The AI Tool Paradox

I signed up for Cursor yesterday. It's impressive how easy it is to deploy code with AI tools now. A decade ago, the limiting factor in building software was often writing the code itself. Now it's something else entirely.

What's interesting about tools like Cursor is that they solve problems we've always had while simultaneously creating new ones. The deployment friction is gone. But debugging can become oddly circular. The AI sometimes removes code you actually wanted to keep or fails to identify the true source of a problem.

This creates a strange paradox. You save time on the creative aspects of coding but can lose it in these new debugging dead ends. I've found myself feeling lethargic about working on my basic blog project. The initial enthusiasm has worn off, replaced by a low-grade frustration with these circular debugging sessions.

The solution isn't abandoning AI tools. Rather, it's getting better at using them. Prompting is becoming a genuine skill—one that most programmers didn't need until recently. Learning to communicate precisely with these systems is surprisingly nuanced. It's not unlike learning to work with a new junior developer who's brilliant in some ways but has peculiar blind spots.

What I need now isn't to abandon the blog but to supplement it with something fresh—another project to reignite my enthusiasm for the week. And ironically, I'll be using AI tools for that too. Because despite the frustrations, they're still incredibly powerful for rapid prototyping and exploration.

The rhythm of software development is changing. The cycles of excitement and lethargy happen faster. The tools accelerate everything—both the progress and the problems. The key is learning to ride these waves rather than being overwhelmed by them.

I suspect the best developers of the coming years won't necessarily be those who memorize the most algorithms or language features, but those who become fluent in this new kind of collaboration.