“The next revolution will catch us all off guard, as they always do. Said another way: if the crowd is anticipating the revolution, it can’t be the revolution.” —Doc Searls, 2002

It’s been nearly two years since OpenAI introduced ChatGPT to a public mostly unfamiliar with artificial intelligence. Initially it appeared to have no obvious relevance to book publishing. Since then, everything has changed. And nothing has changed. Publishers are now delving into the new AI tools, exploring the edges, and engaging in tentative chats with ChatGPT. But there’s no sign of a true revolution in the practice of publishing; it’s just too soon.

There is an abundance of uncertainty around AI in writing and publishing. Major controversies surround AI’s use. Still, for many, there’s excitement about the possibilities.

I’m going to focus here more on promise than on peril, but I do include an overview on the concerns and risks surrounding AI, particularly as they apply to authors and publishers. I’m not seeking to belittle the concerns. They’re just not what this book is about.

I consider this publication to be more of a ‘booklet’ than a book. The UNESCO definition of a book is “at least 49 pages” (why not 50?), and, at 300 words per page, this one could fail to qualify. But, more importantly, it’s not intended to be the definitive word on anything—it’s more of a progress report. Artificial intelligence, and its role in book publishing, are changing at a breakneck pace, which shows no signs of slowing down. So this book(let), this report, is just a snapshot.

Using the Leanpub platform, I’ve been releasing my research and analysis as a work in progress—it’s revised as a living document. I’ve never attempted this before. My reasons are two-fold: to offer something useful in the short-term, and to have the facility to revise it as required. The AI story is far from over.

I hope my approach will prove useful for readers, though I’m aware that too many updates could soon become annoying. I’ve tried to reduce the topicality of this book, to reduce the focus on the stuff that’s going to change the fastest.

The version that’s been (quietly) available since April 2024 I marked as “75% complete” and was offered for free to readers. July 23 was the official pub date for the “100% complete” version, which also found its way into larger distribution outside of Leanpub. I’ll continue to update the 100% version: but those updates will only be available to Leanpub buyers—it’s unwieldy to constantly update print, ebooks and audiobooks, that are in wider distribution.

I’m hearing daily about the pain that people across the publishing industry are facing trying to come to terms with AI. Why is it so painful? Here are three reasons that I’m seeing:

• The technology is complex and mysterious, too complex for non-scientists to understand. (There’s a fascinating study positing an “AI anxiety scale (AIAS)” measuring 21 different reasons that people are anxious about AI.)

• AI has become vastly controversial in publishing, mainly because of copyright issues that strike at the heart of authoring and publishing.

• Yet there’s also an atmosphere of FOMO—Fear Of Missing Out. Despite AI’s many flaws, as with other alluring technologies (the Apple watch? Smart speakers?), we face that gnawing feeling: what if I’m missing out on the next really big thing, like I did on a couple of the last really big things. I felt stupid then; I don’t want to feel stupid again.

All right: It makes sense that publishing people are feeling some AI pain.

So then what’s the use case for this book?

The use case is that you work in book publishing, or you’re an author trying to better understand book publishing, and you’re sick of feeling stupid and confused around AI. Too many of your colleagues appear to understand it better than you do, and you’re tired of not being able to engage intelligently on the topic.

My goal for readers is that, by the end of this book, they’ll feel enabled to join the conversation, to express an informed opinion. I want you to feel equipped to make personal choices around the technology, and have a path for learning how to use AI, if you choose to do so.

I do not want to inundate readers with lots of blah-blah about the underpinnings of AI technology—I’m not going to talk much about AI in the abstract. Instead, I want to give you a grounding in AI specifically for book authoring and publishing. If someone asks you, what does natural language processing really mean, you can answer, as I do: I’m not an AI expert. But I do know how AI is being used in my industry.

I dislike the phrase “what you need to know” because it so rarely lives up to its billing, often missing wide of the mark, either far more than you need to know, or far less. Will I get it right? I’m serious about trying to convey the minimum. I think I know what publishers need to know about AI, as I’ve been talking to lots of them, while hosting seminars and webinars designed very specifically to provide just the basics.

After the summaries that introduce many sections, I’ll expand on the main concepts. I’ve also kept these as short as possible, though, truth be told, sometimes I do go on.

A short-form I’m going to use is “Chat AI.” When OpenAI first released ChatGPT, it was the only kid on the block, but now there are several important competitors. I’ll go into the specifics shortly, but you’ve heard of some of them already, from Google and from Microsoft. They (and several other companies) now offer online AI chat software that’s similar to ChatGPT. And so rather than saying “ChatGPT and its competitors” I’ll just refer to the whole category as “Chat AI.” If I write “ChatGPT” I’ll be referring specifically to OpenAI’s software.

The book is mostly practical, but in the final section I posit an essay on what may be the real-world consequences of AI on the book publishing industry, alongside a few other conjectural spinnings.

The primary audience for this book is trade (consumer) book publishers. A secondary audience is all other book publishers (scholarly, educational…) to the extent that there are overlaps in these industry sectors.

The subtitle states this is a guide to AI for both writers and publishers. Authors are my very important third audience. In part that’s because I want publishers to understand what writers are doing with this technology. I think that’s important. At its recent annual meeting, the BISG (the Book Industry Study Group), which mostly represents trade publishers, indicated that “it is looking to draw from a broader pool including, literary agents and authors.” That’s a welcome move.

But I want writers to read this book, because it can help them understand what publishers are doing with their manuscripts. Self-published authors always keep one eye on traditional publishing—they are, de facto, publishers themselves—and so the AI technology of interest to publishers will be of interest to them.

But, make no mistake, this is not a hands-on how-to for authors on using AI to be better and more productive writers. There are numerous books on that topic (sadly, many of them just scammy Amazon rip-offs). And lots of YouTube videos (of varying quality).

Within trade book publishing my ideal reader is not necessarily a decision-maker, but rather one of the many people who work across the industry every day, creating books and finding readers. I’ll be talking about all of the key publishing functions in this book, from editorial through to sales and distribution. And about the intersection of writers with readers.

Though I grew up in Canada, and began my publishing career there, I’m based in San Francisco, in the United States, and so I default to thinking about the U.S. publishing industry. But just about everything in this booklet should be as relevant to a publisher in Madagascar as it is to a publisher in Manhattan. While publishers in smaller countries face challenges of scale, so do small publishers in America, and this book is intended for companies of all sizes.

When I talk to small publishers about AI, and when I talk to very large publishers about AI, the big difference that I notice is the notion of the publishing ‘enterprise.’ The largest publishers are often part of (or adjacent to) public companies, and once you’re a public company, issues around reputation and security loom so much larger than they do for an everyday publisher, not accountable in the same way to shareholders and the news media. While an editor at a Big 5 publisher has the same use for AI tools as a freelance editor working for smaller outfits, the context in which they use the tools is quite different.

How much do you need to know about AI to read this book?

I’m assuming that my audience has roughly zero knowledge about AI’s innards, but that they’re smart readers. So I’ll be treading that fine line between zero knowledge, and not talking down to the reader.

I do imagine that just about everyone has heard about the new generation of AI: a YouGov poll, in March 2024, suggests that only 7% of Americans know “nothing at all” about AI. Writers and publishers have almost certainly heard about some of the copyright complaints, but probably don’t have a grasp of the issues. So I don’t have to explain that AI stands for “Artificial Intelligence.” But I won’t assume readers can define what AI is, what it means, its potential impact, and so on. That’s why you’re here.

My mantra for the publication of this book is ‘accessibility.’

For most people, that merely means something akin to ‘availability,’ and, indeed, I intend to make this book available in as many forms and formats as I can conceive of. I want to expand the container. I’m trying to make my book an exemplary publication, highlighting the full range of what can be achieved today when publishing a single book.

On Leanpub you’ll find the book available as a PDF file, an EPUB file, and as a free-of-charge web-based publication. You’ll find translations into 31 languages, including the major Asian and European languages. There will be multiple audiobooks in major languages.

I’ll also make the book available, in print and digital formats, on all the ecommerce platforms that can be accessed via Ingram and Amazon. That includes most of the book online retail sites, in much of the world. This way libraries will also be able to buy the book, though, understandably, they have slight space available for self-published work. (I’m happy to donate print and digital copies to any library in the world that requests them.)

Besides the free web version of the full book, I’ll create free excerpts, a Q&A, and infographics, for those who just want to taste, or sample. They’ll be available via my blog.

It doesn’t make sense to record a video of the entire book, when audiobooks are available. But some people prefer to access content via video. So I’ll offer abridged video versions, in multiple lengths.

And what about the human touch? I’ll also be available via webinars or one-on-one online consultations, via my website and blog. I can even be hired to show up in person!

‘Accessibility’ means much more than its dictionary definition. As the folks at the Canadian National Network for Equitable Library Service (NNELS) put it, “An accessible book is one that can be used and understood by everyone.” And so my English EPUB file meets the W3C 1.1 recommendation for the print-disabled, including robust alt text descriptions for images. We hope to meet this accessibility standard for the foreign language translations as well.

The alt-text was created with ChatGPT. I didn’t edit ChatGPT’s version, so that anyone reading the EPUB can see how good it is, and where it’s still lacking. As my colleague Bill Kasdorf reminds me, good alt-text describes what a sighted reader derives from an image, not just what it appears to be.

For book publishers, accessibility is no longer an option: it’s a default setting.

(I have two mea culpas to share: Because of limitations within the publishing platforms, the default PDF file will unfortunately not meet the PDF/UA spec, nor will I be able to offer a large print version of the book on ecommerce sites.)

As indicated, this book is (relatively) short. When you call a short-ish document ‘a book’ you run the risk of buyers protesting, “I thought I was buying a book, but it’s only 50 pages long!” I’ve done everything I can to make it clear on the sales page that this is not a full-length book, but if you’re disappointed, keep in mind that Leanpub offers a 60-day money-back guarantee, and I’ll happily extend that refund offer to ‘forever.’ I want readers to be delighted, not disappointed. (If you bought the book on another platform, email me: I’ll send a check!)

The book is heavily hyperlinked (these appear in different ways in different formats, but often as footnotes). I’ve always believed in linking to original sources in my work, so that readers can verify my sometimes grand claims. But I’m aware that hyperlinks and footnotes can be distracting, particularly if you’re using an e-reading device. I apologize for that. The best bet is to ignore the links as you read, and return to them if you want to go deeper on a topic.

I don’t know in which format you’re reading (or listening to) right now. Nor in which language. I’ve tried to make the “ergonomics” of the book as user-friendly as possible, but links and footnotes are not always optimal from that perspective.

This book has sponsors, something that I know is unusual for ‘a book.’ I explain how the sponsorship works in my ‘disclosures’ section at the end of the text.

The rising cost of print manufacturing has been vexing for several years, though it now appears to be levelling. Increased distribution costs are challenging for all players. These lead to pricing pressures—there’s evidence that readers are beginning to chafe at current pricing for new hardcovers and trade paperbacks. I classify these as ‘analog’ problems—digital technologies bring little to the table to address them.

Then there are the broader ‘existential’ problems that are of ever-increasing concern, but they are inchoate issues, without obvious solutions. Included here are:

• Content discovery and audience engagement via online and social media.

• Changing consumer behavior and reading habits.

• Competition from other digital media.

I believe that AI could be tremendously helpful to publishers in these areas, but the methods are non-obvious and will take time to implement. I’ll describe this in later chapters of the book.

Before I get into the boring technical background, I invite you just to jump right into the online software. The top tools are free to play with. There’s ChatGPT. And Claude.ai. You can dally with Microsoft Copilot. Or try Google’s Gemini.

That’s how most people start with AI—I bet you’ve already tried one or more of these toys. I also bet that you haven’t tried them for very long. Most people I talk to devote just a few minutes. They try a few questions, get back some pretty obvious answers, and they move on.

Wrong approach.

I side with Ethan Mollick, whose work I’ll describe a few times in this book. In his blog and in a recent interview, Mollick talks about his ‘10 hour rule’:

“I want to indicate that 10 hours is as arbitrary as 10,000 steps. Like, there’s no scientific basis for it. This is an observation. But it also does move you past the, I poked at this for an evening, and it moves you towards using this in a serious way. I don’t know if 10 hours is the real limit, but it seems to be somewhat transformative. The key is to use it in an area where you have expertise, so you can understand what it’s good or bad at, can learn the shape of its capabilities.”

Another commenter, reviewing Mollick’s recent book on AI, pointed out an equally-true ‘rule of ten’: “An hour of experimenting with these tools is worth ten hours of reading about them.”

You get the idea. This is about doing, not pondering. My book is a ponderance. You need to get your hands dirty.

Understanding the roughly 70-year development of AI can be fascinating, but it’s in no way required to appreciate what’s going on today.

Our World In Data does a nice job of briefly recording the history—I’ll leave it to them.

The current generation of AI was developed mostly over the last decade. Then, suddenly, ChatGPT appeared on November 30, 2022. Two months later it had 100 million monthly users, the fastest that any technology has ever moved into the consumer space (by comparison, Facebook took over two years to reach 100 million users).

Why the rapid adoption? First, it’s fascinating and fun. Second, it’s free. Third, you don’t need to buy a new device to use it. And fourth, you don’t need any training to access ChatGPT (or its Chat AI competitors). But those same factors applied also to Facebook, so why ChatGPT?

As Arthur C. Clarke famously noted, “Any sufficiently advanced technology is indistinguishable from magic.” Chat AI is magic. The experience of ‘talking’ in everyday language to a machine… it’s magical. The experience of saying “I want an image of a book in a balloon in a cloud near the sun,” and, seconds later,

… also magical. GPT-generated images are starting to look similar in style, colorful and fanciful. So I sent a second prompt “now in a style that looks like a 15th century illustration.” And so:

If I want a video of a book in a balloon in a cloud near the sun, there are over a dozen tools to choose from, and presto. And a musical soundtrack to go with the video. Well, how does this sound? It’s just like magic.

