Should I Be Smarter Than AI Code Assistants Like Cursor/Copilot/Qodo etc?

4 min read7 hours ago

AI-powered code assistants like Cursor, GitHub Copilot, and Tabnine are transforming how developers write code, debug issues, and optimize performance. But as a Staff Software Engineer, should you rely on them blindly, or should you aim to be more knowledgeable than the AI?

The short answer: AI should assist you, not replace your expertise. Your role is to use these tools strategically while maintaining a deep understanding of system design, performance optimization, and security best practices.

1. AI as an Accelerator, Not a Decision-Maker

AI code assistants are great for speeding up development, but they lack context and deep understanding of your system’s unique requirements.

Example: Generating Boilerplate Code

AI tools can quickly generate Spring Boot controller classes, DTOs, or API clients. Instead of writing repetitive code, you can let AI handle it:

AI-generated snippet:

@RestController
@RequestMapping("/users")
public class UserController {
    private final UserService userService;
    
    public UserController(UserService userService) {
        this.userService = userService;
    }
    
    @GetMapping("/{id}")
    public ResponseEntity<User> getUserById(@PathVariable Long id) {
        return ResponseEntity.ok(userService.getUserById(id));
    }
}

✅ Use AI for: Repetitive and standard tasks like creating controllers, DTOs, entity mappings, and REST API endpoints.

❌ Don’t rely on AI for: Business logic decisions, security configurations, and performance optimizations.

2. Validate AI Suggestions Against Best Practices

AI generates code based on patterns from existing open-source repositories and documentation. However, it may not always align with best practices for performance, security, or scalability.

Example: String Concatenation in Loops

An AI might generate inefficient code like this:

String result = "";
for (String word : words) {
    result += word + " ";
}

This is problematic because string concatenation in a loop leads to excessive memory allocation. Instead, a more efficient approach is:

StringBuilder result = new StringBuilder();
for (String word : words) {
    result.append(word).append(" ");
}

Lesson: Always cross-check AI-generated code against Java performance best practices.

3. Use AI for Repetitive Tasks, But Own the Complex Decisions

AI is excellent at writing unit tests, generating documentation, and handling simple refactoring. However, it cannot make critical architectural decisions.

Example: Choosing Between Monorepo vs. Microservices

AI might suggest either approach, but it lacks the deep understanding of your business needs and scalability requirements.

Microservices: Good for independent scaling, but complex in terms of deployment and inter-service communication.
Monorepo: Simplifies code sharing but may introduce versioning challenges.

Your experience is crucial in making these decisions based on factors like team size, deployment strategies, and maintainability.

4. AI Should Enhance Debugging & Optimization

AI assistants can help debug issues by suggesting fixes, but they lack real-time insights into production systems.

Example: Debugging a Memory Leak in Spring Boot

AI might suggest increasing the heap size:

JAVA_OPTS: "-Xmx4G"

However, the real issue could be unreleased database connections or inefficient caching. Using New Relic or JProfiler gives better insights than just relying on AI-generated fixes.

✅ Use AI to: Suggest fixes, generate queries, and provide debugging hints. ❌ Rely on real APM tools like New Relic, Grafana, and Prometheus to diagnose performance bottlenecks.

5. Stay Ahead of AI by Learning Emerging Trends

AI models are trained on past data, meaning they might not be aware of the latest advancements like Structured Concurrency in Java 21 or AI-powered API security.

Example: Using Structured Concurrency for Improved Performance

If you ask AI to write concurrent code, it might suggest traditional thread management:

ExecutorService executor = Executors.newFixedThreadPool(5);
Future<Integer> future = executor.submit(() -> compute());

However, Java 21 introduces structured concurrency(although in preview state), which is more efficient:

try (var scope = new StructuredTaskScope.ShutdownOnFailure()) {
    Future<Integer> future = scope.fork(() -> compute());
    scope.join();
    return future.resultNow();
}

Lesson: AI might not always suggest the latest Java features — stay updated and guide it accordingly.

Final Thoughts: Be Smarter Than AI

Use AI as a productivity booster, not a replacement for critical thinking.
Cross-check AI-generated code against best practices for security and performance.
Own architectural and business logic decisions — AI doesn’t know your system like you do.
AI is great for debugging, but real APM tools give deeper insights.
Stay ahead of AI by learning and applying the latest technologies.

🔹 Your expertise will always be more valuable than AI-generated code. The best engineers are those who leverage AI effectively while applying deep technical understanding to make informed decisions.

What Do You Think?

How do you use AI code assistants in your workflow? Let’s discuss in the comments! 🚀