From Skeptic to Power User: My Shift on AI in Coding
If you had asked me a few years ago what I thought about AI in coding, I would have told you it was a shortcut to mediocrity. I believed that relying on a machine to write your logic would inevitably dull your problem-solving skills and turn you into a "copy-paste" developer. But my perspective took a 180-degree turn when I stepped into the high-velocity world of startups.
The Realization
AI isn't a replacement for the developer's brain; it's a force multiplier that handles the repetitive to let you focus on the creative.
The Skeptic Phase
Initially, I prided myself on writing every single line of code from scratch. I felt that using AI was somehow "cheating" the learning process. I worried that if I didn't struggle through every semicolon and boilerplate setup, I wouldn't understand the deep mechanics of how my applications worked. This mindset, while noble in intent, was actually a bottleneck to my growth.
The Startup Pivot
When I started working at Winedrops, the pace was unlike anything I had experienced. Features needed to be shipped yesterday. It was here that I was explicitly asked to utilize AI tools to speed up our cycles. By integrating tools like Cursor, v0, Claude, andAntigravity into my daily flow, I saw an immediate impact on my output. Reluctantly at first, then with increasing enthusiasm, I started using them for:
Boilerplate Generation
Setting up new components, API routes, or test files in seconds instead of minutes.
Function Scaffolding
Asking for the base logic of a complex utility and then refining it to fit the specific needs.
Explaining Legacy Code
Quickly getting the 'gist' of a complex function I didn't write, so I could modify it safely.
Refactoring Assistance
Finding more concise ways to write existing logic or suggesting modern syntax.
Using Antigravity and Cursor for deep-logic refactoring, andv0 for rapid UI component generation, redefined my role at Winedrops. These weren't just "helpers": they were the engines that allowed us to iterate at such a high frequency without compromising on code quality.
Multiplier, Not Replacement
The key realization was that AI doesn't replace the need for software engineering skills. It actually demands higher-level skills. You still need to know what to build, how it fits into the architecture, and most importantly, you need the skill to verify if the AI output is correct and secure.
I learned that being "fast" wasn't about typing quickly: it was about spending less time on the mundane and more time on the architectural decisions that truly matter.
Avoiding the "Skill Dull"
To ensure my skills don't dull, I treat AI as a junior partner. I always review what it generates, I never accept code I don't understand, and I still take the time to build things manually when I'm trying to master a brand new concept.
AI hasn't made me a lazy developer; it's made me a more productive one. It has allowed me to bridge the gap between idea and implementation faster than ever before, without losing the soul of the craft.