Before you commission a technical assessment, find out what decision it is going to be used in.

Companies commission them for three reasons and only one is the reason people give. To learn something nobody knows. To create a record, usually because a decision has already been made and needs support. Or to win an argument with a counterparty who disagrees about money.

All three produce real work. Only the first one is a question.

Water goes down

On the night of October 15, 1984, a four-alarm fire broke out at 475 L’Enfant Plaza in Washington DC, the building housing both the US Postal Service headquarters and PBS. It started on the ninth floor shortly after eleven and burned through the upper floors. Around 190 firefighters brought it under control in about an hour.

The water went through the building and into the computer room in the basement.

PBS put preliminary damage to its property above twelve million dollars, and a spokeswoman listed broadcasting equipment, tape and editing machines, and computers. Insurance investigators toured the building the next day.

An insurance company hired one of the Big Five, and that firm hired us. The question was whether the computers still worked and, more to the point, whether they were still accurate.

Working and correct are different questions

Nobody had done this before as far as anyone knew. We designed programs from scratch that ran the equipment and compared output against known good results. Most in the general-purpose language of the era, my portion in assembler because I was fast in it and it needed to be quick.

The hard part is the reference. Something has to establish what the right answer looks like, and the machines that would normally tell you are the ones in question.

The deeper problem is that damaged equipment does not fail cleanly. A machine that will not turn on is easy. A machine that turns on, runs, produces output, and is wrong once every few thousand operations is what ruins a company.

Every restore from backup after a ransomware incident faces the same question and almost nobody asks it. The system came back. Nobody checked whether the data came back correct.

Twenty percent, and months of assessment

The insurance company wanted to pay a certain amount. PBS wanted new computers. Our project existed to establish whether refurbishing cost more than replacing, and to establish it straight.

The difference between the two positions was around twenty percent.

Nobody was deceived. The client told us plainly what the project was for. We knew from the beginning and we did honest work anyway. The engineering was real, the findings were real, and the project was still an instrument in a negotiation between two parties who each wanted a particular answer.

Neither side wanted it to end

When two sides are using an assessment to argue, neither is in a hurry for it to finish. The work continued for a long time. We never really finished it, and I left without learning the verdict.

From my seat it was a good assignment. Real money to spend, the best hotels in Washington, and a visit to the Smithsonian. It remains the only time I have been to DC.

Meanwhile the flooded computers sat there and the argument kept running.

The insurance company eventually said buy the new computers. They paid considerably more than the original settlement would have cost, after months of assessment work on top.

Both sides behaved rationally the entire time and the combined outcome was worse for everyone than an early agreement.

Four questions before you commission one

What decision does this report feed into. Who else will read it, and do they have a position already. What happens if the finding goes the other way. And what is the difference between the two positions, in money.

The last one gets skipped. If an assessment costs a meaningful fraction of the amount being argued over, it is not a way to save money.

The full article is on my site: https://thewritingking.com/assessment-as-a-negotiating-instrument/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

A resume and an interview tell you what somebody says they can do. Neither one tells you what they can do.

Every signal a hiring manager relies on has become cheap to produce. Resumes are written with AI. Cover letters are generated in seconds. Application volume has climbed past the point where reading everything is possible, so companies filter with software that candidates work around using more software. Interview answers get rehearsed against the same tools.

None of that makes candidates dishonest. Most are not. It means the evidence you were using to sort them no longer sorts them.

The hire I got wrong

Early in my time at a consulting firm we were building a set of disk utilities as a product. One piece was a directory program. The version shipping with the operating system was painfully slow, and our bet was that rewriting it in C would make it much faster. That bet was correct.

I needed somebody who knew that language. The person I hired said on their resume that they knew it inside and out. They interviewed well. Confident, articulate, easy to talk to.

I was a baby manager at the time. I had the title of vice president of consulting and no training in interviewing whatsoever. Nobody had ever taught me how to run one and I had never thought to ask.

So I hired on a resume and a good impression.

The tell was in the output

The program ran slower than the one we were replacing. Not slightly. Far slower than the version shipping with the operating system, which was the thing we had set out to beat.

I read the code and it was awful. Not stylistically awful, structurally awful, in ways that told me the person writing it did not understand the language.

I sat down with them and asked hard questions. After a while they admitted they had never programmed in that language in their life. Their background was somewhere else entirely.

I let them go and the project finished without them.

I already knew better

My own first real job came from an interview in Rick Shirley’s apartment, where he sat me down and had me write programs on the spot. He did not ask what I knew. He watched me do it.

That is how I got hired with almost no experience and a background nobody would have shortlisted. The demonstration was the whole application.

Then I became a manager and hired somebody off a resume and a good conversation. I had experienced the right method personally and still defaulted to the easy one, because the easy one is what everybody does and nobody had told me otherwise.

Trust but verify. I learned that phrase the expensive way.

A short paid work sample, then an explanation

A small piece of actual work, an hour or two, from your real problem domain. Not a puzzle, not a whiteboard exercise, not an algorithm nobody uses. Something resembling what the job involves on a normal Tuesday.

Then have them explain what they did. Why this approach, what they would change with another day, where they were unsure. Somebody who did the work can answer those questions for as long as you keep asking. Somebody who did not will run out quickly.

Pay them for the time. A few hundred dollars against a mistake that costs months, and candidates worth hiring will decline unpaid work from strangers.

The honest ending

We never made that directory program fast. The bet on the language was right, the code was not salvageable, and what we shipped was tolerable rather than good.

Not every story ends with a recovery. Some end with a system that works well enough and a lesson that cost more than the system was worth.

I have hired a great many people since, and I have never again taken somebody’s word for what they could do.

The full article is on my site: https://thewritingking.com/trust-but-verify-hiring/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

Software that keeps customer rules in code has a ceiling, and you find the ceiling at the worst possible time. Software that keeps them in a table has no ceiling at all.

Integrates with everything means the connection works

It means the connection works. Connection is the part vendors demonstrate, because connection demos well. Data goes out of one system and arrives in another and everybody nods.

The connection was never the hard part. The hard part is that the receiving system wants the data arranged its own way, on its own schedule, with its own exceptions, and the next customer wants something different.

Multiply that by every customer you have and you have the actual project. Nobody sells you that half.

Four hundred newspapers over modems

In the early 1980s I led a project for National TV Log, and their entire business model depended on solving exactly that.

The idea was clever. Reach into a newspaper’s computer, take the file, insert small boldface advertisements, and put the newspaper back. Fully automated, no humans in the middle.

Around four hundred newspapers, each dialed up over a modem, because modems were what existed. Grab, insert, return, hang up, move to the next one.

Then the part that made it real work. Every newspaper had its own rules about where the advertisements went and how they were formatted. Four hundred customers, four hundred sets of rules, no two the same.

The ceiling arrives without warning

The obvious solution is to handle the differences in code. The first customer is easy. The second is easy. By customer five you have branching logic. By customer forty you have a system nobody can safely change, because every change risks all forty.

The modern version hides behind better words. Per-tenant configuration. Customer-specific business logic. Feature flags. Same problem, and the same teams hardcode the first three customers and discover it at the fortieth.

The rules lived in a table

Outside the code, in a table the program read.

Building that table was not trivial, because no databases existed to put it in. No structured query language, no commercial database engines available to us. We built the table and everything around it ourselves.

With no tools and no prior art, the design was still right. One program, and adding a newspaper meant adding a row rather than writing code. Four hundred was what they happened to have. The design had no number in it at all.

Teams today have databases, configuration frameworks, and rules engines available for free, and a great many of them still put the customer differences in the code.

Three signals the logic needs to come out

A conditional statement that names a customer. The moment code says if this is customer Jones, the rules have leaked into the wrong layer.

A deployment required to onboard a customer. If adding a customer means shipping code, every new customer carries the risk profile of a release.

A change nobody wants to make. When a small adjustment for one customer requires regression testing everybody else, the differences are entangled with the logic.

Doing it at customer five is a morning of work. Doing it at customer forty is a project.

Losing the account to your own success

The code was finished and delivered and the system worked well for them. Then somebody from my team took a job at National TV Log and took the work over. We lost the account.

That outcome is fine, and I mean it. They got a good product and the person who understood it best was now inside their building rather than billing them.

A consultancy that builds something durable and documents it properly will sometimes lose the work to its own success. The alternative is staying indispensable by leaving the thing half explained, which is worse for everyone.

Anyone hiring a consultant should ask what happens when the engagement ends. A good answer describes how the knowledge transfers. A bad answer explains why you will always need them.

The full article is on my site: https://thewritingking.com/rules-belong-outside-the-code/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

Asking for something is not the same as verifying you got it, and the gap between those two costs more project time than any other mistake I have watched.

This failure has not changed in forty years. It has only found new places to happen. A vendor sends a specification that does not match what their interface returns. A migration runs against documentation nobody updated. An AI tool gets pointed at a branch that no longer reflects production and confidently rewrites code that is not there anymore.

