There's a version of this blog that opens with something like "AI is revolutionizing software development." I'm not writing that version.
I'm writing the one where I stare at my screen at 7 PM, waiting to leave from office, wondering why the AI just rewrote a working function into something that breaks three other things, and whether this is actually saving me time or just shifting where I waste it.
The Loop Problem
This one got me bad last month.
I was working on a filtering feature in a Laravel project, nothing complicated. I asked Claude to fix a bug where a date range filter wasn't querying correctly. It gave me a fix. I tested it. It broke something else. I told it about the new break. It fixed that. New bug. I told it. It fixed that by reverting the first thing it changed.
Twenty minutes in, I was essentially watching an AI play Whack-a-Mole with my codebase.
The brutal part is that I kept thinking "one more prompt and it'll get there." That's the trap. The model doesn't get tired, it doesn't feel embarrassed, and it has zero awareness that it's going in circles. It's not trying to frustrate you. It just doesn't know what it doesn't know. Eventually I closed the chat, read the actual query myself, spotted the issue in two minutes, and fixed it with four lines.
This happens more than I'd like to admit.
Confidence Without Accountability
AI tools write code with the same tone whether they're 100% right or completely guessing. There's no "I'm not sure about this part." There's no hesitation. You get a clean, well-commented, confidently presented solution that might be subtly wrong in a way that won't surface until someone hits that edge case in production.
I've merged suggestions that looked perfectly fine on review. Good variable names, readable structure, nothing obviously wrong. The bug showed up three weeks later because the logic misunderstood a business rule that wasn't in my prompt. Whose fault is that? Mine, obviously. But the confidence of the output made me trust it more than I should have.
Senior developers know what uncertainty looks like in code. They leave comments, they write defensive checks, they flag things for review. AI doesn't do that unless you specifically ask, and even then it's just following the pattern of what "uncertain code comments" look like in training data.
Context Amnesia
Here's a thing that still catches me off guard even though I know it happens.
You've been working with an AI on the same feature for an hour. You've explained your data model, your constraints, a weird legacy decision that exists for business reasons you can't change. The AI has been helpful. You start a new chat because the old one got long and slow.
Gone. All of it. You're starting with a stranger.
The new session gives you a textbook answer that ignores every constraint you just spent an hour explaining. You give it context again, but you always miss something. You compress a week of architectural decisions into two paragraphs and wonder why the suggestions feel slightly off.
Working memory in conversation isn't permanent storage. It's not even reliable within a single session once the context gets long enough. I've noticed models start to drift or lose track of earlier instructions when a chat runs long. You're not building a relationship with something that understands your project. You're repeatedly briefing a smart consultant who forgets everything between meetings.
The Test Coverage Illusion
AI is very good at writing tests. It will write a lot of them. They will look thorough. They will pass.
This might be the sneakiest problem on this list.
I reviewed a test suite once that had 94% coverage. The tests were all generated with AI assistance. They were testing that functions returned values, that variables were assigned, that arrays had the right length. What they were not testing was whether the logic was actually correct for the requirements. The tests were proving that code ran, not that it worked.
Coverage numbers are a proxy for confidence. AI can inflate that proxy without giving you the actual thing the proxy is supposed to measure. You end up with a green CI pipeline and bugs you'd only catch with integration tests or, more often, angry users.
It Optimizes for Looking Right
This is related to the test issue but it goes deeper.
AI is trained on code that was written, reviewed, and merged. Code that looked good enough to ship. It has very limited exposure to the conversation that happened before that code existed, the rejected approaches, the design decisions, the context buried in a Slack thread from eight months ago.
When you ask it to solve a problem, it gives you something that would look right in a code review. That's genuinely useful. But it doesn't know your actual constraints. It doesn't know that the "obvious" architectural choice is one your team tried and reversed six months ago. It doesn't know that this particular client has a weird edge case in their data that changes what "correct" even means.
I've started thinking of AI output the way I think about StackOverflow answers. Useful starting point, check the date, read the comments, verify it applies to your specific situation before you copy-paste anything.
What I Actually Do Now
I still use AI every day. I'm not writing this to tell you to stop.
But I've changed how I use it. I don't let it run too long on the same problem. If it's not getting there in two or three exchanges, I stop and think about whether I'm asking the right question at all. I treat generated tests as a first draft, not a finished product. I read the output the same way I'd read code from a junior developer I trust but can't verify yet.
The productivity gains are real. The risks are also real. The ones that bite you hardest are the ones that look fine on the surface.
The AI doesn't know what it doesn't know. That's your job now.
