AI writes the code fast. It breaks in the same three spots every time.

Let me catch you up. Your team is shipping faster than ever with AI coding tools. Founders are launching MVPs in days. Engineers are moving work through in minutes that used to take hours. That part is real.

But the failures are real too, and they are boringly predictable. The same holes show up in product after product. If you run a team leaning on this, you need to know where to look before your users do.

The three spots that break every time

Rishi Sharma audited four vibe-coded MVPs for four different founders. Different products, different stacks, different teams. All four had shipped. All four had paying customers. And all four broke in almost the same three places.

Those three spots: the signup and invite flows, anything with timing or background work like webhooks, and the settings and billing pages where users can wreck their own account or someone else's. He could predict the bugs before opening the code.

The reason is simple. These flows depend on business rules nobody told the model. The AI has the least context about what "correct" means, so it guesses. Everywhere else, correctness is obvious. Here, it is a decision your team owns.

The work did not shrink. It moved.

Here is the piece leaders keep missing. AI does not cut your workload. It shifts it. One engineer building an internal tool with an AI partner found that AI redistributes the cognitive load rather than reducing it. The bottleneck stopped being typing code. It became understanding the problem and checking the architecture.

His app looked fine. A high-priority customer defect just went missing from the dashboard. No errors, clean network calls, normal UI. The cause: the AI had copied a hardcoded list of team members across several modules instead of building one shared source. As the org changed, the lists drifted apart.

The fix was easy. Spotting the flaw took human judgment. The model will happily optimize a bad design because it rarely pushes back on your premise. Context beats prompt tricks every time.

Agents that hold the floor while you raise the ceiling

The smarter use of AI is narrow, not all-knowing. Aakreit Sachdeva built a set of focused agents for his design work: one interrogates requirements, one checks against the design system, one tests components in a real browser, and separate agents fix and verify. He drew a hard line: the agent that makes a change should not be the only agent deciding whether the change is correct.

On one Toggle component, the loop tested all 25 configurations in about 10 minutes. Doing it by hand would have run 90 minutes, and by configuration 18 his attention would be gone. The model cost was about $5, roughly what manual prompting would run.

The payoff was not the money. It was 90 minutes of his attention freed for harder questions, while accessibility checks stayed consistent. His frame is worth stealing: agents protect the quality floor, humans raise the ceiling. "The human was busy doing strategy" is not a quality standard.

The approval that looks like all the others

The risk lives in the moment you stop paying attention. Hamza Boulahia points out that your agent will pause and ask to run some chained git commands. You will approve it, usually correctly. Then one day it asks for something that looks just as harmless, like a recursive delete that wiped a Cursor user's files outside the project.

This is why guardrails beat good intentions. Nick Babich makes the case that written instructions in a config file are only probabilistic. Tell the model "always run the formatter" and it will sometimes forget. Hooks fix that by attaching an action to an event instead of asking the model to remember. The action runs no matter what.

Same lesson, two forms. Do not trust the model to remember your standards. Wire them in so they fire automatically.

The deep cut

Everyone reads "AI writes the code" as a headcount story. Fewer engineers, faster ship. That is the wrong read, and it will cost you.

Code is now the cheap part. Babich argues software developers are becoming software creators who orchestrate agents and make architectural calls. That means the value moved to the exact places AI is weak: business rules, edge cases, and checking the work. Those three predictable break spots are not bugs to squash once. They are a permanent audit checklist.

So do not cut the review budget. Redirect it. Put your best people on requirements, on the destructive corners, and on verification loops that a machine cannot own alone. Treat AI coding as a quality-control problem, and you keep the speed without shipping the same three holes as everyone else.

Three questions for your team

• Who owns the audit of our signup, async, and billing flows before we ship, and do they know those are where AI code fails?
• Where in our workflow do we have a separate check that verifies AI changes, instead of trusting the same agent that made them?
• What standards, formatting, accessibility, and design-system rules, are we still hoping the model remembers, and which ones should we wire in as automatic guardrails?