In every one, somebody asked, somebody answered in good faith, and the answer was wrong.

A port that should have been simple

A company that made the nails for roofing trusses gave their customers a free program. You fed it inputs and it designed a roof truss. The nails are flat pieces of metal with sharp points that hold the wood together, and that was the whole business.

The program ran on an older machine and they wanted it on the personal computer becoming standard at the time. A straight port. No new features, no redesign.

I was not the project lead. I was the interface to the client, which means the files came through me.

We asked them for the sources. They sent them. Nobody skipped a step and nobody was careless.

Then the port would not work. The team tried, and tried differently, and burned weeks on a job that should have been simple. Eventually we got the right sources, and it worked immediately.

The sources we started with were not the ones in production. The person who pulled the files believed they were current. They were not, and nobody in the building knew.

The step that takes an hour

Build what you were handed, run it, and check the output against the version customers are using.

If they differ, you have found the problem on day one instead of week three. Skipping it cost us weeks, and there was no way to detect the difference from outside. Same file names. Same structure. Same everything except behavior.

The same test applies to almost any handoff. Run the vendor’s example against their interface and compare it with the documentation. Take one record through the process the runbook describes and see whether the runbook is accurate.

The 2026 version arrives in seconds

An AI tool reading a stale branch produces work that is internally consistent and externally wrong. It refactors functions removed months ago. It updates calls to an interface that changed. It writes tests for behavior nobody ships.

None of it looks wrong. That is the difficulty. Bad output from a confused tool arrives formatted, commented, and plausible.

Nobody to blame, which is why it keeps happening

The client believed they sent the right files. The person who pulled them believed the folder was current. We believed what we were given. Every individual acted reasonably and the project still lost weeks.

The failure lives in the handoff itself rather than in either party.

I sat in that handoff. I asked correctly and I received what I asked for. Doing my job properly was not enough to keep the wrong thing out. Anyone who sits between a vendor and their own team occupies the same position, because the engineers cannot verify what they were handed when they are not the ones talking to the people who have it.

Three questions before you start

When was this last used in production, and by whom. Where did you get it, and is that the same place the running version comes from. Is there another copy anywhere, and do the two match.

None of those is an accusation. They are the same questions you would ask about a used car.

Then run the thing and compare it against reality. The questions establish provenance. Running it establishes truth, and only one of those is evidence.

The full article is on my site: https://thewritingking.com/asking-is-not-verifying/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

Every security vendor now sells anomaly detection with AI in the name. Some of it is new. A good deal of it is a statistical baseline with better marketing, and telling the two apart is worth money to anyone signing the contract.

Three things, whatever the label says

Nearly all of it computes the same three. What normal looks like for this specific customer, user, or device. How far the current behavior sits from that normal. Whether the distance is far enough to act on.

A baseline, a deviation, and a threshold. Modern systems may build the baseline in more dimensions and update it faster. The shape of the answer has not changed.

Ask a vendor to describe it in those terms. A good one will explain how their baseline is built and where the threshold comes from. A weak one will say the model learns.

The system we built in the late 1980s

I worked at a consulting firm that built fraud detection for phone cards. The client sold the cards, people used them, and some of those people were not the people who bought them.

My idea was to give every customer a profile describing what normal looked like for that person. Where they called from, when, how often, for how long.

Then every call got measured against that profile using standard deviation. Not a rule saying calls from Guatemala are suspicious, because plenty of legitimate customers call from Guatemala. A measure of how unusual this call was for this particular person.

A man who lives in Florida makes several calls from Guatemala at two in the morning. Outside his profile, but within tolerance, because he might be on vacation.

Then a call comes in from New Orleans. Now the pattern is not just unusual, it is impossible, because a person cannot be in both places. The flags accumulate and the card gets cut off.

No model, no training run

It had a profile per customer, a deviation measure, and a threshold. It caught fraud, the client was happy, and the firm turned it into a product.

I am not arguing nothing has improved since. Modern systems handle far more signals, adapt faster, and find patterns a person would not think to look for. Those are real gains.

The argument is narrower. A great deal of what gets sold as AI detection is the older method with more dimensions, priced as though it were new.

The threshold is the whole product

Set it tight and you block real customers. Every legitimate person who travels or has an unusual week becomes a support call and an angry email. Set it loose and fraud walks through.

Our design required accumulation rather than a single event. One odd call moved a number. It did not cut anyone off. The card stopped only when the pattern had built past the point where an innocent explanation still fit.

That approach is available to any company running any kind of detection today, and it has nothing to do with the technology. Decide what a single anomaly is worth, decide what the total has to reach, and write both numbers down.

Four questions for a vendor, none of them technical

What does the system compute, described without using the words AI or learning. How is normal established for a new customer with no history. What happens when the system is wrong, and who finds out. Who can change the threshold, and does that require the vendor.

A vendor with a real product answers all four in a few minutes. A vendor selling a label will move to case studies.

The last one matters more than it looks. A threshold you cannot adjust is somebody else’s risk appetite running inside your business.

Nobody called any of this intelligence in 1990, and nobody would have paid extra for the word.

The full article is on my site: https://thewritingking.com/what-anomaly-detection-computes/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

Developers using AI tools merge far more code than they used to and their organizations ship at about the same rate. One large study found pull requests nearly doubling while review time rose almost as much and delivery stayed flat. A controlled trial found experienced developers slower with AI assistance while believing they were faster.

Something is absorbing the gains. I think a large share of it is people fixing generated code instead of throwing it out and asking again.

Nobody deletes, and the reason is sunk cost

Because rewriting feels like admitting the last three days were wasted. They were already wasted. The only question left is whether you spend three more.

That is the sunk cost trap, and knowing the name of it does not stop anyone at hour forty, when you are close, when the next idea is going to be the one. The next idea is never the one. If it were, you would not be at hour forty.

Generating a fresh version now costs almost nothing. The economics that justified grinding on broken code are gone. The instinct that grew out of them is still here.

Weeks on a bug that was not mine

Early in my career I was the lead developer on a system for a rivet company. Rivets were the entire business, and they were among the biggest in the country doing it. We built their accounting and merchandising systems from nothing and delivered them.

Then I hit a bug I could not find. The program crashed hard and dumped a full screen of garbage. Not diagnostics. Garbage.

I spent weeks on it. I read my code, rewrote sections, tested pieces in isolation, and got nowhere. Every day I was sure I was close.

The bug was in the compiler.

I proved it, which took a good part of those weeks, then opened a ticket with the vendor. That ticket was never resolved. As far as I know it is still open somewhere.

Being right did not ship the system

Steve Davis came in and fixed the project. He did not find the bug.

He took the modules I had been fighting with, threw them away, and rewrote them. That was the entire intervention. The rewritten code did not hit the compiler defect, the system worked, and the project moved.

I was right about the cause. Steve shipped the system. Only the second one mattered to the client.

I had confused winning the argument with the vendor for delivering the work. Those are separate projects, and only one of them is the job.

The same trap with AI-generated code

When you debug code you wrote, you are working with a model of it in your head. When you debug code a machine wrote, that model does not exist. You are reverse engineering a stranger’s work in order to repair it, and reverse engineering is slower than writing.

Independent analysis has found rising code churn, meaning more lines written and then quickly changed again. One review reported a substantial increase in security defects in AI-assisted code. That is the shape of work accepted before it was understood.

The alternative takes a minute. Throw it out, change what you asked for, generate again.

Two habits that cost nothing

Set a limit before the work starts. Half a day, one day, whatever fits. When the limit is reached, somebody else looks at it. Not because you failed, because that is the rule. Competent people do not ask for help because they are competent, and that belief is what keeps them in the hole.

And when work has resisted you long enough, delete it and build it again with what you now know.

One more thing I still think about. Steve Davis never chewed me out. He explained what had gone wrong and we moved on. No lecture about the weeks. Forty years later I am writing about it, which tells you which approach lasts.

The full article is on my site: https://thewritingking.com/when-to-delete-instead-of-debug/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

Nine out of ten enterprise AI agent pilots never reach production. That number comes from Deloitte’s 2026 research, and companies that did deploy have started pulling their agents back out.

Every explanation blames the model. Not enough context, bad prompting, wrong framework, wait for the next release. I think the real cause is older and simpler. Nobody decided, before the build, what the thing was supposed to do when it was on its own.

Autonomy is not a feature you buy

A system is autonomous when somebody has written down what it may decide alone, what it must wait for, and what it does when waiting is not an option. A capable system with no such document is not autonomous. It is unsupervised.

The industry has spent three years on capability and almost none on permission.

A water system that ran alone for entire winters

In the 1980s I led a project for a water district in New Haven, Connecticut. Several thousand remote devices across the region. Sensors reading levels and flows, mechanisms that opened gates and dams and drained tanks, all reporting to a central server where operations staff made the calls.

