Working on plabOS lately, there's one thing I've become absolutely certain of. Projects rarely fall apart because AI can't write code well. Even if there are shortcomings, today's loop-back structures and iterative improvements can compensate adequately.
Yet there are still moments when projects start to wobble. Most of these occur when people fail to properly read what AI has produced.
The Problem Isn't Generation—It's Neglect
The typical flow of vibe coding looks like this: write a prompt, get code, run it immediately, get an error, paste the error message back in.
Repeat this loop a few times, and at some point, nobody can explain "why it's not working." Code keeps getting added, but not a single line of understanding has accumulated.
This isn't a simple mistake. It's a new anti-pattern of the AI era. Generation speed has increased, but comprehension speed hasn't kept up. When this gap accumulates, projects quietly collapse.
In the past, the bottleneck was how fast you could write code. Now, the bottleneck is how fast you can understand code. Yet many teams are still focused only on generation speed. It's like only caring about how fast you're pouring water, without checking if the container has holes.
The Clear Difference Between Projects
The difference between projects with few bugs and those with many is surprisingly simple. It comes down to whether these three things were actually "read and understood":
First, did you carefully read the planning documents AI created? Second, can you visualize the folder structure AI organized in your head? Third, do you understand the DB schema AI proposed?
Just following these three things cuts bugs in half, whether you're using AI or not. Because most problems arise not from "building it wrong" but from "modifying without understanding the context."
AI quickly proposes structures. But interpreting those structures to fit your organization's context is still a human responsibility. The most stable projects I've seen weren't stable because the AI was exceptional—they were stable because team members fully digested AI's proposals before moving forward.
The Illusion of "It'll Figure It Out"
I went through the same process myself.
"AI will handle this much." "Let's just get it running and look at it later."
The results were always the same. Code that collapses entirely with the slightest modification. Directory structures with complex hierarchies. Ultimately, time spent reading everything from scratch again.
That's when I realized: time saved by skipping reading doesn't disappear. It always returns as a larger cost.
This isn't a development problem—it's a decision-making problem. It's the choice between "reading for 30 minutes now" or "debugging for 3 hours later." And most of the time, we choose the 3 hours later. Because the 30 minutes right in front of us feels more precious.
Now 'Interpreters' Matter More Than 'Writers'
Many people say, "The era of writing code yourself is over."
I'd put it this way: the time spent writing code has decreased. But the time spent reading and judging code has become far more important.
Looking at AI-generated code, we need to make judgments. Does this structure fit the current business model? Is this design scalable? Can the next person understand it?
Simple bugs can be fixed through automation. But structural errors don't resolve automatically. When a service that was running normally suddenly experiences an unexplainable failure, the responsibility ultimately falls on humans. You can't blame AI then.
There's a certain nuance to the term "vibe coding." By feel, roughly, quickly. But when I look at people who are actually good at vibe coding, they read more carefully, not less. They grasp the intent behind AI-generated code, adjust it to fit the context, and request a complete redo if necessary. It's not that their intuition is good—their reading comprehension is good.
The Culture We Need to Build First
Before learning how to write better prompts, there's a culture I want to establish first:
"Read first, then execute."
Don't skip planning documents. Only modify when you can explain the folder structure. DB changes are made by people who understand the schema.
This approach may seem slow at first. But in the long run, it's the fastest path. Because this approach doesn't depend on any particular developer's intuition. A process where anyone can reach the same conclusion. I believe that's the condition for a scalable organization.
As AI advances, the developer's role shifts from "the person who builds more" to "the person who understands to the end."
Why This Matters for Non-Developers Too
This isn't just a story for developers. It applies equally to planners, operators, and PMs who use AI tools.
Organizations that execute AI's output as-is appear fast, but their maintenance costs eventually explode. Conversely, organizations with a culture of reading, understanding, and verifying results achieve both speed and stability.
Whether AI or a human wrote the planning document, if you don't read it properly, the result is the same. The moment someone says "that was in the document" during a meeting, a reading failure has already occurred.
Conclusion
The core of vibe coding isn't intuition. It isn't input skills. It's reading comprehension.
The moment you skip reading, AI stops being a productivity tool and becomes a chaos amplifier.
The opportunity for developers going forward isn't in making people use AI more—it's in the process of ensuring organizations understand AI's output to the very end.
How much is your team actually reading AI's output?
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