TL;DR: AI writes like a brochure because it’s trained on averages. It hits the common arguments, lands the common conclusions, and never says the one risky, specific thing only a person who lived the work would know to say. The shallowness is structural, and rewriting a few sentences doesn’t touch it. The fix is starting from your real material, in your real voice, and writing it yourself. Here’s how voice capture works.
Here’s the mechanism in detail, why no amount of prompting solves it, and the cost a reader pays for picking up the result.
Here’s an AI paragraph on running a customer service operation:
Effective customer service operations require a strategic balance of technology, well-trained staff, and clear processes. Leading organizations use data-driven insights to optimize the customer journey, ensuring consistent quality across all touchpoints. By helping frontline agents with the right tools and authority, companies can drive both customer satisfaction and operational efficiency, creating sustainable competitive advantages in today’s dynamic marketplace.
Here’s a paragraph by a human who has run a customer service operation:
The first thing you learn running a customer service team is that the worst hour of the week is Monday morning at 9, because that’s when every weekend’s worth of broken purchases lands at once, and your best agents are still on coffee. The second thing you learn is that the agents who solve the hardest problems are the ones who don’t read the script, and your job is to figure out who they’re before HR fires them for not reading the script.
Both paragraphs are technically correct. One is dead.
The deadness has a name. Shallowness in AI prose breaks more attempted books, articles, and reports than any other failure mode, and it cannot be edited out. Here’s why.
What is AI shallowness?
Shallowness in AI prose isn’t a vocabulary problem. It’s not a grammar problem or a tone problem. You can read AI output and find no single sentence you’d cut. Every sentence is technically fine. Every paragraph holds together. The piece reads like a piece somebody would write.
You can read AI output and find no specific sentence you’d cut.Share on X
The problem is what isn’t there.
The AI paragraph on customer service tells you nothing you didn’t already know. The strategic balance of technology and people. The data-driven insights. The customer journey. The frontline agents with the right tools. None of it is wrong. None of it is specific. None of it is the thing only somebody who ran the operation would know to say.
Shallowness is the absence of the specific, risky, slightly controversial detail a working writer puts in because they earned it. Monday morning at 9. The agents who don’t read the script. The HR problem of keeping the good ones. None of those show up in an AI paragraph, because none of them is average. You only know them if you sat in the chair.
Why this happens
The mechanism is structural to how large language models work. It’s not a flaw better prompting or more training can fix.
A language model produces text by predicting the most likely next word, given everything that came before. Across millions of training examples on a subject, the “most likely” word is the average by definition. The word most writers would use in that position. The phrase most writing on the topic includes. The conclusion most pieces in the genre reach.
The system isn’t choosing to be average. It’s no concept of choosing. It’s a probability machine producing the highest-probability output. Across millions of training examples, the highest-probability output is the consensus. The consensus is the average. The average is shallow.
This is why prompting doesn’t fix it. Ask the AI to “write in a punchy specific voice with concrete details” and it produces text that looks like text with concrete details. The details will be the average details people write when they’re trying to look specific. They won’t be the ones only somebody who lived the work would know.
The system can fake the texture of specificity. It cannot produce specificity, because specificity comes from lived experience, and the system hasn’t lived anything.
Why can’t editing fix AI shallowness?
People try. I see it constantly with clients who drafted something with AI and want to “warm it up” before publication. The instinct is to read the draft and swap the most obviously average sentences for sentences with more voice.
It doesn’t work, because the shallowness isn’t in the sentences. It’s in the structure underneath them. The AI organized the content the way AI organizes content. It hit the common points in the common order. It made the common arguments. It used the standard examples. It reached the standard conclusion. Rewriting a few sentences leaves all of that in place.
I covered this in a piece on AI-written books that get sent to me for review. The author tries to save the chapters by adding a story here, a joke there, a personal anecdote in the middle. The result beats raw machine output and is still no good. It reads, in the words I used there, like a hollow thing wearing a couple of human accessories, because that’s what it is. The full version is in You Used AI and It Shows.
The only thing that works is rebuilding from real material in your real voice. That’s not editing. That’s rewriting from scratch, with the AI draft as a scaffold you throw away.
What readers actually notice
From the podcast
Trevor Sumner makes the same point from the supply side, on Leaders and Their Stories:
One of the things about Gen AI is it’s going to output a tremendous amount of content. And the question is, how good is that content?The volume at the bottom explodes. The value at the top goes up, because there’s suddenly so much more to be better than.
The reader cannot always name what they’re noticing. They can feel it. The piece reads competent and they put it down without finishing. The book is about a subject they care about and they never recommend it. The article lands in the tab pile and gets closed without action.
Ask them why and they shrug. They might say “it didn’t grab me” or “I wasn’t really into it.” What they sensed was the absence of the specific. Somewhere below words, they expected a person who knew the subject to say something surprising, to commit to a position, to risk being wrong, to include a detail only that person would have included. The absence of all that produces the disengagement. The reader didn’t notice the AI. They noticed the work was hollow and stopped reading.
This is the part most people writing about AI prose get backward. The usual argument is “readers can tell when AI wrote something.” That overstates it. Readers can tell when something is hollow. They go looking for why, and they find the AI. The hollowness is the signal. The AI is the explanation they land on second.
The implication is dangerous, because it lets writers believe that hiding the AI better will save them. It won’t. The reader responds the same way to hollowness whether it came from AI, from rushed human writing, from a writer who didn’t know the subject, or from any other source of vacancy on the page. The fix isn’t hiding where the hollowness came from. The fix is writing something that’s not hollow.
What working writers actually do
Working writers, the ones whose books and essays land, are doing something specific that AI cannot do.
They’re including the one detail only they would have included. The 71-year-old client I work with on a memoir is doing this on every page. He includes the specific street his uncle worked on in 1972. The fact that his mother kept a glass jar full of buttons and no two were ever the same color. The argument with his father that lasted three years and ended over a single sentence neither of them remembered later but he can still quote.
Every page has at least one detail like that, and the details are what make the page worth reading.
The AI would have written the same chapter as “a complicated relationship with my father that shaped my early adulthood, marked by both warmth and disagreement.” Technically true, completely useless. The human writes “my father said one thing in 1976 that I haven’t been able to get past and I’m seventy-one and probably never will.” Specific, risky, says something only this writer could have said. The full profile of how he does this is in The 71-Year-Old Memoirist Who Uses AI Better Than You Do.
That’s the skill. The skill is including the things only you would have included. The skill isn’t faked by the machine, ever, because the machine has nothing only it would have included. It’s averages.
The mechanism, named
Shallowness is the structural inability of a language model to produce specificity, because specificity comes from lived experience, and the language model has lived nothing.
You can’t prompt around it. You can’t edit around it. You can’t sprinkle around it. You can’t disclose your way around it. The only fix is starting from your real material in your real voice, the work AI was supposed to save you from doing, and the work that produces something worth reading.
This is the failure mode The Death of Thinking is about at scale. What happens to a profession, an industry, a culture, when the loudest output in the room is the structural average of everything written before it, dressed up in clean grammar, with nothing of any specific person in it. The answer isn’t encouraging, and it’s the answer the next decade will produce unless individual writers, individually, keep doing the work the machine cannot do.
That work is yours. It starts with the detail only you would have included.