New Haven gets a lot of snow. Communications ran over phone lines, because that was the entire list of options, and everyone understood those lines would go down in winter. Not might. Would.

And the water still had to flow. So we had a system that would routinely lose contact with every human responsible for it, for weeks and sometimes months, and could not stop working during that time.

The answer was to give every remote station a profile of its own behavior, built from the previous few years of water flows and weather at that specific location. When the line dropped, the station stopped waiting and started deciding from that picture. What to let flow, what to hold, what to open.

No model. No training run. No learning of any kind. A profile, a set of parameters, and a decision table.

The system was not intelligent by any standard. It was still autonomous, because somebody decided in advance what it could do alone.

The boundary, decided before any code

Under normal conditions the operations staff decided. The stations decided only when the staff could not be reached, and what they could decide was bounded by the profile, which meant they could act inside the range of normal and could not invent anything new.

The station was not trusted to be clever. It was trusted to keep doing the ordinary thing while nobody was available to ask for something unusual.

Compare that with an agent handed broad system access, a general-purpose model, and no written boundary. That agent is being trusted to be clever. Some night it will be clever about the wrong thing with nobody there.

The question worth asking this week

Ask what your agent does when a service it depends on is unavailable. Most of the time nobody knows.

The agent calls a service. The service was there in testing and in the demo, so everybody moved on. Then one day it is not there, and the agent retries, retries differently, produces a confident answer built on nothing, or loops until somebody notices the cost.

The New Haven design started from that condition rather than discovering it in production. We built the failure mode first and the normal mode second. I have not seen an AI project do that.

One page beats a governance platform

A large enterprise has a governance function and a committee. A company with forty people has neither, and gets told constantly to move fast and sort it out later.

The smaller company has one advantage. One person can keep the entire picture in their head, so the decision about what an agent may do alone can be made by somebody who understands the business, in an afternoon, on a single page.

That page is worth more than any governance platform on the market. Almost nobody writes it, because writing down limits feels like slowing down.

The companies that skip the page are the ones filling out the ninety percent.

The full article is on my site: https://thewritingking.com/autonomy-is-a-design-decision/. It runs longer, carries the diagrams, and answers the questions readers ask most.

These are AI-made summaries of longer articles on my site, written with AI assistance from my own interviews and my own career. Nothing goes out that I have not read and approved.

I spent thirty-three years in enterprise technology, twenty of them as Director of Computer Operations and Technical Services at a major national retailer. Budgets, vendors, outages, migrations, capacity plans that were wrong, and an annual audit from a CISSP-certified VP of Security whose entire job that week was finding what I had missed. He did that …

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If you have gone looking for an affordable ghostwriter, you already know the punchline. Professional ghostwriting is not cheap. A full-length nonfiction book from an experienced ghostwriter runs $50,000 to $150,000, sometimes more. People who assumed it was a few thousand dollars tend to close the browser tab at that point.

I have finished 54 ghostwriting projects. I am not going to pretend the number is small or apologize for it. What I want to do instead is tell you the truth about where that money goes, why the cheap version costs more than the expensive version, and what your actual options are if $50,000 is not sitting in your budget right now.

Why an affordable ghostwriter is hard to find

A ghostwriter is not typing your ideas into a document. If that were the job, it would be cheap.

The job is ten to twenty hours of interviews to learn how you think and how you talk, so the book sounds like you and not like me. It is research to make sure every claim holds up. It is structuring 50,000 words into something a reader will actually finish. It is writing, then revising, then revising again until the manuscript reads like you on your most articulate day.

That is four to eight months of committed work. During that stretch the ghostwriter is turning down other projects to focus on yours. The price reflects the time, the skill, and everything they are not doing because they are doing your book.

The cheap ghostwriter trap

The internet is full of people offering a full book for $2,000 to $5,000. Here is what that math actually buys.

At $3,000 for a 50,000-word book, the writer earns six cents a word. To make a living at that rate they have to run several books at once at high speed. That means almost no interviews, almost no research, almost no revision. What you get back reads like a long blog post with chapter breaks. The voice is generic because nobody spent the twenty hours learning yours.

I know this because I meet these clients. They come to me holding a manuscript they cannot publish under their own name without embarrassment, having already spent $3,000 to $5,000. Now they pay again to have it done right. The cheap option turned out to be the expensive option with an extra bill attached.

Your book carries your name. Prospects, investors, and peers judge your thinking by it. A bad book does not sit there neutrally. It quietly costs you the opportunities you never find out about.

What to actually do if your budget is under $50,000

You have real options, and none of them is a cheap ghostwriter.

Book coaching. If you can write but need professional guidance, coaching is the most cost-effective path. You do the writing. A coach makes sure the structure holds, the argument works, and the finished book is professional. At $200 an hour, most projects run 20 to 40 hours, so $4,000 to $8,000 total for expert guidance on a book you author yourself.

Self-guided writing. The most affordable route. A good handbook walks you through structure, pacing, revision, and publishing on your own schedule, for the price of a book.

Doing your own prep. The interview and research phase is a big chunk of any ghostwriting project. If you arrive with organized notes, transcribed interviews, and a clear outline, you concentrate the ghostwriter’s time on the writing itself. Thorough preparation can cut a project’s cost by 15 to 20 percent.

The real question

The question is not “how do I find a cheap ghostwriter.” The question is “what will this book do for my business, and what is that worth.”

One client’s book helped raise $30 million. Another’s led to TEDx invitations and an international expansion. A third became a university textbook and a steady stream of speaking engagements. Measured against outcomes like those, the cost stops looking expensive and starts looking efficient.

If the book will generate revenue, attract clients, or build authority, the investment is measured against those returns, not against the sticker price. If you want to figure out which path fits your situation and your budget, that is a conversation worth having.

Richard Lowe is a professional ghostwriter and author. He has written 113+ books under his own name and ghostwritten 54+ more for executives, founders, physicians, and public figures, whose books have raised over $30 million in venture capital, earned TEDx invitations, and been adopted as university reading.

Thinking about a book? Start a conversation about your project or read the full version of this piece on my site: https://thewritingking.com/affordable-ghostwriter/

People ask me to ghostwrite their book for free more often than you would guess. They have a great idea. They are certain it will be a bestseller. They want to split royalties instead of paying, or offer exposure, or a cut of future speaking fees, or just ask if I would do it as a favor.

The answer is always no. Not because I am greedy. Because free ghostwriting produces nothing you can use, and a bad book is worse than no book at all.

Why “free” and “royalty split” offers always fail

Free ghostwriting does not exist in any real sense. A writer who agrees to work for free, for a royalty split, or for exposure is a writer who will drop your project the moment a paying client shows up. The book stalls. Deadlines slip. Communication fades. Eventually the project dies and you have spent months waiting for something that was never going to be finished.

The royalty split fails for a more specific reason. Most business books do not generate meaningful royalty income. The value of a business book is in what it makes possible: speaking fees, consulting contracts, client inquiries, credibility. Offering a ghostwriter a percentage of book royalties is offering them a percentage of the least valuable part of the whole thing. It is not a real offer, and any experienced writer knows it.

Celebrity ghostwriters on major publisher deals sometimes negotiate royalty participation. That market runs on completely different economics. For a professional hiring a ghostwriter to build authority, upfront payment tied to milestones is the arrangement that works, because it is the only one that keeps the writer committed.

What the money actually pays for

A ghostwritten book is not someone typing for a few weeks. My process runs four to eight months across four phases.

Interviews come first. Ten to twenty hours of conversation before I write a word. This is where I find the stories, the insights, and the way you naturally talk about your work. It is where I learn your voice, which is the single most important element of the book. A book that does not sound like you fails no matter how well it is written.

Then structure. The raw interview material has to become a book that builds an argument and holds a reader. Chapter order, pacing, what stays, what goes. These are strategic calls that decide whether someone finishes the book or quits at chapter four.

Then writing. A 50,000-word book is 300 to 400 hours of drafting, with every chapter passing through multiple rounds so it sounds like you and every claim holds up.

Then revision. You review each chapter, I revise, sometimes twice, sometimes five times, until it reads as if you wrote it yourself.

That is 400 to 600 hours for a typical book.

What free ghostwriting really costs you

A book with your name on it is a permanent public statement about the quality of your thinking. A good one opens doors for years. A bad one closes them, and it does it silently.

A prospect who reads a weak book and decides not to hire you will never tell you why. A conference organizer who passes will never explain that the book was the reason. The damage leaves no trace, which is exactly what makes it worse than visible failure. You never learn what the book cost you, because the opportunities that never showed up cannot send you a note.

The book you got for free was not free. It was the most expensive book you never should have published.

If you are serious about a book that represents your thinking at the level your career actually requires, the investment is real, and it is one of the highest-return investments a professional can make. That is a conversation I am glad to have.

