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Vibe coding, especially when paired with AI tools, is reshaping how developers build software, but it comes with hidden costs that aren’t immediately obvious. At first glance, the speed and efficiency feel almost magical—features that could take weeks to develop can now be slapped together in an hour or two. However, this rapid pace creates a thick layer of abstraction that distances developers from truly understanding their own code. The real challenge isn’t just about getting things done quickly, but about internalizing the core fundamentals to avoid piling up what the author calls “Knowledge Debt.” This debt is like a hidden interest you’ll have to pay later as your shallow understanding starts to cause problems.
Understanding code isn’t black and white; it’s more like a spectrum. On the lowest level, you might know just enough to prompt an AI to write your code. A bit higher, you can fix errors when the AI stumbles. At the highest level, you can build everything from scratch without external help. The danger is mistaking the middle level for true mastery. AI often tricks you into thinking you’ve learned something deeply when really, it’s only surface-level. This shallow understanding fades quickly if you don’t struggle through concepts yourself, and using AI to bypass that struggle can actually harm your learning progress.
For junior engineers today, things are more complicated than ever. Before AI, the main focus was on learning fundamentals deeply. Now, they have to juggle two things: using AI tools effectively and knowing when to put those tools aside to focus on the basics. Developers have always needed to know when to deep dive and when to skim, but AI supercharges the temptation to stay shallow, because it makes skimming easier. This can lead to an endless loop of surface-level understanding that’s enough to ship features but not enough to solve deeper problems.
The author of the article experienced this firsthand while building LinkUp BU, an app developed with lots of help from AI coding assistants. The initial phase felt like wielding a superpower. Tasks that might’ve taken more than a year as a solo dev with no Flutter experience got done in a few months. But that convenience came with a massive Knowledge Debt. Over time, the app started breaking in subtle ways that AI couldn’t fix, forcing major refactoring. The AI’s early architectural choices were often flawed or dumb, leaving the author to bear the burden of fixing mistakes and understanding underlying issues. While the app works well today, the real learning happened after the build, when the author had to go back and pay off the debt by understanding the “why” behind decisions, not just the “how.”
Despite the AI’s help, this wasn’t a passive journey. The author still had to engage deeply with the app’s architecture to debug and ship it properly. But this AI-assisted learning felt a lot like cramming for an exam: just enough knowledge to get through, but not enough to retain long term. When asked to apply these skills in a deep technical interview, the author admits needing to revisit the basics to refresh knowledge. The high-level models stick around, making re-learning faster than for a true beginner, but the depth isn’t quite there yet.
In conclusion, coding with AI can sometimes feel hollow, turning development into a game of trying to outsmart a generic model instead of solving meaningful architectural puzzles. There’s no universally accepted approach yet for how junior developers should integrate AI into their workflow. What is clear, though, is that AI must be used intentionally, not because it’s the easiest option. The satisfaction of struggle, of working through tough problems yourself, remains essential for becoming a well-rounded engineer who understands not just how to build something, but why it’s built that way.
