What ChatGPT Doesn't Tell You About the Code It Writes

Let's be honest: ChatGPT, Claude, and Cursor are incredible. You describe what you want, and they write working code in seconds. Features that used to take days now take minutes.

But here's what nobody talks about:

AI writes code that works—not code that's safe.

And when you launch with vulnerable code, that's when things go wrong. Data breaches. Crashes. Angry users. Bad press. The stuff that kills apps.

Here's exactly what AI coding assistants skip, and what you need to add yourself.

What AI Does Really Well

Before we talk about problems, let's give credit where it's due. AI is exceptional at:

• Writing boilerplate: CRUD operations, API routes, database models
• Implementing features: Authentication, file uploads, search
• Fixing syntax errors: Missing semicolons, typos, wrong imports
• Explaining code: "What does this function do?"
• Converting between languages: Python to JavaScript, etc.

For getting an MVP running fast, AI is unbeatable. The problem is what happens after that.

What AI Consistently Misses

1. Security Isn't a Priority

When you ask ChatGPT to build a login system, it'll give you code that works. Users can sign up, log in, log out. Perfect, right?

Except:

• Passwords might be stored in plain text
• SQL injection vulnerabilities in database queries
• No rate limiting (bots can spam your forms)
• Missing CSRF protection
• Session tokens that never expire

Why? Because AI is trained on code examples from the internet—and most tutorial code online isn't production-ready.

2. No Error Tracking or Monitoring

Your app is live. A user hits an error. Your app crashes for them.

Do you know? Nope. Because AI didn't add error tracking.

AI will write perfect happy-path code: "User logs in → sees dashboard → clicks button → action happens." Great!

But what if:

• The database is down?
• The API times out?
• User has a slow connection?
• Third-party service is unavailable?

Without monitoring, you're flying blind. Users experience crashes, you lose customers, and you don't even know it's happening.

3. Performance Is an Afterthought

AI will give you code that works, but not code that's fast. Examples:

• N+1 queries: Loading a list of 100 posts? AI might hit the database 100 times instead of once.
• No caching: Same data fetched over and over instead of caching results.
• Huge bundle sizes: Importing entire libraries when you only need one function.
• Unoptimized images: Serving 5MB images when 50KB would look the same.

Your app works, but loads slowly. Users leave. Google ranks you lower. You lose business.

4. Missing Edge Cases

AI generates the happy path. But real users don't always follow the happy path.

What happens when:

• User submits an empty form?
• They type 1 million characters into a text box?
• Upload a 10GB file?
• Click "submit" 100 times in a row?
• Their session expires mid-action?

If AI didn't code for it, your app crashes. Or worse—creates security vulnerabilities.

5. No Input Validation

AI trusts user input way too much. It'll take whatever users type and use it directly. This causes:

• SQL injection: Hackers can steal your database
• XSS attacks: Malicious code runs on your users' browsers
• Buffer overflows: App crashes from unexpected input
• Business logic bypass: Users send negative numbers to get refunds

Always validate user input on the backend. Never trust what comes from the frontend.

6. Secrets in the Code

This one's dangerous. AI will often put API keys, database passwords, and tokens directly in the code:

Why this is bad: If you push this to GitHub, bots will find it within minutes. Your accounts get hacked, your bills skyrocket, your data gets stolen.

The fix: Use environment variables (.env files) and never commit secrets to version control.

Why This Happens

AI isn't trying to write bad code. It's just optimizing for a different goal than you.

AI's goal: Generate code that demonstrates the requested feature and compiles without errors.

Your goal: Code that's secure, monitored, performant, and handles edge cases.

AI delivers code fast. But "fast" doesn't mean "production-ready."

What To Do About It

You don't need to become a security expert. You just need to check for common issues:

1. Scan your code: Use tools like VibeCheck to find security holes automatically
2. Add error tracking: Sentry, LogRocket, or Rollbar (takes 10 minutes to set up)
3. Test edge cases: Try to break your own app before users do
4. Validate all inputs: Check length, format, type on the backend
5. Use environment variables: Never hardcode secrets
6. Enable HTTPS: Always. No exceptions.

The Bottom Line

AI code assistants are game-changers. They let you build faster than ever. But they're tools, not replacements for thinking about security and monitoring.

Use AI to write code fast. Then check it for the boring stuff AI skips.

That's the difference between launching successfully and dealing with a crisis on day 2.

Your users are counting on you to protect their data. Don't let AI's shortcuts become your launch day disaster.