Richard Lowe is a professional ghostwriter and author. He has written 113+ books under his own name and ghostwritten 54+ more for executives, founders, physicians, and public figures, whose books have raised over $30 million in venture capital, earned TEDx invitations, and been adopted as university reading.

Thinking about a book? Start a conversation about your project or read the full version of this piece on my site: https://thewritingking.com/free-ghostwriter/

Ghostwriting is hiring a professional writer to create a book credited to you. The writer does the writing. You provide the ideas, the stories, and the expertise. Your name goes on the cover. Theirs does not.

That is the whole concept. Everything else is how it gets done, which is what this is about.

I have ghostwritten 54 books. My clients have been executives, founders, coaches, consultants, physicians, and public figures. Their books have helped raise over $30 million in venture capital, land TEDx invitations, secure publishing deals, and get adopted as required reading at a major university. None of those outcomes came from the client sitting at a keyboard for six months. They came from a process that turns expertise into a finished manuscript.

Who actually uses ghostwriters

Most of my clients are people whose time is worth more running their business than writing 50,000 words. They have the knowledge, the stories, and the credibility. What they do not have is six months to sit down and write.

Executives use a book to establish authority. A published book changes how people treat you in a meeting, on a stage, in a negotiation. It is a credential a website cannot replicate.

Founders use a book to document what they built and why it matters, and it becomes a business development tool that opens doors to speaking, media, and client conversations.

Coaches and consultants use a book to do the selling before the prospect ever calls. By the time they reach out, they already understand your approach and are half sold.

Public figures use a ghostwriter because having a remarkable life and being able to write about it compellingly are two different skills.

The process, phase by phase

It starts with a conversation, not a commitment. We talk about your book, your goals, and whether ghostwriting is even the right path.

If it is, we talk for real. One to three recorded interviews a week. These conversations are where the book comes from. I ask questions built to get past your rehearsed talking points to the material underneath. The best content in most books comes from a story the client did not plan to tell.

From the interviews I build a detailed outline, which you approve before any writing starts. Then I write chapters and deliver them as they are finished. We revise as we go. When the full draft is done, you do a final review, send corrections, and I deliver the final manuscript.

The whole thing runs six to ten months depending on scope. You own everything. Copyright transfers to you. I do not discuss the project publicly unless you give written permission.

What ghostwriting is not

It is not plagiarism. Plagiarism takes someone’s work without their knowledge or consent. In ghostwriting, both parties agree from day one that the client gets the credit. It is contractual, consensual, and paid.

It is not fraud. The ideas, stories, and expertise belong to the client. They came from the client’s life and career. The ghostwriter’s contribution is turning that raw material into a manuscript that reads well. The substance is yours. The craft is mine.

It is not a shortcut to a bad book. A professional produces a better manuscript than most people could write themselves, because writing books is what we do every day. Every modern president has used speechwriters. The publishing industry runs on ghostwriting at every level. The practice is older than the printing press.

What a book does for your career

A book is not a brochure. It is the single most effective authority-building tool available to a professional, and it positions you differently than any other credential.

My clients use their book as the center of everything else: articles, talks, keynotes, media, all drawing from the book’s material for years after publication. The return is rarely measured in book sales. It is measured in the speaking invitation you would not have received, the client who called because they read chapter four, the investor who took the meeting because your book was on their desk.

If you are thinking about a book, the first step is a conversation. No commitment. We talk about your book and whether this is the right fit.

Richard Lowe is a professional ghostwriter and author. He has written 113+ books under his own name and ghostwritten 54+ more for executives, founders, physicians, and public figures, whose books have raised over $30 million in venture capital, earned TEDx invitations, and been adopted as university reading.

Thinking about a book? Start a conversation about your project or read the full version of this piece on my site: https://thewritingking.com/guide-to-ghostwriting/

After ghostwriting 54+ books, I can tell you the topic is rarely the real problem. Most people who contact me already know what their book should be about. They just do not trust the answer yet.

The real question is not “what should I write about.” It is “will this topic sustain an entire book, and will anyone care.” Those are different questions with specific answers. Here is how to run the test on your own idea before you hire anyone.

Start with the problem you solve

The strongest business books are built around a specific problem you solve for a specific audience. Not a topic. Not an industry overview. A problem.

One of my clients ran a cybersecurity company. He did not write a book about cybersecurity. He wrote about the specific vulnerabilities companies ignore until they get breached. That focus gave the book a clear audience, a clear argument, and a reason to exist that nothing else on the shelf provided.

If you are a consultant, coach, or business owner, the topic is usually sitting inside the conversations you already have. What do clients ask you about over and over? What do you explain in every first meeting? What do you know that your competitors either do not know or will not say? That is the book.

Your story is the vehicle, not the topic

A lot of people want to write their story. The instinct is right, but the framing matters. “My entrepreneurial journey” is not a topic. It is a structure, and structure alone does not carry a book.

The topic is the insight your journey produced. What did you learn that other people in your position have not figured out? Your story is how you deliver that insight and make it credible. But the insight is the reason someone picks up the book.

The test: “After reading my book, the reader will know how to ___.” If you cannot fill that blank with something specific, the topic needs more work.

Go narrow, not wide

The most common mistake is a topic that is too broad. “Leadership” is not a topic. “How I built a leadership culture in a company where the previous three CEOs failed” is a topic. “Marketing” is not a topic. “The framework that took my company from $2M to $20M in three years” is a topic.

Narrow feels risky because it seems to exclude readers. The opposite is true. Narrow attracts readers because it promises specific value. Nobody picks up a book called “Business Strategies.” Everybody picks up the book that promises to solve their exact problem. Across my projects, the tightest-focused books were the ones that opened doors. The broad ones sat on shelves.

Check whether you have enough material

A book needs 40,000 to 60,000 words, roughly 15 to 25 chapters of real content. Before you commit, make sure you have enough to say.

List every subtopic, story, framework, case study, and lesson under your main topic. If that list runs to 15 or more distinct items that could each carry a chapter, you have the material. If it stalls at six or seven, the topic is either too narrow or you have not dug deep enough into what you know.

The traps to avoid

The autobiography trap. “I want to tell my whole life story” with no unifying theme. Memoirs work when they are about something larger than chronology.

The everything trap. “I want to cover everything I know.” That is a textbook, not a book anyone reads voluntarily. Pick one thing and go deep.

The revenge trap. “I want to expose what happened at my old company.” These books exist and some are excellent, but they need careful legal review and a story that transcends the grievance.

The me-too trap. “I want to write a book like the one I just read.” If it already exists, yours needs a different angle, audience, or argument.

When you are ready

The right topic passes three tests. You have genuine expertise in the subject. Your audience has a real need for what it delivers. And it can sustain 40,000 to 60,000 words of original content without padding.

Pass all three and you are ready to start. Pass two and you probably need help closing the gap. Pass one or none and you need more time before the book makes sense.

Richard Lowe is a professional ghostwriter and author. He has written 113+ books under his own name and ghostwritten 54+ more for executives, founders, physicians, and public figures, whose books have raised over $30 million in venture capital, earned TEDx invitations, and been adopted as university reading.

Thinking about a book? Start a conversation about your project or read the full version of this piece on my site: https://thewritingking.com/book-topic/

Slang is one of the hardest parts of a client’s voice to capture, and one of the fastest ways to break the whole illusion if you miss it.

A retired Marine does not talk like a Silicon Valley founder. A chef from New Orleans does not use the same expressions as a hedge fund manager from Connecticut. Those differences go deeper than vocabulary. They carry where someone grew up, what industry shaped them, which generation they belong to, and how they relate to the people around them. Get it right and the book sounds like the person whose name is on the cover. Get it wrong and something feels off on every page, even if the reader cannot say what.

The interview is where you hear it

The interview is not just about extracting information. It is about listening to how the client delivers it. I record every session and review the recordings for speech patterns as much as content.

I am listening for the phrases they return to without noticing, the jargon they use fluently, the regional expressions that mark where they come from, the generational slang that places them in an era, and the informal language they drop into when they stop being careful about how they sound.

That last one matters most. Clients usually start an interview speaking formally. They know it is being recorded and will become a book. As the conversation deepens and they relax, the real voice comes out. The slang appears. The sentence fragments show up. The expressions they would never put in an email but use every day start surfacing. That relaxed voice is the one the book needs to sound like.

The kinds of slang a ghostwriter runs into

Industry jargon. Every profession has its own language. A tech founder talks about runway and burn rate. A developer talks about cap rates and getting to vertical. A veteran talks about boots on the ground and squared away. Strip it out and you sanitize the voice. Keep it in and the book sounds authoritative. The skill is calibration: heavy jargon works for an insider audience, but for a general reader it has to appear naturally and stay understandable from context.

Regional and cultural expressions. A client from the South might say “fixing to.” Someone from Brooklyn might say “deadass” for “seriously.” A British client might call a bad situation “a proper shambles.” These carry identity. In memoir especially, they are essential. A memoir about growing up in South Boston that reads like it came from nowhere in particular has lost something fundamental.

