Where AI Fits Into My Writing Process – and Where It Doesn’t

I use AI in almost everything I write. That does not mean AI does the writing.

I use artificial intelligence in almost everything I write – but probably not in the way people assume.

That sentence requires some explanation, because “I use AI” has become almost meaningless.

It can mean asking a search engine for help finding a source. It can mean using a chatbot to challenge an argument or organize research. It can also mean entering a prompt, receiving several thousand words and publishing them under your own name.

Those are not the same thing.

So, before I describe my process, I want to draw the most important line clearly:

AI does not write my books. Every word in my manuscripts is my own.

I use AI while researching Cryptogeddon, Holy Wars and other projects. I use it to explore technologies, interrogate ideas, identify gaps in my knowledge and find sources worth reading. But when it is time to write the book, I write it.

My blog workflow is somewhat more flexible. A weekly article has a different purpose, lifespan and production cycle than a novel. AI may help me organize the argument, test structures, identify repetition or refine a difficult passage. Even there, however, I am not asking it to invent a subject and then publishing whatever comes back.

AI participates in the process. It does not own the work.

Here is what that actually looks like.

The Process Begins Before AI

My writing rarely begins with a prompt.

It begins with something that catches my attention: a book, a news story, an historical question, a development in cybersecurity, an experience at the gaming table or an idea that refuses to leave me alone.

I capture those thoughts in Apple Notes. Some are only a sentence. Others grow into collections of links, quotations, questions and fragments. Most never become finished pieces at all.

Before I involve AI, I try to know at least three things:

1. What am I curious about?
2. Why does it matter to me?
3. What question am I trying to answer?

Those decisions need to come from me. Otherwise, I am not using AI to develop an idea. I am asking it to supply one.

That may produce content. It is unlikely to produce anything only I could have written.

Stage One: Mapping the Territory

Once I have a subject, I often use AI to help me understand its shape.

Suppose I am researching a technology for Cryptogeddon. I may begin with broad questions:

• How does this system work?
• What would have to go wrong for it to fail?
• Who controls it?
• What assumptions does it depend upon?
• How might an attacker abuse it?
• What secondary consequences am I overlooking?
• What terminology should I understand before researching further?

The purpose is not to collect prose for the manuscript. It is to improve my mental model of the subject.

AI is particularly good at revealing the structure of an unfamiliar field. It can identify major concepts, show how they relate and suggest questions I would not have known to ask. That makes it a useful starting point.

But a starting point is not a source.

Stage Two: Moving From Answers to Sources

Language models are dangerously good at sounding authoritative.

They can present established fact, reasonable inference and complete invention in exactly the same confident tone. A polished answer can be helpful, but fluency is not evidence.

My research loop therefore looks like this:

1. Ask AI to help map the subject.
2. Identify the claims that matter.
3. Find the original or most authoritative sources.
4. Read those sources myself.
5. Compare competing interpretations.
6. Ask better follow-up questions.
7. Keep only what the evidence supports.

For technology and cybersecurity, that may mean technical documentation, research papers, incident reports or reporting from sources I trust. For Holy Wars, it may mean historical scholarship, primary accounts and competing interpretations of the same event.

The AI conversation helps me navigate. The sources determine what I can responsibly claim.

My rule is simple:

AI can help me discover a claim. It cannot be the authority for that claim.

Stage Three: Using AI to Create Friction

The most valuable thing AI gives me is not an answer. It is resistance.

Once I have developed an idea, I can ask the machine to attack it:

• What is the strongest objection?
• Which assumption is doing too much work?
• What evidence would weaken my conclusion?
• Am I confusing correlation with causation?
• Which stakeholder am I ignoring?
• Is this genuinely plausible, or merely convenient for the story?
• What would an informed critic say?

This is especially useful because writers become attached to their own ideas. After enough time with an argument or story, it becomes difficult to see what we have assumed rather than established.

AI does not eliminate that problem, and its criticism is not automatically correct. But it can create enough distance for me to reconsider something I had begun treating as settled.

Sometimes I reject its objection. Sometimes I return to the research. Occasionally, it exposes a weakness that changes the direction of the work.

The decision remains mine, but it is a better-tested decision.

Stage Four: The Process Splits

This is where my book and blog workflows become different.

For books

Research, questions, timelines and technical explorations may all involve AI. The manuscript does not.

