│ ││ Ask AI for code ──> It generates something ──> ││ ││ ──> Looks roughly correct? ──> Commit it ──> ?? ││ ││ Missing steps: ││ ✗ Understanding every line ││ ✗ Knowing why this approach over alternatives ││ ✗ Verifying security implications ││ ✗ Testing edge cases ││ ✗ Ability to debug when it breaks ││ │└─────────────────────────────────────────────────────────────┘The problem isn’t using AI. It’s using AI as a replacement for understanding instead of a tool for acceleration.
The Real-World Damage
Let me show you what vibe coding looks like in practice. I saw this exact scenario last month:
# Developer asks AI: "Add caching to my API"# AI generates:
@app.route('/api/users')@cache.cached(timeout=300, key_prefix='all_users')def get_users(): users = User.query.all() # No pagination! return jsonify([u.to_dict() for u in users])
# Vibe coder: "Looks good, it has caching!"# Reality: No pagination, returns ALL users, memory bomb# When it crashes in production, vibe coder can't debugThe developer saw caching decorators and thought: “Great, performance optimization!” He didn’t notice:
- No pagination - Returns every user in the database
- Cache key is static - Same cache for all users regardless of filters
- No cache invalidation - Stale data when users update
- Memory explosion - Large datasets crash the server
When this crashed in production with 50,000 users, the developer had no idea where to start debugging. He’d never understood the code in the first place.
Why Senior Developers Are Worried
The Reddit thread revealed specific concerns from experienced developers:
1. AI is trained on bad code too
One commenter put it bluntly: “AI is still, and probably will only ever be, a junior dev at most. It’s trained on bad code, and even if you give it a bunch of rules, it still gets it wrong.”
2. The confidence trap
AI generates code with confident syntax. It looks correct. It compiles. It even runs. But looking correct and being correct are different things.
┌───────────────────┬────────────────────────────────────────┐│ AI Suggests │ Hidden Problem │├───────────────────┼────────────────────────────────────────┤│ "Use prefetch" │ Fires hundreds of API requests ││ "Add caching" │ No invalidation, stale data ││ "Optimize query" │ N+1 problem hidden in ORM magic ││ "Secure this" │ SQL injection via string concatenation │└───────────────────┴────────────────────────────────────────┘3. Security vulnerabilities
Another warning from the thread: “Otherwise it will suggest all kinds of egregious bullshit and you will have no idea if it’s secure, performant, and maintainable.”
I’ve seen AI suggest:
- Storing passwords in plain text “for debugging”
- Disabling CSRF protection “to fix the error”
- Hardcoding API keys “so it just works”
- Exposing internal endpoints “for convenience”
All of these “solved” the immediate problem. All of them were security disasters.
4. Career stagnation
The harshest truth: developers who vibe code become AI operators, not engineers. They lose the ability to:
- Debug complex issues
- Design systems from scratch
- Evaluate trade-offs
- Mentor junior developers
- Pass technical interviews
The Checklist I Use Now
After the caching disaster, I developed a checklist for AI-generated code. Every time AI writes code for me, I answer these questions before committing:
□ Can you explain every line to a teammate?□ Do you know why this approach over alternatives?□ Can you debug this code if it breaks in production?□ Are there security implications you understand?□ What are the edge cases?□ What happens when input is null/empty/malformed?□ What's the performance impact at scale?□ How would you test this manually?If I can’t answer all of them, I don’t commit. I either:
- Ask AI to explain the code
- Research the concepts I don’t understand
- Rewrite it myself
Proper AI-Assisted Development
Here’s the same caching problem solved the right way:
# Developer asks AI: "Explain caching strategies for REST APIs"# AI explains: key-based caching, pagination, cache invalidation
# Developer writes (understanding WHY each piece exists):@app.route('/api/users')def get_users(page=1, per_page=50): cache_key = f'users_page_{page}_{per_page}' result = cache.get(cache_key)
if result is None: # Developer understands why pagination matters users = User.query.paginate(page=page, per_page=per_page) result = jsonify({ 'users': [u.to_dict() for u in users.items], 'total': users.total, 'pages': users.pages }) cache.set(cache_key, result, timeout=300)
return result
# Developer can explain:# - Pagination prevents memory issues# - cache_key includes page params to avoid wrong cache hits# - timeout=300 means data refreshes every 5 minutesThe difference isn’t the code itself. It’s that the developer can explain:
- Why pagination at the database level (not just slicing results)
- Why cache_key includes pagination params
- What happens when a user is updated (and why we might need cache invalidation)
- How to debug if the cache gets corrupted
Common Vibe Coding Mistakes
Mistake 1: “The Code Runs, So It Must Be Right”
Running code is the lowest bar. Malware runs. Infinite loops run. Code that deletes your database runs.
Fix: Test edge cases. Test failures. Test with bad data. Running is not correct.
Mistake 2: Not Reading AI-Generated Code
I’ve seen developers commit 200-line files without reading them. They trusted the AI.
Fix: Read every line. If you can’t explain it, don’t commit it.
Mistake 3: Skipping Code Review
“It’s just AI-generated code, review won’t find anything.” Wrong. Review finds AI’s confident mistakes.
Fix: Treat AI code like any other code. Review it. Question it. Test it.
Mistake 4: Assuming AI Knows Best Practices
AI is trained on public code. Public code includes:
- Stack Overflow answers from 2015
- Deprecated tutorials
- Bad examples with good SEO
- Security vulnerabilities that weren’t discovered yet
Fix: Verify AI suggestions against current best practices. Don’t assume authority.
When AI Help Is Actually Helpful
AI is valuable when it accelerates your understanding, not replaces it:
Good uses:
- “Explain this error message” - then understand the explanation
- “What are common patterns for X” - then evaluate which fits your case
- “Write a first draft” - then review and improve every line
- “Generate boilerplate” - then customize it for your needs
- “Find bugs in this code” - then understand each bug found
Bad uses:
- “Just fix it” - without understanding the fix
- “Make this work” - without understanding how
- “Add this feature” - without reading the implementation
- “Optimize this” - without verifying the optimization
The Verdict
Vibe coding is a symptom of using AI as a crutch rather than a tool. The antidote is understanding every line of code you ship.
The senior developers warning against vibe coding aren’t anti-AI. They’re pro-understanding. They’ve seen what happens when developers ship code they can’t debug: 3 AM incidents, frustrated teammates, and careers that plateau.
Use AI. Let it accelerate your learning. But never let it replace your understanding. The code you commit should be code you can defend in a code review, debug at 2 AM, and explain to a junior developer next week.
If you can’t do those things, you’re not coding. You’re just typing.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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