The conversation around AI in software development continues to evolve, and I've recently experienced some breakthroughs that have fundamentally changed how I view these tools. The key insight? AI serves as a helper, not a replacement – and this distinction matters more than we might think.
Tools Define Our Trade
Throughout my career across different industries, including construction, I've learned that tools are everything. Yet developers often have strong, sometimes puzzling preferences. Why do some developers choose VS Code for PHP development when PHPStorm offers comprehensive, purpose-built functionality? The answer lies not in the tool itself, but in how we choose to leverage it.
Reducing Development Arrogance
One of the most unexpected benefits I've discovered in working with AI tools is how they've reduced my tendency toward development arrogance. As a contractor, I've encountered numerous approaches to writing code:
- The general framework-specific best practices
- Internet-wide accepted standards
- Internal company style guides (often defended with "that's how we've always done it")
- My own preferred methods
Previously, I might have stubbornly advocated for my approach. Now, AI helps me evaluate these different perspectives objectively. This shift is particularly valuable when working on greenfield projects or MVPs, where I've learned to view everything as potential technical debt until it reaches production.
The Power of Rapid Iteration
AI has transformed my development workflow by enabling rapid scenario testing. I can now:
- Compare different approaches and their trade-offs
- Analyze package documentation against framework requirements
- Evaluate compatibility issues across different components
- Consider multiple implementation strategies simultaneously
What previously took a week of research and experimentation can now be accomplished in hours. This isn't about cutting corners – it's about exploring more possibilities and making better-informed decisions.
Ownership and Responsibility
Here's a crucial point: when you use AI to help write code, it remains your code. You are responsible for it. Others will review it as if you wrote every line yourself – because ultimately, you did. AI is simply another tool in your toolkit, similar to linters, static analysis tools, or any other development aid.
This perspective drives home the importance of:
- Implementing comprehensive testing
- Ensuring proper test coverage
- Verifying that tests actually validate functionality
- Maintaining code quality standards
Beyond Copy-Paste Development
Using AI effectively isn't about laziness. Developers who use AI poorly are often the same ones who blindly copied from Stack Overflow without understanding the code. The tool may have changed, but the underlying methodology and thought process should remain rigorous and thoughtful.
Learning Through Application
Perhaps most surprisingly, I've learned more in recent months using AI tools than in years of traditional development. Working on unique, complex projects – including replicating legacy applications with their quirks intact – has pushed me to understand not just how to code, but why certain approaches work better than others.
The ability to quickly generate multiple test cases, explore edge conditions, and consider alternative implementations has made me a more thorough and thoughtful developer. When replicating legacy systems, AI helps identify potential bugs that might actually be features, allowing me to document and preserve intentional behaviors while building in flexibility for future corrections.
Conclusion
AI in development isn't about replacing human judgment or creativity. It's about amplifying our capabilities, reducing cognitive load, and enabling us to focus on what matters most: solving problems effectively. By embracing these tools while maintaining our professional standards and responsibility, we can create better software faster – without sacrificing quality or understanding.
The key is remembering that AI assists us in writing code; it doesn't write code for us. This distinction shapes how we approach development, how we maintain quality, and ultimately, how we grow as professionals in an evolving technological landscape.
Top comments (0)