For fear of seeming dismissive of AI’s extraordinary abilities by relegating it to the category of inscrutable “magic,” it’s fun to learn that many of the scientists responsible for the current generation of AI admit that they really don’t understand exactly how it works. As a report in a recent issue of the MIT Technology Review noted, “for all its runaway success, nobody knows exactly how—-or why—-it works.” Exciting, but a little scary.

In preparing this book I’ve struggled with what is the ‘responsible’ thing for me to do as an author of a book about AI. The conventional approach is to provide a short explanation of the science and a review of frequently-used terms.

I’m not going to do that.

I’m going to offer here a few external links to what I think are some reasonably comprehensible short descriptions of AI basics.

What’s the future of AI?: McKinsey & Co. (April, 2024) has a good set of explainers.

Likewise Gartner’s Generative AI (undated) isn’t bad.

Futurepedia offers a not-bad summary of AI Fundamentals (May, 2024)

Having disposed of the how-to, I’m now going to introduce some terms that I do think are valuable to understand. Not because you need to know them to use the software. Only that this set of terms references some key aspects of how the current generation of AI actually operates.

My use case for tackling these terms and concepts is authors and publishers who (i) want to go a level deeper on AI, for whatever reason, or (ii) want to understand the context of the current criticisms of AI, or (iii) want to contribute to strategic discussions of how their colleagues or organizations should approach AI.

In other words, this is not what you need to know, but, rather, what you might like to know. Here they are, in non-alphabetical order:

You can open up Chat AI software and just type in a question, very much as you do currently on Google.

Large Language Models work by analyzing huge amounts of (mostly) written material, allowing them to predict what words or sentences should come next in a conversation or a piece of writing. They don’t ‘understand’ language in the human sense, instead processing text by breaking it down into smaller pieces (called tokens), and then converting the tokens into numbers. They process the text as numbers, regurgitating more numbers, which are then converted back into text on output. That’s an overly simplified explanation of why Chat AI does not ‘contain’ copyrighted work: it’s built with numbers that represent a vast abstraction from the underlying texts.

LLMs are trained on how language is typically used and then generate responses based on this understanding. We tend to underestimate just how predictable most language is. Chat AI can generate text that is (sometimes shockingly) similar to existing literature, but, by design, it doesn’t have the capability to retrieve specific excerpts or copies of copyrighted texts. (I know, many of you have heard about the New York Times lawsuit against OpenAI—the Times was able to get ChatGPT to regurgitate some portions of previously-published articles verbatim.

The most important thing to understand this term is the “generative” part. Generative AI generates new text.

This, the nerdiest of the terminology here, describes a specific type of LLM developed by OpenAI. “Generative” indicates its ability to create text, “pre-trained” signifies that it has been trained on a large body of text data, and “transformer” references the software that it uses. Knowing what GPT stands for is helpful only so that you understand what the GPT in ChatGPT represents.

ChatGPT is the software you see; GPT is what’s behind the software. Users experience ChatGPT, not the GPT behind it. As noted above, ChatGPT is just one of several Chat AI online software systems, with similar functionality.

One more term that you’ll encounter frequently that is unfamiliar to many is:

The dictionary definition of corpus is “a collection of written texts” (though, in fact, it’s not always text). The term is used in reference to what GPTs are trained on: vast corpuses of (mostly) text. We’re told that the largest corpuses contain hundreds of billions of words. For mere mortals that’s impossible to comprehend. Don’t you think of Wikipedia as enormous, containing a vast number of words? Well, there are a mere 4.5 billion words in Wikipedia—GPT-4 was trained on well over a trillion.

I think that it’s important to consider this scale. Authors, understandably, are worried that the 75,000 words, plus or minus, in their book might have been sucked into a large language model. Perhaps they have (more below). But assuming this is the case, consider just how little value any one book has to the total power of today’s large language models. It’s truly insignificant. Beyond insignificant. Even 10,000 books is chump change.

They each work more or less the same and deliver similar results. So which one is best? That question takes you down a rabbit hole. It’s a bit like asking which town offers the best beach vacation, or which SUV is best for families. There is a huge amount of nuance in the issues and a vast amount of pride and prejudice filtering the responses.

So too with this seemingly simple but still enormously complex software. I’ve heard people argue that Claude has more nuanced skills with language, or that ChatGPT is better with software code. Microsoft Copilot, they point out, integrates better with the Microsoft software suite. Google Gemini can plug into Gmail, Google Maps and YouTube. Some of the differences are meaningful; many are slight. And they’re constantly changing with each new software release.

There are two top tiers of AI software. The first is what could be called the foundational software, software like ChatGPT and its competitors (Chat AI). That’s how most people experience AI today.

The next tier is all of the other software that provides what is essentially a window into the foundational software. Newcomers to AI chat will mostly not have encountered this software, though it is not rare or obscure. Some are familiar with Jasper (marketing software) or Perplexity (search). This software would simply not exist without ChatGPT and its brethren.

Keep in mind that mainstream software from Adobe, Microsoft, Grammarly, Zoom, at al., now incorporate AI tools, but this is different from software that’s built from the ground up based on chatbot interfaces. (Others like Otter (transcriptions) predate ChatGPT but are now imbued with large language model technology.)

Below we’ll look at publishing-specific software that capitalizes on generative AI’s abilities.

Way back when software was code and only engineers understood how it worked. What made personal computers succeed, beyond their relatively low cost, was software that was (relatively) easy to use. We don’t give much thought to all of this; it just surrounds us.

But consider, for a moment, Microsoft Word, a program that I can safely assume is used by 99% of the readers of this book. It takes about 10 minutes to show someone how to write a paragraph of text in Word. Then you can show them how to create bold text, and italics, and how to change the base font, perhaps from Calibri to Arial or Times Roman. Then, how to save the file and print the file. How to search and replace.

And then you leave them on their own.

Years later you find out that they’re still formatting their Word paragraphs with spaces and tabs. And still forgetting to save their files, or getting mixed up about which version they saved, sometimes having to recreate text from scratch. But they “know how to use Word.”

Word has hundreds of features, but a third of the time users employ only 5 commands: Paste, Save, Copy, Undo and Bold. It takes several weeks of training to properly learn Word’s advanced features; features that could help a publisher save many hundreds of hours of time. But most never do. I’ve been using Word since 1985, nearly forty years, and I’ve never taken a course. I don’t even know what I don’t know about how to extract the full power from the software.

I go on about this because it’s a helpful way to get your head around why AI software is so very different from any software you’ve ever worked with. It doesn’t come with a manual, not even a Quick Start guide. It comes with a blank screen and a question: “What can I help you with.”

There’s no menu bar, no button to push, no keyboard shortcuts. You ask a question, start a chat, and you get some answers.

Claude’s response:

“There are several key differences between working with modern conversational AI like myself compared to traditional tools like Microsoft Word or Excel:

“1. Natural language interaction: With conversational AI, you can communicate your intent using everyday language rather than having to learn and navigate complex menus and functions. You simply explain what you want to accomplish conversationally.

“2. Contextual understanding: AI assistants maintain context and understand the flow of the conversation, allowing for more natural back-and-forth dialog. Traditional software has no contextual awareness from one command to the next.

“3. Adaptability: AI can dynamically adapt and respond to a wide variety of requests, while traditional tools are limited to a predefined set of functions. An AI assistant can help with writing, analysis, math, coding, ideation, and more within a single interface.”

That was useful, but I wanted something easier to grasp. So I asked:

Claude:

“Here’s a simple way to explain the difference to someone without a technical background:

“Think of traditional software like Microsoft Word or Excel as a tool, like a hammer or a calculator. It has a fixed set of functions that it can perform, and you need to learn how to use it properly to get the result you want.

“On the other hand, think of modern conversational AI as a smart assistant, like a knowledgeable colleague or friend. You can just talk to it using plain English, and it understands what you’re trying to do. It can help you with all sorts of tasks, from writing to math to coming up with ideas.

“You don’t need to learn any special commands or techniques—just chat with it naturally. It adapts to your needs and remembers the context of your conversation. Over time, it gets to know you and your preferences.

“So while traditional software is like a static tool that you use, conversational AI is more like an intelligent partner that you interact with naturally to get things done. It’s a more flexible, personalized and efficient way of working.”

Hmm. Not quite what I wanted. So I continued the conversation:

Claude:

“Sure, let me try again without using technical terms:

“Imagine you’re talking to a friend who is really knowledgeable and eager to help you out. You can just talk to them in plain English about what you’re trying to do, whether it’s writing an essay, solving a math problem, or brainstorming ideas for a project.

“Instead of having to learn and remember a bunch of specific steps or commands, you can just explain what you need in your own words. Your friend understands the context and adapts their help based on what you’ve already discussed.

“It’s a back-and-forth conversation, where you can ask questions, clarify things, and get advice. And the more you talk to this friend, the better they understand your unique needs and communication style.

“Now, imagine if you could have that same type of helpful, easy interaction, but with a computer program. That’s the basic idea behind new types of software you might be hearing about.

“Instead of the old style of software, where you have a rigid set of functions and have to learn the right way to use it, this new software is more like a knowledgeable friend. You can just talk to it conversationally and it figures out how to help you based on the natural flow of your discussion.

“So it’s a smarter, more personalized, and easier way to get help with all sorts of tasks on your computer, just by talking to it like you would a person. That’s the key difference from the older, more rigid style of software.”

Summary: I was trying to do two things with these examples. One was to offer a dead simple instance of why chatbot software is so different from any software we’ve ever used. And the other was to illustrate how a single chat can evolve. It’s not just a single question and a single answer. It’s a conversation. That’s a big part of the breakthrough.

Chat AIs are some sort of combination of intern and knowledgeable consultant. You can’t assume that they know what you mean. You need to explain what you want, why you want it, and how they can be most helpful.

The first, AI foundational platforms, like ChatGPT, are core AI technology. The second, let’s call them AI add-ons; software that’s been freshly built on top of the core platforms. You won’t have heard of many of these, tools like Jasper or Rewind.

My third category is software featuring AI tools perhaps crudely bolted onto their existing product. For example the Microsoft suite of software features new AI tools in Word, Excel and PowerPoint. Adobe has added AI technology to Photoshop and Acrobat. Grammarly is being rebuilt around the new generation of LLM-based technology. It’s mostly the same product, but with AI added.

It’s becoming clear that nearly all of the software you use today will soon enough incorporate AI features. You’ll have to make your own assessment of whether those tools are genuinely useful or just AI window-dressing.

I’m more interested in the new tools, built from the ground up, to provide the value that only AI can engender. That’s the next generation of software.

I’m a Windows guy, sadly (it’s a long story as to why), and so I’m talking about my experiences accessing AI software via a Windows computer, not a Mac. Because all of the software here is browser-based, there shouldn’t be a difference using these tools on a Mac. But I offer no guarantees.

You will need some money to play the AI game, but not a helluva lot. As described above, much of the foundational software can be accessed via free versions. But you’ll usually want a paid subscription while you test it, and the subscription should be about $20/month. Cancel after you’ve tried it (don’t forget!). If you see something that’s $79/month or, god forbid, $159/month, you can ignore it. They’re not thinking of you as a potential user; they’re thinking of large mainstream corporate users.

In order to access its intelligence sometimes you have to talk to it as if it were an idiot. Or perhaps an idiot savant. It’s a bit like the character played by Dustin Hoffman in Rain Man.

Anyone can use ChatGPT. Just go to chat.openai.com and you can access the most recent version, GPT-4o for free (there are some premium features available for an extra $20/month).

When you go to ChatGPT you find essentially a blank screen and the implicit question “How can I help you today?”

You can ask it questions. Better still, you can upload a large PDF and ask questions of the file. You can also upload images, which it can describe, or a scanned page: it can recognize the text, even if handwritten.

What has emerged as a gating issue for the successful use of Chat AI is learning how to ‘speak’ with it (which you can literally do in the latest mobile app version). In Chat AI-ese is called creating “prompts” or “prompting.” Users have discovered that the more precise and detailed their prompts are, the better the responses they receive from ChatGPT. Further, prompts are not just one-off. Chat AI can continue a conversation for quite a while (though not indefinitely), and if you don’t get the answer you’re looking for you can revise and refine your prompts. This takes a lot of getting used to, and has spawned a series of how-tos, written and online, to train users on how to get the most out of prompting.

Try asking any of the Chat AI software to explain a concept like developmental editing. Then ask it to craft an explanation that a 12-year-old could understand. The results are dramatically different. Amusingly, Chat AI seems also to respond to emotional pleas. Adding “this is very important to my career” to a prompt can coax more useful responses.

(This is as good a time as any to add a parenthetical: It’s both reassuring and deeply troubling that the top scientists working on language-based AI are unable to explain why things like this occur.)

Of the various co-opted terminology seized upon during the AI gold-rush, “prompts” and “prompting” are as good as it gets. You don’t really “talk” to a Chat AI. You don’t “ask” it questions (or, you shouldn’t—that’s not how it works). You prompt these engines for responses, not for simple answers.

At first I thought an analogy might be theatrical prompts. The prompter gooses an actor who loses track of a line, causing them to jump back into action. But this isn’t accurate because an AI prompt doesn’t loosen a chunk of memorized text—that’s not how generative AI works. Give an AI engine a single prompt repeatedly, and the answers will always be at least a little bit different.

Just now I asked ChatGPT to “list the key elements of effective prompting.” I asked a second time and the answers were thematically consistent, but quite different in the specifics. Topics that were highlighted included:

• Be specific

• Avoid ambiguity

• Provide background

• Use clear instructions

In a sense it’s quite odd: AI has a good grasp of language—shouldn’t it have a sense of what you mean without pedantic prompting? But it’s precisely that mastery of language that makes prompting most powerful. Tell it exactly what you mean, not approximately.

