A vibe-coded app does not have technical debt simply because AI wrote some of the code.
The real warning sign is simpler: the product is becoming harder to change safely than it should be.
Your app may still look polished. Sign-up may work. Payments may work. Customers may even be using it. But if adding one field, changing one permission or adjusting one payment rule regularly breaks something unrelated, the problem is no longer prompt quality. The system has accumulated decisions nobody fully controls.
That is technical debt.
For founders using Lovable, Cursor, Codex, Supabase and similar tools, this matters because AI compresses the time between idea and working software. It can also compress the time between a clean prototype and a codebase full of assumptions.
The goal is not to stop using AI. It is to know when fast generation needs stronger engineering discipline.
Technical debt is a change-cost problem
Technical debt is often described as messy code. That definition is too narrow.
A better business definition is:
Technical debt is the extra cost and risk you pay later because an earlier implementation decision made the system harder to understand, verify or change.
That can appear as duplicated components, but it can also live in places a founder never sees:
- the same business rule implemented differently in three screens;
- database tables that no longer match the product model;
- permissions enforced only in the interface instead of at the data layer;
- production database changes that were never captured in version control;
- payment state that depends on a browser redirect instead of verified server events;
- no tests around authentication, billing or other critical workflows;
- deployment settings that only one person understands;
- a feature that works only because several undocumented side effects happen in the right order.
This is why a working app and a maintainable app are not the same thing.
AI-assisted development makes one kind of debt easier to create
AI coding tools are excellent at producing a plausible implementation from the context they can see.
The risk appears when the product has accumulated more context than the current prompt, conversation or file selection contains.
A request such as “add team billing” sounds local. In a real SaaS product it may affect account ownership, permissions, subscriptions, invoices, cancellation, plan limits, email notifications and audit history.
If those boundaries were never made explicit, an AI agent can make a locally sensible change that creates a globally inconsistent product.
This is not just a theoretical concern. Software Improvement Group’s 2026 State of Software research reports lower maintainability in AI-generated code and roughly twice the security-risk violations in the AI-generated code it analysed. The useful conclusion is not “AI code is bad.” It is that faster generation increases the value of review, architecture and verification.
Source: https://www.softwareimprovementgroup.com/publications/state-of-software-2026/
The seven warning signs I would take seriously
1. Small changes repeatedly break unrelated features
This is one of the clearest signals.
Changing the profile page should not unexpectedly break billing. Adding a status should not require edits across ten unrelated components. A minor form change should not create a login regression.
Occasional regressions happen in every software project. The pattern matters.
If unrelated breakage becomes normal, the codebase probably has hidden coupling or duplicated logic.
2. The AI keeps fixing the same bug in different ways
A recurring loop looks like this:
- a feature breaks;
- you ask the AI to fix it;
- the fix works;
- a later prompt changes the same area;
- the old problem returns;
- the AI introduces another workaround.
At that point, more prompting may be adding patches without resolving the underlying model.
Stop asking only “how do I fix this error?” Ask “why can this class of error keep returning?”
That question often exposes the architectural debt.
3. Nobody can explain the important state transitions
For a serious product, someone should be able to explain what happens when a user:
- creates an account;
- joins or leaves a team;
- changes role;
- pays;
- receives access;
- cancels;
- requests a refund;
- deletes data;
- retries after a failed operation.
If the answer is effectively “the app somehow handles it,” the system is fragile even if today’s happy path works.
The more valuable the state, the less comfortable you should be with unexplained behavior.
4. Your database changes have no reliable history
This becomes especially important with Supabase-backed apps.
Supabase’s current documentation recommends tracking schema changes through versioned migrations and warns that direct changes to a remote production database can bypass migration history and create synchronization problems.
Source: https://supabase.com/docs/guides/deployment/database-migrations
A founder may see “add a column” as a tiny change. In production, schema changes are part of the software release process.
If your live database has been changed repeatedly through dashboards or ad-hoc SQL with no reproducible history, the problem is not aesthetic. You may be unable to recreate, test or safely evolve the system.
5. Critical workflows have no tests
You do not need thousands of tests because your MVP has real users.
You do need evidence around the workflows whose failure would hurt the business.
