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One thing that became very common after the AI boom was a lot of clients arriving with apps that were already built using AI coding tools. When we audited some of these apps, we saw recurring production issues.
AI-assisted development and generative AI tools are great at speeding up output. However, the problem with these tools is that engineering still determines production reliability. With AI systems, you can easily generate working screens and data flows. Unfortunately, you’ll begin to see several weaknesses and issues when real load, real data, and real users are introduced.
Teams that rely on generative AI produce apps quickly. After all, AI technology writes the code, AI generation creates the UI flows, and AI outputs fill entire repositories. This speed of production is why there is a shift towards the use of AI. Yet, this doesn’t guarantee the production success of a product.
Therefore, in this article, we’ll discuss the issues and risks of AI-generated apps you need to know as a founder. Bear in mind that the findings we’ll share are based on real audits and not just theory. We found these same issues across every codebase we checked.
What Counts as an Issue in Production
Even though a working demo is a milestone that needs to be celebrated, that doesn’t mean you’ve got a working product. When we talk about production success, we measure it by reliability, scalability, data integrity, and cost under real usage.
You see, it’s easy for a working demo app to load one record at a time for five test accounts. But when you take that same app and introduce a thousand users at once, it can get overwhelmed and melt. This is why a demo app is not always capable of revealing major issues.
As a matter of fact, a lot of impressive demos hide incorrect AI language models or single-model assumptions. It’s common for large language models to generate code that compiles. The issue with these models is that they need to be fed missing pieces or undocumented behavior to generate excellent results.
Another common issue is AI hallucinations. These often appear when the system goes ahead to invent unavailable methods or libraries. Yes, this might not break a demo, but it will definitely affect production deployment once it becomes recurring.
Once such an app enters production and users begin to input massive data, these issues surface. Weak assumptions around state, retries, and concurrency all turn into support tickets, outages, and rollback cycles. As any founder knows, these are not good for business.
What We Found Repeatedly
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We audited several AI-generated apps from different founders, domains, and stacks. From these audits, we discovered patterns of structural failures that were almost identical. These findings are not edge cases or isolated issues. Instead, they reflect consistent outcomes when generative AI is used to code apps without proper engineering guardrails.
No Real Architecture
One app that we audited that really revealed architecture issues was the Kids’ Storytelling App. This app was developed to deliver a single massive class in just one screen that mixed UI, logic, and data.
There were no layers, no separation of concerns, nothing like dependency injection or stable contracts. It was just a lump of features together. Since everything was tightly coupled, if you made any change, you ran the risk of breaking unrelated features.
While this app was the perfect example of architectural issues, we found the same issue with other apps. A lot of them had UI and business logic mixed together with data access right inside the same file.
Scalability Problems
When we audited the Reader App, we saw a perfect example of scalability problems. This app had one write per record instead of batching, which caused performance to collapse under minimal load.
Work was done per record with no sort of caching or batching. We also found the concurrency handling to be weak. As a result, when there is a large increase in load, it causes a request timeout and a throughput collapse.
AI-generated code doesn’t pay much attention to batching, async workers, queues, caching, and backpressure. It often defaults to per-record operations without reasoning about scale. The result is scalability issues that appear as soon as there is an increase in load.
Production Unreadiness
Neither of the audited apps came with logs, metrics, or traces. That meant that no one could answer simple questions like: What happened before the crash? How many users were affected? Has it happened before?
When a program has no observability:
- Every bug is a mystery.
- Repairs depend on “try something and release again.”
- Incidents take longer because no one knows what changed.
We also noticed that the SLIs and SLOs were not defined. This meant there was no target for reliability. In addition, none of the apps had load tests or chaos drills. As a result, the first time they experienced real traffic was during launch. Basically, the systems were shipped blind with no supporting evidence.
Data Layer Mistakes
We noticed the Reader App’s data model was not consistent across screens and per-record writes.
Here are the issues that we found repeatedly:
- No idempotent writes: So, whenever the network retried a request, records were duplicated.