Talk to it like the helpful assistant, the learned counselor, that you want it to be, not like a kid off the street.

The takeaway here is that, at least for the time being, the effective use of LLM-based AI tools requires at least conversancy with prompting best practices. A prompting mastery will be appropriately rewarded.

Cambridge Dictionary’s Word of the Year for 2023 was “Hallucinate,” whose definition has been expanded to include “When an artificial intelligence… hallucinates, it produces false information.” (Other additions to the 2023 dictionary include “prompt engineering,” “large language model,” and “GenAI.”)

AI hallucinations, Cambridge notes, “sometimes appear nonsensical. But they can also seem entirely plausible–even while being factually inaccurate or ultimately illogical.” This, sadly, is quite true, and as of July 2024 remains a dramatic limitation for using generative AI for mission-critical tasks. It’s one of the several great oddities of AI, and it takes people a while to get their heads around it. Remember, generative AI is mostly a next word prediction engine, not a database of facts. Hence the need for HITLs, Humans-In-The-Loop, as we’re now known, double-checking AI output. And again, it’s remarkable that we can get such extraordinary value from a technology that can produce provably inaccurate output. So it goes.

Gary Marcus, an experienced and well-informed AI-critic, compares AI hallucinations to broken watches, which are right twice a day. “It’s right some of the time,” he says, “but you don’t know which part of the time, and that greatly diminishes its value.”

Ethan Mollick, a keynote speaker at the Publishers Weekly September 2023 conference, notes that people using AI expect 100% accuracy. Hallucinations, he says, are similar to “human rates of error” which we tolerate daily.

Andrej Karpathy, a noted scientist specializing in AI, who currently works at OpenAI, writes about hallucinations:

“I always struggle a bit when I’m asked about the ‘hallucination problem’ in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines.

“We direct their dreams with prompts. The prompts start the dream, and based on the LLM’s hazy recollection of its training documents, most of the time the result goes someplace useful.

“It’s only when the dreams go into deemed factually incorrect territory that we label it a ‘hallucination.’ It looks like a bug, but it’s just the LLM doing what it always does.”

It’s not just the problem of making stuff up. Chat AI is deeply flawed software.

For many queries, particularly from novices, the responses are mundane, off-target or simply unhelpful. Chat AI has trouble counting: Ask it for a 500-word blog post and you’ll be lucky to get 150.

And each of the AI companies, in order to reduce bias and to avoid answering “how-to-build-a-bomb” queries, has erected tight response guardrails around their products: all too often, the response to a question is, essentially, “No, I won’t answer that.” I asked Google Gemini to review a draft of this text and was cautioned that “it’s essential to get the author’s approval before publishing.”

Consider this: I asked four Chat AI’s to fact-check the following statements:

• As of 2024, there are 6 big multinational publishers based in New York City. They are known as the Big 6.

• Ebooks continue to dominate book sales in the United States.

• Borders and Barnes & Noble are the two largest bookselling chains in the United States.

• After a sales decline during Covid, U.S. book sales are again growing by double-digits.

All of them spotted the errors in the first three statements. Each of them became a little confused on the fourth, uncertain of the extent of the Covid sales bump, and of subsequent sales patterns. It’s a tiny, non-representative experiment, but these Chat AIs, which are not meant to be fact-based, can do a credible job assessing facts most casual observers would miss.

They are relevant to anyone’s understanding of the AI revolution. But the discussion is too large for the space available: I just wouldn’t do it justice.

Recommended for authors and publishers: play with the image generation features in ChatGPT—they’re free and fun to use. Then do a search under “video and AI” and marvel at some of the examples you’ll find in the sites linked. Though the tool is not yet publicly available, OpenAI’s Sora provides some stunning examples of videos generated just from text prompts.

There is little built-from-the-ground-up AI software specifically for book publishers (though there’s lots available for authors).

Scholarly publishers have more options, with a variety of AI tools for research, writing and publishing.

For trade publishers most of the options relate to AI and audio. Outside of audio, the choices are coalescing around editing and marketing tools. There are also several AI content-detection tools, and content licensing tools and services.

The editing software is positioned towards authors, not professional editors — there are many more authors than editors, and the quality demanded by professional editors is far more onerous than for (mostly self-published) authors.

The marketing tools likewise aim more broadly than just book publishers, but to marketers everywhere, with tools for web content, copy generation and SEO. Jasper.ai is a leader in this category, and claims HarperCollins as a customer.

Shimmr, a sponsor of this book, is an AI-powered ad creation tool specifically for book publishers. The company, and its founder, Nadim Sadek, were profiled in a May 2024 article in Publishers Weekly.

“What we do is use AI to consider the psychological profile of a book and match it to the frame of mind of a specific audience, ensuring a more effective connection between readers and books,” Sadek told PW. “We call it ‘Book DNA,’ and it involves not only knowing the characters and plot of a book, but the values, interests, and emotions of the book.”

Calling on the Book DNA, Shimmr’s AI tools then create targeted advertisements for search and social media channels (currently Google and Meta). The ads take the form of display ads, featuring AI-generated images accompanied by taglines.

I point out to publishers evaluating Shimmr is that it’s software can generate incremental revenue, which is what we hope a new advertising/marketing tool will bring to the table.

Another new vendor born out of the opportunity with AI, is Veristage (also a sponsor of this book). Veristage offers Insight, its “AI Publishing Assistant,” a task-specific front-end across multiple publishing functions.

The Insight journey starts with the manuscript. Uploading an early version unlocks a range of tools, some more valuable to editorial, others more valuable to marketing. After working with any and all of the features, you can download a PDF report that includes editorial aspects, like writing tone, tropes, cliches and use of adverbs and adjectives, and then marketing content, like descriptions, metadata, unique selling points, comps, Amazon-optimized content, and suggested social media posts.

What I like best about Insight is that it takes a holistic approach to applying AI to the publishing process, rather than having to gather multiple software tools, each for a different function.

I want also to highlight here Leanpub, the publishing platform that hosts this book. They’ve been amazing to work with. Small is beautiful: they take chances with technology and services that larger companies would steer away from. Of top interest to authors and smaller publishers is their new TranslateWord service, where you can translate a book written in Microsoft Word into up to 31 languages, via the GPT-4o API (which powers ChatGPT). That’s what I’m using to translate this book.

I looked elsewhere to try to find a service for book translation. There are tons of translation firms, some employing AI. The only one I could find that offers book translation is DeepL. But it’s not a focus for the company. Right now Leanpub is the place to go.

I’ve spoken with several of the publishing system software vendors. They’re all looking at the opportunities, but treading carefully.

knk has run two webinars on AI, and released a whitepaper, but (as of October, 2024) has not announced any AI features in its products.

Virtusales has launched its first set of AI-enabled tools, including image tagging, alt-text generation, copyediting tools, sales and marketing copy generation, and translations of that copy. But you’ll have to dig deep into their site to find out more.

I spoke with Klopotek on its Klopotek Publishing Radio. They have “started an AI initiative in the area of Customer Services,” though no more details are available.

Supadu, which offers publishers “web design, ecommerce and data solutions,” now features “Supadu Smart AI,” (pdf) with “‘avatar-led’ video book reviews, fully integrated with Supadu Smart Buy Buttons, and easy author video translations for global markets.”

Some people are familiar with the work I’ve done around book publishing technology-based startups. There is a report in Publishers Weekly that describes the work, and links to the database.

As you’ll see in the report, I look pretty broadly at startups across the book publishing spectrum. I don’t include new book publishing companies, unless they are doing some unusual things with technology. The basic criteria is: do you use technology to try to invigorate some aspect of the book(ish) publishing process? I’ve got over 1,600 companies in the database, most launched after Amazon released the first Kindle in 2007.

After you get an overall sense of the database, you can start to dive in more deeply. On the far right tab you’ll see a way to sort only by the AI-related publishing startups.

As you can see in the illustration, there are over 240 AI-related publishing startups (as of early July, 2024). We count the audio publishing startups that employ AI as a separate category and the total of the two is over 300 companies. The majority, over 280, were launched after ChatGPT first appeared in November of 2022. This volume of new business startups is unprecedented within book publishing. It’s astounding.

As is characteristic of the full database, these AI startups mostly target authors (70%). Some 13% are looking, per se, to serve publishing companies. 10% are children’s publishing-focused.

About 50 of the startups target readers with a range of inventive offerings. Several are storytelling platforms. There are quite a few summarizers. There are multiple discovery sites, “Use AI to find your new favorite book.” Fast-improving AI-generated voices have led to a selection of “read it to me” tools.

And some interesting ideas that wouldn’t be possible without AI.

Bookshelf: Reading Tracker is a combination book discovery and personal library app that includes “automatic time tracking, reading goals & reminders, and insightful stats & trends.” Its AI librarian “can generate summaries and flash cards, discuss key ideas and takeaways, and much more.”

My friend Ron Martinez recently launched a site called Inventionarts.ai which introduced “a new conversational medium.” As the site explains, “Talk to multiple AI personalities, each with their own identity, expertise, and awareness. Invite different personas to join you in scenarios. And you can play a fictional role, too!” A literary game, of a sort.

As is characteristic of the larger startup cohort, many of these startups are between tiny and miniscule, just one person with a website and a half-baked idea. I include them all—who knows where they’re headed.

I strongly encourage you to play with the database. Click a few links. I think you’ll be tickled by the innovation and audacity of many of these organizations.

One of the sponsors of this book, Book Advisors, specializes in mergers and acquisitions in the publishing industry. I’m always advocating to innovative startups that they look for partners, and Book Advisors is where I send them to talk through the process. Book publishing has several respected M&A firms to call upon, including The Fisher Company and Oaklins DeSilva+Phillips. Book Advisors is the only firm I know that also works with technology startups.

The Association of American Publishers “filed reply comments in the U.S. Copyright Office inquiry into the intersection of copyright law and artificial intelligence (AI)...” The U.K. Publishers Association notes that “it is of the utmost importance that the Government puts in place tangible solutions as soon as possible to protect the human creativity and knowledge that underpins safe and reliable AI.”

On the other hand, the Independent Publishers Guild (IPG), with more than 600 members, offers AI guidance and training to its members, including its “Practical Guide to AI in Publishing,” conducted by a noted AI and publishing expert, George Walkley.

Here in the U.S., I’m privileged to be part of the Book Industry Study Group’s (BISG) AI Working Group, which reports to the Workflow Committee. The Working Group has outlined several potential strategic initiatives:

• Best Practices and Standards Development: Recommending best practices related to AI usage, linked to ethical guidelines, existing regulations, and laws. This includes transparency in AI-generated content and its disclosure to consumers.

• Industry Surveys and Definitions: Conducting surveys to gather broad industry insights on current and potential AI uses and establishing clear definitions and scopes for what AI means within the context of book publishing.

• Periodic Reporting and Reviews: Offering periodic reports to help the industry foresee and adapt to the rapid changes brought about by AI technologies.

Other writing and publishing associations are trying to get in front of the topic. Some take stands in opposition to AI; others are just trying to help their members understand the technology and, perhaps, to experiment with it.

A year ago the question of what publishing companies are currently doing brought an answer of, “not much.” Not a whole lot has changed since, but there has been some activity. I’ll focus on trade book publishers; scholarly and academic publishers are more active. Here’s some of what I’ve uncovered.

Hachette: According to a November 2023 report in The Bookseller in the UK, Hachette has published a position statement on AI, offering a distinction between ‘operational’ uses and ‘creative’ uses. The company “made it clear it encourages ‘responsible experimentation’ for operational uses but is opposed to ‘machine creativity... in order to protect original creative content produced by humans’.”

HarperCollins: According to an April, 2023 report in Publishers Weekly by Andrew Albanese and Ed Nawotka, HarperCollins CEO Brian Murray said:

“We know that it’s going to be important and it’s going to have a major impact on our industry over the next three to five years. And so I think a lot of us are trying to figure out how and to make sure we employ it in an ethical, moral way that helps us serve authors and provide professional services to authors and doesn’t compete with authors and storytelling.

“And that’s, I think, a challenge. Not so much maybe for all the publishers that are here, but I guarantee you there are a lot of little tech teams around the world that might be coming after our business. They’re not publishers, they’re not editors. They’re the technologists and they see an opportunity.”

Penguin Random House (PRH): Bertelsmann, the parent company of PRH, offers a white paper called State of Play: Exploring Generative AI’s Transformative Effects on the Media & Entertainment Industry, which includes a section on book publishing.

Also, according to a report in Publishers Lunch, the company “introduced its own internal AI application, called PRH ChatGPT.”

The article states that PRH internal documents explain that the program “‘can be used to streamline processes, enhance creativity, and provide data insights’ across departments. The company suggests that among the uses of the app are summarizing books and documents; revising emails; drafting blog posts or job descriptions; analyzing text-based data, and “generat[ing] ideas for content.’”

Simon & Schuster: Company CEO Jonathan Karp earned a mention in the New York Times “Most Memorable Literary Moments of the Last 25 Years” with a quote reported in Publishers Weekly from its May 2024 U.S. Book Show. He said that AI was not the “elephant in the room” but rather, “more like the cicada in the world. You know, lots of buzzing and lots of screwing.”

He acknowledged that AI “is definitely a valuable tool. It’s definitely going to make us more efficient. It’s going to help us process and gather information better, and hopefully allow workers to do a higher level of work that’s more interesting and creative.”

In March, at the London Book Fair, as reported in The Bookseller, Karp said that the company would also be looking at foreign language versions of AI-generated audiobooks “in territories where [that author’s] works would never otherwise have a chance because of the cost of [audio production].”

