AI Pair Programming: The Good, The Bad, and The Future

Nov 20, 2024

After spending six months actively using AI coding assistants in my daily work, I've developed a nuanced view of their role in software development. These tools are neither a magic solution nor a threat to replace developers - they're powerful augmentations to our existing workflow.

Key observations:

1. AI excels at boilerplate code and repetitive tasks In my experience, AI assistants can reduce boilerplate coding time by 60-70%. For example, when setting up a new React component with TypeScript, what would normally take 10-15 minutes of typing interfaces, props, and basic structure takes just 2-3 minutes with AI assistance. Here's a typical time savings breakdown:

   Task               Manual    With AI    Savings
   Component Setup    15 min    3 min      80%
   Test Boilerplate  12 min    4 min      67%
   API Layer Code    20 min    7 min      65%

2. It's surprisingly good at explaining complex code patterns AI has become my go-to tool for understanding legacy code. Recently, I encountered a complex Redux-Saga flow that was poorly documented. The AI assistant broke down the code path, explained the async patterns, and even identified potential race conditions I hadn't noticed. It's like having a senior developer available 24/7 to walk through code with you.

3. The tools sometimes suggest outdated or insecure practices A critical caveat I've discovered is that AI models can suggest deprecated methods or security anti-patterns. In one instance, the AI suggested using the vulnerable eval() function for JSON parsing instead of JSON.parse(). Another time it recommended the outdated componentWillMount React lifecycle method. Always verify security-critical code against current best practices.

   Common AI Security Mistakes:

   • Using innerHTML instead of safer alternatives
   • Suggesting direct DOM manipulation in React
   • Missing input validation in API endpoints
   • Implementing weak password hashing

4. Human oversight remains crucial for architecture decisions While AI excels at implementation details, it struggles with bigger architectural decisions. When designing a new microservice architecture, the AI suggested a structure that would have created tight coupling between services. The human ability to consider business context, future scalability needs, and team dynamics remains irreplaceable.

   Architecture Decision Matrix:

   Decision Type        AI Reliability
   Implementation      ★★★★☆
   Code Structure      ★★★☆☆
   Design Patterns     ★★★☆☆
   System Architecture ★★☆☆☆
   Team Organization   ★☆☆☆☆

The most effective approach I've found is treating AI as a junior developer - one that can work incredibly fast but needs supervision and code review. It's particularly valuable for:

• Writing test cases
• Documenting code
• Refactoring repetitive patterns
• Exploring alternative implementations

The future likely holds even more sophisticated AI tools, but the core skills of problem-solving and system design remain fundamentally human.