- Retries without backoff: As a result of rapid-fire retries on the database, we noticed cascading timeouts.
- No pagination: When a query demands heavy work like pulling thousands of records at once, the UI might freeze.
- No connection pooling: A new connection had to be opened every time a request was made. This, in turn, exhausted limits under load.
Security Gaps
What we categorize as small AI-generated apps were not spared from security issues. A lot of these apps leaked secrets into code across multiple internal audits.
This wasn’t malicious in nature. Instead, it was the default result of AI-generated code where inline strings are treated as credentials.
Some of the common issues we saw:
- Weak authentication and validation that only checked client-side.
- No threat modeling, so no one asked, “What happens if someone abuses this?”
- No SAST or DAST in CI, which leads to undetected vulnerabilities.
Founders often think that small apps bear low risks. This is not the case, as these apps are often the ones that get breached first due to missing guardrails.
Technical Debt From Day One
Another thing we discovered in the Kids’ Storytelling App was that fixing one bug broke three other features. This was because the same logic was copied and pasted across files. And since there were no tests, no one knew the features that were safe to modify.
Domain Logic Missing or Wrong
The Reader app lacked domain logic, which made its main feature, text selection, next to impossible. Since there was no rendering engine, UI elements were positioned by guesswork. Whenever there is a change to the font scale or line wrapping, selections break.
This problem happens mainly because AI-generated code doesn’t model the domain but assumes it.
API and Contract Drift
The DTOs for both apps were AI-generated, and this caused a problem where the fields changed between screens. All of this happened without the backend knowing. This, in turn, broke compatibility.
Patterns we observed:
- No API versioning on endpoints
- Changes deployed without notice
- Backward compatibility unmaintained
State and Sync Issues
When we audited the Kids’ App, background tasks were racing each other. In other words, they were written at the same time to the same data. This led to unpredictable state changes.
Sometimes, updates vanished. At other times, duplicates appeared. Worst of all is that there were no timestamps to resolve these conflicts.
Most of these issues can be attributed to a lack of an offline-first plan, among other reasons.
Build and Release Hygiene
Neither project had CI gates. As such, they were released by manually building the app on a developer’s machine and uploading it.
This led to:
- Hardcoded configs across environments
- No review gates
- No artifact tracking
Performance Pitfalls
In the Reader App, every item triggered a network call due to N+1 calls.
We also saw:
- Heavy parsing on the main thread
- Image lists loaded without limits
- Memory leaks
- Repeated queries for the same data
The performance issue was a design flaw rather than a tuning issue. Moving heavy work off the main thread and batching calls should be baseline, not advanced tuning.
Rewrite vs Refactor
Bug fixes don’t fix structural failure. So, refactor only when:
- A viable core exists
- Modules can be isolated
- Tests already exist
- API contracts are versioned
Rebuild when:
- Concerns are mixed
- DTOs drift from release to release
- No tests exist
- Every change has a large blast radius
For both the Kids’ App and the Reader App, isolation wasn’t possible. The cost of refactoring exceeded the cost of rebuilding because reaching a safe deployment kept getting farther away, not closer. So, both were rebuilt.
What We Fix First After an Audit
Here’s a breakdown of our actions after a typical audit:
- Architecture backbone: Layers, DI, config, versioned contracts
- Scale patterns: Batching, caching, queues, async workers, backpressure
- Data integrity: Idempotent writes, pagination, pooling, migrations
- Observability and testing: Logs, metrics, traces, SLIs, SLOs, load tests, test pyramid
- Security and governance: Threat modeling, SAST, DAST, SBOM, secrets management, review gates in CI
All these steps make sure we have our bases covered.
What This Means for Founders
AI can produce code. It doesn’t produce a product. If you already shipped an AI-generated app, assume an audit will surface architecture gaps, scale risks, and security holes.
Fix the backbone first before adding features. If the core is missing, rebuild instead of patching. Consider rebuilding is often taking less time comparing to refactoring (using AI-assisted coding). Strong engineering still defines whether a product lives or dies in production.
