I’ve been seeing something lately, and now I can’t stop it. At first, it felt like a series of small moments. Unusual, but easy to brush. And so it happened.
For example, a client’s technology team was asked to provide notes to help me turn their technology into a profit-oriented content marketing piece. Instead, submit a complete outline of the article. Nice, isn’t it?
But when I asked them to go deeper into one of the concepts, there was a moment. They asked where it was in the text. They read it, and beat a little. Then one of them said, “Ask Claude.” They both laughed.
That’s when it clicked. They didn’t use AI to refine their thinking. They had used it to produce something they did not fully recognize as their own and could not explain. In short, AI makes it easier to produce work, but harder to tell who really understands it.
Once I started seeing it, I saw it everywhere.
- A student submitted an excellent final project. Clear thinking, solid structure, polished writing. It is much better than any of his previous work. But most categories had that defining space at the beginning. I asked if he had used AI. He had it. I told him that he had to reveal it, and he said he would reveal it. But I’m not sure he got that information right.
- The marketing agency I partner with shared a deck full of detailed tables, charts, and timelines. It looked impressive. But when I asked questions, there were no answers. At one point, the leader showed how great Claude was at building these.
I see more polished work than before. I also see many people who cannot explain what they have produced.
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What really happened
AI is great at helping us produce faster, cleaner, and more organized output than we could ever create ourselves. In many ways, that’s a win.
But we have begun to confuse productive work with cognitive work. Those are not the same. With age, the gap between them becomes harder to see. This is what I think of as the productivity fallacy of AI: where the output gets better, but the understanding doesn’t.
Before AI, tools helped us do what we already intended. Now, AI can generate strategy, messaging, and analysis that looks complete and reliable, even if we don’t fully understand it.
This happens because AI can produce a seemingly finished task without requiring the user to process or internalize the thinking behind it. If we’re not careful, we skip that step altogether. That’s when things change, and that’s where things start to break.
Why this is a problem – especially for marketers
There is a logical decline in advertisers.
First, credibility begins to crack. If you can’t explain your reasoning, you can’t defend it, and at some point, someone will ask. “AI suggested” is not a strategy.
At the same time, the strategy becomes… decorative. The output looks fine. Clean frames, detailed timelines, polished messages. But without real understanding, they are just artifacts. (Good charts don’t count if you can’t walk someone through them.)
This is also reflected in the work itself. If you don’t fully understand what you are talking about, messages lose their edge. You default to high-level thinking instead of translating features into meaningful benefits or distinguishing in a meaningful way.
Finally, the parties feel. Questions are asked, answers are unclear, and trust is destroyed. It was quiet at first. But it adds up.
Symptoms of illness
Once you start looking for this, it’s surprisingly easy to see. There are several indicators. Some have been around since the early days of ChatGPT (like the use of em characters). Some are subtle, but just like saying.
- A language is more civilized than the person who speaks it.
- Vague explanations when asked, “Why?”
- Excessive use of terms such as “prepared” or “strategic” without clarification.
- Effects that look weird but feel disconnected.
- Copy/paste artifacts (like space at the beginning of paragraphs).
- My personal favorite: the undo tool.
But AI itself is not the problem. The AI is incredibly powerful. I use it and recommend it. This is not about rejecting the tool. It’s about how we use it.
Right now, in most cases, we copy rather than process, skip the thinking step, and treat AI as a replacement for the editor. That’s where things start to fall apart.
How to use AI without losing insight
The good news is that this is fixable. You don’t need to stop using AI. You just need to use it differently.
To use AI effectively without losing insight, follow these four practices.
1. Do not copy and paste. Retype.
Yes, it’s slow. That’s the point. Retyping forces you to process what you read.
Retyping what the AI generated is helpful. Rewriting it in your own words helps a lot, especially if your AI isn’t trained in your voice. If you can’t rewrite it, you don’t understand it yet.
2. Prove that you understand it
Before using anything generated by AI, stress test it. Can you describe it? Make it easy? Answer “why”? If not, you’re not done yet.
3. Use AI to build understanding
Don’t just ask AI to generate the work. Ask it to explain, challenge, and test you. Used in this way, AI becomes a thinking partner, not just a content machine.
4. Add a layer of understanding
Right now, most workflows look like this: produce, then deliver. Missing in the middle: generate, translate, verify, and explain.
Skip those steps, and you get instant output. Put them in, and get a job you can stand behind.
A big change
We are entering a world where output is simple. When everyone can produce something that looks right, the separator is no longer an output. The thinking behind it. Ability to question, adapt, and explain.
This is where the gap begins to appear. The most prominent people will not be the ones who produce the most content. They will be the ones who really understand you.
AI can make you more productive. But if you can’t explain what you’ve created, you don’t really own it. That will become more important as AI becomes part of everyone’s workflow.
Disclosure: AI tools have been used to assist in drawing and editing this article. All opinions and examples are my own, based on my experience and observations.