Generational markers. A client in their sixties uses different casual language than one in their thirties. “First-rate” versus “next level.” Neither is wrong. But a 65-year-old CEO whose memoir suddenly includes “that meeting was lowkey a disaster” will confuse everyone who knows him.

Rough language. Some clients swear constantly. Others never do. The book has to match. Someone who drops an f-bomb every third sentence should have a book with that energy, cleaned up enough to read well but not so sanitized that the voice disappears. I capture the full range first, then we decide together how much of it serves the book.

When slang does not belong

Not every voice includes much slang, and not every book benefits from it. A thought-leadership book in a conservative field may need a measured voice. A medical book for other doctors may run dense with technical language and no slang at all. The job is accuracy. If the client speaks formally, the book sounds formal. Injecting slang to make a reserved person sound casual is just as wrong as stripping it from someone who talks that way naturally.

Fifty-four books, fifty-four voices

Every client I have worked with had a different voice. Different industries, regions, generations, levels of formality. The retired officer did not sound like the entrepreneur. The chef did not sound like the financial advisor. Getting the slang right was part of making each book sound like the person on the cover.

That is the job. Not writing in my voice. Writing in theirs.

Richard Lowe is a professional ghostwriter and author. He has written 113+ books under his own name and ghostwritten 54+ more for executives, founders, physicians, and public figures, whose books have raised over $30 million in venture capital, earned TEDx invitations, and been adopted as university reading.

Thinking about a book? Start a conversation about your project or read the full version of this piece on my site: https://thewritingking.com/modern-slang/

In the first part of this series I argued that AI search does not kill your website, it raises the stakes on having a good one, and I meant every word. The opportunity is real. I would not have spent months of my own time chasing it if it were not. But I would be lying to you by omission if I stopped there, because the same machines that create that opportunity are broken, and almost nobody in the business of selling AEO will tell you how.

I will. I went deep into this for my own business, and a lot of what I found was ugly. These are the cracks you need to know about before you build, because some of them are merely frustrating and some of them can do real damage to your name while you are not looking.

You cannot see how you are doing, or why

In old-fashioned search, you could at least see the board. There were tools that told you where you ranked for a given term, whether you were moving up or down, what was working. It was imperfect, but it was legible. You could form a theory, make a change, and watch what happened.

AI search gives you almost none of that. There is no reliable rank tracker for whether ChatGPT recommends you. The criteria these engines use to choose a source are opaque, and they are opaque even to the people who do this for a living. I could see, sometimes, that I showed up in an answer. What I could never see was why that source got chosen over another, or what to change to show up more. You are optimizing in the dark, working from guesses and indirect signals, with no scoreboard to tell you if any of it landed.

It gets stranger. Ranking well in traditional search does not guarantee you get cited in the AI answer. A page sitting at the top of Google can be completely absent from the AI’s response, while a lower-ranked page gets named again and again. The two systems do not move together the way you would expect, which means the playbook you spent twenty years learning does not reliably transfer.

There is no “the AI.” There are dozens, and they disagree

Here is the thing that quietly makes every other flaw worse. People say “the AI” as if it were one thing. It is not. ChatGPT is one machine. Gemini is another. Perplexity, Copilot, Claude, Grok, and whatever launches next month are all separate, and that is before you count the fact that each one ships new versions that behave differently from the version before it. You are not optimizing for a system. You are optimizing for a crowd of them, and they do not agree.

They pull from different sources. They weigh authority differently. They make things up at different rates. One leans hard on Reddit and reviews, another favors established publications, another quietly prefers whatever it was trained on and barely looks at the live web at all. A change that makes you more visible in one can do nothing in a second and actively bury you in a third. There is no single lever, no shared rulebook, no version of “do this and you will show up.” You do the work, then watch it land in one place, miss in another, and backfire in a third, with no way to tell in advance which will happen.

For a business owner this is exhausting in a specific way. You cannot pick one and ignore the rest, because your potential client might be asking any of them, and you have no idea which. So you are forced to chase a dozen moving targets at once, each opaque, each slow, each wrong in its own way, none of them telling you the rules. That is not a search engine you can learn. That is a field of them, all drifting, and you are trying to be the right answer in every one at the same time.

I did not arrive at that from theory. I ran the exact same question, a request for a factual profile of me with sources, cold through every major engine on the same day. The spread was not subtle. One of them, Copilot, pulled my current numbers, found the right material, and gave a clean profile with no hedging. Two others handed back a mix of current and years-old figures and openly flagged that they could not reconcile them. One repeated a flat error I will get to in a moment. And one, Gemini, generated an answer and then erased it in front of me before it finished, producing nothing at all. Five engines, the same question, the same day, and a range that ran from accurate to useless. There is no “the AI” to satisfy. There is a committee that does not agree, and you have to win over all of them.

It is slow to learn and slow to forget

Here is the one that surprised me most. AI search is slow in both directions, and both directions hurt.

It is slow to pick up new or corrected information, and slow is generous. Here is the one that has beaten me for years. I am a working professional with ten years of changes behind me. My book count goes up. My rates change. The kind of work I take shifts. That is what a real career looks like from the outside: the facts move, because the person is alive and still working. The machines cannot handle it. When I ran my profile test, the gap was right there in the numbers. The current, correct figure for what I have written sat on my own pages, and one engine found it cleanly. The others reached past it and pulled a book count I passed years ago off scattered third-party pages, then served it up as current. Two of them surfaced the old number and the right number side by side and admitted they could not tell which was true. I have corrected my own source again and again, and the dead figures keep crawling back into the answers because they are sitting on enough other pages to outvote me. It is the hardest problem I have hit in my own AI presence, and I have not solved it. A static fact is easy. A fact that changes the way every living person’s facts change is something these systems are simply bad at holding onto.

And it is slow to let go of bad information. The freshness problem cuts the other way too. Content that is not kept current decays out of the answers, and stale errors can sit in the machine’s picture of you long after you have fixed the source. So you are punished twice: slow to be believed when you are right, slow to be cleared when the machine is wrong. The only real defense is to keep your home base current and consistent enough that, whenever the machine does finally update, it updates toward the truth.

When it is wrong about you, there is no one to call

This is the flaw that should worry a business owner most, and it is the one people understand the least until it happens to them.

AI engines get things wrong. Not occasionally, structurally. They generate confident, fluent, completely false statements, and the rate is not trivial. Depending on the topic, studies put the rate of fabricated output anywhere from a few percent to more than a quarter of responses, and it climbs higher on niche subjects, which is to say, on exactly the specific, lesser-known topics that describe most individual businesses and professionals. The more particular your niche, the more room the machine has to make something up about you.

Now the part with no good answer. When the machine invents something false about your business, there is no edit button. No form. No support line. No “report this” link that goes anywhere real. It states the falsehood to whoever asked, and you usually never find out it happened. This is the same wall as the stale book count, except now the wrong fact is not just outdated, it is invented. In my own profile test, three separate engines credited me with the same book. I did not write it. It is not mine. One engine making that mistake is a hallucination. Three engines making the identical mistake means there is a page somewhere out in the crawled web that has wrongly tied my name to that title, and every engine that found it repeated the error with total confidence. There is nowhere to send the correction, no one to email, no lever to pull. I own my source and can make it say anything I want. The machines kept saying what they had picked up somewhere else.

This is not a hypothetical risk. A small solar company in Minnesota found that an AI engine was repeatedly and falsely telling people it had been sued by the state. The company tied real client cancellations and lost sales to it. They did not do anything wrong. The machine simply manufactured a damaging falsehood and told it to their potential customers, and there was no easy button to make it stop. Think about what that does to a business that runs on trust, which is every business.

There is no appeal, and “the AI said it” is not a defense

Search had a feedback loop, clumsy as it was. You could dispute things, file requests, work through a process. AI search effectively has none. There is no court of appeal for what the model believes about you. You cannot challenge it, cross-examine it, or force a correction. The documented recourse, when there is one at all, is a lawsuit, which is an absurd and unaffordable answer for a small business that just wants a falsehood taken down.

And do not assume the company behind the machine will absorb the blame. The accountability tends to flow toward the business, not away from it. When Air Canada’s own chatbot invented a refund policy that did not exist, the airline tried to argue it was not responsible for what its bot said. A tribunal disagreed and held the company liable. The lesson cuts in an uncomfortable direction: “the AI said it” did not protect the business when the AI was its own. You should not count on it protecting you when the AI belongs to someone else and is talking about you.

It can be corrupted on purpose

This is the one that genuinely unsettled me, because it turns a passive problem into an active threat.