Keith Riegert, CEO of Ulysses Press and Perfect Bound, presented at the Publishers Weekly U.S. Book Show in May, 2024, offering the most comprehensive overview I’ve seen about AI use cases within publishing companies. Though Perfect Bound is a sponsor of this report, I stand by that statement. Keith offers “20 practical ways you, as a publishing professional, can start using AI right now.”

His presentation, Getting Started with AI, can be viewed and downloaded from the Perfect Bound website.

I’m deliberately borrowing this section title from a May, 2023 edition of Ethan Mollick’s newsletter —there’s no need to try to improve on it. Mollick is a professor at the Wharton School of the University of Pennsylvania, who studies entrepreneurship & innovation. His newsletter, which I recommend frequently, is calm, refreshing, and uniquely insightful.

Among the things that qualify Mollick as a commentator are that he has no skin in the game. He doesn’t need to sell AI, nor to trash it. He has merely committed to exploring AI and its many impacts, mostly upon education, culture, writing, and publishing. And he’s a fine, clear writer.

If you browse through Mollick’s newsletter archives you’ll see that he didn’t begin to focus on AI until December 2022. It wasn’t his beat—like most of us, AI dropped in on his day job, and he couldn’t take his eyes off it.

In this post his insights falls closest to our interest as publishing professionals. “Might AI,” he asks, “change the way we interact with books?”

To answer the question, Mollick notes, “we need both an AI with a memory large enough to hold a book, and an author who knows their own book well enough to judge the AI’s results.” Mollick tests one of his several titles (he doesn’t specify which one, but from the chats it’s clearly The Unicorn’s Shadow: Combating the Dangerous Myths that Hold Back Startups, Founders, and Investors, a book favorably reviewed on Amazon, though not a current bestseller).

Mollick considers different aspects of AI’s potential value to an author, publisher or reader, including “AI as reader and editor,” and “a practical use: help for instructors.” He asks a Chat AI to summarize the book. It succeeds to Mollick’s satisfaction.

Then a tougher challenge: “Give me examples of metaphors in the book.” Metaphor, he points out, “is challenging for even human readers, as it involves finding a use of figurative language without any clear markers (unlike a simile, there are no “likes” or “as”).” The results, he records, “are impressive, though there are minor errors.”

The LLM is less successful as an editor: its failings in this department, Mollick notes, illustrate “something that has become clear about the current state of AI: if you are a very good writer or editor, you are better than current AI…”

Nonetheless, “AIs have, or at least have the appearance of having, an understanding of the context and meaning of a piece of text.” As a result, Mollick believes that “how we relate to books is likely to change as a result of AI.”

I think so too.

Software for the automated typesetting of books dates back to at least the 1970s. In the mid-1980s I supervised a software project called PageOne, based on Donald Knuth’s TeX, which could typeset a book in minutes. SGML appeared around the same time, based on a document standard introduced in 1969. It was largely succeeded by XML, introduced in 1996. These robust markup languages create solid structures for automation.

Desktop publishing ushered in another round of automation for QuarkXPress and Adobe InDesign, as well as Adobe Illustrator and Adobe Photoshop. Publishing workflows can be managed with various programs and systems.

An organization to watch is the Coko Foundation. They offer a suite of open source production and publishing management tools, including Kotahi, a scholarly publishing platform, and Ketty for book production, which includes an AI Assistant. The Kotahi AI PDF Designer, “transforms PDF design into a straightforward, interactive process.”

There are some early initiatives to bring AI into InDesign workflows. In April 2024 Adobe announced a Text to Image feature. Third parties may be getting ahead of Adobe here: the innovative prepress and production vendors in India, such as Hurix Digital and Integra, are showing more initiative than Adobe in harnessing AI for production.

The ‘low-hanging fruit’ is obvious. Ask Chat AI to help with a product description or a press release. Ask it to suggest some keywords. This it can do, without breaking a sweat. But most publishing professionals can do the same thing, with only a little moisture on the brow.

Keith Riegert’s use cases, linked above, include suggestions for brainstorming titles, drafting a digital marketing report, and creating a digital marketing campaign tracker in Google Sheets.

Shimmr software, described above, hints at the shape of automated marketing to come.

Metadata is core to book discoverability. You’ve heard that enough times to be nauseated by the admonition. It’s off-putting mainly because “metadata” remains elusive to most non-techies. If you say, “it’s just the basic info about the book, the title, description, price, subject categories, that kind of thing,” people exhale. That they’re comfortable with. But that’s about all.

I regret to remind you that there’s actually far more to metadata than just a few details about the book. There’s so much more. Much more than I can encompass in this little book. I’ve co-authored a whole book on the topic. Ingram publishes Metadata Essentials, an excellent short volume. I’ll say it here, and not for the last time: authors and publishers pay short-shrift to their metadata at their peril.

AI can help with metadata generation. For example, self-publishing vendor PublishDrive, offers an “AI-Powered Book Metadata Generator” which offers AI recommendations for the book title, blurb, Amazon categories, BISAC categories, and keywords.

Insight, from Veristage, described above, can generate descriptions, keywords, BISAC categories, and define target audiences.

With his typical deep wisdom, Graham Bell, EDItEUR’s director, notes that “one reaction to (the controversies surrounding the technology) is to forswear use of AI or to avoid trading in AI-created products. A more realistic option is simply to be transparent with trading partners and readers when AI has been used. And as some resellers limit or ban AI-based content from their platforms, it is important for reputable publishers to highlight those products that do use generative AI techniques to create content.”

Bell goes on to outline ways that publishers can specify in metadata:

• AI contributors

• AI-based voices in audiobooks

… as well as a method to indicate in the metadata for digital products that the publisher explicitly opt outs of text and data mining (TDM) for uses other than research. There’s also a way to specify a separate license covering commercial or non-research TDM.

As is often that case, what is specified in ONIX may not be uncovered down the food chain, but at least a best effort has been made.

But this is not a great time to be timid with technology.

There are calm and rational approaches available for integrating AI into your publishing operation, regardless of the size of your organization.

The impetus must come from the top. The very top. Senior executives need to embrace a vision of AI’s potentially transformative impact and communicate a program to staff across the organization. The program may be little more than “experiment, document your experiments, and share.” That’s a good start.

The Economist recently referenced an organizational tactic for new technology adoption called “the lighthouse approach.” You create a beacon by selecting one high-profile proof of concept that can be implemented quickly, that everyone can relate to.

Publishing companies are handicapped by the hubbub surrounding copyright: authors are up in arms. A May 2023 Authors Guild survey found that “90 percent of writers believe that authors should be compensated if their work is used to train generative AI technologies,” and 67 percent said they “were not sure whether their publishing contracts or platform terms of service include permissions or grant of rights to use their work for any AI-related purposes.” Those uncertain authors are now asking their publishers if AI is being used in the editing or production of their work, and some powerful authors are insisting that it not be. They’re looking for the AI equivalent of a peanut-free bakery.

This is a thorny problem for publishers—if you can’t use AI on the books you’re planning to publish, what can you use it for?

Despite its widespread use, few publishers have publicly defined their AI policies, and communicated their approaches to AI to the public. The term ‘the public’ has a slippery significance here, when you consider the different publics addressed by trade, scholarly and educational publishers.

For trade publishers the most important audience is authors and their agents. Scholarly publishers face different obstacles, when they consider AI’s promising impact on research, and then AI’s more problematic impact upon converting research into narrative (Avi Staiman wrote a thoughtful post on this topic). For educational publishers, establishing policies is tricky, as AI’s encroachment on the practice of teaching, of education, is multifaceted and complex.

I think that publishers face two big challenges as they move forward with AI technologies. The first is to develop a company position about how to approach AI generally, on how to incorporate AI into their workflows. The second challenge is communicating that position, clearly and unambiguously, to their constituents.

The publisher policies I have seen are mostly flawed. Some of them are in fact policies directed externally, at authors, with a range of admonitions about what is acceptable practice (not much) and what is not acceptable (lots). O’Reilly’s “AI Use Policy for Talent Developing Content for O’Reilly” goes on for pages and pages, with esoteric guidance, such as “DO NOT use any OSS GenAI Models that produce software Output that is subject to the terms of a copyleft or network viral open source license.”

On the other hand scholarly publisher Elsevier, in the “Elsevier Policies” section of its website, includes statements on “Responsible AI Principles,” “Text and Data Mining,” and “The use of generative AI and AI-assisted technologies in writing for Elsevier.”

The few internal, unpublished, publisher policies that I’ve seen are conservative, excessively so. These publishers reacted too quickly to the range of perceived and possible threats, and to their authors’ anxieties, and have hamstrung their own ability to engage robustly with this fast-developing, fast-changing technology.

It’s a given that they will use AI ‘responsibly,’ whatever that means. It’s a given that they have the utmost concern for authors’ intellectual property and for aggressively protecting author’s copyrighted work. (Although, of course, these broad principles must be declared publicly, and often reiterated.)

But what else?

• Will they allow AI to have a role in editorial acquisitions? Can AI take a look at the slush pile?

• Will they allow AI to have a role in developmental editing, line editing and copyediting?

• Will they allow AI to have a role in determining print runs and allocations?

• In creating accessible ebook files, including alt-text?

• In aiding audiobook creation in cases where it’s not economically-realistic to hire talented human narrators?

• In aiding foreign language translation into markets where rights would never be sold?

• In developing marketing material at scale?

• In communicating with resellers?

If so they must make this clear, and clearly explain, the thinking behind these policies. Publishers must be brave in countering the many objections of most authors at this time of fear and doubt.

At the February 2024 PubWest conference in Arizona a speaker from outside the publishing industry suggested that one of the uses for AI will be replacing interns. The room burst into flames. She meant well—indeed an April 10, 2024 report in the New York Times describes how Wall Street investment banks are looking to replace many of their interns with AI. Similar to the case in publishing, an obvious concern is: how do you find senior analysts if they can’t start off as junior analysts?

The publishing industry has always relied on internships. A 2019 study found that 80 percent of the people who had worked in publishing for less than fifteen years had previously interned.

In part it’s a way to get the grunt work dispatched at a reasonable cost. But that pales against the larger reality that no publishing school can equip someone to join a publishing company at the level of middle-manager. The only way to develop the skilled staff of tomorrow is to train interns and apprentices today.

The objective here is not to seek to replace interns with AI, but instead to make their work more productive and rewarding using AI tools, benefitting both the intern and the publishing company.

Using AI for audiobooks is not new; I first reported on the trend in Publishers Weekly in 2021. But the newer large language models have reinvigorated the technology for automated audiobook narration.

Back in 2021 I noted “Is it perfect? Certainly not. Can it be good enough? Probably, if a publisher is willing to spend the necessary time in the voice editing phase of the project.” Two-and-a-half years later, by many accounts, AI-voices are undetectable from human voices, unless you’re listening very closely.

Last November Meta (Facebook) introduced “Seamless,” which is able to “transfer tones, emotional expression, and vocal style qualities” into the translation of 200 languages. An audiobook can be immediately translated into multiple languages with extraordinary quality.

Also in 2021 I reported that “Audible’s block on the distribution of audiobooks with non-human narrators is a real problem that may take some time to resolve.” In the meantime, both Google and Apple announced programs to allow authors to create audiobooks with AI-generated voices. On December 5, 2023, Findaway Voices by Spotify began accepting “digital voice narrated audiobooks from Google Play Books for distribution to select retail partners.”

In early November 2023 Amazon announced that Kindle Direct Publishing (KDP) authors would soon have access to a service that would allow them to “quickly and easily produce an audiobook version of their ebook using virtual voice narration, a synthetic speech technology.” In January, 2024, Jane Friedman reported (paywall) “Audible quietly started allowing AI-narrated audiobooks to enter its storefront late last fall, long after other retailers had done the same.”

And, of course, I’m using AI for the audiobook versions of this book.

I hosted a webinar on AI for book translation, sponsored by BISG, in June, 2024. The video is online on YouTube.

The subject is complex and nuanced. One thing I find fascinating is how long people have been trying to automate translation. It’s a reminder that books, which fill our universe, are such a small proportion of written communication, even more so in this online age.

Warren Weaver, credited as the father of machine translation (MT), noted to a colleague, “When I look at an article in Russian, I say: ‘This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.’” For a machine, language is just a code. It’s not culture and feeling and the grandeur of written language. It’s a task where letters are numbers.

Clearly the fiction/nonfiction divide will loom large in AI translation. Chat AI is strong on style, but it falls short on facts. It’s very early days, but I’m inclined to think that Chat AI will shine with fiction, but come up short with nonfiction. (Nonetheless, I’m proceeding to machine-translate this fact-filled book!)

Literary fiction is very much the elephant in the room. It is precious and revered and rightly so. Translators can spend hours arguing about a single word or phrase. Chat AI must tread cautiously in those waters.

But this is fertile territory. As far as I can determine (from scant data), there were only 9,500 trade book translations in 2023. Even if I’m off by a large factor, it’s clear that few books are being translated from foreign languages into English.

Similarly, I found a statistic indicating that in 2023 there were only 7,230 translations from English into Spanish (in Spanish book markets). That seems ludicrously small.

There’s a vast opportunity here.

Most of the use of AI for book translation will be for books where translation was never considered economically feasible. There is bound to be a job impact on translators of “mid-market” books; the job growth will be managing projects and in QA. Will that offset the job loss? Unlikely.

As with most aspects of AI, there are challenging issues to be addressed, and no easy answers.

As I noted above, Leanpub and DeepL are two companies offering AI-assisted book translation services to authors and publishers.

I see two reasons for the advance within this community. First, the authors within scholarly publishing are academics by trade, and in the STM sector (scientific, technical and medical), they’re often scientists with advanced degrees. Quite apart from publishing, they are investigating, and often embracing, AI within their work. They would be more surprised to find that their publisher was not exploring the use of AI in editing and publishing their work.