For many products that means:
- authentication and account recovery;
- user A cannot access user B’s private data;
- role changes actually change permissions;
- successful payment creates the correct access exactly once;
- failed payment does not create paid access;
- webhook retries do not duplicate orders or ledger entries;
- cancellation changes the correct state;
- the most important form or transaction survives expected failure cases.
A green interface is not a substitute for these checks.
If you are still preparing an AI-built product for real users, start with my AI-built app production checklist.
6. Production is the only realistic test environment
When every meaningful change must be tried against live data, the cost of experimentation rises sharply.
Supabase documents a workflow with separate development, staging and production environments, migrations and CI/CD so schema changes can be tested before they reach the live database.
Source: https://supabase.com/docs/guides/deployment/managing-environments
The exact tooling can vary. The principle should not: a risky change should have somewhere safe to fail before customers experience it.
7. The first developer spends more time discovering than changing
Developer handoff is a useful maintainability test.
A new engineer should need time to understand a product. But if basic questions require reverse-engineering the entire codebase, reading old AI chats or experimenting directly in production, you have created comprehension debt.
For Lovable projects specifically, my developer handoff guide covers the ownership and transfer side of that problem.
The most expensive debt is often in the data model
UI debt is visible. Data debt is quieter.
Imagine an MVP that originally had one user per business. Later, customers need teams.
If subscriptions, permissions, documents and billing records were all attached directly to the individual user because that was easiest in version one, adding teams is no longer “add a team screen.” The original ownership model is wrong for the new product.
You now have a decision:
- keep adding exceptions around the old model;
- migrate the data model deliberately;
- or rebuild the affected domain around the correct ownership boundary.
This is where product thinking matters as much as code quality.
Before changing tables, define the real entities and relationships. Who owns what? Which roles exist? Which records belong to a person, account, organisation or transaction? Which states are valid? Which history must remain auditable?
A clean answer here can remove more future debt than refactoring fifty React components.
Refactor, harden or rebuild?
Do not jump from “technical debt exists” to “rewrite the app.”
Almost every useful product has some debt.
Use the smallest intervention that restores control.
Refactor when the model is right but the implementation is messy
Good candidates include duplicated validation, repeated UI logic, oversized components, unclear naming and tangled service boundaries.
The behavior can remain the same while the internal structure becomes easier to change.
Harden when the product works but lacks production safeguards
This can include tests, monitoring, access-control review, backups, idempotency, rate limiting, error handling, staging and a disciplined deployment process.
The product concept may be sound. The operational safety around it is incomplete.
Rebuild a layer when its underlying model is wrong
A rebuild becomes more defensible when the problem is structural: the wrong ownership model, incompatible payment state, irrecoverable schema design, unsafe authorization architecture or a dependency that prevents the product from doing what the business now requires.
Even then, “rebuild” does not have to mean throwing away the whole product.
You can preserve the validated UX, content, customer knowledge and working modules while replacing the part that cannot evolve safely.
A practical maintainability audit for an AI-built MVP
Before paying for a rewrite, ask a developer to answer these questions from the actual repository and production architecture:
- Can we describe the core data model in plain English?
- Is every sensitive record tied to a clear owner or access rule?
- Are database changes reproducible from version-controlled migrations?
- Are secrets separated from public client configuration?
- Are the highest-value workflows covered by automated or repeatable tests?
- Can we reproduce a realistic environment without touching production?
- Do payment and webhook operations tolerate retries safely?
- Can errors be observed without waiting for a customer screenshot?
- Can a new developer understand how the main workflows fit together?
- Can we change one core feature and state exactly what needs regression testing?
The answers produce a much better decision than asking whether the code “looks clean.”
Do not confuse fast development with disposable development
AI-assisted development is valuable precisely because it lets a small team test ideas faster.
But once an experiment becomes a product, the objective changes.
The first objective was prove that this should exist.
The next objective is make it safe to keep changing.
That transition is where architecture, migrations, tests, ownership and operational discipline become valuable. They are not ceremony added to slow the builder down. They protect the speed you gained in the first place.
If your AI-built MVP is working but every new feature feels increasingly risky, I can help you assess the architecture, product state and production gaps before you commit to a rewrite. See my MVP development service or contact me with the current stack and the problem you are trying to solve next.
