A line in a recent Content Marketing Institute article stopped me recently because it named something I have been circling for a while.
Robert Rose was writing about AI, content debt, and what he called “formative debt,” but the part that stayed with me was his point that our industry has been focused on training people to operate tools rather than developing the judgment those tools cannot supply.
That is the tension I keep coming back to with AI.
I do not think the problem is the tool. I use AI every day, and I am not interested in pretending it is not useful. It can help organize ideas, summarize information, pressure-test a thought, and give shape to something that might otherwise stay stuck in my head too long. But knowing how to use a tool is not the same as knowing whether the work is any good.
A person can know how to prompt a tool and still not know whether the answer is useful, accurate, or relevant. They can generate a polished caption, strategy, or report summary and still miss the larger question of whether any of it serves the audience, the client, or the purpose of the work.
That is not an age issue. It is not a young-person problem, and it is not a senior-person virtue signal. I have seen experienced people produce shallow work, and I have seen newer people bring sharper instincts than people who have been in the room much longer. But I do think judgment takes time to build.
The output can look finished before the thinking is
One of the strange things about AI is how quickly it can make something look complete. A caption has structure. A report has a summary. A strategy has sections. If you are moving fast, it can be easy to mistake that shape for substance.
Experience helps you notice the difference. It helps you look at a clean piece of writing and recognize that it still does not sound like the person who is supposed to be saying it. It helps you look at a report and see that the numbers are technically there, but the story is not. It helps you recognize when a recommendation sounds reasonable in theory but will probably fall apart the moment someone tries to act on it.
Those are not always things a tool can tell you. A tool can give you language, options, and even a decent first pass, but it cannot replace the years spent learning what makes something useful in the real world.
I have more than 20 years of experience across sales, marketing, entrepreneurship, agency life, and running my own business. That experience does not make me right about everything, and it certainly does not mean I have nothing left to learn, but it has given me pattern recognition.
I can usually tell when something feels thin. I can tell when a piece of content is saying a lot without really saying anything. I can tell when a report is polished but not clear, or when a strategy is technically organized but not grounded in what the audience, client, or business actually needs. That kind of judgment is hard to shortcut because it is not built only by learning the tool. It is built by doing the work, seeing what happens after the work leaves your hands, and learning which details actually mattered.
The skills trap
The phrase “AI-native” has started to bother me a little, not because comfort with AI is a bad thing. There is real value in knowing how to use the tools well, and I do think people who learn to work with AI thoughtfully will have an advantage. But there is a trap in confusing tool fluency with work fluency.
Someone can be fast with AI and still not understand the business problem. They can produce more content and still not understand the brand voice. They can generate a clean answer and still not know which part of the answer deserves the most attention.
That is where companies may get into trouble if they treat AI fluency as a replacement for judgment instead of one input into it. The CMI article made the point that companies racing to hire “AI-native” talent may soon discover what they actually bought. That line feels blunt, but I understand why it landed with me. If what you bought was speed without discernment, output without experience, or tool use without editorial judgment, the gap will eventually show.
It may not show immediately because the work will look finished, which might be the most dangerous part.
More is not always better
AI is very good at giving you more: more ideas, more headlines, more angles, more copy, more explanation, and more ways to say the same thing. Sometimes that abundance is helpful, especially when you are trying to get unstuck, but more information does not automatically make the work better. Sometimes it only gives you more to sort through.
That is where editing becomes its own skill. I do not mean editing only in the grammar sense. I mean the harder kind of editing: deciding what belongs, what distracts, and what needs to be cut even if it is technically accurate.
That part has never come easily to me. I am detail-oriented by nature, and when I am trying to understand something, I want all the context. I want the source material, the background, the contradiction, the side note, and the detail from three emails ago that changes how everything else should be interpreted. That instinct helps me, but it also creates its own problem because at some point, I still have to decide what the audience actually needs.
Not everything I know belongs in the final piece. Not every detail that helped me understand the issue will help the reader understand it. Some information exists to support the thinking, not to appear in the finished work.
That is the part I think gets harder when tools make output so easy. If AI gives you ten ideas, someone still has to decide which one is worth developing. If it gives you a full draft, someone still has to know what sounds generic, what is missing, and what should be removed. If it gives you a report summary, someone still has to know whether the summary reflects what actually matters.
The work is not only creating more. It is knowing what stays.
Experience lives in the decisions no one sees
Good work often depends on decisions that are invisible by the time someone sees the final version: the sentence removed because it sounded impressive but did not add anything, the chart left out because it created more confusion than clarity, or the recommendation changed because experience said, “This may sound right, but it will not work that way in practice.”
That, to me, is judgment.
And judgment is not built only by reading best practices or learning better prompts. It is built through repetition, mistakes, feedback, client conversations, revised drafts, missed expectations, and the slow accumulation of seeing what actually happens after the work leaves your hands.
You learn what people ignore, what they misunderstand, and what looks good in a document but creates problems in execution. You learn that clarity is not the same as saying everything, and that sometimes the strongest version of something is the one with less in it.
That is why subtraction matters. The ability to cut is not just a writing skill. It is a judgment skill. It requires you to know what the piece is really trying to do, to trust that the point will be stronger when it is not buried under every supporting detail, and to let go of material that may be interesting, accurate, or hard-earned because it does not serve the person on the other side.
I still struggle with that sometimes. There are plenty of times when I want to include everything because everything feels connected. In investigative pieces especially, I want to show the whole trail because the trail is part of how I arrived at the conclusion. But the reader does not always need the trail. Sometimes they need the thing the trail revealed.
The tool can help, but it cannot make the call
None of this makes me anti-AI. If anything, using AI has made me more aware of the human part of the work.
The tool does not care whether the piece sounds like me. It does not know which detail matters because of something that happened five years ago. It does not understand the client relationship, the audience expectation, or the quiet tension underneath the assignment unless a human brings that context into the process.
Even then, someone still has to make the call, and that is where experience helps: not by making you right all the time, but by helping you recognize what the tool cannot know on its own.
AI can help us create more than ever, but more was never the same as better. The harder work is still knowing what matters, what to keep, what to cut, and what only a human with enough lived experience can recognize as the point.
