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AI Does Not Know You, and That Is the Whole Problem

TL;DR: A model can produce competent prose about your subject in seconds. What it produces is the of what people in your field generally say, and that’s the one thing your book mustn’t be. It doesn’t know which received idea you stopped believing in 2011, or why.

Ask a model to write a chapter on your specialism and it’ll produce something competent in seconds.

Read it carefully and you’ll notice something specific. It’s the average of your field. Every claim is one most practitioners would accept, every emphasis is the standard emphasis, and nothing in it would surprise anybody who has worked in the area for five years.

That’s the tool working as designed. It has no way to know which received ideas you’ve stopped believing, or why, so the average is all it can give you.

This is the problem I care most about when somebody asks whether they should write their book with AI. The tool is fine for plenty of jobs. The trouble starts when an author hands it the part only they can supply, because the book comes out sounding like everybody and the name on the cover is the only thing in it that’s theirs.

What does a model know about your subject?

The consensus, weighted by how often it’s been written down.

It knows what the textbooks say, what the popular articles say, and what the widely repeated framings are. On most subjects that’s a great deal of accurate information and it’s useful for orientation.

What it doesn’t contain is the private half of your expertise. The thing you were taught that turned out to be wrong in practice. The rule everybody repeats that you stopped applying in 2011 because you watched it fail. The exception you’ve seen forty times that nobody has written up because it’s inconvenient.

That private half is your entire competitive position as an author. It’s the only reason somebody should read your book instead of the three that already exist.

I’d go further. An author who publishes the average version has spent months and real money telling readers what they could have found on the first page of a search, and then signed it.

The book on this: The Birth of the Augmented Human is 31 chapters on using these tools as an amplifier, with a companion volume on what happens when the judgment goes across too.

Why does the average version feel so plausible?

Why the average version of your subject feels so plausibleConsensus is usually correct, and correct is what makes it dangerous. If the draft were wrong the author would catch it. The problem is that it is right, in the way a good textbook is right, and reading your own subject stated correctly produces a pleasant sensation of the work being done. Then it ships, it reads like every other book in the category, and the author cannot work out why nobody responded to it. The books that do anything all contain something a competent generalist would not have written, not because it is contrarian but because it came from somebody who had been there.Why the average version feels rightIf it were wrong you would catch it. It is right, which is the problem.1The draft comes backStating your subject correctly2It reads as competentThe way a good textbook reads3That feels like work being doneA pleasant sensation, and a false one4Then it shipsAnd reads like every other bookin the category, for the same reasonThe ones that do anything contain something a competent generalist would not have written.
Why the average version of your subject feels so plausibleConsensus is usually correct, and correct is what makes it dangerous. If the draft were wrong the author would catch it. The problem is that it is right, in the way a good textbook is right, and reading your own subject stated correctly produces a pleasant sensation of the work being done. Then it ships, it reads like every other book in the category, and the author cannot work out why nobody responded to it. The books that do anything all contain something a competent generalist would not have written, not because it is contrarian but because it came from somebody who had been there.Why the average version feelsrightIf it were wrong you would catch it. It is right,which is the problem.1The draft comes backStating your subject correctly2It reads as competentThe way a good textbook reads3That feels like work being doneA pleasant sensation, and a false one4Then it shipsAnd reads like every other bookin the category, for the same reasonThe ones that do anything contain something acompetent generalist would not have written.

Because consensus is usually correct, and correct is what makes it dangerous.

If the draft were wrong you’d catch it. The problem is that it’s right, in the way a good textbook is right, and reading your own subject stated correctly produces a pleasant sensation of the work being done.

Then it ships, and it reads like every other book in the category, and the author can’t work out why nobody responded to it.

More and more of the books people bring me to fix began exactly this way. They wrote it with AI, it came out as crap, and now they want it rescued. Rescuing it means sitting the author down and interviewing the private half out of them, the same work they’d hoped the tool would spare them.

The books in my library that do anything all contain something a competent generalist wouldn’t have written. It came from somebody who had been there, and being contrarian had nothing to do with it.

The book on this: The Death of Thinking is that companion volume, and it argues the case this piece is making at book length.

Can you tell the model about yourself?

Up to a point, and the ceiling is lower than the enthusiasm suggests.

