GrowingVibe Coding Is Cheap. Engineering Remains Expensive.
Writing code is getting cheaper and faster. Engineering remains expensive.
This is not hype. It is reality. And it is measurable.
A recent study by Anthropic looked at how AI-assisted coding affects learning new programming skills. Developers unfamiliar with a Python library were split into two groups. One used an AI assistant, the other relied on documentation and web searches. After completing the tasks, participants were tested on understanding the library’s concepts. The result was clear. The AI group scored about 17 percent lower on comprehension, without finishing tasks any faster overall
Think about that for a second. You can have code that works perfectly but not actually understand why it works. Some developers who relied on AI finished faster, but when it came to reasoning about the code, they struggled the most. Producing output is not the same as building skill. Speed without understanding is a trap
The study also showed that how you interact with AI matters. Developers who used the AI for everything learned the least. Those who asked targeted questions, explored why the code worked, or generated snippets and then probed them preserved far more of their learning[^1]. The lesson is simple. AI can teach, but it cannot replace thinking. Treat it like a collaborator, not a magician.
This matches what I see in real-world software work. Someone who knows how to vibe a prompt can get a function running in minutes. Someone who knows systems moves faster when complexity grows, not because they type faster, but because they anticipate what could break, what could scale poorly, and what might cause headaches down the line. That is judgment. That is engineering. That is the thing AI cannot fully outsource.
Code is an artifact. Systems are living organisms. They accumulate state, fail in unpredictable ways, scale differently than expected, and evolve over time. Engineering is expensive because it works with what persists and what matters. Producing code fast is only part of the picture. Understanding, anticipating, and designing for durability is where the real value and the real cost lives.
Vibe coding works beautifully for small problems, narrow contexts, or throwaway prototypes. But as soon as a system grows, complexity hides in plain sight. AI-generated code may look clean on the surface, but intent is implicit. Decisions are baked in without explanation. Debugging, extending, or refactoring that code becomes a treasure hunt you never signed up for. Suddenly, the cheap, fast code feels anything but cheap.
This is why engineers continue to outpace prompt specialists. They do not necessarily write more code, or write cleverer code. They reduce future cost. They know what precision matters, where to add structure, and where to leave flexibility. They can delete or rewrite confidently because they understand the system. AI accelerates their work, but it never replaces their judgment.
The bigger picture is this. The emerging divide is not AI versus human, or coder versus non-coder. It is between people who can reason about systems and people who only generate output. Code is becoming abundant. Judgment is scarce. AI makes writing software easier. Engineering determines whether that software survives reality.
Here is a little truth that always makes me smile. Fast code without thought is like a skateboard with no brakes. It might be fun at first, but try turning a corner and you will find yourself in the ditch. AI can give you the skateboard. Engineering teaches you how to steer.
Footnotes and Sources
Anthropic. How AI Assistance Impacts the Learning of Programming Skills. Controlled study on AI-assisted coding and concept retention
The Decoder: AI coding tools hurt learning unless you ask why, Anthropic study finds
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