AI-generated code is not free. It is delayed billing.
Cursor, Bolt, Lovable, v0, and Replit can help you build a working app quickly. That part is real. A founder can go from idea to demo in a weekend, sometimes in a single evening. For prototypes, that is brilliant.
The problem starts when the demo becomes the product.
The app looks finished. The pages load. The button works. A Stripe checkout opens. A database row appears. So it feels like the expensive part is over.
Then real users arrive, and the bill shows up in a different form: support tickets, failed payments, security fixes, emergency developer hours, slower feature work, and sometimes a full rebuild.
This is the real cost of AI-generated code. Not the prompt. Not the subscription. The cost is what happens when unreviewed code touches customers.
The free code trap
AI coding tools make software feel cheaper because they remove the first visible cost: paying a developer to create version one.
That is useful. It also changes how founders judge risk.
A traditional development quote might be £8,000, £20,000, or £50,000. You see the number upfront, so you scrutinise the plan. You ask what is included. You worry about quality.
With vibe coding, the first version costs a few subscriptions and a lot of your time. The price looks low, so it is tempting to skip the boring checks.
No proper error handling. No security review. No tests. No database indexing. No rollback plan. No monitoring.
For a prototype, that may be fine. For a product with users, it is gambling.
The hidden costs nobody sees in the demo
Most AI-generated code problems do not announce themselves on the homepage. They hide in edge cases.
A user submits the same form twice. A payment webhook arrives late. Someone uploads a 20 MB image. A database query works with 30 records but times out at 30,000. A logged-out user opens an old dashboard URL. A Supabase policy protects one table but leaves another readable.
None of that shows up in the happy-path demo.
The costs usually land in five places.
Bug fixing time
Small bugs are rarely small when nobody understands the code.
A founder might lose half a day trying to fix a broken signup flow by prompting the AI tool again and again. The tool changes three files, fixes the visible error, and quietly breaks something else.
Then the founder loses another half day reverting changes, copying errors into chat, restarting the dev server, and hoping the next patch sticks.
If your own time is worth £50 an hour and you spend 10 hours a month fighting avoidable bugs, that is £500 a month before you hire anyone.
If you hire a developer in panic mode, the price goes up. Debugging unfamiliar, AI-generated code under pressure is slower than building with a plan.
Security fixes
Security debt is expensive because it comes with urgency.
Common AI-generated security problems include:
- API keys committed into frontend code
- Admin routes protected in the UI but not on the server
- Missing rate limits on login, signup, and password reset
- Database tables readable by the wrong users
- Webhooks accepted without signature verification
- User input saved or rendered without proper validation
A basic review can catch many of these before launch. After launch, the same issues become emergencies.
Rotate exposed keys. Patch the backend. Check logs. Notify users if needed. Review data access. Rebuild trust.
That is not a £99 problem anymore.
Lost users
Most users do not report bugs. They leave.
If 1,000 people visit your launch page and 4% should sign up, that is 40 signups. If a broken form, slow page, or auth bug cuts conversion to 2%, you just lost 20 potential users.
If each customer is worth £200 over their lifetime, that is £4,000 in lost value from one leaky flow.
This is why vibe coding true cost is hard to feel early. You see the users who made it through. You do not see the people who bounced because the app froze, the mobile layout broke, or the checkout failed silently.
Rewrite cost
Some AI-built apps can be cleaned up. Some need surgery. A few need a rebuild.
The difference usually comes down to structure.
If the app has a decent architecture, clear separation between frontend and backend, and isolated bugs, fixing is usually the right move.
If everything lives in one giant component, business logic is scattered through UI files, authentication is half-implemented, and database access has no consistent pattern, a rebuild may be cheaper than months of patching.
That is the painful bit. A free first version can create a £10,000 rebuild later.
A simple cost example
Imagine a founder builds a small SaaS with AI tools.
They launch with 500 visitors from LinkedIn and Product Hunt. The app works for the first 50 users, then issues start appearing.
The contact form drops messages when users double-click submit. A dashboard query gets slower every day. Two customers hit a permission bug. Stripe webhooks sometimes fail, so paid users stay on the free plan.
Here is a realistic month-one cost:
- 12 founder hours debugging: £600 at £50/hour
- Emergency developer help: £1,200
- Lost paid upgrades from billing bugs: £1,000
- Lost signups from broken flows: £2,000 to £4,000
- Security cleanup after exposed keys: £500
That is £5,300 to £7,300 in one month. The app still needs proper tests and monitoring afterwards.
The first version was cheap. The recovery was not.
Fix early vs fix later
Early review is boring. That is why it works.
Before launch, a reviewer can say:
- This API key is in the wrong place
- This endpoint needs authentication
- This form needs server-side validation
- This query will slow down as data grows
- This payment webhook can be spoofed
- This error path leaves users stuck
Most fixes are still contained. The codebase is smaller. There are no angry users waiting. There is no incident history to clean up.
After launch, the same issues cost more because every change has risk. You need to fix the bug without breaking active users. You may need database migrations, support replies, hotfix deployments, and careful testing.
The code did not become more complicated overnight. The consequences did.
How to estimate your own technical debt
You do not need to be a developer to make a rough estimate.
Ask these questions:
- Does the app handle errors without crashing?
- Are private pages blocked on the backend, not just hidden in the UI?
- Are API keys and secrets kept out of browser code?
- Do forms validate input on the server?
- Can you explain how payments, auth, and database access work?
- Does the app stay fast with realistic data?
- Is there any monitoring, logging, or alerting?
- Could a new developer run the project from the README?
If the answer is “I don’t know” for most of these, that uncertainty has a price.
When cheap is actually cheap
AI-generated code is fine for experiments, landing pages, throwaway prototypes, internal tools with no sensitive data, and early demos where the goal is learning.
It becomes risky when the app handles payments, customer data, private messages, business workflows, health information, legal data, or anything users depend on.
At that point, the question is not whether AI code is bad. It is whether the code has been reviewed like production software.
The ROI of a professional review
A £299 code review will not magically turn a messy prototype into Stripe overnight. It can do something more useful: tell you where the real risk is.
A good review should find the issues that could cost you money later:
- Security gaps
- Broken auth assumptions
- Fragile payment flows
- Missing validation
- Slow database queries
- Risky architecture choices
- Bugs on core user journeys
If it prevents one failed launch, one exposed API key, one broken payment flow, or one unnecessary rebuild, it has paid for itself.
The best time to review AI-generated code is before users rely on it. The second best time is before the next release.
Worried the “free” code is carrying expensive risk? Get a free code assessment. A £299 code review can save you £10,000 in fixes later.