I Trusted AI Code for 6 Months. It Created 47 Subtle Bugs.
ChatGPT's code worked. Then production load hit 10K users.
Month 1: “This AI coding is amazing.”
Month 3: “Wait, why is this endpoint returning null sometimes?”
Month 6: “We’ve been shipping broken code for six months.”
Stack Overflow’s 2025 survey says 66% of developers deal with “AI solutions that are almost right, but not quite.”
I’m one of them.
Here’s what six months of trusting AI code cost us.
The Setup: We Went All-In on AI
January 2025. Our backend team of 4 decided to use ChatGPT for “everything we can.”
Not just boilerplate. Everything.
API endpoints. Database queries. Authentication logic. Error handling. Background jobs.
The pitch: “Move faster. Ship more features. AI writes the code, we review it.”
Three months in, we’d shipped 23 new features. Double our usual velocity.
Management loved us.
We felt like superheroes.
The First Sign Something Was Wrong
March 12, 2:34 PM.
Customer support: “User can’t log in. Says their session expired but they just logged in 5 minutes ago.”
I checked the logs. Session token was valid. JWT decode worked. Redis cache returned the right data.
Everything worked in testing. The code looked fine.
Then I noticed: The AI-generated session middleware checked if (tokenAge > maxAge) instead of if (tokenAge >= maxAge).
One character. Off by one error.
Sessions expired exactly at the boundary. If you logged in and immediately hit an endpoint exactly at the session length, you got kicked out.
Happened to 0.3% of users. Only those who got unlucky with timing.
We wouldn’t have caught this in testing. We only found it because one customer complained.
I checked how many similar issues we might have.
That’s when I started finding the others.
Bug #1-12: The “Edge Case Collection”
I spent the weekend reviewing every AI-generated function from the past 3 months.
Found 12 bugs. All similar pattern.
Bug #3: Password reset link validation checked if (resetToken) instead of if (resetToken && !isTokenExpired(resetToken)). Expired tokens still worked.
Bug #7: Pagination logic: offset = page * limit instead of offset = (page - 1) * limit. First page showed items 0-19. Second page showed items 20-39 instead of starting at 20. Off by one page.
Bug #9: Email validation regex from ChatGPT accepted user@domain without TLD. Emails like admin@localhost passed validation.
Bug #11: API rate limiter checked if (requestCount > limit) instead of if (requestCount >= limit). Users got one extra request above the limit.
Every single one: “Almost right, but not quite.”
Every single one: Worked in basic testing.
Every single one: Broke in production under specific conditions.
Bug #13-28: The Database Disasters
April. I started checking database queries.
ChatGPT loves ORMs. Makes sense. ORMs are cleaner than raw SQL.
Problem: AI doesn’t think about query performance.
Bug #14: User search endpoint. AI wrote:
users = User.query.filter(User.name.contains(search_term)).all()Looks fine. Works fine for 100 users.
We had 400K users. Query took 8 seconds. Full table scan. No index.
The correct version needed:
users = User.query.filter(User.name.ilike(f'%{search_term}%')).limit(50).all()Plus a proper index. AI didn’t mention indexes once.
Bug #19: N+1 query nightmare.
for order in orders:
customer = Customer.query.get(order.customer_id)
# process customer100 orders = 101 database queries (1 for orders, 100 for customers).
Should have been:
orders = Order.query.options(joinedload(Order.customer)).all()1 query. Not 101.
I asked ChatGPT directly: “Why didn’t you use joinedload?”
Response: “You’re right, joinedload would be more efficient. Here’s the updated code:”
It knew. It just didn’t suggest it the first time.
Bug #29-38: The Concurrency Failures
May. Production load increased. Things started breaking under concurrent requests.
Bug #31: Inventory update logic.
product = Product.query.get(product_id)
if product.stock >= quantity:
product.stock -= quantity
db.session.commit()Looks fine. Single request, works perfectly.
Two simultaneous requests for the last item? Both pass the if check. Both subtract. Stock goes negative.
Race condition. Classic concurrency bug.
The fix needed proper locking:
product = Product.query.with_for_update().get(product_id)AI never mentioned with_for_update. Even when I specifically asked about “handling concurrent requests.”
Bug #34: Cache invalidation.
cache.delete(f'user_{user_id}')
db.session.commit()Delete cache, then commit to DB.
If the commit fails? Cache is deleted but database unchanged. Next request gets stale data from DB, caches it again.
Should have been: commit first, then delete cache.
Small detail. Huge impact in production.
Bug #39-47: The Security Holes
June. Security audit.
Auditor found 9 issues. All in AI-generated code.
Bug #41: File upload validation.
if file.filename.endswith('.jpg'):
# processFilename malicious.php.jpg passed the check.
Should have checked MIME type, not filename.
Bug #43: SQL injection via ORM.
query = f"SELECT * FROM users WHERE role = '{role}'"
db.session.execute(query)AI used ORM elsewhere. Used raw SQL here. With f-string. Didn’t sanitize input.
I asked ChatGPT: “Is this code safe?”
Response: “This code is vulnerable to SQL injection. Use parameterized queries instead.”
Again: It knew. It just didn’t write it correctly the first time.
Bug #47: JWT secret in environment variable... that defaulted to “secret” if not set.