The same openness that lets you build authority with a strong home base also lets a determined bad actor poison the well. Researchers have demonstrated this in controlled studies, and the results are sobering. By planting carefully constructed content where the machine would find it, they made a target product several times more likely to be recommended. In head-to-head tests, seeding the right material made one option two to eight times more likely to be chosen over a competitor. Worse, they showed you can attack in the other direction too: content engineered to disparage a rival, to label it unsafe or untrustworthy, can train the machine to quietly suppress that competitor in its answers.

Sit with what that means. A competitor willing to spend the effort, or a stranger who simply wants to hurt you, can plant false signals built to inflate themselves and bury you. There is no referee. No one is checking. The machine swallows what it finds and repeats it with the exact same confidence it uses for the truth, and it cannot tell the difference. The system has no immune response. Anyone who tells you the AI playing field is fair is telling you something they have not tested. It is not fair, it is not policed, and it can be bought.

If you want the broader context on how these systems get unreliable and how the errors compound, I have written more about the mechanics over on my fiction site, including a closer look at what the AI writing research actually says. The short version is that the failure modes are real, measurable, and not going away on their own.

It takes your work and sends no one back

There is a quieter flaw that costs you in a different way. The machine can absorb your content, your insight, the article you spent hours writing, and repeat the substance of it in an answer without sending anyone to you. You did the work. The machine delivered it. The person got what they needed and never learned your name, never visited your site, never had the chance to become a client. The credit, the click, and the relationship all evaporate into a summary. You become an uncredited supplier to a system that gives you nothing back for it.

You cannot audit what it read

And underneath all of it, you cannot see the inputs. You do not know which sources the machine weighed, which it trusted, which it ignored, which bit of stray nonsense it happened to pick up about you. You are guessing at what the machine has read, which means you are optimizing against a black box. Every other flaw on this list gets harder because of this one. You cannot fix what you cannot see, and you cannot see almost any of it.

So what do you actually do

If I have done my job, you are now appropriately wary and wondering whether this is worth it at all. It is. I have not changed my mind from part one. AI search is where a growing share of decisions are getting made, and opting out is not a strategy. But you go in clear-eyed, and you build the one defense that actually works against this whole list of problems.

That defense is the same home base I spent the first part of this series arguing for. You cannot control the machine. You cannot make it fast, make it honest, make it fair, or make it accountable, and you cannot chase every one of the dozen engines on its own terms. What you can control is one thing: how consistently the truth about you appears across the web, anchored to a strong, accurate, owned source. That single move is the one thing that works on all of them at once, because a clear, deep, consistent record is the closest thing there is to a signal every engine respects. When the machine is slow, a consistent home base is what it eventually updates toward. When the machine is wrong, a corroborated record is what makes the falsehood harder to sustain. When someone tries to corrupt the picture, a deep and consistent footprint is what is hardest to drown out. Every one of these flaws punishes businesses with thin, scattered, inconsistent presences, and rewards the ones who built something solid early and kept it current.

The honest takeaway is not doom. It is this. AI traffic is worth pursuing, and the businesses that win at it will not be the ones chasing a hack or buying a service that promises to game the system. They will be the ones who built something real, made it the clearest and most authoritative source about themselves, and made it easy for the machines to get them right. The flaws are exactly why that work matters. In a system this opaque, this slow, and this corruptible, being the strongest honest signal in the room is the only move that holds up. That is what I actually tell clients about AI when they ask whether they should worry about it.

I am Richard Lowe, The Writing King. I ghostwrite business books and memoirs for executives, founders, and experts. If you want help making sure AI gets your business right, here is how I work.

The website is dead. I hear it every week, usually from the same person who, thirty seconds later, asks me how to get their business showing up in ChatGPT. They have read that nobody clicks links anymore, that AI just hands people the answer, that the whole apparatus of having a site is a relic. They have half-buried the website in their head. And they want to know what replaces it.

Nothing replaces it. That is the part they have backwards. The thing that gets you into those AI answers is the website, the same one they were about to write off. Not despite the shift to AI. Because of it. I can say this with some confidence, because two clients have already found me through AI, named me to a machine’s answer, and become real work. The death-of-websites story is not just wrong, it is exactly inverted, and the businesses that believe it are going to spend the next few years making themselves invisible in the one channel they were trying to win.

Let me show you why, because once you see how these machines actually work, the panic turns into something you can use.

Where AI answers actually come from

An AI engine does not know anything about your business on its own. When someone asks it a question about your industry, your service, or you by name, it does not reach into some private store of truth. It assembles an answer from sources it has access to: pages it has crawled, content it was trained on, sites it can pull from in the moment. It is synthesizing, not inventing. The whole thing rests on the material underneath it.

So the real question is not whether the machine answers. It will answer either way. The question is what it pulls from when your name comes up. If there is a strong, clear, consistent source about you and your business, it pulls from that. If there is nothing, or worse, if the only things it can find are thin, outdated, or contradictory, it improvises. And you do not want a machine improvising about your business in front of a potential client.

This is why the website is not dead. It is the source. It is the thing the machine reaches for when it needs to say something about you. No website, or a weak one, and you have not escaped the website problem. You have just handed the answer to whatever else happens to be lying around.

Your website is the one thing you own

Step back from the AI question, because there is a harder truth underneath it. Every channel you use to reach people, you are renting. Your social following lives on a platform that can change the rules, throttle your reach, or vanish overnight, and it has done all three to people you know. Your search rankings belong to Google and move when Google feels like moving them. Your visibility inside AI answers is controlled by companies you will never speak to. None of it is yours. You are a tenant everywhere, one policy change away from eviction.

Your website is the exception. It is the one asset in your entire presence that you actually own. The domain is yours. The content is yours. The structure is yours. Everything else should point back to it. That is what I mean by a home base: the one place that does not disappear when a platform changes its mind.

In the old world, the home base mattered because it was where people landed when they clicked. Now it matters for that and for something bigger. It is where the machines learn who you are. The businesses that understand this are not retreating from their websites. They are pouring more into them, because the site has quietly become the single most important input into how AI describes them to the world.

And here is the part nobody says out loud. Traffic is worthless if it lands somewhere weak. You can win the AI visibility game outright and get nothing for it, because the place you sent people did not close. The home base is where the machines learn about you and where the deal actually gets made. Both roads lead to the same place: build the home base, or stop pretending you are serious about any of this.

What changed: from ranking to being the answer

For twenty years the game was search engine optimization. SEO. Get your page to rank near the top of the list of blue links so the searcher sees you, clicks you, lands on you. One outcome mattered: a position on a page and the click that followed.

That game is ending. What is replacing it has a name, answer engine optimization, AEO, and a completely different goal. You are not trying to rank in a list. You are trying to be the answer. When someone asks ChatGPT to recommend a ghostwriter, or to explain what book ghostwriting costs, the machine does not hand back ten links to sort through. It gives one answer, in a confident voice, and most of the time the person never clicks anything at all. AEO is the work of becoming the source that answer is built from.

That is the whole shift. You are no longer fighting for a slot on a page a human scans. You are fighting to be the source a machine trusts enough to repeat. Treat it as SEO with a new coat of paint and you will optimize hard for the wrong thing and wonder why you are invisible.

Where being the answer beats ranking

Here is what is genuinely good about this shift, and it is more than people expect.

When you rank in traditional search, you are one of ten options on the page. The user still has to choose you, click you, and decide you are worth their time. When you are the answer an AI gives, there is no list to compete against in that moment. The machine has already chosen. Being cited as the source, even once, can carry more weight than ranking on ten different pages, because the citation comes with the machine’s implied endorsement. It is not “here are some options,” it is “here is the answer,” and you are it.

It also reaches people earlier and at a better moment. Traditional search caught people while they were researching, still sorting, still skeptical. AI answers reach people at the point of decision, when they have asked a direct question and want a direct answer. If your business is the one named in that answer, you are showing up exactly when a choice is being made, with the machine’s weight behind you.

I am not speaking in theory here. Two clients have found me through AI. One was using Perplexity, the other ChatGPT. Both had asked for something specific: a memoir ghostwriter with a technical background. That is a narrow query. It is not “find me a writer,” it is a person who knows exactly what they need and is asking the machine to name someone. The machine named me. Not because I gamed anything, but because I had made my site the clearest source on the planet for precisely that combination of memoir, ghostwriting, and a technical past. When the question got that specific, I was the answer. You do not get a more qualified lead than someone who described their exact need to a machine and was handed your name in response.

Here is the part of it I keep thinking about. One of those clients told me I came up as the number two recommendation. Not number one. Second on the machine’s short list. He chose me anyway. Sit with what that means, because it cuts against the thing everyone assumes. You do not have to be the single top result to win. The machine narrowed a whole field down to a couple of names it trusted enough to put forward, and once you are on that short list, a human being makes the actual decision the way humans always have. Being named at all is the win. The old obsession with being position one matters less when the machine is handing the person two or three trusted names and stepping back.

And being cited builds authority faster than ranking ever did. A link in a list is a maybe. Being named as the answer is closer to a recommendation. Do that consistently across enough questions and you become, in the machine’s model of your field, one of the names that belongs there.

