AI-Generated Code Quality: What Founders Need to Know

AI-Generated Code Quality: The Numbers Behind the Hype

The conversation around AI-generated code quality tends to polarize. AI enthusiasts show demos of working applications built in seconds. Skeptics share examples of catastrophically broken code. Neither view captures the full picture.

As an agency that uses AI tools daily alongside traditional development, we have a practical perspective: AI-generated code is neither uniformly good nor uniformly bad. Its quality depends on the task, the tool, the prompt, and -- critically -- whether a human reviews the output before it reaches production.

This article presents what we have observed across hundreds of AI-generated codebases, backed by specific quality benchmarks. If you are a founder considering using AI to build your product, these are the facts you need to make an informed decision.

Quality Benchmarks: How AI Code Measures Up

We analyzed AI-generated code across four dimensions that matter most for production software: correctness, security, maintainability, and performance. Here is what we found:

Correctness

AI-generated code works correctly for standard patterns approximately 70-85% of the time. That sounds good until you consider what "standard patterns" means and what happens in the remaining 15-30%.

The pattern is consistent: the more common and well-documented the task, the better the output. The more specific or unusual the requirement, the more likely the AI produces code that appears to work but contains subtle logical errors.

Security

Security is where AI-generated code quality drops most dramatically. AI optimizes for functionality, not adversarial resistance.

Common security vulnerabilities we find in AI-generated code:

• Missing server-side validation (found in ~60% of AI-generated backends) -- Frontend validation exists but can be bypassed
• Improper error messages (found in ~55%) -- Stack traces, database details, or internal paths exposed to users
• Insecure direct object references (found in ~50%) -- No authorization check on individual resources
• Hardcoded secrets (found in ~35%) -- API keys, database credentials, or encryption keys in source code
• SQL injection vulnerabilities (found in ~30%) -- Dynamic query construction without parameterization
• Missing rate limiting (found in ~70%) -- APIs open to brute-force attacks
• Weak session management (found in ~45%) -- Missing session expiry, no token rotation, insecure cookie flags

These are not obscure edge cases. They are the OWASP Top 10 -- the most common and most exploited vulnerabilities in web applications. AI tools consistently fail to implement protections against them unless specifically and repeatedly prompted.

Maintainability

Maintainability determines how expensive your software will be to modify and extend over time. AI-generated code scores poorly here because the AI has no concept of your codebase's future.

Code quality metrics we typically see:

The result is code that works today but becomes increasingly expensive to change. Every feature addition requires more time because developers must understand and navigate inconsistent patterns.

Performance

Performance in AI-generated code is generally acceptable for low traffic but degrades under real-world conditions:

• Database queries -- AI generates queries that work correctly but are rarely optimized. Missing indexes, N+1 query patterns, and full table scans are common
• Memory management -- Event listeners that are never cleaned up, large objects held in memory unnecessarily, and growing in-memory caches without eviction
• API response sizes -- Returning entire database records when the client only needs three fields
• No caching -- Every identical request triggers the same expensive computation or database query

The Security Problem in Detail

Security deserves deeper examination because it represents the highest-risk gap in AI-generated code. Let us walk through a realistic scenario.

A Real-World Example

A founder uses AI to build a project management application. The AI generates user authentication, project creation, task management, and team collaboration features. Everything works in testing.

Here are the security issues that a professional audit would likely uncover:

Issue 1: Broken Access Control

The AI generates an API endpoint to fetch project details:

GET /api/projects/:id

The endpoint checks if the user is authenticated (logged in) but does not check if the authenticated user has access to the requested project. Any logged-in user can view any project by guessing or iterating through IDs.

Issue 2: Mass Assignment

The user update endpoint accepts whatever fields the client sends and passes them directly to the database update operation. An attacker can add "role": "admin" to a profile update request and escalate their permissions.

Issue 3: Information Leakage

Error responses include database query details, internal file paths, and stack traces. An attacker uses these to map the application's internal structure and identify further vulnerabilities.

Issue 4: Missing Input Validation

File upload accepts any file type and size. An attacker uploads a malicious script disguised as an image, which gets served to other users.

None of these issues are visible during normal usage. The app works perfectly for legitimate users. But any moderately skilled attacker would find and exploit these within hours of looking.

Technical Debt: The Hidden Cost

Technical debt is the accumulated cost of shortcuts in your codebase. Every shortcut makes future changes harder and more expensive. AI-generated code accumulates technical debt at an accelerated rate because the AI consistently takes the fastest path rather than the most sustainable one.

