AI coding tools are spreading through software companies faster than any previous technology. Startups are embracing them for “productivity”, teams are embedding them in every IDE, and developers are boasting about how much code they can generate in an afternoon.
It feels like progress. Is it?
The truth is, using AI to write code is a lot like paying with a credit card. You sometimes get what you want instantly, but you’re paying with understanding. Instead of having an organic accumulation of technical debt that needs to be kept in check, you’re manually requesting it.
Disclaimer: This post is not intended to discourage the use of AI. Every tool has its place. The point of view here is to encourage thinking twice before enforcing the all-encompassing use of AI tools in software development. Even this article has been adjusted and grammar-checked using an LLM. But more discussion is needed to highlight why we as programmers – and humans – still need to keep our critical thinking faculties around.
The Transaction: Borrowed Understanding
When AI writes code for you, it isn’t helping you understand the problem. It’s skipping that step entirely. You’re effectively outsourcing reasoning. The code may work, but the comprehension gap it leaves behind is enormous.
Developers talk about technical debt all the time: legacy systems, messy abstractions, bad or unwanted dependencies. But there’s a new kind of debt AI introduces that’s harder to see: cognitive debt, the loss of shared understanding that once held teams together.
In traditional coding, every decision, even the wrong ones, taught something. AI short-circuits the learning curve. It gives you an answer – sometimes right, sometimes silently wrong – without forcing you to understand the question. You may be saving some time upfront, but you’re not accelerating.
The Interest Rate: Degradation of Culture
AI-generated chunks don’t just bloat your code base, it also quietly reshapes your engineering culture.
When developers stop thinking deeply about how code works, reviews become superficial. Design discussions thin out. Debugging turns into archaeology. Teams stop cultivating judgment because the tools are always ready to fill the silence with “something that compiles.”
This is the real interest rate on AI debt: a gradual atrophy of collective reasoning. Codebases used to tell stories. You could trace the thinking of those who built them. Now they’re filling up with paragraphs of text no one authored or understands.
The Minimum Payment: Delusion
Companies love to justify AI-assisted development with a promise: “We’ll review and refactor it later.“
In most cases, that’ll never happen. Just like a total rewrite instead of gradual refactoring almost never happens.
Once AI-generated code enters production, it becomes part of the scaffolding. Nobody has time to replace it, the next sprint is already planned. The illusion of progress turns into a permanent liability, a pile of “working code” that can’t evolve without breaking.
And since AI doesn’t build understanding, even rewriting becomes hard. You can’t pay off a loan with the same credit card that created it.
The Credit Limit: Human Comprehension
AI allows developers to produce far more code than they can ever understand. Entire modules, frameworks, and pipelines appear with a few prompts, and teams convince themselves they’re scaling.
But they’re not scaling engineering, they’re scaling complexity. The limit isn’t the model’s capacity. It’s the team’s ability to reason about what it built. That limit is being exceeded every day.
When that happens, we’re not writing software anymore. We’re borrowing software written by something else, and hoping we can afford the payments.
The Bill Always Comes Due
AI in programming feels like free money: infinite productivity, no limits. But it’s the same illusion credit cards create: you’re not skipping the cost, you’re just delaying it.
Every time AI writes a function you don’t understand, every time a team merges code no one could have written by hand, you’re borrowing from the future. The bill won’t come only in errors or bugs. It’ll come in the form of engineers who can no longer reason about their own systems.
And when that happens, it won’t matter how fast AI can code. We’ll have outsourced not just our syntax, but our thinking.
Consider Before Full Adoption
The debate around AI in programming keeps circling the same talking points: productivity, speed, automation.
The real question isn’t “how much faster can we code?”.
It’s “what happens when no one knows how the system works anymore?”
The real silent crisis isn’t bad code quality. It’s the death of original thought and comprehension, the erosion of the very skills that made programming a creative craft instead of a vending machine. AI is not just changing how code is written; it’s changing who writes it, and more importantly, who still understands it. The longer we pretend this trade-off is harmless, the deeper the debt gets, until the only thing left that still understands the code is the model that wrote it.
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