The honest tradeoffs

I am not going to sell you the clean version of this, because the clean version is not true and you would catch me on it anyway.

AEO is not a straight upgrade. It is a different game with its own real costs. The biggest one is the lost click. When the AI answers the question completely, the person may never come to your site at all. You got the credit, the citation, the authority signal, but you did not get the visit, the email capture, the entry into your world that a click used to give you. That is a genuine loss, and anyone telling you otherwise is selling something.

The traffic that does come is also harder to measure. The clean dashboards that told you exactly where a visitor came from and what they did get murkier when a chunk of your influence is happening inside a chat window you cannot see into. You can feel the effect without being able to point at a number that proves it.

And you are, to a degree, at the mercy of how the machine summarizes you. You can be the source and still not love the way your work gets compressed into two sentences. You influence it. You do not fully control it.

None of this changes the conclusion. It sharpens it. The tradeoffs are exactly why the owned home base matters more, not less. The more of your fate sits inside systems you do not control, the more it matters that the one thing you do control is strong, accurate, and unmistakably yours.

What I did about my own business

I did not arrive at any of this from a conference talk. I went deep into it for my own business, because I wanted to know what was actually happening before I said a word about it to a client.

I spent months on it. I made my own website the most authoritative source about me, on purpose, treating it as the canonical record of who I am and what I do. Not because it would rank, though it did, but because I had worked out that the machines pull from somewhere, and I would rather they pull from me than guess. I wanted the answer about Richard Lowe to come from the source I control, stated the way I would state it, not assembled from a decade of scraps.

And here is where I ran into the wall I am still standing at. I have ten years of changes behind me. The number of books I have written has climbed year after year. My rates have moved. The work I take has shifted. That is what a living, working professional looks like: the facts about them change, because they are alive and still at it. The machines cannot keep up with that. They will confidently tell someone a book count I passed years ago, or a version of what I do that expired two rebrands back. I have corrected the source over and over. The old numbers still surface. It is the single hardest thing I have tried to fix about my own AI presence, and it is still not fixed.

It is the same logic I bring to a client’s book. The book becomes the authoritative version of their thinking, the canonical source everything else can point back to. The website is that for the business. In an AI world, having a canonical source about yourself is not a nicety. It is the difference between being described accurately and being described by accident.

That is the work. Not chasing the new channel, but building the source the new channel depends on. The businesses that get this will be fine. The ones who abandon their website to chase AI traffic are going to find they have thrown away the one thing that would have gotten them into the AI answers in the first place.

So is the website dead?

No. The opposite. AI search has taken the website, the thing everyone keeps declaring obsolete, and quietly made it more important than it has been in years. It is the home base, the owned ground, the source the machines pull from and the place your conversions happen. The shift to AI did not kill it. It raised the stakes on having a good one.

But I owe you the other half, and it is the half nobody selling AEO will say out loud. This system, for all its promise, is broken. Not buggy. Broken in structural ways that can hurt you. It is a black box you cannot rank in or measure. It is slow enough that it still cannot keep up with how many books I have written. It will state things about your business that are flatly false with no button anywhere to fix them. And it can be deliberately poisoned by a competitor with a budget and a grudge. Walking into that starry-eyed is its own kind of mistake.

In the next part I am going to show you the cracks in AI search, the specific ones that should shape how you approach all of it. The opportunity is real. So is the damage. You need to see both before you build.

I am Richard Lowe, The Writing King. I ghostwrite business books and memoirs for executives, founders, and experts. If you want help building a home base the machines actually pull from, here is how I work.

The most powerful AI model in the world got switched off on a Friday night. Not because it broke. Because the government sent a letter.

On June 12, the Commerce Department told Anthropic that its two newest models could no longer be touched by any foreign national, anywhere, including the company’s own foreign-born staff. There was no clean way to block just those people, so Anthropic shut the models off for everyone. One letter, and a tool that hundreds of millions of people used went dark overnight.

I want to talk about what that means for you, because it isn’t what the headlines said.

The headlines treated it like the end of the world. It wasn’t. A more powerful model went on hold for a while. It’ll come back, or it won’t, and either way most people’s work continues on any of a dozen other tools. The story got loud because loud earns clicks. That’s not a conspiracy, it’s just how every story works now. Somebody has to put a spike in there to raise your blood pressure, or you scroll past.

The boring fact underneath the noise is the part worth keeping. A tool a lot of people depended on got turned off by someone they’ll never meet, in a room they’ll never enter, for reasons they didn’t get a vote on. And that should make you ask a quiet, uncomfortable question about your own setup.

If your business runs on an AI tool, you don’t own that tool. You rent it.

When you subscribe to Claude or ChatGPT or any of them, you’re renting access. Same as Netflix. Same as your phone plan. The company can change the price, change the rules, retire the model you like, or shut the whole thing down, and you get a say in exactly none of it.

I’ve watched this happen in small ways for a while now. I’ve used older Claude models that simply went away. One day they were there, the next day I’d been moved to something newer whether I asked for it or not. In my case the new ones were better, so I came out ahead. But that was luck, not control. The company decided. I went along because I had no other choice.

I pay two hundred dollars a month for the top Claude plan. I use it hard, and I get real value out of it. I’m not complaining about the price or the product. But I’m clear-eyed about what I’m actually buying. Not a tool. Permission to use a tool, and permission can be pulled.

I learned that lesson the expensive way, years before any of this AI business. I had platforms terminate my accounts and wipe out work I’d spent real time building. Nobody asked. Nobody warned me. One day it was there, the next day the login stopped working and there was no human on the other end to argue with. If you’ve ever had an account frozen overnight, you know the specific feeling. You built something on top of a service, and the service decided you were done.

So I run one rule now, and I don’t break it. Never wire a tool you don’t own into the guts of your business.

There’s a real difference between using AI as a tool and baking AI into your product. Using it as a tool is safe. I prompt Claude to help me draft something, and if Claude vanished tomorrow I’d open something else and lose nothing but an afternoon. Baking it in is the dangerous version. If your website calls a specific AI model every time someone loads a page, and that model’s company changes its prices or its terms or its whole business overnight, your site breaks and your customers are the ones who tell you.

The fix is old and unglamorous. Keep your own work at the center. Your own books, your own site, your own voice, your own list of the people who read you. Treat every outside service as a part you can swap out. Use AI to help build your platform. Never let AI be the platform.

A lot of smart people are pouring their content and their authority into these tools right now, and that’s fine, as long as the foundation underneath is theirs. The trouble starts when the rented thing becomes the load-bearing wall. Then somebody else’s Friday-night decision becomes your Monday-morning emergency.

Staying free is harder than it sounds, because the easy path always runs toward getting locked in. Every shortcut you take inside one platform is a small chain you’ll have to cut later. The specialized workflow that only works in one tool. The export you keep meaning to do and never do. The audience that lives entirely on a platform you don’t control. None of it feels like a risk until the day it does.

This won’t be the last switch that gets flipped. The tension between governments and companies and the people who use these tools is just getting started, and it only runs one direction once it starts. Friendly governments and hostile ones, friendly companies and hostile ones, all of them fighting over who’s allowed to touch what. The people who come through fine will be the ones who never confused renting with owning.

So here’s the question I’d actually sit with. If the tool you lean on most disappeared tomorrow, would you still have a business? Or would you have a very expensive habit and a login that stopped working?

Be honest about the answer. Then go build the wall.

I wrote a longer version of this, with the full breakdown of the shutdown and how I structure my own setup so no single service can take me down, over on my site.

I write about ghostwriting, owning your work, and thinking clearly about the tools we all depend on. If that’s your kind of thing, subscribe and the next one lands in your inbox. And if you’re sitting on a book that should carry your name and live on a platform nobody can switch off, that’s the work I do.

A client sent me two chapters he had drafted with ChatGPT, and he was proud of them. I read them twice trying to find something kind to say. They were garbage. Not offensively bad, which would at least have been interesting. Worse than that. They were flat. Clean grammar, tidy structure, every paragraph sitting politely next to the last one, and nobody home.

I didn’t argue with him about it. I rewrote the chapters in his voice, set the two versions side by side, and he never mentioned AI again. He didn’t need a lecture. He needed to see it, and once he saw it he couldn’t unsee it.

I keep coming back to that moment, because the whole industry is busy having the wrong argument. One camp says using AI to write is cheating and you are going straight to hell. The other says it is just a tool, calm down, it’s basically spellcheck. Both of them think the question is whether the machine was in the room.

It isn’t. The question is the same one it has always been. Is the book any good.

Here is the part that took me a while to see clearly. The backlash against AI books is real, but watch what actually triggers it. A reader hits something that reads dead, goes looking for why, and finds the machine. The badness comes first. The AI is the explanation they land on second. A genuinely good book almost never gets dragged into that, because nobody goes looking for a problem they cannot feel.

