来源：https://dev.to/gamya_m/when-judgment-becomes-the-bottleneck-973

Someone left a comment that's been rattling around in my head: "We're moving from an era where implementation was the bottleneck to one where judgment becomes the bottleneck. When anyone can generate code, interfaces, and integrations in minutes, the differentiator becomes identifying worthwhile problems, defining clear requirements, and recognizing whether the result is actually good."

Think about what it took to build something even three or four years ago. You'd need someone who knows frontend, someone who knows how to call an image generation API, someone who knows how to call a language model API, someone who knows how to wire those together, someone who knows how to deploy it. That's a team. I described what I wanted in a paragraph. The implementation step — all of the above — happened in minutes.

If the hard part used to be "can we build this," and that part is now fast, what's the hard part now? Identifying worthwhile problems. Defining clear requirements. Recognizing whether the result is good.

Here's the thing I keep circling back to: judgment isn't something you can prompt your way into. You can ask an AI to "review this code for bugs" or "tell me if this design is good," and it'll give you an opinion. But knowing whether that opinion is trustworthy — knowing enough to push back — that still requires you to understand the problem space yourself.

The easier it gets to generate things, the more it seems to matter that you actually understand what you're generating and why. I'm less worried about "will AI make skills obsolete" and more curious about "which skills are becoming more valuable because of this shift" — and judgment, evaluation, and knowing what questions to ask seem to be high on that list.

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