This flows up into the scholarly publishing ecosystem, where, as often as not, the editors are scholars themselves. For them, technology is not intimidating.

I’m not going to drill down further on the particulars in this book; I’ll report on it in more depth on my blog.

Authors and publishers sometimes seem to be living in two solitudes, connected, but apart.

Times change. Where publishers were once untouchable, top authors now call the shots. As I discuss elsewhere, self-published author are the trailblazers. They carry little of the baggage that burdens traditional publishing.

When publishers look at AI, they see few opportunities. When I talk to authors about AI, the world is their oyster. The possibilities are near-endless:

Authoring and editing

• Trying to write & publish whole books

• Trying to ideate for a new project

• Trying to ideate within a new book

• Fine-tuning of the story

• Research

• Fact-checking

• Writing companion

• Developmental editing

• Copyediting

• Spell-checking and grammar-checking

• Proofreading

Illustration/imaging

• Create illustrations and charts

• Cover design roughs

• Video promotions

Marketing

• Automate submissions to agents, publishers, contests, friends, blurb requests

• Generate marketing material: press releases, blog posts, social media, etc.

• Website generation

Authors are going to drive much of the change in industry adoption of AI, whether for or against.

They are the beneficiaries of much of the startup innovation surrounding AI in authoring and publishing — some 70% of the startups are looking to work with authors on their journeys.

There are so many issues and concerns surrounding AI, that they fill volumes on their own. Here’s a word cloud of the topics I monitor. I’m sure I’m missing a few.

There’s lots of information available on each of these topics, and I encourage you to read as deeply as you can. It’s possible you’ll conclude that the risks outweigh the benefits, and that you don’t want to pursue the use of AI, whether personally or within your organization. That decision brings its own risks; the usual, of being left behind. But it’s a personal choice.

If you google “books regarding the risks of AI” you’ll find a selection of worthwhile volumes. A recent podcast that I found particularly chilling was Ezra Klein’s chat with Dario Amodei, Anthropic’s co-founder and CEO (the company that develops Claude.ai). You learn that these companies are aware of the risks. Amodei refers to an internal risk classification system called A.S.L., for “AI Safety Levels” (not American Sign Language). We’re currently at ASL 2, “systems that show early signs of dangerous capabilities—for example ability to give instructions on how to build bioweapons.” He describes ASL 4 as “enabling state-level actors to greatly increase their capability… where we would worry that North Korea or China or Russia could greatly enhance their offensive capabilities in various military areas with AI in a way that would give them a substantial advantage at the geopolitical level.” Chilling stuff.

Within this grim context, I’ll highlight the most pertinent issues for writers and publishers.

The copyright issues are both specific and broad. It’s well-known that many of the LLMs were trained on the open web—everything that can be scraped from the 1.5 billion sites on the web today, whether it’s newspaper articles, social media posts, Wikipedia, web blogs and, apparently, transcripts of YouTube videos.

It’s provable that at least one of the LLMs ingested the actual text of thousands of books not in the public domain. Possibly others did as well.

Was it legal to ingest all of this text to help build billion-dollar AI companies, without any compensation to the authors? The AI companies make their argument around fair use; the courts will eventually decide. Even if it was legal, was it ethical or moral? The ethics appear less complex than the legal considerations. You decide.

The laws surrounding copyright obviously did not anticipate the unique challenges that AI brings to the issue, and searching for legal solutions will take time, probably years.

Here’s a catalog of fifteen of the most prominent suits, not all of them having to do with books, but also images and music. And here’s another list that updates the status of all of the, by their count, 30 lawsuits.

The U.S. Copyright Office’s position on the copyright-ability of AI-generated content states that AI alone cannot hold copyright because it lacks the legal status of an author. That makes sense. But this assumes 100% of the work is AI-generated. As discussed elsewhere, few authors are going to let AI generate an entire book. More likely it will be 5%, or 10% or… And here the Copyright Office stumbles (as would I).

In a more recent ruling the Office concluded that a graphic novel comprised of human-authored text combined with images generated by the AI service Midjourney constituted a copyrightable work, but that the individual images themselves could not be protected by copyright.“ Jeez!

At the end of July the Office published “Part 1 of its report on the legal and policy issues related to copyright and artificial intelligence, addressing the topic of digital replicas.”

The New York Times offers a glimpse into how the Copyright Office “is reviewing how centuries-old laws should apply to artificial intelligence technology, with both content creators and tech giants arguing their cases.”

Some compare the current litigation to the Google books lawsuit, which took 10 years to legally resolve. Who knows how long the appeals process will drag out for these filings.

But that may not be a publisher’s most serious issue. It’s perception. AI is radioactive within the writing and publishing community. For many authors the well has been poisoned. Anything that even smacks of AI draws intense criticism.

There are numerous examples. In a recent incident Angry Robot, a UK publisher “dedicated to the best in modern adult science fiction, fantasy and WTF,” announced that it would be using AI software, called Storywise, to sort through an anticipated large batch of manuscript submissions. It took just five hours for the company to drop the plan and return to the “old inbox.“

The startup Prosecraft, “the world’s first (and only!) linguistic database of literary prose” was shut down in the blink of an eye.

The unbearable dilemma for trade publishers in using AI tools internally: if your authors find out, you’ll have a hard time weathering the resulting storm. I believe that publishers have no choice but to be brave, to adopt (at least some of) the tools, explain clearly how those tools are trained and how they’re utilized, and push on.

In the UK, The Society of Authors takes a hardline approach: “Ask your publisher to confirm that it will not make substantial use of AI for any purpose in connection with your work—such as proof-reading, editing (including authenticity reads and fact-checking), indexing, legal vetting, design and layout, or anything else without your consent. You may wish to forbid audiobook narration, translation, and cover design rendered by AI.”

The Authors Guild appears to accept that “publishers are starting to explore using AI as a tool in the usual course of their operations, including editorial and marketing uses.” I don’t think that many members of the Guild are as understanding.

Most publishers, and many authors, are searching for ways to license content to AI companies. Everyone has a different idea of what the licensing terms should be, and how much their content is worth, but at least the discussions are underway.

Various LLM vendors are starting to take licenses for specific content, some books, but mostly news or other online text and data. Are the AI companies only going to license the crème de la crème of content? Do they need the skim milk as well?

There are several startups looking to work with publishers (and, in some cases, individual authors). ProRata.ai and Created by Humans are both interesting in this regard.

In mid-July, Copyright Clearance Center (CCC), long the industry’s main player in collective copyright licensing, announced the availability of “artificial intelligence (AI) re-use rights within its Annual Copyright Licenses (ACL), an enterprise-wide content licensing solution offering rights from millions of works to businesses that subscribe.” This is not a blanket license.

Publishers Weekly covered the announcement, quoting Tracey Armstrong, president and CEO of CCC, saying “It is possible to be pro-AI and pro-copyright, and to couple AI with respect for creators.”

It’s not all-encompassing, covering only “internal, not public, use of the copyrighted materials.” Nonethless, this may well be a breakthrough in moving publishing closer to a degree of cooperation with the large language model developers.

In the next few sections I want to dance around the ambiguities surrounding AI use. I’ll try to walk you through the issues, many of them interrelated.

Artificial intelligence, in different forms, has already been integrated into most of the software tools and services we use every day. People rely on AI-powered spell- and grammar-checking in programs like Microsoft Word or Gmail. Microsoft Word and PowerPoint apply AI to provide writing suggestions, to offer design and layout recommendations, and more. Virtual assistants like Siri and Alexa use natural language processing to understand voice commands and respond to questions. Email services leverage AI to filter messages, detect spam, and send alerts. AI powers customer service chatbots and generates product recommendations based on your purchase history.

And much of this is based on Large Language Models, as it is with ChatGPT.

For an author or editor to say, “I don’t want AI used on my manuscript,” is, broadly speaking, all but impossible, unless both they and their editors work with typewriters and pencils.

They could try saying, “I don’t want generative AI” used on their book. But that’s a tough one to slice and dice. Grammar-checking software was not originally built on generative AI. Grammarly has added AI as an ingredient to its product, as will all other spelling and grammar checkers. Generative AI is also core to the marketing software on offer.

Another aspect of authors and the use of AI has similarities to the copyright issue discussed above. In the extreme, we’re seeing 100% AI-generated content being published on Amazon. Most of it (all of it?) is of terrible quality, but that doesn’t prevent it from being published. (See also the Amazon section.) More concerning for publishers is AI-generated submissions. Yes, AI ups the quantity, but large publishers already have a filter for quantity. The filters are called agents. They are the ones who are going to have to figure out how to handle the quantity problem, and apparently they’re going to have to find a solution that doesn’t employ AI.

It’s something of an existential problem—do I want to publish a book written by ‘a machine’? For most publishers that’s an unequivocal ‘no.’ Easy peasy. Well, what about a book where 50% of the content was generated by an LLM, under a capable author’s supervision? Hmm, let’s try a ‘no’ on that as well. OK: then what about 25%, or 10%, or 5%? Where do you draw the line?

And, now that you’ve entered the line-drawing business, how do you resolve the dilemma that spelling and grammar tools now rely, at least in part, on generative AI? What about AI-driven transcription tools, like Otter.ai, or the transcription feature built into Microsoft Word?

I can’t find any trade publisher that has declared they will not publish a work with a pre-specified quantity of AI-generated text. Here’s the Authors Guild on the topic:

“If an appreciable amount of AI-generated text, characters, or plot are incorporated in your manuscript, you must disclose it to your publisher and should also disclose it to the reader. We don’t think it is necessary for authors to disclose generative AI use when it is employed merely as a tool for brainstorming, idea generation, or for copyediting.”

Needless to say, ‘appreciable’ is not defined (Oxford defines it as “large enough to be noticed or thought important”), but the post goes on to explain that the inclusion of more than “de minimis AI-generated text” would violate most publishing contracts. De minimis, in legal terms, is not precisely specified, but, generally speaking, means more or less the same as appreciable.

I hosted a webinar on AI detection, sponsored by BISG, in May, 2024. The replay is online on YouTube. Jane Friedman offered a comprehensive write-up of the webinar in her Hot Sheet newsletter.

For many authors, the toxicity of AI means keeping it far away from their words. Publishers bear a special burden—they don’t create the text, but, once published, they shoulder a substantial obligation to the text. We’ve seen lots of dynamite blow near incendiary books, whether it be around the social implications of the content, or the plagiaristic purloining of other writer’s words and ideas. Now with AI we face a whole new set of ethical and legal issues, none of which were outlined in publishing school.

Part of it seems similar to what people worry about for students, that using AI is somehow cheating, similar to cribbing from a Wikipedia article, or perhaps just asking a friend to write your essay.

One of our webinar speakers, an educator, José Bowen, shared his disclosure for students. It’s not exactly what you use for an author, but it demonstrates some sort of “risk levels” of AI use.

Template Disclosure Agreement for Students

• I did all of this work on my own without assistance from friends, tools, technology, or AI.

• I did the first draft, but then asked friends/family, AI paraphrase/grammar/plagiarism software to read it and make suggestions. I made the following changes after this help:

  • Fixed spelling and grammar

  • Changed the structure or order

  • Rewrite entire sentences/paragraphs

• I got stuck on problems and used a thesaurus, dictionary, called a friend, went to the help center, used Chegg or other solution provider.

• I used AI/friends/tutor to help me generate ideas.

• I used assistance/tools/AI to do an outline/first draft, which I then edited. (Describe the nature of your contribution.)

And so a publisher could draft something like this for their authors. Let’s say the author discloses the top level: I used AI extensively, then edited the results. What then? Do you automatically reject the manuscript? If so, why?

And, meanwhile, if you’re paying attention, you learn that that manuscript you just read and loved, which the author swore wasn’t even spell-checked by Grammarly, could have in fact been 90% generated by AI, by an author expert at concealing its use.

You’re then forced to rethink the question. It becomes, “Why am I so damned determined to detect this thing which is undetectable?”

In part it’s the alarmist concern surrounding the copyrightability of AI-generated text. The copyright office won’t offer copyright protection to 100% AI-generated text (or music, or images, etc.). But what about 50% AI-generated text? Well, we would only cover the 50% generated by the author. And how would you know which half? We’ll get back to you on that one.

Wouldn’t it be great if you could just feed each manuscript into some software that would tell you if AI had been used in creating the text?

Leaving aside the issue that the only way to do this would be by employing AI tools, the more important question is, would the software be (sufficiently) accurate? Could I rely on it to tell me if AI had been used in creating a manuscript? And could I depend on it not to produce “false positives”—to indicate that AI had been used, when in fact it had not?

There’s now lots of software on the market that tackles these challenges. Many of the academic studies evaluating this software point to its unreliability. AI-generated text slips through. Worse, text that was not generated by an AI is falsely-labeled as having been contaminated.

But book publishers are going to want some kind of safeguards in place. It appears that, at best, these tools could alert you to possible concerns, but you would always need to double check. So perhaps it might alert you to texts that need to be more carefully examined than others? Is this an efficiency?

True efficiency will be found in moving beyond concerns about the genesis of a text, instead maintaining our existing criteria as to the quality of the submitted work.

“AI will not replace you. A person using AI will.” —Santiago Valdarrama (January, 2023)

Job loss from AI adoption could be severe. Estimates vary, but the numbers are grim. There are obvious examples: San Francisco’s driverless taxis eliminate… taxi and rideshare drivers. AI-supported diagnostics could reduce the need for medical technicians.

The optimist in me points to, as one example, the introduction of the spreadsheet and its impact on employment. As you see in the chart below, employment in “Accounting, Tax Preparation, Bookkeeping, and Payroll Services” has nearly doubled since 1990—hardly an indictment of spreadsheets and other technologies that have largely automated these tasks.

