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AI helped me ship faster — but every line it wrote made my system harder to debug, scale, and trust. That’s not engineering. That’s gambling.
I started using ChatGPT in late 2022, right after getting laid off. With no job, I had time — so I talked to it non-stop. Some days, I spent 5–6 hours experimenting. Ideas bounced back and forth. It felt like having a new kind of partner. At that point, everything was sunshine. There are no red flags yet.
After landing a new job at another startup, I integrated AI into my coding workflow. It felt like magic. Snippets, tests, and scaffolds appeared instantly. I called it my tireless junior dev. But a few weeks in, I noticed something off: I was shipping more but thinking less. Worse — I felt detached from the codebase.
I used AI to generate controller logic, mock APIs, and especially RSpec tests. It saved me hours. I love TDD, so I didn’t want to outsource that part — but I had to admit, the output was decent. Part of that came from my prompting skill — I had spent 3–4 months sharpening that during unemployment.
On the surface, things looked efficient. PRs moved quickly. I felt productive. But the longer I used it, the more invisible costs started to show.
? Problem 1: Fixing AI output takes time
I spent too much time correcting AI-generated code. Even with multiple options, most suggestions weren’t up to my quality bar. If code quality doesn’t matter to you, this isn’t a problem. But I care. Instead of solving logic problems, I was refining prompts and cleaning up AI guesses. That felt like a different job.
? Problem 2: I outsourced my thinking
This one hit harder: critical thinking got slower. I realized I'd gradually handed over my problem-solving process to an assistant. That’s dangerous. I get paid to think clearly and solve real-world business problems — not to autocomplete someone else's solution.
Problem 3: I became less patient
AI gave me instant answers. So when they failed, I got irrationally frustrated. Pre-AI, tough problems took time, but I stayed calm. With AI, the illusion of speed made me angry when reality didn't keep up.
? Real-World Consequences
When teams use AI without understanding trade-offs, things break:
AI promises acceleration, but if it sacrifices clarity, it backfires. Especially in startups where clarity = survival.
? What I Do Now
After another layoff (at time of writing), I’ve gone back to basics. No AI tools — just human brain + editor. I bought a whiteboard to sketch ideas, design systems, and practice deep thinking.
I still use AI for:
But I no longer use it for core architecture, deep debugging, or problem-solving. There, I want my brain in charge — not a suggestion engine.
? Final Thoughts
AI isn’t bad. But full dependence is. We already saw this with Google, Stack Overflow, and social media — tools that once helped us now often cripple us when overused. AI just magnifies this at scale.
We need to ask:
If your context allows, try going AI-free for a week. Notice how your brain works. Track how often you refactor AI code vs. your own. Does speed always mean quality? Or just comfort?
Tools come and go. AI will stay. But our thinking — our clarity — that’s what defines us.
I started using ChatGPT in late 2022, right after getting laid off. With no job, I had time — so I talked to it non-stop. Some days, I spent 5–6 hours experimenting. Ideas bounced back and forth. It felt like having a new kind of partner. At that point, everything was sunshine. There are no red flags yet.
After landing a new job at another startup, I integrated AI into my coding workflow. It felt like magic. Snippets, tests, and scaffolds appeared instantly. I called it my tireless junior dev. But a few weeks in, I noticed something off: I was shipping more but thinking less. Worse — I felt detached from the codebase.
I used AI to generate controller logic, mock APIs, and especially RSpec tests. It saved me hours. I love TDD, so I didn’t want to outsource that part — but I had to admit, the output was decent. Part of that came from my prompting skill — I had spent 3–4 months sharpening that during unemployment.
On the surface, things looked efficient. PRs moved quickly. I felt productive. But the longer I used it, the more invisible costs started to show.
? Problem 1: Fixing AI output takes time
I spent too much time correcting AI-generated code. Even with multiple options, most suggestions weren’t up to my quality bar. If code quality doesn’t matter to you, this isn’t a problem. But I care. Instead of solving logic problems, I was refining prompts and cleaning up AI guesses. That felt like a different job.
? Problem 2: I outsourced my thinking
This one hit harder: critical thinking got slower. I realized I'd gradually handed over my problem-solving process to an assistant. That’s dangerous. I get paid to think clearly and solve real-world business problems — not to autocomplete someone else's solution.
Problem 3: I became less patientAI gave me instant answers. So when they failed, I got irrationally frustrated. Pre-AI, tough problems took time, but I stayed calm. With AI, the illusion of speed made me angry when reality didn't keep up.
? Real-World Consequences
When teams use AI without understanding trade-offs, things break:
- Codebases become bloated or over-engineered
- Debugging cycles slow down
- Onboarding becomes harder — the code reflects suggestions, not intention
- Velocity drops, morale erodes, and eventually, customers leave
AI promises acceleration, but if it sacrifices clarity, it backfires. Especially in startups where clarity = survival.
? What I Do Now
After another layoff (at time of writing), I’ve gone back to basics. No AI tools — just human brain + editor. I bought a whiteboard to sketch ideas, design systems, and practice deep thinking.
I still use AI for:
- Dummy data generation
- JSON scaffolds
- Boilerplate test cases
But I no longer use it for core architecture, deep debugging, or problem-solving. There, I want my brain in charge — not a suggestion engine.
? Final Thoughts
AI isn’t bad. But full dependence is. We already saw this with Google, Stack Overflow, and social media — tools that once helped us now often cripple us when overused. AI just magnifies this at scale.
We need to ask:
- When does AI increase clarity?
- When does it erode thinking?
- Can I still solve this problem without help?
If your context allows, try going AI-free for a week. Notice how your brain works. Track how often you refactor AI code vs. your own. Does speed always mean quality? Or just comfort?
Tools come and go. AI will stay. But our thinking — our clarity — that’s what defines us.