The Dangerous Illusion of the 'Solo AI Developer'

The Trap of “It Just Works”

We are living in a golden age of generative AI. Tools like Claude Code, Cursor, and GitHub Copilot allow us to ship features faster than ever. But there’s a growing trend I’m seeing in open-source projects and even enterprise codebases: The Absentee Developer.

This happens when a human treats the AI not as a tool, but as a contractor they can blindly trust. They prompt, they copy-paste, they deploy. If it compiles, it ships.

This mindset is dangerous. As I discovered while auditing several “fully AI-generated” web UIs, this approach leads to security holes so large you could drive a truck through them—client-side authentication, unvalidated file paths, and unrestricted shell access.

AI is a Junior Developer on Speed

Imagine hiring a junior developer who:

  1. Types 1000 words per minute.
  2. Knows every library in existence.
  3. Has zero concept of security implications unless explicitly told.
  4. Will confidently lie to you if they don’t know the answer.

Would you let that developer push to production without a code review? No.

Yet, that is exactly what happens when we let AI “solo” a project. The AI optimizes for solving the immediate prompt. It doesn’t inherently optimize for the “unknown unknowns”—like what happens if a user intercepts a request, or if a path parameter contains ../../.

The “Repetitive Loop” of Doom

One of the most common failure modes is the “Context Loop.” You ask the AI to fix a bug. It gives you code. The code fails. You paste the error back. It gives you a “fixed” version.

After 5 iterations, you might have working code, but you also have a Frankenstein’s monster of patches. The AI has lost the thread of the overall architecture. It’s just throwing spaghetti at the wall to satisfy your error message.

This is where vulnerabilities are born. In the rush to “make it green,” security checks get bypassed, input validation gets dropped, and hardcoded secrets slip in.

How to actually use AI (The “Co-Developer” Mindset)

To build secure software with AI, you need to shift your mental model. You are not the “prompter”; you are the Lead Engineer. The AI is your pair programmer.

1. Don’t Accept Magic

If the AI writes a block of code you don’t understand, stop. Ask it to explain. If you can’t verify it’s secure, assume it isn’t.

2. Challenge the Output

Talk to the AI like a colleague.

  • “Hey, you removed the CSRF token in this refactor. Why?”
  • “This input handler looks vulnerable to XSS. Rewrite it with sanitization.”
  • “You’re using dangerouslySetInnerHTML here. Is there a safer way?“

3. Review the diffs, not just the result

Don’t just look at the final file. Look at the diff. Did the AI sneakily delete a TODO comment? Did it remove a permission check? AI models have a limited context window; they often “forget” constraints established earlier in the conversation.

Conclusion

AI is the most powerful force multiplier we’ve seen in software engineering. I love using it. I use it every day.

But it is not a replacement for engineering judgment. It is a tool that amplifies your intent. If your intent is “build this fast and I don’t care how,” it will amplify that recklessness. If your intent is “help me build a robust, secure system,” it will amplify your craftsmanship.

Don’t let the AI drive the car. You drive. Let the AI read the map.

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