Ethan Mollick’s study with the Boston Consulting Group (BCG) was an experiment that aimed to better understand AI’s impact on work, especially on complex and knowledge-intensive tasks. The study involved 758 BCG consultants, randomly assigned to use or not use OpenAI’s GPT-4 for two tasks: creative product innovation and business problem solving. The study measured the performance, behavior, and attitudes of the participants, as well as the quality and characteristics of the AI output.

Among the findings was that “AI works as a skill leveler. The consultants who scored the worst when we assessed them at the start of the experiment had the biggest jump in their performance, 43%, when they got to use AI. The top consultants still got a boost, but less of one.” The full article is revealing, and as with all of Mollick’s work, provocative yet accessible.

Education has been front-and-center in the pro and con debates about AI. The introduction of AI into classrooms is largely seen as a curse, or at least a challenge. Other educators, like PW’s keynoter Ethan Mollick, embrace AI as a remarkable new tool for educators; Mollick insists that his students work with ChatGPT.

The best book on the topic is Teaching with AI: A Practical Guide to a New Era of Human Learning by José Antonio Bowen and C. Edward Watson.

I’m not going to delve into educational publishing in this book—it’s a vast topic, demanding a separate report. Arguably publishing is becoming of secondary interest within education: AI tools are software, not content, per se.

Perplexity goes a step further, rephrasing the information it gathers from multiple sources so that you really don’t have to click a link. It provides links to its sources, but clicking them is usually unnecessary—you’ve already got the answer to your question.

Google is moving in the same direction with its “AI Overviews.”

This seemingly modest shift has huge implications for every company and every product that relies, at least in part, on being discovered through search engines. If searchers are no longer being sent to your site, how can you engage them and convert them to customers? Simple answer, you can’t.

Joanna Penn is at the forefront of thinking about the impact of new technologies on writing and publishing. She tackled this complex topic on her podcast and blog last December.

It’s still early days for AI and the transformation of search.

Amazon now requires authors to disclose details of their use of AI in creating their books. No doubt this can be abused.

Try searching on Amazon for “AI-generated books.” There are lots. Some of the results are how-to books about the use of AI for creating books. But others are, unabashedly, AI-generated. “Funny and Cute cat images-You are can’t see this types of photos in the world-PART-1” (stet) is credited to Rajasekar Kasi. There are no details of his (?) bio on an author page, but six other titles are credited to the name. The book, published August 26, 2023, has no reviews and no sales rank. The ungrammatical title of the ebook doesn’t match the ungrammatical title on the cover of the print book.

But other authors are clearly using AI extensively in the creation of their books, and not disclosing. As I discuss above, detecting AI use is next-to-impossible with skilled ‘forgers.’ Coloring books, journals, travel books and cookbooks are being generated with AI tools in a fraction of the time and effort of traditional publishing.

Search “korean vegan cookbook” and you’ll find the number one title, by Joanne Lee Molinaro, in first place. But trailing right behind it are other titles that are obvious rip-offs. “The Korean Vegan Cookbook: Simple and Delicious Traditional and Modern Recipes for Korean Cuisine Lovers” has two reviews, including one that notes “This is not a vegan cookbook. All the recipes have meat and eggs ingredients.” But the book is #5,869,771 in sales rank, versus the original, which stands at #2,852 on the list.

It’s difficult to determine the extent of the harm caused. Nothing good can come of this, but how bad is it?

Amazon has policies in place that allow it to remove any book that fails to “provide a positive customer experience.” Kindle content guidelines forbid “descriptive content meant to mislead customers or that doesn’t accurately represent the content of the book.” They can also block “content that’s typically disappointing to customers.” It is the sheer volume that defeats Amazon’s watchers? Or is there another reason?

LLMs are trained on that which has already been published online. What has been published online is rife with bias and so LLMs reflect that bias. And of course not just bias, but hate, reflected in its learnings, and now a potential output in AI-generated words and images. Porn is another natural beneficiary of the AI’s remarkable facility with images, and there are recent troubling stories of young women finding fabricated nude images, their male classmates as likely suspects. The New York Times reported separately about an increase in online images of child sexual abuse.

Authors and publishers need to be aware of these built-in limitations when using AI tools.

“AI will never be creative.” That’s a familiar mantra.

We all know what creativity is, don’t we?

Cambridge: “the ability to produce or use original and unusual ideas.”

Britannica: “the ability to make or otherwise bring into existence something new, whether a new solution to a problem, a new method or device, or a new artistic object or form.”

I don’t dispute that creativity, in this sense, is well beyond the reach of LLMs.

My argument is that most of what’s passed off as creative—and often appears quite creative —-is, in fact, iterative. Which works perfectly well for many things, including most advertising.

But to raise the ‘creativity’ stanchion, and then diminish LLMs, is to set an impossibly high barrier, at which AI invariably fails. Then people dismiss AI as “well, I told you it’s not creative.”

And thereby miss the iterative stuff that it’s very good at.

I’ll be posting a long essay on this topic in the not distant future, after I finish reading:

The Creativity Code, by Marcus du Sautoy, and

The Artist in the Machine, by Athur I. Miller

Literary Theory for Robots, by Dennis Yi Tenen

Jeremiah Owyang is an industry analyst (and investor) based in Silicon Valley. He’s an AI booster, but he’s been around hype before.

As he puts it, small teams of programmers now can use LLMs to assemble “sentient creatures”—like a 4-year-old—in two days. They are capable of:

• ‘Seeing’ with computer vision, what’s happening in the real world,

• ‘Hearing’ via voice commands and ambient sounds in the real world,

• ‘Thinking’ through processing the above real-world input,

• ‘Learning’ by accessing the pre-trained data,

• ‘Referencing’ exclusive data sets,

• ‘Speaking’ with life-like audio voices, that have inflection and tone in any language,

• ‘Writing’ through text communication, in any format or style required,

• ‘Drawing’ by creating images spontaneously, and

• ‘Interacting’: it can proactively engage in dialog, ask questions, or assign AI agents to complete tasks on their own.

Owyang continues: “I’ve been in Silicon Valley for 27 years, and have experienced five waves… Friends: I’ve. Never. Seen. Such. Rapid. Evolution. In. Such. A. Short. Time.

“It’s very clear that AI is evolving faster than a humans can, this is exponential capabilities in such as short time.”

Will it matter to publishing? You decide.

In this book I’ve generally considered AI’s short-term impact on book publishing. That’s evolution. But is a revolution in the offing?

You’ll recall Doc Searls’ quotation from the introduction to this book: “The next revolution will catch us all off guard, as they always do. Said another way: if the crowd is anticipating the revolution, it can’t be the revolution.”

By that statement I would likely fail if my call is for an incipient AI revolution. Nonetheless, I want to play with the possibility over the next 20 pages or so.

What got me started was a recent conversation with Peter Brantley, my partner in the AI webinar weeds, about how we might shape upcoming AI programs. Peter works much of the time within the library community, and had just returned from a day-long event in Los Angeles with some 150 of his library colleagues, where they explored, in person, AI’s potential impact on their sector. The conversation was lively and in-depth. Why, he wondered, don’t trade book publishers have gatherings like this? AI is a topic at every publishing event: why isn’t it the topic?

The answer Peter came up with is that trade book publishers haven’t yet concluded that AI is going to have any real-world consequences on their business. On the core of their business. On their ability to conduct business. Sure, it might help write a press release or pen a manuscript rejection letter, but they assume that publishing will still putter along as it’s been doing for decades, with the average time from contract to print book dragging on for up to two years.

At the same time, publishers of course face near-term challenges more pressing than AI. Paper prices. Shipping costs. Shrinking margins. Sales mostly flat overall. With all of those pressures, who has the time or bandwidth to care about AI?

But the other creative industries get it. Art and design gets it. Advertising gets it. Hollywood sees what’s coming, as does the music industry. Journalists are watching with dread.

Other book publishing sectors are starting to get a handle on AI’s impact. Scholarly publishing is deep into the technology. But trade publishers are acting like there will be few real-world consequences from AI.

What might the real-word consequences be? Will it impact the supply of books? Or the demand for books?

Supply: Yes, AI is the culprit behind a bunch of new garbage books on Amazon. Hundreds? Certainly. Thousands? Maybe. Millions? No. How many books are on Amazon? More than 50 million. It’s very crowded already.

That aside, it’s clear that in the near term, AI isn’t going to be writing whole books that people will actually want to read. Its value thus far is as a writing buddy—critiquing, suggesting, occasionally spinning out a few paragraphs of usable text. No, in the near term, AI is not going to be radically changing the outputs of book publishing, the supply of new books.

Demand: Nope, no one is demanding AI-generated books. That’s not a factor. And I can’t foresee any scenario where AI will impact the demand for books more broadly.

What about the process of publishing? Yes, that will change. At least a little. As we discovered at our Publishers Weekly AI event last fall, publishers are looking to AI for help here and there: marketing, editorial, a bit of this and a bit of that.

Is it possible that there will be few real-world consequences of AI on trade book publishing? Perhaps it’s just a toy.

So let’s step back from the AI weeds and revisit the troubled state of trade publishing, and through that, drill down into where AI is likely to have its largest impact.

By varying estimates, the entire book publishing industry in the United States has annual sales of under $35 billion. Even if they were $40 billion, Apple Computer’s annual sales alone are 10 times higher. Apple’s gross profit is 44% of sales, alongside net profits of 25% of sales. Publishing doesn’t even dare dream of margins like these.

Trade book publishing has been in a gentle economic decline for decades, some years up a few percent, some years down. At mid-2024 sales are looking solid year-to-year, but that’s the kind of summer romance publishers are familiar with. We win some, we lose some, but, overall, trade book publishing is not a growth industry.

In 2023, according to AAP’s StatShot “trade revenues were down 0.3%, at $8.9 billion for the calendar year.” As far as I can tell, AAP’s data doesn’t account for inflation. At 3.4% inflation, the sales decline would be 3.6%, closer to the 2.6% decline in unit sales reported by Circana.

Without the growth of alternative formats, ebooks and audiobooks, the industry might be in very bad shape. Ebooks were a growth engine for years. Audiobook sales continue to climb, up nearly 15% in 2022 and another 9% in 2023. Yes, some of that is substitution sales, but a lot of the customers for ebooks and audiobooks are new customers, not necessarily regular book readers. Ebooks and audiobooks accounted for 21% of trade sales last year. Would all of those customers buy print books if print was all that was available?

Retail pricing is becoming an increasing concern as costs and retailer and wholesale discounts squeeze publisher margins. Academic studies suggest that there is a degree of price elasticity for books, but surely we’re reaching the resistance point: hardcover bestsellers are running up against what may be a $35 price ceiling. “Subscription fatigue” is causing video-on-demand customers to drop services, focused on the increasing cost of each. Netflix’s Premium plan now runs to $22.99/month; the Disney Plus Duo Premium is $19.99/month. As a colleague recently remarked to me, how many cash-strapped subscribers will say, oh, let’s cancel our Netflix subscription so that I can buy a book next month?

I’m not going to beat this to death: it’s not only authors who scramble to make a living—publishing personnel are grossly underpaid compared to comparable professions.

I keep a representative smorgasbord of recent publishing job postings. I’m looking at one, for a Marketing Manager at a trade publisher that reported 2023 sales of just over $30 million. The position pays between $60,000 and $70,000 per annum. Here’s another, for a Publicity Manager at one of the imprints of a publisher reporting $750 million in annual sales. “This role will have an annual salary of $74,000–$79,000,” the listing notes.

According to the U.S. Bureau of Labor Statistics National Occupational Employment and Wage Estimates, marketing managers earn, in the mean, across all industries, $166,410. “Public Relations Managers” mean earnings are $159,420.

Book publishing isn’t even in the ballpark. This is not news to anyone working in publishing. It’s always been this way. But can publishing sustain itself at these pay levels? An inability to offer candidates even half of the pay standard does not auger well for an industry growing increasingly digital.

In an age of TikTok, YouTube, social media, SEO, metdata, and author platforms, what the heck is a “publicity manager” anyway?

Three persistent advantages for traditional book publishers are (i) cachet, (ii) access to major media, and (iii) access to bookstore distribution. This doesn’t apply equally to all traditional publishers—the largest ones have more of each: more cachet, better access to major media, and better opportunities for getting their books onto the front tables at the largest number of bookstores. But, taken together, these are the characteristic advantages of the traditional book publishing industry.

One other value that publishers can offer is a curated list of complementary titles, where each single title benefits from its companions. Think of a publisher like Fox Chapel, specializing in books about crafts: if you enjoy one of their titles, there’s a reasonable chance you’ll look closely at their other books (and magazines) on the same topic, even if they’re written by other authors.

This crosses over to something that only a few publishers have managed to develop, a marketable brand. Think of series like Wiley’s “For Dummies,” or Oxford’s “Very Short Introductions.” The brand conveys a consistency of editorial approach and quality that encourages readers to buy multiple titles. Self-publishers often launch their own short series, mostly for fiction, though they can’t inhabit the breadth of these larger publisher catalogs.

All of the other services that traditional publishers provide can be purchased on the open market, with similar quality, at affordable prices (for example, editorial, design, production and essential marketing functions). And, for self-publishers, the per-book-sold income is five times or more greater than traditional royalty schedules.

I’ll drill down a little deeper on this in a short appendix at the end of this book. But let me quickly demean the value of the top three advantages. Cachet is good for bragging rights, but has modest cash value. Book reviews, and other major media exposure, now have a vastly diminished impact on book sales. And bookstores represent roughly 15% of industry sales (and far less for self-published authors)—they are no longer core to a book’s success.

Things have changed.

