Working in big tech is a unique blend of exhilarating problem-solving and relentless pressure to innovate. Over the years, tools promising to revolutionize software engineering have come and gone. When AI-based coding assistants like GitHub Copilot, ChatGPT, and others entered the scene, they were heralded as game-changers, capable of saving time, reducing errors, and accelerating development cycles.
As a Senior Software Engineer, I eagerly integrated these tools into my workflow. Initially, I experienced promising results: faster code generation, automation of repetitive tasks, and reduced debugging times. However, over time, I began noticing subtle yet significant drawbacks, which ultimately led me to stop using AI in my day-to-day development.
The Honeymoon Phase: AI at Its Best
When I first started using AI tools, they felt like a superpower. I could describe complex functionality in plain English, and the AI would provide concise, functional code within seconds. Repetitive tasks were automated, and I found myself saving hours of effort every week.
Example: API Development Made Easy
While building an internal tool for a large-scale data pipeline, I relied on an AI assistant to scaffold API endpoints in…
