In recent years, AI has become the centerpiece of conversation in many engineering teams—and rightfully so. Tools like GitHub Copilot, Copilot Chat, and other AI pair programmers have reshaped how we write code, document systems, and reason about complexity.
But there’s a line between leveraging AI and abdication of engineering responsibility. And when that line is crossed, the result isn’t acceleration—it’s dysfunction.
Here’s a real story from within our company.
🧓 The Veteran Engineer Who Gave Up on Coding
We have a senior developer on our team. He's respected, experienced, and not far from retirement. Over the last few years, he’s become fixated on AI—convinced that within two years, large language models will replace most of our roles. As a sort of “legacy project” before he retires, he’s set out to train and integrate an AI model to automate our internal development pipeline.
At first glance, this seems aligned with the company’s goals. We’ve been early adopters of GitHub Copilot, and we’ve successfully integrated other AI tooling into our workflows. We encourage experimentation.
But what’s happening here goes far beyond “reasonable usage.”
❌ From Developer to AI Prompt Engineer—Literally
This engineer has stopped writing code himself. Completely. He refuses to hand-craft PRs or write patches manually. Instead, his workflow looks like this:
- He copies the contents of Jira tickets into Copilot Chat or a similar AI.
- He waits for a pull request to be generated.
- He does not review or edit the code.
- If the generated code is broken, he refuses to debug it directly. Instead, he tries to "teach" the AI to fix it by adding new comments or prompts.
The result? A simple task estimated to take one day ends up dragging on for five—full of bugs, blocked reviewers, and broken functionality.
His justification? “It’s fine if we’re slower now. Once the AI matures, we’ll recover the time tenfold.”
🧠 But Here's the Reality
This approach is not innovation. It’s negligence. And it reveals a deeper misunderstanding about what AI can and should do in a professional engineering context.
Let’s break it down:
1. AI is a tool—not a replacement for ownership
Copilot is meant to assist, not to take full responsibility. A developer who cannot explain, debug, or adapt generated code is not working with AI—they’re being replaced by it in a very literal sense.
2. Code quality suffers when understanding vanishes
AI-generated code is only as good as the person reviewing and integrating it. When bugs arise—and they always do—the developer must be able to dive in, reason through the logic, and make decisions. Otherwise, debugging becomes a game of prompt roulette.
3. “Training” Copilot with comments is a fantasy
Copilot is not a fine-tunable LLM in your IDE. Adding comments in hopes that it will “learn” better behaviors across sessions is fundamentally flawed. This isn’t Reinforcement Learning. It’s wishful thinking.
4. Your team pays the price
The engineer may believe they're leading innovation, but in practice, they’re slowing the team down. Others must compensate, fix broken PRs, and decipher logic that even the original author doesn’t understand.
✅ Healthy AI Usage Patterns
To avoid this kind of misuse, teams should define clear guidelines. Here are a few that have worked for us:
You own the code you commit. Period.
AI can draft, suggest, and autocomplete—but responsibility doesn’t shift.AI involvement should be transparent.
If 80% of a PR was generated by AI, state it. Let reviewers calibrate expectations.PRs must be understandable and maintainable by humans.
No black-box code. If you can’t explain it, don’t ship it.Review and debugging are not optional.
AI may generate code, but it won’t rescue you from runtime bugs, broken APIs, or business logic mismatches.
🧭 Final Thoughts: Use AI Responsibly
AI is here to stay. It will change how we develop software—but not by removing responsibility from engineers. Instead, it amplifies both good and bad engineering practices. If you cut corners, AI cuts them faster. If you build clean, modular, well-documented systems, AI can help accelerate that process.
But the moment we stop thinking, stop understanding, and stop caring—that’s when AI stops being a tool and becomes a crutch.
Let’s not mistake delegation for abdication.
Let’s use AI as professionals.
Summary: Over-relying on AI isn't innovation—it's negligence. Use AI to assist, not replace your understanding, ownership, and responsibility.
AI is a powerful tool, but we should never underestimate how reluctant people can be to think for themselves.