Self-publishing has been the main driver of growth in consumer (trade) publishing in the last decade and more. Accurate sales data around self-publishing remains hidden—it’s easy to ignore its importance. But the available data shows that self-publishing claims a substantial portion of the trade publishing market.

As Kobo CEO Michael Tamblyn (somewhat) famously noted, “One in 4 books we sell in English is a self-published title, which means that effectively, for us, self-publishing is like having a whole other Penguin Random House sitting out in the market that no one sees. It’s like the dark matter of publishing.”

The trends within the self-publishing market are arguably the only significant trends in trade publishing. Self-published authors are showing the way. They are adventurous and uninhibited by the customs of the traditional industry. Many are fluent with social media. They are close to their readers, their customers. If you want to learn the latest marketing techniques, follow author forums, blogs, and newsletters.

Dig deep on author income stats, the economics of writing, and then compare the income decline of traditionally-published authors against the income gains of self-published authors. In an international survey and report that I conducted with Steve Sieck for ALLi, in 2023 (pdf), we discovered that “the median writing and self-publishing-related income in 2022 of all self-publishers responding was $12,749, a 53% increase over the previous year. Average (mean) incomes skewed much higher: $82,600 in 2022, a 34% increase.”

In a separate survey, conducted by Peter Hildick-Smith for the Authors Guild, “full-time self-published authors, who had been publishing since at least 2018, reported a mean income of $24,000 compared to $13,700 in 2018, a 76 percent increase.”

For the sake of thoroughness, I want also to mention hybrid publishers. I’ve become increasingly enthusiastic about the hybrid model, and the hybrid publishing segment is significant and growing. No one estimates hybrid sales separately. But the impact is most clearly seen in their dominance of two categories: book by celebrities, and in popular business books. These categories have long been reliable income sources for trade publishers, but hybrid publishers’ “concierge services,” and much more favorable income sharing, are irresistible for many high-profile authors.

Recent instances, like Authors Equity’s profit sharing relationship with its authors, and Keila Shaheen’s 50/50 profit sharing with Simon & Schuster, suggest a trend toward the normalization of the hybrid model. This is good news for authors; less promising for traditional publishers.

I’m co-authoring a report with Rüdiger Wischenbart, based on his original study “Publishing Beyond Publishers.” We’re trying to understand, and to quantify, as best we can, all of the book-ish publishing activity, worldwide, that could (and should) be included as part of a more comprehensive view of the modern publishing ecosphere.

Once you move beyond the bookish container you find glimpses of where AI could really make an impact.

The poster child for our project is Wattpad, where “97 million people spend over 23 billion minutes a month engaged in original stories.” The stories are experienced online, mostly on smartphones, in short chunks. Few become published books. They are just as likely to find their way into “Wattpad WEBTOON Studios, the company’s TV, film, and publishing counterparts.”

Younger readers are more attuned to online digital reading than their more senior brethren. According to a January 2024 Wattpad survey, “digital formats are increasingly popular among younger generations, with 65% of Gen Z and 71% of Millennials embracing webnovels, ebooks and webcomics, while less than half of the Gen X and Boomer generations say the same.”

And, of course, as the traditional publishing industry is now learning: “Diversity is a key driver for embracing digital formats: 61% of Gen Z and 70% of Millennials agree that ebooks, webnovels, and webcomics give them access to content that’s harder to find in bookstores and libraries, including LGBTQ+ and minority-focused content.”

Wattpad has competitors. Several are nonprofit and open source. Inkitt is the top commercial competitor, which, according to Publishers Weekly, recently raised $37 million from investors “including Stefan von Holtzbrinck, the owner of Holtzbrinck Publishing Group, which owns Macmillan, and former Penguin CEO Michael Lynton; it has now pulled in a total of $117 million in investments. In February 2023, the Financial Times said that Inkitt was the eighth fastest-growing company in Europe, and #1 in Germany.” A July 9 article in Esquire outlined Inkitt’s use of AI, its role in their success.

How many of you have even heard of Inkitt, visited its publishing platform, or downloaded its Galatea reading app?

But our “Publishing Beyond Publishers” report goes beyond online story platforms to consider how “content can be created and disseminated in a diversity of formats (print, digital), media (books, audio, movies, games), distribution channels (communities, platforms, streams) and business models (sales of products, subscriptions, streaming, freemium, paid models) in mostly digitally-defined supply and marketing chains.” How many opportunities are book publishers missing because they’re not an obvious fit within their current business model?

With the exception of ebooks, modern publishing has never faced a threat from technology. The dawn of the Internet era provided publishers with more opportunity than threats; it’s just a single retailer, Amazon, enabled by the Internet, who truly upset the apple cart.

I’ve never seen a study on whether Amazon has been (when all aspects of its operations and impact are fully factored) a net positive for the book publishing industry. It reaches many buyers who aren’t served by bricks and mortar, and is willing to sacrifice margin to keep prices low. Ebooks and audiobooks are delivered at scale. But Amazon is able to cut prices in part because it demands high discounts and fees from suppliers, and it chokes other retail channels. There are painful trade-offs.

Still, don’t try criticizing Amazon in front of a self-published author. They would not be in business without The Everything Store.

To gain a perspective on AI’s coming impact on book publishing I recommend Clayton Christensen’s The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, first published in 1997 by Harvard Business Review Press.

Christensen looks at how existing (incumbent) firms can succumb to the forces of innovation.

Successful, well-managed companies often fail when disruptive changes come to their industry. Conventional management practices, which had helped them to become industry leaders, make it difficult for these companies to be nimble in confronting the disruptive technologies that could cannibalize their markets.

They ignore the products spawned by the disruptive technologies, because, at first blush, they compare poorly to their existing products. Their most profitable customers generally can’t use the purported innovations and don’t want them. Companies try to fight off disruptive threats by doubling down on existing products and services.

Christensen’s key insight is that by doing the apparently ‘right’ thing, including listening to customers, successful companies leave themselves wide-open to disruptive innovation. They focus on their current customers and ignore important new technologies—which initially target small, less profitable markets. It creates an opening for agile startups to disrupt the leaders.

Chris Dixon, in discussing the book, points out that “the reason big new things sneak by incumbents is that the next big thing always starts out being dismissed as a ‘toy.’” Hmm, yes, a lot of people see Chat AI as a toy.

Are publishers failing to recognize that innovations built with generative AI might disrupt their fragile business model?

The impact of AI on book publishing will certainly have a dramatically different impact on fiction publishing than it will on nonfiction.

The book publishing industry isn’t often enough analyzed as two bifurcated industries, one that publishes fiction books and another than publishes nonfiction.

While most trade publishers offer both fiction and nonfiction titles, the industry-wide gulf between the two forms is stark both in the number of titles published, and in book sales. Estimates vary, but fiction titles represent only roughly 10% of books published each year. Sales, however, greatly favor fiction. Again, there are year-to-year shifts, but fiction captures roughly half of annual trade book sales. Last year 21 of the top 25 bestsellers were fiction. Using Wikipedia’s compilation, of the books in English that have ever sold 20 million copies or more, 84% are fiction titles.

Book sales trends are notoriously fickle, but fiction sales appear to be on a continuing upward slope. After cratering at 32% in 2019, they claimed 40% of the adult market in 2022, and grew slightly again in 2023. Adult fiction sales were up an additional 6.3% in the first half of 2024.

While AI is going to be increasingly used by fiction authors to aid in the process of creation, as discussed elsewhere, there seems little likelihood that AI-generated stories will take over the bestseller shelves. A “good-enough” reach for some genre fiction is conceivable, but again not something that should give authors and publishers sleepless nights.

The writing and publishing of nonfiction, on the other hand, will be aided and abetted by AI across the board. It’s happening already. Nonfiction authors are taking advantage of Chat AI’s many talents both as a research assistant and a writing aid. And nonfiction publishers will increasingly call upon Chat AI for help in manuscript development, fact-checking, editing, marketing and distribution.

I spoke to a very smart author’s agent, who has been thinking a lot about what AI means to her clients, and to all professional authors. The conversation turned to the idea of the book as a ‘container,’ and I asked her to expand on that idea. Her response:

“The basic idea is that when authors/agents are placing a book with a publisher, it is just that: a book. There is an understanding that it might later become one of the myriad forms of derivative works we have come to know (translation, dramatic adaptation, graphic novel, etc.) but all of that is secondary to the ‘the Work’ itself. The Work is not a bag of words or phrases or facts, it is something structured by the author in a way unique to that author. Breaking the container, shaking the Scrabble bag of letters and drawing out a random selection (that is not entirely random because it is a Scrabble bag made up of the syntax and semantics and style of the specific author), is not what is being considered when we make a grant of rights to a publisher.

“The book is the integral whole that is greater than the sum of its parts. It’s the product of a creative epiphany (and a ton of creative intellectual work) that brought it all together as a book, not just ‘a’ book, but the book by that particular author.”

When I think about nonfiction books, breaking the container is one of the upsides of the AI era. You break the container, and like Fabergé eggs, there are treasures contained within.

The constraints of the container are both a feature and a drawback. On the one hand “the work is not just a bag of words or phrases or facts, it is something structured by the author in a way unique to that author.” On the other hand, in creating the container, the author, by dint of the inherent limitations of the bookish container, was forced to reduce, reject, rewrite, and reconfigure. The work is polished, but it is distilled. Some things were lost (while others gained) along the way.

People fail to read long nonfiction books all the way through, in part because they’ve become conditioned to skimming all the text they encounter on the web—even in their emails. The “book summary” model fails for the same reason—instead of an indigestible 250-page nonfiction book you get an indigestible 8-page summary of the book.

Books are like lunch boxes–all the stuff in one place. But I just want the cookies.

It’s not going to be easy for established authors to throw off their chains. But they may have to learn to do so, or risk being overshadowed by a new generation of skilled researchers and writers who can express themselves with or without formal containers, as the occasion calls for. Rigid containers impose far too many limits, and limitations.

What it comes down to is that the business of writing has irredeemably changed. It’s like the end of the scribes. In this case, our monkish authors must get out from behind their desks and their containerized view of the book, and engage with form and with their audiences.

Oh yes, I know well the author rejoinders: But I’m a writer, I’m not a promoter. I’m not on social media. I don’t have a platform. I speak to my readers via occasional interviews and ever-more-occasional appearances. I have a website (but, truth be told, I never update it). My work is my gift to readers. They can accept my work, or reject my work, but I will not be joining them in their living rooms.

Well, you can put away the scrolls and the ink made from the blood of ermines. The occupation of ‘author’ now demands that you get out from behind your desk and meet your readers where they live, in their living rooms, and on their Facebook pages. You can lament it all you want, as we curtail your grants and your tenure and your publishing opportunities. This is the brave new world of writers and readers, sans publishers, those dreadful intermediaries who work hard to enhance your work, yet constantly interrupt the direct connection to your readers, the people who most value your work.

None of this precludes great narrative nonfiction books reaching an attuned audience. Au contraire. Those books can continue to shine. Look at the recent Pulitzer Prize nonfiction winners. No one wants publishing to lose its ability to incubate books of this exceptional quality. That would be tragedy. The current publishing business model doesn’t need to disappear for the new model to thrive.

I think of these great nonfiction books as ‘beautiful.’ ‘Beautiful’ as in beautifully-written, carefully edited, designed with care and deliberation, and printed, with deckled edges, on FSC-certified paper. They are sold in boutiques.

The boutiques are called ‘bookstores,’ but they have all the characteristics of boutique retail—lovely objects, chosen with care by the owner and the managers, a bit pricey, but for those who treasure such objects, well worth the cost.

The task of most nonfiction authors is now to move beyond the container, as well as the publisher as gatekeeper.

The reading public, particularly its younger members, is increasingly ‘media-agnostic.’ In search of entertainment or knowledge, they can be watching a YouTube or TikTok video, a Netflix series, listening on Spotify, scrolling through Instagram (“Insta”), catching up on the news, or, occasionally, reading a book.

Yet the market model is still focused on the unique container. The publishing industry. The film industry. The television industry. The music industry.

Movies are not books. YouTube videos are not movies. Podcasts are not audiobooks.

These silos were not an historical inevitability but a result of a series of business decisions made by powerful organizations over time.

It’s not hard to disrupt a business model built upon a single container.

For all of its facility with language, the current generation of generative AI works its magic with even more bravado producing sounds, images and video. AI affords this book with audiobooks, in multiple languages, alongside the 31 translations of the ebook. None of this would be even remotely possible without AI—the economics of traditional production makes no sense for me.

Content creators are traditionally trained to excel in just a single container silo. We have writing programs and film programs and music programs. It rarely occurs to us that creative writers might also take a ‘minor’ in film or music. Or in computer programming or in app development.

Whatever happened to transmedia? Where are the programs that teach creatives to be, as Apple once framed it, “Masters of Digital Media”?

The best content will always win. But, over time, the content containers matter less and less. The digital generation may not be container-agnostic. But they are container-flexible. They encounter most of their content digitally, via smartphones. Print will continue to find an audience, but it will never again command the center.

The “publisher of the future” needs also to be container-flexible. Most of the growth in content distribution is not via ink on paper, nor words in EPUB files. The growth of audiobooks is no accident, nor is the popularity of YouTube and TikTok.

The larger challenge lies in the necessity of making new and existing content discoverable, whatever its form, and then upon discovery, to turn awareness into a purchase decision.

The online discovery problem has been with us for some time; AI did not create it. The problem is one that industry veteran Mike Shatzkin has highlighted more than once. There are so many titles available in print, and digital formats allow them to remain continuously in print. Millions of out of print books can be found from used book retailers. On top of that two million or more new books are published each year, just in English. AI will make the saturation problem worse, but it’s already out of hand.

