from Hacker News

I’m a software developer and repeat founder who’s genuinely excited about what AI can do for our industry. In past projects, I’ve relied on AI coding assistants (e.g., GitHub Copilot, ChatGPT, Claude Code) to help me write boilerplate, generate suggestions, and speed up routine tasks. But every time I lean on these tools, I notice myself understanding concepts less deeply and becoming a bit “lazy” about fundamental problem-solving. To counter that, I’ve habitually limited my AI usage—kind of like how I allow myself only 30 minutes per day on Hacker News.

However, I’m now pulling together a new startup, and the stakes have changed. Speed-to-market isn’t just a fun challenge anymore; it’s a competitive necessity. If I don’t use AI to accelerate development, I risk falling behind. At the same time, I worry that over-reliance on AI will stunt my learning curve and leave me unable to debug tricky issues or refactor in a meaningful way. Has anyone else experienced this tension between “true learning” and “rapid launch”?

I’m not advocating for an “AI doom” mentality far from it. I want to embrace AI’s productivity gains, but I also want to make sure I’m still learning, able to debug, able to build robust architecture and improving as an engineer. If you’ve navigated this trade-off (particularly in a high-stakes startup environment), I’d love to hear: - What concrete strategies or workflows you put in place. - How you measure your own “proficiency” so you know you’re not losing the fundamentals. - Any pitfalls you ran into by either over-relying or under-relying on AI tools.

Thanks in advance for sharing your experiences and advice!