The Signal #036 — Dakota’s read on the AI news that actually matters to people running a business.

Working code is not the same as good code. That distinction sounds obvious. But it becomes easy to forget when the output is fast, the tests pass, and you have seventeen other things on your plate.

A developer named Vinicius Brasil wrote about this recently. His post is short and direct, and it is one of the clearest descriptions of a problem that reaches well beyond software development.

What happened

Vinicius published “When I reject AI code even if it works” and laid out the specific conditions under which he refuses AI-generated output, even when that output runs without errors.

His list is worth reading straight. He rejects AI code when he cannot explain the approach in his own words. He rejects it when the diff (the list of changes the AI made) is bigger than the problem itself. He rejects it when the AI introduces abstractions before proving they are needed. He rejects it when it works locally but makes the system harder to reason about. And he rejects it when he catches himself trusting the output more than his own understanding.

He also makes a point about his own process. Before AI coding tools, he would spend days exploring a codebase, thinking through different solutions, and only then write anything. That depth meant he could explain every decision to a colleague without hesitating. With AI, he says completing big tasks still takes days. He often rejects all of the AI’s changes and starts over. The difference in quality between the first session and the second, he writes, is not the AI model. It is the person behind the screen, who now has more time to understand the problem before directing the agent toward a solution.

His conclusion is pointed: “code that runs and makes the CI green can still be a bad solution, and engineering has always been about implementing adequate, scalable, and extensible solutions.”

Why it matters for operators

If you are not in software, you might read this and think it is a technical problem for technical people. It is not.

The underlying pattern shows up in any workflow where AI is producing volume faster than humans can evaluate it. A marketing agency where an AI is generating a dozen campaign briefs before anyone has read the first one. A healthcare billing team where an AI is flagging claims faster than coders can verify the logic. A SaaS company where AI-drafted support responses are going out before anyone has checked whether the answer actually fits the customer’s situation.

In every one of those cases, the output can look correct and still be the wrong answer. The test that determines whether something “passes” in those contexts might be a green checkmark, a supervisor who is moving too fast, or a customer who does not know enough to push back. None of those are reliable filters.

Vinicius frames the review problem clearly. With implementation getting faster and faster, he writes, the real bottleneck moves to reviewing the volume of code the AI generates. That is the sentence operators should sit with. Speed shifted from production to review. If your review capacity did not scale with your production capacity, you have a gap. The outputs are accumulating faster than anyone can evaluate them well.

What most people get wrong

Most teams treat AI review as a courtesy step. Someone glances at the output, confirms it roughly matches the request, and moves it forward. The assumption underneath that behavior is that if the AI produced it and it looks right on the surface, it probably is right.

Vinicius describes the alternative to that assumption as requiring more understanding before you direct the agent, not after you receive its answer. His second session with the same task produces better results not because he uses a different AI model, but because he has consolidated his understanding of the problem first. He is driving the agent rather than being driven by it.

That is a meaningful distinction for anyone managing an AI-assisted team. The question is not just whether your people are reviewing outputs. It is whether they understand the problem well enough to know what a good output would look like before the AI starts producing.

If the answer is no, you are not really reviewing. You are confirming.

The short version

AI tools raise output volume quickly. Review capacity rarely keeps pace. The operators who manage this well are not the ones who approve everything that looks fine. They are the ones who have built enough problem understanding into their process that they can recognize when something that works is still not good enough.

Working and adequate are different standards. Engineering has always known that. It is worth the rest of us learning it now.

If you want to think through where that gap might exist in your own workflows, xovionlabs.com is a good place to start.