Code reviews have always been a critical part of professional software development. In mature teams, they are not just about catching bugs. They are about knowledge sharing, enforcing standards, improving design quality, and reducing long-term maintenance cost.
With the rise of AI-generated code, the nature of code reviews is changing significantly. Reviewers are no longer only evaluating code written by humans. They are now reviewing code written with AI or sometimes entirely by AI. This shift affects how reviews are conducted, what reviewers should focus on, and what skills matter most.
This article explains how AI-generated code is changing code reviews, with a strong focus on Angular and frontend enterprise applications. It is written for senior developers, tech leads, and architects who are responsible for maintaining quality in large codebases.
No hype. No fear-mongering. Only practical realities and best practices.
1. Traditional Code Reviews: What They Were Optimised For
Before understanding what has changed, it is important to understand what code reviews were originally designed for.
Core Goals of Traditional Code Reviews
In most Angular teams, code reviews traditionally focused on:
Correctness of business logic
Code readability and clarity
Adherence to Angular best practices
Consistency with project architecture
Performance and memory concerns
Security and error handling
Test coverage and quality
Reviewers assumed:
The author understood the code deeply
The author made conscious design decisions
The code reflected the author’s skill level
This assumption no longer always holds true with AI-generated code.
2. What AI-Generated Code Actually Looks Like
AI-generated code is not bad by default. In fact, it is often:
Syntactically correct
Well formatted
Cleanly structured
Consistent in style
But it has unique characteristics that affect reviews.
Common Traits of AI-Generated Angular Code
Over-verbosity
Code tends to be longer than necessary.
Generic Patterns
Uses common examples rather than project-specific conventions.
Over-engineering
Introduces abstractions that are not needed.
Superficial Correctness
Works for the happy path but ignores edge cases.
Weak Context Awareness
Misses business rules, domain nuances, and historical decisions.
A senior reviewer must recognise these patterns quickly.
3. The Biggest Shift: Reviews Are Now About Intent, Not Just Code
With AI-generated code, the most important question in a review is no longer:
“Does this code work?”
It is now:
“Why does this code exist in this form?”
Example: Angular Service Layer
AI might generate a service like this:
@Injectable({
providedIn: 'root'
})
export class UserService {
constructor(private http: HttpClient) {}
getUsers(): Observable<User[]> {
return this.http.get<User[]>('/api/users');
}
}
This code is correct. But reviewers must ask:
Should this be provided in root?
Should it go through a facade?
Is error handling missing?
Are retries needed?
Should caching be applied?
AI does not ask these questions. Reviewers must.
4. Code Reviews Are Becoming Architecture Reviews
AI can generate code faster than humans, but it does not understand:
Why the architecture exists
Which trade-offs were made
What future changes are expected
Impact on Angular Applications
In large Angular applications:
Feature modules exist for isolation
Shared modules are carefully controlled
State management is intentionally designed
Performance decisions are deliberate
AI-generated code may violate these silently.
Example: Accidental Architecture Drift
AI might:
Import shared modules directly into lazy-loaded modules
Bypass facades and access services directly
Duplicate state instead of reusing selectors
Each change looks small. Over time, architecture erodes.
Reviewer Responsibility
Code reviews must now:
Enforce architectural boundaries
Detect subtle violations early
Reject “working” code that damages structure
5. Increased Volume, Reduced Signal
AI increases development speed. This leads to:
More pull requests
Larger diffs
Faster iteration cycles
The Review Problem
Reviewers face:
AI-generated code often looks “clean”, which creates a false sense of safety.
Best Practice
Reduce PR size aggressively
Enforce smaller, focused changes
Reject large AI-generated dumps
Require clear PR descriptions explaining intent
Speed must not compromise understanding.
6. Reviewing Code the Author May Not Fully Understand
This is one of the biggest cultural changes.
In the past:
With AI:
Authors may not fully understand generated code
Explanations may be shallow
Ownership becomes unclear
New Rule for Reviews
If the author cannot explain:
Then the code should not be merged.
AI assistance does not remove accountability.
7. AI and Boilerplate: What Reviews Can Safely Ignore
Not everything requires deep scrutiny.
Low-Risk Areas
AI-generated code is usually safe for:
High-Risk Areas
Review carefully:
State flow logic
Authentication and authorisation
Error handling
Performance-sensitive components
Change detection strategies
RxJS streams and subscriptions
Senior reviewers must prioritise attention.
8. AI-Generated RxJS Code: A Major Review Hotspot
RxJS is an area where AI often struggles.
Common Problems
AI-generated RxJS code often:
Overuses switchMap
Misses takeUntil or cleanup
Nests observables unnecessarily
Ignores cancellation behaviour
Creates hard-to-debug streams
Example Problem
this.userService.getUsers().subscribe(users => {
this.users = users;
});
AI may omit:
Error handling
Unsubscription
Async pipe usage
Review Checklist for RxJS
Is subscription lifecycle handled?
Can async pipe be used instead?
Are operators correctly chosen?
Are side effects isolated?
RxJS mistakes can cause serious production issues.
9. Testing Code Reviews Are Changing Too
AI can generate tests quickly, but test quality varies.
Common AI-Generated Test Issues
Angular Testing Example
AI may generate:
it('should create', () => {
expect(component).toBeTruthy();
});
This adds almost no value.
New Review Focus
Reviewers must ask:
Does this test validate behaviour?
Would it catch a real regression?
Is it testing implementation or outcome?
Test quantity is meaningless without intent.
10. Code Reviews Are Becoming More About Deletion
One surprising effect of AI is code bloat.
AI tends to:
Senior Reviewers Must Be Comfortable Saying:
Removing AI-generated excess is now part of the job.
11. Security Reviews Matter More Than Ever
AI-generated code can introduce subtle security issues.
Angular-Specific Risks
Unsafe DOM manipulation
Improper sanitisation
Token leakage
Hardcoded URLs
Weak error handling
AI may produce code that works but violates security policies.
Reviewer Responsibility
Security cannot be delegated to AI.
12. Changing Reviewer Skill Sets
The skills needed for good reviews are shifting.
Less Important Than Before
Syntax checking
Formatting feedback
Simple lint issues
More Important Than Ever
AI raises the bar for reviewers.
13. How Angular Teams Should Adapt Their Review Process
Practical Changes
Update review guidelines to mention AI usage
Require authors to disclose AI-generated code
Emphasise intent and design explanation
Strengthen architectural checks
Encourage deletion and simplification
Tooling Adjustments
Strong lint rules
Automated formatting
Static analysis
Performance monitoring
Automation should catch basics so humans focus on thinking.
14. Cultural Changes in Teams
AI changes team dynamics.
Healthy Practices
Dangerous Practices
Auto-approving AI-generated code
Skipping reviews due to “confidence”
Treating AI as a senior engineer
Culture matters more than tools.
15. The Future of Code Reviews in an AI World
Code reviews will not disappear. They will evolve.
What Will Increase
Design discussions
Architecture validation
Cross-team alignment
Code ownership clarity
What Will Decrease
Reviews will become more valuable, not less.
Conclusion
AI-generated code is fundamentally changing code reviews. Not by making them obsolete, but by shifting their focus.
For Angular teams, the role of code reviews is now:
Senior developers and reviewers are now the last line of defence against architectural erosion, hidden complexity, and silent risk.
AI writes code faster than humans.
Humans must ensure that code deserves to exist.