How Technical Debt Compounds

The cost of technical debt is not linear -- it is exponential. Each layer of hastily written code makes the next layer harder to add. This is why vibe-coded applications often hit a wall around month 6-9 where progress effectively stalls.

Specific Debt Patterns We See

1. Copy-Paste Architecture

AI frequently solves similar problems differently in different parts of the codebase. Instead of creating a shared utility for date formatting, it writes the formatting logic inline everywhere it is needed. When the format needs to change, you have to find and update every instance.

2. Over-Reliance on Dependencies

AI tends to install an npm package for every small task. We have seen AI-generated projects with 200+ direct dependencies for simple applications. Each dependency is a potential security vulnerability and a maintenance obligation when it needs updating.

3. No Error Boundaries

When one component fails, the entire application crashes. Professional code isolates failures so a bug in the notification system does not take down the checkout flow.

4. Implicit Assumptions

AI-generated code makes assumptions about data formats, timezone configurations, locale settings, and environment variables that are never documented. These assumptions create time bombs that explode when the deployment environment differs from the development environment.

Testing Coverage: The False Confidence Problem

AI can generate tests, which sounds like a solution. But AI-generated tests have a specific quality problem: they test what the code does, not what the code should do.

Example:

The AI writes a function that calculates a discount. Due to a logic error, it applies the discount twice for orders over $100. The AI then generates a test that confirms the function returns the (incorrect) doubled discount. The test passes. The code is wrong. The test just confirms the wrong behavior.

What Good Testing Looks Like

The testing gap is particularly dangerous because passing tests create false confidence. A codebase with 80% test coverage but only happy-path tests is not well-tested. It is well-measured.

The Code Review Imperative

Given these quality issues, code review is more important for AI-generated code than for human-written code. This is counterintuitive -- you might expect AI code to need less review because it follows patterns consistently. But the consistency is precisely the problem. AI consistently makes the same categories of mistakes, and those mistakes are invisible to someone who does not know what to look for.

What Professional Code Review Catches

A senior engineer reviewing AI-generated code checks for:

1. Security vulnerabilities -- Authorization checks, input validation, secrets management
2. Logic errors -- Off-by-one errors, incorrect conditions, missing edge cases
3. Architecture problems -- Tight coupling, missing abstractions, scalability blockers
4. Performance issues -- Unoptimized queries, memory leaks, missing caching
5. Dependency assessment -- Are all dependencies necessary, maintained, and secure?
6. Test adequacy -- Do tests actually verify correct behavior or just confirm existing behavior?

At Soatech, every line of AI-generated code goes through the same review process as human-written code. This is not optional and is a core part of how we use AI in development.

Practical Recommendations for Founders

If You Are Using Vibe Coding Tools Directly

• Never deploy AI-generated code without a security review -- Even a basic scan with tools like Snyk or SonarQube catches common issues
• Assume the code has bugs -- Test with unexpected inputs, empty fields, special characters, and large data volumes
• Do not store sensitive data in vibe-coded applications until a professional has reviewed the security model
• Budget for a professional code audit if the prototype becomes a real product -- $2,000-5,000 for a thorough review can save $50,000+ in breach costs

If You Are Hiring a Team

• Ask how they use AI -- Good teams use AI to accelerate boilerplate and review every line. Bad teams ship AI output directly
• Request test coverage reports -- Not just the number, but what types of tests are included
• Ask about security practices -- OWASP alignment, dependency auditing, and penetration testing should be standard
• Verify code quality processes -- Code review, linting, and architectural standards

If You Are Evaluating Code Quality

Use our project calculator to estimate what professional development with proper quality controls would cost for your specific project. Often, founders discover that the cost difference between "cheap and risky" and "professional and secure" is smaller than they expected, especially when you account for the cost of fixing quality issues later.

The Bottom Line

AI-generated code quality is good enough for prototypes and internal tools where security, performance, and maintainability are low-priority concerns. It is not reliable enough for production applications that handle customer data, process payments, or need to grow over time.

The solution is not to avoid AI -- it is to pair AI with experienced human engineers who catch the mistakes that AI consistently makes. This combination produces better software faster than either approach alone, which is exactly how the best development teams work in 2026.

Concerned about the quality of your codebase? Talk to our team -- we offer code audits that identify security vulnerabilities, performance issues, and technical debt, with a clear remediation plan.