JWT_SECRET = os.getenv('JWT_SECRET', 'secret')We forgot to set JWT_SECRET in staging. Ran with default value for 2 weeks.
AI added the default. I didn’t notice. Neither did code review.
What I Learned: AI Optimizes for “It Works” Not “It’s Right”
Six months. 47 bugs. All from AI code that “worked.”
Here’s the pattern:
AI writes code that passes basic tests. But production isn’t basic tests.
Production has:
Edge cases AI doesn’t consider
Concurrency AI doesn’t think about
Performance at scale AI doesn’t optimize for
Security implications AI doesn’t flag
The code compiles. The tests pass. It ships.
Then production breaks it.
The 45% Statistic Is Real
Stack Overflow 2025: “45% of developers say debugging AI-generated code is more time-consuming than writing code themselves.”
I didn’t believe it until I lived it.
Time to write the code with AI: 30 minutes for a feature.
Time to debug that code later: 4 hours to find the bug, understand why AI wrote it wrong, rewrite it correctly, test all edge cases AI missed.
The math doesn’t work out.
We thought we were moving faster. We were just deferring the debugging to later.
What Changed: How We Use AI Now
We didn’t stop using AI. We changed how we use it.
Before: “AI writes code, we review it.”
Now: “We write critical sections, AI helps with boilerplate.”
What AI still does:
CRUD endpoints (but we verify the SQL/ORM queries)
Test scaffolding (but we write the actual test logic)
Documentation (but we fact-check it)
Boilerplate setup code
What we stopped letting AI do:
Authentication/authorization
Payment processing
Database migrations
Anything involving money or user data
Concurrency-critical sections
Security-sensitive logic
The new process:
I write the core logic manually
AI fills in the repetitive parts
I review everything line-by-line
I specifically check for: race conditions, edge cases, performance, security
I ask AI: “What could go wrong with this code?”
That last step matters. When you prompt AI to critique code instead of write code, it’s surprisingly good at finding issues.
The Trust Collapse Is Real
Stack Overflow 2025: Trust in AI accuracy dropped from 70% to 60% in one year.
I get why.
AI is good at generating code.
AI is bad at understanding context, thinking about edge cases, considering performance, and flagging security risks.
The difference between those two skills is where the 47 bugs came from.
If You’re Using AI to Code
Some things I wish I’d known in January:
1. AI optimizes for “it compiles” not “it’s correct”
Green tests ≠ correct code. Especially for edge cases, concurrency, performance.
2. AI doesn’t know your context
It doesn’t know you have 400K users. It writes code for the examples it was trained on — probably small datasets.
3. AI doesn’t think about security by default
You have to explicitly ask “Is this secure? What are the attack vectors?”
Even then, verify.
4. The “almost right” code is the most dangerous
Completely wrong code fails immediately. “Almost right” code ships to production and breaks under specific conditions.
5. AI-generated code needs adversarial review
Don’t just ask “Does this work?” Ask “How can this break? What did AI miss? What assumptions are wrong?”
📬 What I’m Working On
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Built it because I got tired of spending 8 hours writing postmortems for bugs like these. Early access is open.
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The Uncomfortable Truth
66% of developers deal with “AI solutions that are almost right, but not quite.”
45% say debugging AI code takes longer than writing it themselves.
Trust in AI dropped from 70% to 60% in one year.
These aren’t just statistics. They’re warnings.
AI makes you faster until it doesn’t. Then it makes you slower than if you’d written the code yourself.
The 47 bugs I found? Those are just the ones I caught.
I wonder how many are still in production.
What Would I Do Differently?
If I could go back to January:
1. Use AI for scaffolding, not logic
Let AI generate the structure. Write the important parts yourself.
2. Treat AI code like junior dev code
Review it with the assumption that it’s missing edge cases, performance considerations, and security checks.
3. Test adversarially
Don’t just test happy paths. Test the boundaries. Test concurrency. Test at scale.
4. Ask AI to critique, not just create
“What could go wrong with this code?” gets better results than “Write this code for me.”
5. Check everything that touches money, auth, or user data
Don’t trust AI with critical sections. Period.
Real Production Resources
If you’re dealing with production systems and AI-generated code:
Free:
📋 Production Incident Prevention Kit — Checklists used before deployments. Catches issues before they hit production.
🐍 Python for Production Cheatsheet — The stuff AI doesn’t tell you about production Python.
Paid:
🔥 Backend Failure Playbook — How real systems break and how to fix them. Java, Spring, SQL, Cloud.
🎯 30 Real Incidents That Cost Companies Thousands — Actual production failures and how to prevent them. The kind AI code creates.
Everything I learned: devrimozcay.gumroad.com
Weekly: Real Stories, Real Failures
I write about production engineering, AI code reviews, and the stuff that breaks at 3 AM every week.
Not the polished version. The honest version.
— The worst part about the 47 bugs? We only found them because things broke in production. How many times did users encounter issues and not report them? How many bugs are still there, waiting for the right conditions to surface?
Stack Overflow says 66% of developers trust AI code and then debug it later. I was one of them. Now I’m the guy who writes the code first and asks AI to review it. Takes longer upfront. Saves weeks on the backend.
— If you’re using AI to write production code, do yourself a favor: Run a security audit on everything it generated in the past 6 months. You’ll probably find issues. Better you find them than an attacker does.