Judgment is the skill that matters most in the AI era

AI has made generating plausible output incredibly fast and easy. Words, code, images, presentations, analyses — ask for something and the machine will produce it right now.

What AI has not made fast and easy is judgment.

The people who will get the most value from AI are not the best prompters. They’ll be the ones who can critically engage with what comes back, recognizing when it’s wrong, when it’s shallow, when it’s synthetic, and when it can be improved.

I’ve spent the last year using AI heavily in my professional work, and here and there on my personal blog. I came to AI with good judgment built over decades as a writer and engineering leader. If anything, using AI has deepened and sharpened my judgment.

Where judgment comes from

Conventional wisdom says that judgment comes from years of making your own mistakes. The software developer who debugs their own bad code until the lesson is internalized. The writer who rewrites their own bad drafts until they develop an eye for what isn’t working. Failure as the teacher.

But judgment does not require creating the flawed work yourself. It requires sustained critical engagement with flawed work regardless of origin.

Did you know I never set out to be a writer? I fell into it when writing technical manuals was the best job I could get after engineering school. I did that for a few years and built baseline writing and editing skills.

Then I got a job editing technology books for a major publisher. I’ve written before about how I was David Pogue’s editor. His work was excellent and precise, requiring only a light hand and collaborative refinement. I can think of a couple other authors whose work required something closer to reconstruction. Every other author I edited fell somewhere in between.

Pogue showed me what good looked like and how to make it better. Those other authors showed me what broken looked like so I could learn how to fix it.

I learned some editorial judgment by writing my own bad drafts in that first job. But I learned it deeply by engaging critically with output that arrived in front of me.

I don’t want to overplay this hand. I was just 25 and 26 when I edited those books. My judgment has deepened over more than three decades of additional experience, some through making my own mistakes and some through working out the mistakes of others.

But the path to judgment in the AI era is going to be critically engaging with AI’s output.

What critical engagement looks like

Earlier this year I used Claude to help me write a personal essay about Carmel, Indiana. I’d been thinking for 30 years about why I never felt like I fit there. Finally I believed I was close to having the answer. I turned to Claude to help me finish thinking it through and work on an article explaining it.

The process was weeks of conversation, dropping observations and photographs and honest admissions about class and bias and values, asking Claude to hold it all simultaneously while I kept thinking.

Then I asked for a draft. Claude gave me something reasonable to start with, but it didn’t sound like me and made some assertions I wasn’t comfortable with.

I didn’t accept what came back — I wrestled with it. I pushed Claude hard on those assertions, leading me to remove some and sharpen others. I cut whole sections. I wrote new material from scratch. “Bulldozed bones” wasn’t in the draft; I wrote that. The Rottweiler wasn’t in the draft. I put her there. The VW Jetta and Kia Soul weren’t in the draft. Those details came from my life, and I knew they belonged in the article.

Judgment isn’t just about catching errors. It’s also about knowing when technically correct isn’t actually right, and knowing when the output has drifted from your truth or standard even when it hasn’t made a mistake.

The second example comes from my consulting work. I used Codex to generate technical documentation for an entire codebase. It would have taken me weeks, maybe months, to document it manually. But it would have also taken me weeks to months to verify all of AI’s output.

A colleague and I spent considerable time creating the prompt, iterating through failures and correcting what didn’t work. The prompt we arrived at does something specific: it makes Codex surface its inferences explicitly, and it prevents Codex from claiming more than the evidence supports. Documentation generated by that prompt will tell you “this function appears to handle authentication based on naming conventions, but the implementation is unclear” rather than asserting what it can’t verify.

That discipline — making AI transparent about what it doesn’t know — is itself a judgment call. We built it through critical engagement with output that failed, identified the failure mode, and wrote a constraint that prevented it.

A legitimate concern

The concern is that AI takes away the struggle that helps build judgment. The student who has AI write the essay never develops the writer’s eye. The developer who accepts AI-generated code without reviewing it never builds the intuition that catches subtle errors. The cultural pressure runs toward acceptance and speed, not critical engagement.

AI has a way of making mediocre work look finished. The corporate world in particular rewards throughput far more than discernment, and AI makes plausible output cheap.

Trouble is, AI can’t make errors cheap. Judgment remains crucial. Passive acceptance won’t build judgment. Critical engagement will. That’s a habit, and it’s teachable.

Not long ago I taught a group of IT recruiters how to use AI to generate marketing content. They’re not writers, but they don’t have a marketing department. The primary lesson wasn’t prompting. It was editing. How to read AI output and know when it sounds synthetic rather than like them. How to catch claims they can’t stand behind. How to pull a draft back toward something true and human. Through that process, they gradually develop a sharper eye for what good looks like.

That’s the same lesson I learned from David Pogue’s chapters and the difficult authors’ chapters 30 years ago.

The implication

AI lowers the cost and increases the speed of generating words, code, and all kinds of other output. It does not lower the cost of being wrong.

This kind of judgment is learnable and always has been. What’s changed is the source of the output. You will develop this judgment by engaging critically, every time, with everything AI gives you.

The recruiters I taught will get better over time, if they keep at it. The ones who engage critically will get better.

That’s always been how craft works.

I consult with engineering organizations on exactly these problems. If that’s useful to you, I’d enjoy talking. Reach me here.