The Debugging Divide: How LLM Tools Might Be Widening the Skills Gap
There's a traditional differentiator in software engineers between those who are great at debugging - and those who find it tough.
This is not a relative quality judgement - we've all seen developers who are perhaps not great at debugging produce fantastic work, innovative and creative. The work may have had a bug or two, but the general shape was good, and the ideas underneath it even better. I can think of multiple examples where this kind of work pushed everyone forward.
The Skills Behind Effective LLM Use
The more I use LLM tools, the more I notice that the skills I'm applying to produce a better output are grounded in debugging, and precision of thought. Iterative exploration and methodical improvement, with a strong "theory of the system" informing choices.
Where I see LLM based approaches fail hardest is when the vibes are strong and this theory of the system was absent or deemphasised. Fast forward all the things, auto-accept, little to no checks or tests. The mess it can make in this situation is truly ferocious.
To put this in context - I wonder if there's a correlation between people who find LLM/AI tools like Claude Code to be the literal devil and those who find debugging harder.
I get it - if I were faced with hundreds/thousands of LOC suddenly, that didn't work and was incomprehensible, then yeah - crap.
The Expert Paradox
The expert paradox here is strong - tools that are supposed to democratise access to development end up being the most powerful in the hands of people who already know what they want. But - explaining why is next to impossible.
It's not helpful to just say "you just need to get better at reading and debugging" when someone just wants something to work. It negates their ability to contribute novel or challenging ideas, things which push us all forward.
The Widening Gap
I worry the gap is only going to become more pronounced. It's a trend I'm keeping an eye on in my personal practice, to counter the imbalance where we might lose more than we gained.
The traditional learning path in software development has always been messy and indirect. Someone might start with terrible debugging skills but have incredible intuition for system architecture, or struggle with syntax but excel at understanding user needs. The gradual progression through increasingly complex problems gave different types of minds time to find their strengths and develop complementary skills.
But if the new gatekeeping mechanism becomes "can you immediately make sense of thousands of lines of generated code?" then we're selecting for a much narrower cognitive profile. We risk losing the people who think in pictures, who need to build understanding through experimentation, or who process information more slowly but more thoroughly.
Some of the most valuable contributors to software have been those who thought differently about problems, who saw patterns others missed, who asked uncomfortable questions that led to better designs. If we accidentally optimise them out of the pipeline, we lose not just individual talent but cognitive diversity - exactly the kinds of minds we need most for complex systems challenges ahead.