So I draw a hard line in my own work, and it is not about purity. I use AI to organize interviews, build summaries, handle email. The book itself, every word, is human, because the machine produces the exact hollow thing that gets a book rejected, and my whole job is to produce the opposite.

I wrote the long version this week, the backlash, what the machine actually puts on the page, and why you can’t fix it by sprinkling humanity on top. If you want it: https://thewritingking.com/not-the-ai-its-the-crap/

Richard

The standard ghostwriting contract ends at manuscript delivery. Author thanks ghost, ghost wishes author well, book goes to editing and design, ghost is gone. The author exhales, the writer moves to the next client, the relationship resolves cleanly.

Six weeks later the author discovers that publishing the book was the easier half. The launch needs a back cover, a one-sheet, a press kit, a podcast pitch deck, a launch email sequence, social posts, a LinkedIn newsletter announcement, and ideally a keynote built from a chapter. The author looks at this list and realizes that all of the material exists, that it’s inside the 80,000-word manuscript they paid to have written, and that the only person on earth who can extract it quickly already wished them well two months ago.

I have watched this gap eat author launches for ten years. The launches that work are not the ones with bigger budgets. They are the ones where the ghostwriter stayed involved through launch, converting book material into marketing material in the voice the book already established.

The economics of post-manuscript work favor the ghostwriter who already wrote the book by a wide margin. A back cover and one-sheet that would take a new writer 15 hours to produce (because they have to read the book first) takes the original ghostwriter five hours. A podcast pitch deck that would take 20 hours from a stranger takes the original ghostwriter eight. A 52-week LinkedIn newsletter content plan that would take a content strategist three months takes the original ghostwriter three weeks.

The fix is small. Ask, before signing the main contract, whether the ghostwriter offers post-manuscript services: back cover and one-sheet, podcast pitch deck, LinkedIn newsletter content plan, keynote outline.

If they offer none of this, that conversation is normal. Most don’t. If they offer all of it at the hourly rate, you’ve found someone who treats the book as a business asset rather than a literary deliverable, and the next 12 months of your launch will go very differently than they would have otherwise.

The price difference between the two kinds of ghostwriter at signing is usually small. The post-launch value gap is large.

I wrote up the full set of post-manuscript deliverables and what each tends to cost: https://thewritingking.com/ghostwriter-role-after-manuscript/

Richard

A client sent me two chapters he had drafted with ChatGPT, and he was proud of them. I read them twice trying to find something kind to say. They were garbage. Not offensively bad, which would at least have been interesting. Worse than that. They were flat. Clean grammar, tidy structure, every paragraph sitting politely next to the last one, and nobody home.

I didn’t argue with him about it. I rewrote the chapters in his voice, set the two versions side by side, and he never mentioned AI again. He didn’t need a lecture. He needed to see it, and once he saw it he couldn’t unsee it.

I keep coming back to that moment, because the whole industry is busy having the wrong argument. One camp says using AI to write is cheating and you are going straight to hell. The other says it is just a tool, calm down, it’s basically spellcheck. Both of them think the question is whether the machine was in the room.

It isn’t. The question is the same one it has always been. Is the book any good.

Here is the part that took me a while to see clearly. The backlash against AI books is real, but watch what actually triggers it. A reader hits something that reads dead, goes looking for why, and finds the machine. The badness comes first. The AI is the explanation they land on second. A genuinely good book almost never gets dragged into that, because nobody goes looking for a problem they cannot feel.

So I draw a hard line in my own work, and it is not about purity. I use AI to organize interviews, build summaries, handle email. The book itself, every word, is human, because the machine produces the exact hollow thing that gets a book rejected, and my whole job is to produce the opposite.

I wrote the long version this week, the backlash, what the machine actually puts on the page, and why you can’t fix it by sprinkling humanity on top. If you want it: https://thewritingking.com/not-the-ai-its-the-crap/

Richard

You’ve Been “Planning to Write That Book” for Five Years

You’ve been planning to write that book for five years.

I know because you’ve told me. Not you specifically. But someone exactly like you. A coach with a methodology that works. A consultant with eighteen years of expertise. A speaker who commands rooms but has nothing to sell at the back.

You’ve got the framework. You’ve got the stories. You’ve got clients who would write testimonials tomorrow if you asked.

You just don’t have the book.

And every year you wait, the coach down the street gets a little further ahead.

She published last fall. Now she’s getting the podcast invitations you’re not getting. The speaking fees you’re not charging. The clients finding her instead of you.

The Excuses You Keep Making

Here’s what you tell yourself:

“I need to get my thoughts organized first.”

“Once things calm down with clients...”

“I’m still refining the methodology.”

These sound like reasons. They feel like reasons. But you’ve been saying them for years, and nothing has changed. The book isn’t getting written. The methodology isn’t getting more refined. You’re just getting older while your competitors get published.

Planning isn’t progress. Planning is hiding.

The Real Fear

Let’s name the thing you don’t say out loud.

You’re successful. Your clients get results. People pay real money for your expertise. And yet there’s a voice that whispers: What if I put everything I know into a book and it turns out to be ordinary? What if the thing I’ve built my identity around isn’t as special as I thought?

Eighty-four percent of business owners report struggling with imposter syndrome. Not beginners. Owners. People with track records and revenue and teams.

You’re not broken. You’re normal.

But normal keeps you invisible.

What You Lose By Waiting

The cost of waiting isn’t just the book you don’t have. It’s everything that book would have opened for you.

Speaking fees. One of my clients went from free talks to $15,000 keynotes within eighteen months of publishing. The book was the only thing that changed. Same expertise. Same delivery. Different price tag because now there was proof on a shelf.

Clients you’ll never reach. Your methodology works for people who hire you. What about the thousands who never will? A book scales your expertise beyond the limits of your calendar.

Your legacy. Your framework lives in your head right now. In scattered workshop materials. In slide decks nobody will ever open after you’re gone. When you exit, it exits with you.

That’s not a business. That’s a ticking clock.

Why People Stall

I’ve ghostwritten 53+ books for coaches, consultants, and executives. Every one of them came to me with the same mix of urgency and hesitation.

The urgency is obvious. They know their expertise deserves a bigger platform. They feel it every time they watch someone with half their experience get twice the visibility.

The hesitation comes from three places.

First, they think writing a book means clearing their calendar for six months. It doesn’t. My process runs on a few hours of conversation spread across four months. You talk. I write. You keep running your business.

Second, they worry the book won’t sound like them. That’s why I don’t write books. I capture voices. Every client who’s read their finished manuscript has said some version of the same thing: “This sounds exactly like me, only more organized.”

Third, they’re not sure what they’d even write about. You don’t need to know. That’s what the discovery process is for. I ask questions you haven’t thought to ask yourself. The book emerges from the conversation.

You don’t need to be ready. You need to start.

What a Book Does For Your Business

Forget about becoming a bestseller for a minute. Think about what a book does at the business level.

It positions you as the authority. When a prospect is choosing between you and three other consultants, the one with the book wins. Not because the book proves you’re smarter. Because it proves you’re serious enough to put your thinking on the record.

It opens doors that stay closed to non-authors. Podcasts want guests with books. Event planners want speakers with books. Journalists want sources with books. A book is a credential that keeps working while you sleep.

It multiplies your reach. You can only take so many clients. A book lets your methodology help people you’ll never meet. That’s not charity. That’s market expansion.

Your competitors figured this out. That’s why they published first.

Proof It Works

An entrepreneur used his book to secure investor funding for his new company. The book became the centerpiece of his pitch deck and helped him close critical early investment.

A financial strategist turned his methodology into a 5-star client acquisition tool. The book simplified complex concepts for his target audience and built trust before the first meeting.

A Web3 consultant landed a publisher and built an executive platform. The book positioned him as the go-to expert in an emerging field.

An AI futurist wrote the book that led to global speaking invitations and media recognition. The book shaped how companies think about innovation.

As Joseph Rockey Jr. put it: “He knows all the ins and outs of how to write a book, but more importantly, how to use a book.”

More client stories: https://thewritingking.com/ghostwriting-case-studies

Stop Planning

Here’s what separates people who write books from people who talk about writing books:

They start before they feel ready.

They don’t wait for perfect conditions. They don’t wait until imposter syndrome goes away. It never does. They decide the fear of staying invisible is worse than the fear of being seen.

Then they get help, because they know themselves well enough to know they won’t finish alone.

That window doesn’t stay open forever.

You don’t need to write a single word. You don’t need an outline or a plan or a timeline. You just need to start talking. I handle everything else.

If you’re ready to stop thinking about it and start doing it, book a call and let’s talk about your book.

No pitch. No pressure. Just a conversation about whether it’s actually time, or whether you need another year of planning.

Book a call:

https://thewritingking.youcanbook.me

Richard Lowe | Ghostwriter | thewritingking.com