Why AI Makes Us Lazy (And How to Fight It)
The framework is simple. Following it is hard. Not because it's complicated — because we're human. Here are the temptations, the warning signs, and how to build the discipline.
I wrote about the responsible AI development framework. Six phases, five practices, clear guidelines.
And I still struggle to follow it sometimes.
This post is about why.
The Temptations
Generation is exciting. Verification is boring.
AI gives you code in three seconds. Verification takes ten minutes. Generation gives you a dopamine hit — look at all this output! Verification is just… tedious.
The temptation: skip verification, move to the next exciting generation.
Shipping feels productive. Being careful feels slow.
When you’re behind on a deadline, “being responsible” feels like a luxury. You tell yourself you’ll be careful next time, when there’s more time.
There’s never more time.
Accepting is easy. Understanding is hard.
AI gives you an answer. You could dig in and understand it. Or you could just accept it and move on.
Moving on feels like progress. But it’s not.
AI seems confident. Self-doubt makes you defer.
When you’re in unfamiliar territory, feeling like an imposter, and AI responds with complete confidence — it’s tempting to think “who am I to question it?”
You’re the one who has to live with the consequences. That’s who.
You’re tired. AI never is.
At 11pm, when you’re exhausted and AI is still generating perfect-looking code, the discipline required to verify feels impossible.
This is when the worst bugs get shipped.
The Warning Signs
You’re doing it wrong if you catch yourself thinking:
“I couldn’t explain this code if asked.”
If you can’t explain it, you don’t understand it. If you don’t understand it, you can’t debug it. If you can’t debug it, you own a liability.
“The explanation was suspiciously clean.”
Real systems are messy. If AI’s explanation is too clean, it’s probably wrong. Verify against actual behavior.
“I haven’t actually run this yet.”
Code that hasn’t run is fiction. Run it. See what happens. Reality always has surprises.
“AI will fix it.” (on repeat)
The fix-it loop: AI generates code, it doesn’t work, you ask AI to fix it, it doesn’t work, repeat forever.
Sometimes you need to stop, read the code yourself, and think.
“This feels wrong but AI said so.”
Your intuition exists for a reason. If something feels off, investigate. Don’t defer to AI’s confidence.
“I’ll verify it later.”
You won’t. Later never comes. Verify now or accept that you’re shipping unverified code.
The Antipatterns
The Copilot Coma
Accepting suggestions without reading them. Tab, tab, tab through completions. Shipping code you never actually looked at.
Wake up. Read what you’re accepting.
The Context Dump
Throwing everything at AI and hoping it figures it out. Massive prompts, vague requests, frustration when results are wrong.
Better: small, specific questions with clear context.
The Fix-It Loop
Asking AI to fix bugs in code AI wrote, creating more bugs, asking AI to fix those, forever.
Sometimes you have to read the code yourself. Use your own brain. Debug like it’s 2015.
The Confidence Bias
Assuming AI is right because it sounds confident. AI always sounds confident. Even when it’s completely wrong.
Verify everything.
The Complexity Excuse
“This is too complex for me to understand, so I’ll just trust AI.”
No. If it’s too complex to understand, it’s too complex to debug. Simplify until you can understand it, or don’t ship it.
Building the Discipline
Knowing the temptations isn’t enough. You need systems that make the lazy path harder.
Make verification a gate
Don’t let yourself merge without tests. Set up pre-commit hooks. Create checklists. Make “skip verification” require conscious effort.
Time-box generation
Give yourself 30 minutes to explore with AI. Then stop and verify what you have. Don’t let generation become endless.
Verbalize understanding
Before accepting AI code, explain it out loud. To yourself, to a rubber duck, to a colleague. If you can’t explain it, you don’t understand it.
Track your near-misses
When you catch a bug AI introduced, write it down. Review your near-misses weekly. Notice patterns. Learn from them.
Accept slower
You will be slower than people who don’t verify. That’s okay. You’ll also ship fewer bugs. The math works out.
The Counter-Mantras
When you catch yourself rationalizing, counter with:
“I’ll verify later” → No. Verify now.
“AI seems confident” → Your judgment matters too.
“Everyone else ships fast” → You can’t see their bugs.
“I’m too tired to verify” → Then you’re too tired to ship.
“This one time won’t matter” → That’s what you said last time.
The Bottom Line
The framework gives you structure. But you have to do the hard part: actually following it when it’s inconvenient.
When you’re tired. When you’re behind. When shortcuts are tempting.
That’s the discipline. It’s hard. But that’s what separates responsible AI use from reckless AI use.
The next post is about making this easier — automation and tooling that forces responsible behavior.
Previous: The Responsible AI Development Framework
The Journey Here
The updates that led to this moment.
Writing the honest part of the series — why the framework is hard to follow. Generation is exciting, verification is boring. We're tired, AI never is. I catch myself cutting corners too. That's the real talk.