When I move from research into scenes, narration, dialogue and chapters, I write the words myself. That boundary is deliberate.

A novel is more than an efficient delivery system for a plot. Its language carries the author’s sensibility: what receives attention, what remains unsaid, how a character is judged, where a sentence accelerates and where it pauses. Those decisions accumulate into voice.

I do not want to outsource that discovery.

I may later use tools to help locate inconsistencies or interrogate whether some technical element is believable. But I do not ask AI to generate chapters for me, rewrite my prose in bulk or manufacture a voice I can claim as my own.

Every sentence in the book has to pass through my mind and my hands.

For blog posts

A weekly blog operates on a shorter cycle. I am often responding to something timely, developing an argument in public or sharing work in progress.

Here I may use AI more directly as an editorial tool. Depending on the article, I might ask it to:

• compare two possible outlines;
• suggest a clearer order for ideas I have already assembled;
• identify repetition;
• flag an unsupported leap;
• show where a reader might misunderstand me;
• test alternative headlines;
• or help tighten a passage that is not working.

That is closer to an extended editorial conversation than manuscript generation.

The topic, underlying argument, personal perspective and final judgment still have to be mine. I decide what the article says. I decide which suggestions are useful. And I remain responsible for every sentence published under my name.

The distinction is not that books are sacred while blogs do not matter. It is that the tools are allowed closer to the prose in one workflow than in the other – and even there, they remain tools.

Stage Five: Knowing When AI Is Making the Work Worse

AI has a gravitational pull toward competent blandness.

It likes orderly explanations, symmetrical lists and conclusions that neatly restate whatever came before. It can turn an awkward but interesting thought into a polished paragraph that sounds like it could have been written by anyone.

That is useful when clarity is the problem. It is destructive when the awkwardness contains the writer’s voice.

There are warning signs that AI is getting in the way:

• the writing becomes smoother but less specific;
• every argument acquires an artificial balance;
• uncertainty is replaced with a tidy lesson;
• the same phrases and rhythms begin appearing repeatedly;
• the prose explains things the reader already understands;
• or the article sounds finished before I have decided what I think.

That last danger may be the most serious.

Writing is partly how I discover what I believe. If I accept a polished formulation before doing the underlying thinking, I may end up with something coherent that is not actually mine.

The struggle to articulate an idea is not wasted motion. Often, it is where the insight comes from.

Stage Six: The Final Human Pass

Before anything is published, I want to be able to answer several questions:

• Is this accurate?
• Do I believe it?
• Does it sound like me?
• Is there something specific here, or only a competent summary?
• Have I distinguished fact from inference?
• Am I saying anything I would be unwilling to defend?
• Does every sentence earn the right to carry my name?

AI cannot answer those questions for me because they are not simply questions about text quality.

They are questions about authorship.

The final decision to publish is not a mechanical checkpoint at the end of the pipeline. It is the point at which I accept responsibility for the work.

What AI Is – and Is Not – in My Writing

If I reduce the process to its simplest form, AI plays four useful roles:

1. Research guide – helping me map unfamiliar territory and locate questions worth pursuing.
2. Sceptical reader – testing assumptions, arguments and plausibility.
3. Organizational tool – helping me compare structures and manage complicated material.
4. Editorial assistant – identifying repetition, ambiguity and weak transitions, particularly in shorter work.

What it is not is the novelist.

It does not create the pages of Cryptogeddon. It does not choose the language of Holy Wars. It does not decide what my characters fear, what they value or what their choices mean. It does not turn personal experience into something I can falsely claim to have written.

AI can help me arrive at the blank page better informed, more thoroughly challenged and with a clearer sense of what I am trying to accomplish.

Then I have to write.

When the Research Starts Resembling the Fiction

Lately, AI has begun playing one more role in my writing process: subject matter.

There is an old expression that reality is stranger than fiction. For someone writing a near-future cyberthriller, that is becoming less an observation than a weekly professional hazard.

I imagine autonomous systems, coordinated cyberattacks and institutions struggling to control technologies they barely understand. Then I return to the research and discover events uncomfortably close to what I had imagined.

It is getting harder to invent an AI future that does not begin arriving before I finish the draft.

That is unsettling.

It is also irresistible material.

Next week, in The Cryptogeddon Briefing, I am going to look at some of those recent developments – and ask a question that no longer sounds quite as melodramatic as it once did:

How scary is AI getting?