Amazon’s algorithms are not selfless—they do a very good job of surfacing what’s saleable, the book that you’re most likely to purchase next. That book isn’t characterized merely by its sales figures. It sold best not because some scammer optimized the online listing. It sold best because it was the best, and delighted readers told others to buy it, both online and in person. (Amazon has encouraged advertising that distort its algorithms, part of the enshittification of the platform.)

I talked above about metadata. Conversion is as vexing as discovery. Can AI help authors turn browsers into buyers?

The concept of copyright has been fundamentally challenged by developments in artifial intelligence. It’s not that people don’t want or deserve protection for their work—arguably they deserve it more than ever. And it’s not that AI renders copyright unenforceable (at least in some form, if not in the current form).

It’s that the “protection of copyright” can also become a veil behind which your work disappears. If your book cannot be at least referenced via a conversation with Chat AI then it, in essence, does not exist. Why bother protecting that which cannot be found?

This is where the discovery problem might become a catastrophe. Google does a great job discovering books only through their metadata. Omniverous AI prefers the whole enchilada. Unlike traditional search engines, AI rarely responds to user queries based only on the abstract metadata about a work.

At the same time, changing content containers, containers other than whole books, suggest different kinds of copyright challenges. If the text of a book can change on-the-fly, in response to reader input, what then is the copyright of the text? And how can the original author be fairly compensated?

Publishers need authors; authors don’t need publishers.

The future of publishing is the intimate relationship between writers & readers. It’s far stronger than the relationship between writers and publishers and readers and publishers. Publishers can be a roadblock in the relationship between writers & readers. In many cases they do not enable the relationship; they impede it.

For a long time, the only way to access high-quality written content was through books or via a modest selection of periodicals. That’s certainly no longer the case. Quite apart from the distractions of other media, there are now so many different ways to access (non-containerized) high-quality written content. Books no longer hold the primacy they once enjoyed.

“What’s the perfect book for me to read next?” Generative AI can answer that question with an eloquence and a precision that has never before been possible. As AI becomes a reliable recommender engine, authors will need to communicate via that engine. (Amazon, of course, is working on this, and has the benefit of knowing what you’ve previously purchased.)

So where does this leave us?

I felt it necessary to catalog the challenges that publishing faces. Chat AI is arriving at a time that trade publishing is troubled. It’s not arriving at a time when the industry is robust, and able to say: “we don’t need some newfangled technology; we’re doing just fine.”

I talked above about the most pressing challenges publishing faces: rising costs and shrinking margins.

The wolves will never be sated in their demands for ever steeper discounts; margins will not improve. Retail prices are near a ceiling. The future of the current trade publishing model lies in cost reduction.

Salaries cannot go any lower, so we’ll need to cut costs within the production cycle.

But publishers have been trying to cut production costs for decades. There have been some notable successes, but we’ve exhausted the current options.

I’ve shown that AI can bring efficiencies to publishing, across the workflow. They’re not instant and they’re not easy: you need to work at AI. But the opportunity is there.

Publishers are not looking to reduce staffing, so the objective has to be more books coming more quickly to market based on current staff resources. AI tools can further that objective.

And, of course, there’s always a goal of selling more copies of the books being published. AI can help there as well.

I describe above how AI can be transformative to the longer-term future of publishing as well.

Don’t worry about that. Get your house in order, and we’ll talk further.

There is now a near-endless supply of material available to help inform you on AI, far too much, of course. Choose your preferred medium: books, blogs, newsletters, videos, podcasts, courses and psychic divination. I’ve linked to some key sources through this book, it’s worth clicking a few random links to see where they take you.

There’s only one excellent AI book for beginners, Ethan Mollick’s Co-Intelligence, which was released in April, 2024. It’s drawn from his equally excellent (free) newsletter, “One Useful Thing.”

I didn’t want to burden the introduction with this information, but I know that people are skeptical of the folks writing about AI today, suspicious that many of these authors are fashioning themselves as latter-day AI experts. No doubt some are. I hope that I have acceptable credentials to undertake this project.

I read my first book about artificial intelligence in 1988, a book published two years previously, Understanding Computers and Cognition: A New Foundation for Design by Terry Winograd and Fernando Flores. The book is dense—much of it was beyond my understanding—but it was the first time I got excited about the possibilities of artificial intelligence in book publishing.

By that time I was deep into the weeds of the digitization of publishing, occasioned by the explosion of desktop publishing, Apple computers, PageMaker and the like. In 1986 I supervised the development of Page One automated book publishing software, a program that could take a book manuscript as a Microsoft Word file and typeset it into a whole book in minutes. That’s very easy to do today; it wasn’t easy then.

I continued to work in publishing technology thereafter, as a consultant and analyst, working with some of the leading software and hardware vendors, including Adobe, Apple and Microsoft. I also became involved with, and a part-owner, of a software company called Enfocus. We created an automated system for prepress workflows.

We didn’t have access to AI at that juncture, but we got very good at unleashing fully automated publishing production systems. As AI can be indistinguishable from magic, so too can the best of automation appear to resemble AI.

Fast forward to 2016, and my colleague Cliff Guren helped me focus on AI in book publishing: it was beginning to look like something was finally happening. The Bestseller Code (Archer and Jockers) appeared in September, 2016, making it clear that computers could interpret the texture of literature with a high degree of insight and precision.

By that time I was paying close attention to book publishing startups, and a small number emerged with some degree of AI technology wound into their software offerings.

Fast forward again to October 2022.

Months before it became the topic everyone in publishing was talking about, Tim O’Reilly presented to the PageBreak Conference on “AI and Publishing Transformation.” O’Reilly is well-known in the publishing community, both for O’Reilly Media and for the Tools of Change conference. He’s one of tech’s top visionaries.

O’Reilly was not merely enthusiastic about the new advances in AI, he was over the top. “We’re at a point that’s very similar to how I felt when we discovered the Worldwide Web in 1992,” he said, and followed that with “this is as transformative as VisiCalc, the PC, and the web browser.”

He admitted that (at that point) the use case was still fuzzy, pointing to a couple of pilot projects at O’Reilly Media. But, he said, “this is getting better scarily fast. Machine learning is not a future thing anymore. This is about the democratization of AI.”

O’Reilly talked about how publishers should approach these new technologies, saying that they need to “know when to burn the boats and go all in. There’s a time when you have to commit.”

ChatGPT wasn’t released until a month later. PageBreak was the first publishing conference to put it front and center, via Tim’s insights.

This book has five sponsors. I knew when writing the book that there would be few riches to be found, and I decided to underwrite my efforts by inviting sponsors to participate.

As I indicate on my website, working as a consultant, an analyst, and as a journalist, I support the International Federation of Journalists’ Global Charter of Ethics for Journalists. Paragraph 13 is clear on the obligation to avoid conflicts of interest or “any confusion between (my) activity and that of advertising or propaganda.”

Having sponsors implies a conflict of interest and a confusion around advertising. If I was working for the New York Times it would be simple: “No.” Working for myself, disclosure is my weapon to satisfy these obligations: I’ll describe the consideration I’ve received and you can judge, in my work, if I have been compromised.

I chose the sponsors that I invited to this project because I was familiar not only with their work but with the individuals involved in their organizations. They are colleagues and friends. I told them that their products might be discussed in the text of this book and they would have no control over those words. What they could control was their advertisements at the end of the book—I would post those as supplied. That’s what I’ve done.

I have received payments from others that may have influenced my work on this book—I can catalog the following:

• I have not done any paid consulting work for the AI vendors described in this book, including the sponsors.

• I received some profit-sharing revenue from Publishers Weekly for the AI webinars in September, 2023.

• I am paid a standard rate for my Publishers Weekly articles.

Please let me know if you detect any favoritism that you think may have resulted from these engagements. My bias in favor of AI was formed before the vast riches flowed.

I wouldn’t be able to talk with any credibility about AI if it weren’t for the support of Publishers Weekly, including the editor emeritus, Jim Milliot, and the CEO & Publisher, Cevin Bryerman. Jim supported my early writing about AI, and Cevin (alongside Krista Rafanello and the rest of the team) were instrumental in the success of the 2023 fall conference, AI and the Revolution in Book Publishing. Andrew Albanese is now the executive editor at PW; Ed Nawotka, a senior editor. I’m lucky to be working with both of them.

And further thanks to numerous colleagues…

• Peter Brantley

• Cliff Guren, long-time sparring partner and insightful reader of the drafts of this book and other projects

• Two other ‘beta’ version readers offered valuable feedback: Joe Wikert and Brad Farmer

• Hannah Johnson, many thanks for the cover design

• My Publishing Technology Partners, Ken Brooks, Bill Kasdorf, Bill Rosenblatt, Bill Trippe, Steve Sieck, and our newest partners, Lettie Conrad and Linda Secondari.

• My sister, Anne Pashley, who helps me keep the publishing startup database current, and constantly energizes my research efforts.

• My partners in my monthly AI webinars: Brooke Horn, Liz Bartek, and Brian O’Leary at BISG.

• Bill Kasdorf helped me drill down on the accessibility issues for my book.

• Peter Armstrong and Len Epp at Leanpub were patient with my questions and quibbles and helped me through the process of offering the best possible outcome on the Leanpub platform.

• Hugo Rayne at ElevenLabs for audiobook support.

• My good friend and reliable detector of my writing and reasoning foibles, Bob McArthur.

• I wouldn’t have returned to San Francisco and dug into generative AI if it weren’t for my long-time friend Zach Stewart at the Canessa Printing Company and Gallery.

The cachet of traditional publishing brought most of us to the industry. We grew up loving books, went on to study English or something similar in college, kicked about for a while, perhaps got a job in a bookstore, perhaps an expensive masters of publishing certificate, then worked as an intern at a publishing company, and here we are today. You love your job, though the pay sucks and the working conditions are not always pleasant.

That same cachet feeds the publishing supply chain. On the input side of the supply chain: it means a lot to a lot of authors. We see it all the time. The economics of the traditional publishing route makes no sense, and it takes four times as long to get into print, but, maybe, just maybe, I’ll be the next unexpected winner, like Delia Owens (Where the Crawdads Sing) or second-time megastar, Amor Towles (A Gentleman in Moscow).

It’s like Vegas: except the odds are better in Nevada. Play a single number in roulette and the odds against you are 36 to 1. Play traditional publishing and the odds against you are… far worse.

The odds of getting a contract with any of the traditional trade publishers, let alone one of the big 5, are poor. But that’s the least of an author’s problem. Finding readers is the mega-challenge.

One thing that can help is landing a spot on a bestseller list.

Let’s consider PRH, Penguin Random House. The company publishes just 15,000 new titles each year. In 2021 they managed to get 196 books onto the Publishers Weekly bestseller lists. As the linked article highlights: “PW publishes four adult bestseller lists, each with 20 titles: hardcover fiction, hardcover nonfiction, trade paperback, and mass market paperback. That means that over the course of a year, there are 2,080 hardcover positions on our lists and 2,080 paperback positions.

Not all of the bestsellers were newly-published that year—let’s guess generously that 90% were, 1870 slots.

Meanwhile The New York Times has 15 bestseller slots per week for each category of book (fiction, nonfiction, children’s; separate lists for YA and how-to; hardcover separate from paperback, plus combined lists). For an individual author, that means 780 available slots per year, although most of those slots are filled with repeat sellers. (The Body Keeps the Score by Bessel van der Kolk has been on the paperback list for five-and-a-half years!) So let’s divide that in half, 390 available slots. You have to sell a lot of books to grab one of those.

What about coverage in the New York Times Book Review? In 2016, Pamela Paul, then the editor of the Review, claimed that “The Book Review at the Times reviews about 1% of the books that come out in any given year.” Clearly she means 1% of the books from the top publishers—she can’t be thinking about small presses or self-published authors. And so perhaps 150 of PRH’s 15,000 new titles are reviewed each year.

Most observers agree that while getting attention from top media is still important, and can be powerful, the impact of reviews is not what it once was, if for no other reason that fewer book readers interact with the major review outlets.

Let’s turn our attention to the supply chain downstream, to distribution. While publisher brands carry weight with authors, it’s always been a truism that, unlike many consumer goods, publisher brands carry next to no weight with book buyers and readers. But they do carry a lot of weight with distributors and booksellers. This reflects the historic business-to-business nature of publishing (rather than B2C—to consumers). Publisher cachet is disseminated via distribution and retail channels: Barnes & Noble carries a book because it’s from PRH, and then places it in front of buyers’ faces; thus is cachet disseminated to readers.

David Lamb and Susan Reich formed Book Advisors LLC in 2016 to consult on mergers and acquisitions in publishing. We have completed more than a dozen transactions, as well as a variety of financial, distribution, and operational consulting projects, including fair market valuations. We are pleased to have recently worked in association with Clarke & Esposito; Thad McIlroy of The Future of Publishing; and Kuo-Yu Liang of Ku Worldwide.

We pride ourselves on our relationships, diligence, and discretion. Whether you are interested in buying, selling, financing, or consulting, please get in touch for a confidential discussion.

bookadvisorsllc.com | bookadvisors@gmail.com

Firebrand Technologies is a software and service company that has been assisting book publishers with data and workflow management solutions for more than 35 years.

NetGalley is an online platform that connects publishers and authors to reviewers, librarians, booksellers, media, and educators who discover new books on NetGalley and recommend them to their audiences.

As developers of software and digital solutions, Firebrand Technologies and NetGalley are also industry experts and active members of the publishing community. Our solutions benefit all aspects of the publishing process, from acquisitions all the way to sales and marketing.

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Perfect Bound was created in 2022 to help address the supply-chain issues that have been plaguing the book publishing industry for years. Periods of excess capacity at printers are followed by short windows of delays due to supply crunches. Perfect Bound seeks to open up the printing market to independent publishers and authors by allowing printers from all around the globe to easily connect and do business with publishers looking for new and dynamic suppliers.

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