AI coding tools do not make the same mistakes because they are stupid.
They make the same mistakes because of how they work.
Cursor, Bolt, Lovable, v0, Replit Agent, and similar tools are very good at producing code that looks plausible. They know common patterns. They can scaffold pages, wire up components, and suggest fixes faster than most humans can type.
But production bugs are rarely about typing speed.
The bugs we see in AI-generated code usually come from missing context, missing feedback, and missing judgement. The tool can generate the next likely chunk of code. It cannot reliably know whether that chunk is safe in your actual product unless the surrounding system gives it enough information.
That is why the same issues keep coming back: weak error handling, missing validation, broken permissions, hardcoded assumptions, messy state, and code that works for one demo path but fails under normal use.
AI does not experience runtime failure
A human developer runs the app, sees the crash, checks logs, reproduces the bug, and learns from the failure.
An AI coding tool does not naturally experience that loop unless you provide it.
It can write this:
const profile = await getProfile(userId);return profile.avatarUrl;It looks fine. It may even compile.
But what happens if the user has no profile yet? What if getProfile() returns null? What if the database times out? What if the avatar field is empty because the user skipped onboarding?
A developer who has been burned by production bugs will ask those questions. An AI tool may not, unless the prompt or project patterns push it there.
That is one reason AI code has bugs: the tool often optimises for a complete answer, not a tested answer.
The context window is smaller than the product
Most real apps are bigger than the code the AI tool is actively looking at.
A bug in one file may depend on rules in another file, a database schema, an environment variable, a third-party webhook, or an old migration nobody has opened in months.
AI tools have context windows. Some are large. None are the same as understanding the entire product, its history, and the promises made to users.
This creates familiar problems:
- A new component uses a different validation pattern from the rest of the app
- An API route skips the auth helper used elsewhere
- A database query ignores an index that already exists
- Error messages vary across similar forms
- One feature stores dates in UTC while another assumes local time
The tool is not trying to create inconsistency. It is working from whatever context it has.
If the current file has a bad pattern, AI often repeats it. If the current prompt omits security, the answer may omit security too.
The code can look right and still be wrong
This is the uncomfortable part.
AI-generated code often has the shape of good code. Nice names. Clean indentation. Modern libraries. TypeScript types. Sensible-looking folder structure.
That makes problems harder to spot.
Bad hand-written code often looks suspicious. AI-generated code can look professional while hiding a broken assumption.
For example:
if (user.role === 'admin') { return await getAllCustomers();}That looks simple. But where did user.role come from? Can the client modify it? Was it loaded from the server session? Is there a separate permission model? Should some admins only see customers in their organisation?
The code reads well. The risk is in the business rule.
AI tools are much better at syntax than product judgement.
Pattern matching is not the same as understanding intent
Large language models learn from patterns in code and text. That is useful. Most software uses repeated patterns.
The trouble is that your app’s intent may not match the most common pattern.
A generic login flow might be fine for a toy app. Your product may need multi-tenant permissions, audit logs, invite-only access, or strict data separation between teams.
A generic checkout flow might be fine for a demo. Your business may need invoice billing, VAT handling, coupon rules, plan downgrades, failed payment recovery, and webhook reconciliation.
If you prompt “add subscriptions,” the tool may build something that looks like subscriptions. That does not mean it handles the boring financial edge cases.
This is where AI coding limitations show up. The tool can infer what usually comes next. It cannot reliably infer what your business will regret later.
Training data includes demo code
AI coding tools learned from a lot of public code. Some of it is excellent. Some is outdated, rushed, insecure, or written for tutorials where the author skipped production details on purpose.
That is how you get code that feels familiar but is not production safe: API routes with no rate limiting, auth checks that only protect the UI, database calls with no pagination, payment examples that ignore webhook verification, and form handlers that trust browser input.
The tool is not malicious. It is pulling from patterns that were never meant to be shipped unchanged.
AI is bad at knowing what it has not checked
Good developers are annoying in useful ways.
They say things like:
- “I need to see the schema first.”
- “This depends on how auth is implemented.”
- “We should test the webhook path.”
- “This fix might break the mobile flow.”
- “I don’t know yet. Let me reproduce it.”
AI tools tend to be more confident than they should be. If you ask for a fix, they give a fix. If you ask for a feature, they build one. That can be great for momentum and bad for risk.
The missing sentence is often: “I need more information before changing this.”
When the tool does not ask that, the user has to.
Why the same bugs keep appearing
After reviewing enough AI-built apps, the pattern becomes boring.
One app is a marketplace. Another is a dashboard. Another is a booking system. The underlying mistakes repeat.
Forms trust the frontend. APIs trust the user. Queries assume small datasets. Errors go to the console instead of the user. Loading states are missing. Permissions are implemented in one place and forgotten in another.
These are not the parts that make a demo impressive. Production hardening is invisible when it works. Nobody claps because the password reset token expires correctly. Users only notice when it does not.
How to prompt better
You can reduce these mistakes by changing how you use the tool.
Instead of:
Build a dashboard for users to manage projects.Try:
Build a dashboard for users to manage projects. Before writing code, identify the auth rules, error states, loading states, empty states, and data validation needed for production. Ask questions if anything is ambiguous.Instead of:
Fix this error.Try:
Find the root cause of this error. Do not patch symptoms. Explain what changed, what else could break, and how to test the fix.Instead of:
Add Stripe subscriptions.Try:
Design the Stripe subscription flow with webhook verification, failed payment handling, plan changes, database state, and test cases. Do not write implementation code until the flow is clear.A better prompt will not turn the tool into a senior engineer. It will force more of the missing thinking into the conversation.
Add feedback loops
The most useful improvement is not a clever prompt. It is feedback.
Run the app. Run tests. Check logs. Use the browser console. Seed realistic data. Try slow network mode. Open private URLs while logged out. Submit bad inputs.
Then feed real errors back into the tool. AI coding tools are much more useful when they are reacting to evidence instead of guessing from a clean prompt.
What this means for founders
You do not need to stop using AI coding tools.
You do need to stop treating a working demo as proof that the code is safe.
The tool can help you build quickly. It can help you understand unfamiliar code. It can suggest fixes. It can generate tests if you ask for them properly.
But someone still has to check the production assumptions:
- What happens when this fails?
- Who is allowed to do this?
- What data can this user access?
- How does this scale beyond the demo account?
- How will we know if it breaks?
Those questions are where real software starts.
The practical takeaway
AI coding tools keep making the same mistakes because they are often used in the same way: build the visible feature, accept the plausible code, move on.
To get better results, add the missing loop. Give the tool more context. Ask it to identify risk before writing code. Test the result under ugly conditions. Review the parts that touch data, money, auth, and user trust.
AI can get you to version one faster than ever. Version one still needs adult supervision.
Understanding why AI code breaks is step one. Fixing it is step two. Get a free code assessment, and we’ll handle the production risks before your users do.