Recently, I have been relying heavily on vibe coding.
It has undeniably accelerated my research. Problems that once required repeated searches, debugging, and verification can now be resolved much faster with AI.
But this efficiency comes with a cost: as my projects grow more complex, my sense of control over them is gradually weakening.
When an error appears, I often cannot quickly identify which module is responsible or reconstruct the entire chain of failure. Instead, I find myself mechanically executing commands suggested by AI and then checking the result. Although AI can usually resolve the issue in the end, I do not necessarily retain the reasoning that led to the solution.
I am left with only a vague memory of the context in which an error occurred and the general logic behind it. The next time a similar problem appears, I still cannot immediately locate the relevant code or determine what needs to change. If a project continues expanding in this way, it may eventually drift beyond my control. That is something worth taking seriously.
AI can make me more efficient, but it cannot replace my understanding of the system. It can make changes on my behalf, but it cannot build my judgment for me.
What truly becomes mine is not the volume of code AI generates, but the knowledge, experience, and methods I accumulate through repeated analysis, verification, and repair.