I recently made a non-negotiable call for my engineering team: Using AI is no longer optional. I’m pushing my developers to integrate AI bots into their workflows for writing, refactoring, and testing code every single day. In the current landscape, the tech world is moving at a velocity that makes “manual-only” coding feel like trying to win a Formula 1 race on a bicycle. If we don’t leverage these tools, we aren’t just slowing down; we are becoming obsolete.
But if I’m being completely honest, this mandate makes me incredibly uneasy. As a Head of Engineering, I’m balancing the need for speed with the terrifying possibility of “automated mediocrity.” Here is why I’m mandating AI, the traps I’m watching out for, and the rules I’ve set to ensure we don’t lose our souls to the LLMs.
Falling into “The Dead Loop”
The biggest productivity killer I see isn’t a lack of tools; it’s the Dead Loop. We’ve all experienced that hypnotic trance where you believe the AI is just one prompt away from the perfect solution.
It usually goes like this:
- The AI generates a block of code (let’s say, a complex Java Spring Boot controller) that looks correct but fails on execution.
- You feed the error back to the AI.
- The AI “apologizes” for the oversight and gives you the exact same broken logic, perhaps swapping a variable name or two.
- You repeat this until two hours have vanished.
In those two hours, a seasoned engineer could have written the logic from scratch, unit-tested it, and grabbed a coffee. The “Dead Loop” is dangerous because it feels like work, but it’s actually just expensive wheel-spinning. We cannot let the convenience of a “generate” button override our fundamental problem-solving instincts.
Losing the “Big Picture”
AI is a master of the micro, but a novice of the macro. It can write a flawless regex or a concise helper function in seconds. However, it has zero concept of how that function impacts the long-term scalability of our entire application architecture.
When developers lean too hard on copy-pasted AI snippets, the codebase starts to look like a “Frankenstein” project—a collection of parts that work individually but don’t quite belong together. We risk creating Leaky Abstractions and massive amounts of Technical Debt that won’t reveal itself today, but will make our lives a nightmare a year from now when we try to refactor.
As a leader, my fear is that we stop building cohesive systems and start just “managing” a series of disconnected scripts.
My 3 Simple Rules
To keep our engineering edge sharp, I’ve established three “ground rules” that every developer on my team must follow:
1. Treat it Like a High-Speed Intern
Think of the AI as a very fast, very eager junior intern. An intern can save you hours of grunt work, but you would never commit their code to production without a line-by-line review. You are the senior architect; the AI is the helper. If you can’t explain what the AI wrote, you aren’t allowed to merge it.
2. Let it Type, Don’t Let it Think
Use AI for the “mechanical” parts of coding—repetitive boilerplate, converting data formats, or writing basic UI components in Vue or Tailwind. But the architectural decisions—the “why” behind the database schema or the security protocols—must come from a human brain. We use the bots for the labor, not the logic.
3. The 10-Minute Rule
If you have spent more than 10 minutes arguing with a bot or trying to “prompt engineer” a fix for a specific bug, turn it off. This is the circuit breaker for the Dead Loop. Sometimes, the “old school” way of opening documentation and typing it out yourself is still the fastest, most reliable path to a solution.
The Bottom Line
We are entering an era where the definition of a “Senior Engineer” is changing. It’s no longer just about how well you know a syntax; it’s about how well you can direct a suite of tools to produce a secure, scalable result.
I want my team to have the best tools in the world. I want us to be the fastest software house in the market. But I refuse to let us lose our “engineering gut.” Use the bots, stay in control, and never let the AI do the heavy thinking for you. The moment we stop questioning the output is the moment we stop being engineers and start being data entry clerks for the LLMs.
