Your developer just shipped a feature in three hours that would've taken three days. The code looks clean. Tests pass. You're thrilled about the velocity. Then users start complaining because every time someone likes their post, they get an email. And a push notification. And an in-app notification. All at once.

Nobody caught it because the AI understood "add notifications when posts get liked" and did exactly that. It also wrote tests that verified notifications worked. Everything passed. The problem? The AI didn't know users expect to control how they get notified, not just receive all notifications through every possible channel.

This is the AI coding problem nobody's talking about. It's not that AI writes bad code. It's that AI writes perfectly good code that solves the wrong problem, then writes tests that confirm it solved that wrong problem beautifully.

Your code reviews can't catch this. Your CI/CD pipeline can't catch this. Because technically, nothing is wrong. The code works. The tests pass. You just built the wrong thing.

What's Actually Happening

Pre-AI, when developers wrote code slowly, they had time to think about what they were building. Writing tests forced them to consider edge cases. Code reviews caught logical errors because reviewers understood the problem space.

Now? A prompt becomes code in seconds. Tests generate just as fast. Ship it. The thinking step disappeared. We accidentally optimized it out.

The developers who succeed with AI aren't the ones generating code fastest. They're the ones who realized they need to think more before generating code, not less.

Because AI will happily build whatever you imply you want, whether that's what you actually need or not.

For Technical Teams: The New Workflow

Stop doing: prompt → generate code → generate tests → ship

Start doing:

Write the specification first. Before touching AI, write down the actual system behavior: "When a post gets a like, send one notification based on user preferences. Check notification_settings table for preferred channel (email, push, or in-app). If user disabled like notifications, send nothing. Batch multiple likes within 5 minutes into a single notification."
Generate tests from that specification. Let AI create tests. Then ask: do these tests verify my specification or just validate AI output? This is where you catch that AI interpreted "send notifications" as "send all notification types" instead of "respect user preferences."
Generate implementation only after tests match your spec. Now you're reviewing AI code against a specification you understand, not just checking if code looks syntactically correct.
Tag AI-generated code in commits. Mark what came from AI assistants. This lets your team track which code creates maintenance burden over time.

This takes 10 minutes per feature. Your velocity is still 3x faster than pre-AI. You just stopped optimizing for generation speed and started optimizing for building the right thing.

For Leaders: What to Ask and Enforce

Most teams are reviewing AI-generated code the way they reviewed human-written code—looking for bugs, not requirement mismatches. Here's how to fix it:

Questions to ask your engineering leadership:

What percentage of AI-generated features had written specifications before code generation?
How are we tracking bugs in AI-generated code vs. human-written code?
Can developers explain the system behavior their AI code implements, not just what the code does?

Policies to implement:

Mandate specification-first development. No AI-generated code merges without a written specification of intended behavior. This isn't documentation theater—it's forcing systems thinking before implementation.
Track AI code separately in your repos. Tag AI-assisted code and measure its bug rates, time-to-fix, and maintenance costs over 6 months. Early data from teams doing this: AI code has fewer implementation bugs but more requirement mismatches. The specification step fixes this.
Change how code reviews work. Stop asking reviewers "is this code correct?" Start asking "does this code match the specification? What breaks if our assumptions change?"

What This Actually Costs (And Saves)

Real numbers: Teams using AI assistants with specification-first workflow maintain 3-4x velocity gains while keeping quality comparable to pre-AI development. The specification step costs about 20% of raw generation speed but catches requirement mismatches before any code exists.

Teams skipping this? Initial 5x velocity that degrades to 1x within six months as technical debt accumulates and nobody understands what their system actually does.

The company that lost $40K on bad authentication now spends 10 minutes per feature writing specifications first. They haven't had a requirement-mismatch bug in six months. Their velocity is still 3x faster than before AI. They just stopped measuring speed by how fast code generates and started measuring it by how fast correct features ship.

The Real Shift

AI coding assistants are incredible at implementation. They're terrible at understanding what you actually need. The teams figuring this out aren't fighting AI or trying to slow it down. They're recognizing that AI accelerates the wrong part of development if you let it.

The velocity gain is real. But it only compounds if you preserve the thinking that makes sure you're building the right thing. Otherwise you're just accumulating technical debt at impressive speed.