You can supply background, examples, and preferences, and that improves the output. What you can’t transfer is the thirty years of pattern recognition that makes you notice one thing in a situation and dismiss another. Those are weightings you can’t list, and a prompt can only carry what you can list.

Try it directly. Write down the ten things you know about your field that aren’t in the books. Most people manage four and then stall, and the stall is the point. The rest surfaces when a situation triggers it, so it comes out in conversation and interview. A good question from another person triggers it far more reliably than a blank prompt box ever will.

The book on this: Family Cybersecurity contains the private half of thirty-three years in enterprise technology, the part no model has.

What does AI not knowing you mean in practice?

Use the tool where the average is fine and stay out of its way where it’s not.

The average version is perfectly adequate for the connective material. Background a reader needs but doesn’t care about. Definitions. The bridge between two chapters. A first pass at a structure you’ll then rearrange.

It’s not adequate for the argument, the examples, or anything that establishes why you’re worth reading. Those parts have to come out of you, and the tool can’t tell you which is which, because it doesn’t know which parts are the average.

A reasonable division: it drafts, you supply, you decide. The moment the decision moves across is the moment the book becomes the aggregate.

How do you tell if it has happened to your manuscript?

Give a chapter to somebody senior in your field and ask what surprised them.

If the answer is nothing, the chapter is the average, whether or not a machine was involved. Plenty of manuscripts written entirely by hand have the same problem, because the author wrote what they thought a book on the subject should contain instead of what they know. The second test is subtraction. Remove your name from the chapter. Would anybody in your field be able to guess who wrote it? If not, there’s nothing in there that only you could have supplied. That test is uncomfortable and it takes about a minute.

I’d run it before a manuscript goes anywhere near a publisher. Authors hate hearing that anyone could have written a chapter, but hearing it from a colleague costs a minute, and hearing it from readers after launch costs the book its one chance.

The rest of this series covers the neighboring gaps: AI never writes in your voice, and the labor split that works on a book. The AI and Writing Hub collects the rest, and my ghostwriting service is largely the business of getting the private half of somebody’s expertise onto a page, the half no model has.

Experts with decades in a field who publish something a model assembled from everybody else’s writing waste their best asset, because the readers who needed what that expert knew never get it. The tool is fine for the connective tissue. Hand it the argument and you’ve thrown away the only reason anybody would buy a book with your name on it.

Frequently Asked Questions

What does an AI model know about my field?
The consensus, weighted by how often it’s been written down. Textbooks, popular articles and widely repeated framings. It doesn’t contain the private half of your expertise, meaning the rules you stopped applying and the exceptions nobody has written up.
Why does AI-drafted writing feel plausible but fall flat?
Because consensus is usually correct, and reading your subject stated correctly produces a pleasant sensation of work being done. The draft isn’t wrong, so it survives review. It simply reads like every other book in the category.
Can you give an AI enough context to write like you?
Only up to a point. You can supply background and examples. That helps. You can’t transfer decades of pattern recognition, because that’s a set of weightings you can’t enumerate.
Which parts of a book can AI reasonably draft?
The connective material. Background a reader needs but doesn’t care about, definitions, bridges between chapters, and a first pass at a structure you’ll rearrange. Not the argument, the examples, or anything establishing why you’re worth reading.
How do you tell if a manuscript has become generic?
Give a chapter to somebody senior in your field and ask what surprised them. If nothing did, it’s the average. Then remove your name and ask whether anybody could guess who wrote it.

About the Author
Richard Lowe, professional ghostwriter

Richard Lowe is a professional ghostwriter and author with 113+ books authored and 54+ ghostwritten. Before writing full time he spent 33 years in enterprise technology, including 20 years as Director of Computer Operations and Technical Services at Trader Joe's. He writes nonfiction, fiction and memoir, and works with executives and experts on books that build authority.

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Disclaimer

The views and opinions expressed in this blog post are solely those of Richard Lowe and are based on personal experience and research. This content is for informational purposes only and should not be construed as professional legal, financial, accounting, or business advice. Always consult with qualified professionals before making important business or legal decisions. Richard Lowe is not a lawyer, accountant, or licensed professional advisor, and this content does not establish any professional relationship.

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