The Hook
Last week, I pushed a feature that worked beautifully on the first try. No breakpoints. No frantic Stack Overflow searches. No 2 a.m. commit messages full of regret.

ChatGPT wrote 80% of it.

And honestly? That scared me more than it impressed me.

Not because the code was bad—it wasn’t. It was clean, well-structured, and even included error handling I would have added later. The problem was something else entirely.

I didn’t truly understand one critical loop inside it. And three days later, when a subtle edge case surfaced in production, I spent six hours debugging code I hadn’t written.

That’s when I realized: AI isn’t replacing developers. But it is changing what “being a developer” actually means.

The New Workflow
Here’s how I build software now, compared to two years ago.

Before (2022):

Think about the problem for 20 minutes.

Write pseudocode in a notebook.

Type the code slowly, checking docs every few lines.

Run → crash → debug → repeat.

Google the error, read three conflicting answers.

Fix it, feel mildly proud.

After (2024 with AI):

Describe the problem in plain English to ChatGPT.

Get back a working function skeleton in 10 seconds.

Accept 80%, rewrite 15%, wonder about the remaining 5%.

Run → works immediately (usually).

Worry about the parts I didn’t think through.

The speed is addictive. But the cognitive shift is massive.

What AI Does Well (And What It Hides)
After three months of active use, here’s my honest breakdown.

AI excels at:

Boilerplate and CRUD APIs

Regular expressions (finally, someone who enjoys them)

Converting between data formats (JSON ↔ XML ↔ YAML)

Writing unit tests for well-defined functions

Explaining error messages in plain English

What AI obscures:

Why a specific algorithm was chosen over another

The hidden assumptions about scale, concurrency, or null values

Non-obvious side effects (e.g., mutation of external state)

Security implications in context (no, it won’t warn you about injection unless you ask)

And the biggest one: ownership.

When I type every line, I remember it. When I accept AI-generated code, I often forget it within an hour.

Real Example: The Logging Parser Incident
Two weeks ago, I needed a function to parse mixed-format log files (Apache + JSON + plain text lines). I asked ChatGPT. It delivered a 60-line Python function in five seconds.

It worked. I tested it on three sample files. All passed.

I committed it. No second thought.

Three days later, a production log contained a line with escaped quotes inside a JSON string. My AI-generated parser broke. Not catastrophically—it just skipped 200 legitimate events silently.

Because I hadn’t written the parsing logic myself, I didn’t immediately recognize the flaw: the regex was too greedy, and the fallback branch was incorrectly prioritized.

Fixing it wasn’t hard. Finding it took half a day.

The lesson: AI gives you confidence without competence. That’s dangerous.

The Three New Skills Developers Need
If AI writes the code, what do we do?

I think we shift up the abstraction ladder. These three skills matter more than syntax now.

1. Spec-First Thinking You can no longer “just start coding and figure it out.” The AI will happily generate garbage if your prompt is vague.

Learning to write clear, testable specifications (in English or a lightweight DSL) is now a core skill.

Bad prompt: “Write a function to process user data.”
Good prompt: “Write a Python function that takes a list of user dicts, validates email format, removes duplicates by user_id, and returns a new list sorted by created_at. Raise ValueError for missing required fields.”

The latter produces production-ready code. The former produces a mess.

1. Reading Code Like a Security Auditor You no longer write everything. But you must review everything—with suspicion.

Treat AI-generated code like a junior developer’s pull request. Ask:

Does this handle empty inputs?

What happens at 10x scale?

Are there hidden O(n²) loops?

Could this introduce injection or data leaks?

If you can’t answer each question in 30 seconds, you don’t trust the code.

1. Debugging Without Primal Memory The hardest shift is psychological.

When you debug your own code, you remember writing it. You have context, intention, and a model of the logic.

When you debug AI-generated code, you have none of that. You’re reverse-engineering a stranger’s work.

That means logging and observability are no longer optional. You need:

Structured logs at key decision points

Simple feature flags to roll back suspicious changes

Small, testable functions (AI loves huge functions—don’t let it)

What I’ve Changed
After the logging parser incident, I adopted three personal rules:

I never use AI-generated code I can’t explain line-by-line in a code review. If I can’t, I rewrite it myself.

I always add at least one edge-case test that wasn’t in the AI’s original output. If the test passes, fine. If it fails, I learn something.

I treat AI as a pair programmer, not a replacement. I prompt, review, question, and modify. I never “accept all.”

This isn’t about fear. It’s about responsibility.

The code may be generated by a model. But the bug, the security breach, the outage—that’s owned by a person. And that person is still me.

The Future Isn’t Less Coding. It’s Better Thinking.
I don’t think AI will kill software engineering.

But I do think it will kill cargo-cult coding—the kind where you copy-paste from Stack Overflow without understanding, tweak random things until it works, and move on.

That was already bad practice. AI just automated it.

The developers who thrive will be those who use AI to think faster, not to think less. They’ll write more tests, better specs, and clearer documentation—because the execution layer is now cheap.

The bottleneck is no longer typing speed. It’s reasoning quality.

Let’s Talk
I’m still figuring this out. Some days I feel like AI doubles my productivity. Other days I spend hours debugging its clever mistakes.

What about you?

Have you shipped AI-generated code to production?

Did you fully understand it?

Would you let an AI refactor a critical payment service?

Drop a response. I’m genuinely curious.

If you enjoyed this, follow for more essays on building software in the age of LLMs. No hype. Just real experience.