Many people have found the intelligence of AIs to be shocking. This will seem quaint compared to a far bigger shock coming: highly emotional AIs. The arrival of synthetic emotions will unleash disruption, outrage, disturbance, confusion, and cultural shock in human society that will dwarf the fuss over synthetic intelligence. In the coming years the story headlines will shift from “everyone will lose their job” (they won’t) to “AI partners are the end of civilization as we know it.”

We can rationally process the fact that a computer could legitimately be rational. We may not like it, but we could accept the fact that a computer could be smart, in part because we have come to see our own brains as a type of computer. It is hard to believe they could be as smart as we are, but once they are, it kind of makes sense.

Accepting machine-made creativity is harder. Creativity seems very human, and it is in some ways perceived as the opposite of rationality, and so it does not appear to belong to machines, as rationality does.

Emotions are interesting because emotions clearly are not only found in humans, but in many, many animals. Any pet owner could list the ways in which their pets perceive and display emotions. Part of the love of animals is being able to resonate with them emotionally. They respond to our emotions as we respond to theirs. There are genuine, deep emotional bonds between human and animal.

Those same kinds of emotional bonds are coming to machines. We see glimmers of it already. Nearly every week a stranger sends me logs of their chats with an AI demonstrating how deep and intuitive they are, how well they understand each other, and how connected they are in spirit. And we get reports of teenagers getting deeply wrapped up with AI “friends.” This is all before any serious work has been done to deliberately embed emotions into the AIs.

Why will we program emotions into AIs? For a number of reasons:

First, emotions are a great interface for a machine. It makes interacting with them much more natural and comfortable. Emotions are easy for humans. We don’t have to be taught how to act, we all intuitively understand results such as praise, enthusiasm, doubt, persuasion, surprise, perplexity – which a machine may want to use. Humans use subtle emotional charges to convey non-verbal information, importance, and instruction, and AIs will use similar emotional notes in their instruction and communications.

Second, the market will favor emotional agents, because humans do. AIs and robots will continue to diversify, even as their basic abilities converge, and so their personalities and emotional character will become more important in choosing which one to use. If they are all equally smart, the one that is friendlier, or nicer, or a better companion, will get the job.

Thirdly, a lot of what we hope artificial agents will do, whether they are software AIs or hard robots, will require more than rational calculations. It will not be enough that an AI can code all night long. We are currently over rating intelligence. To be truly creative and capable of innovations, to be wise enough to offer good advice, will require more than IQ. The bots need sophisticated emotional dynamics that are deeply embedded in its software.

Is that even possible? Yes.

There are research programs (such as those at MIT) going back decades figuring out how to distill emotions into attributes that can be ported over to machines. Some of this knowledge pertains to ways of visually displaying emotions in hardware, just as we do with our own faces. Other researchers have extracted ways we convey emotion with our voice, and even in words in a text. Recently we’ve witnessed AI makers tweaking how complimentary and “nice” their agents are because some users didn’t like their new personality, and some simply did not like the change in personality. While we can definitely program in personality and emotions, we don’t yet know which ones work best for a particular task.

Machines displaying emotions is only half of the work. The other half is detection and comprehension of human emotions by machines. Relationships are two way, and in order to truly be an emotional agent, it must get good at picking up your emotions. There has been a lot of research in that field, primarily in facial recognition, not just your identity, but how you are feeling. There are commercially released apps that can watch a user at their keyboard and detect whether they are depressed, or undergoing emotional stress. The extrapolation of that will be smart glasses that not only look out, but at the same time look back at your face to parse your emotions. Are you confused, or delighted? Surprised, or grateful? Determined, or relaxed? Already, Apple’s Vision Pro has backward facing cameras in its goggles that track your eyes and microexpressions such as blinks and eyebrow rises. Current text LLM’s make no attempt to detect your emotional state, except what can be gleaned from the letters in your prompt, but it is not technically a huge jump to do that.

In the coming years there will be lots of emotional experiments. Some AIs will be curt and logical; some will be talkative and extroverts. Some AIs will whisper, and only talk when you are ready to listen. Some people will prefer loud, funny, witty AIs that know how to make them laugh. And many commercial AIs will be designed to be your best friend.

We might find that admirable for an adult, but scary for a child. Indeed, there are tons of issues to be wary of when it comes to AIs and kids, not just emotions. But emotional bonds will be a key consideration in children’s AIs. Very young human children already can bond with, and become very close to inert dolls and teddy bears. Imagine if a teddy bear talked back, played with infinite patience, and mirrored their emotions. As the child grows it may not ever want to surrender the teddy. Therefore the quality of emotions in machines will likely become one of those areas where we have very different regimes, one for adults and one for children. Different rules, different expectations, different laws, different business models, etc.

But even adults will become very attached to emotional agents, very much like the movie Her. At first society will brand those humans who get swept up in AI love as delusional or mentally unstable. But just as most of the people who have deep love for a dog or cat are not broken, but well adjusted and very empathetic beings, so most of the humans that will have close relationships with AIs and bots will likewise see these bonds as wholesome and broadening.

The common fear about cozy relationships with machines is that they may be so nice, so smart, so patient, so available, so much more helpful than other humans around, that people will withdraw from human relationships altogether. That could happen. It is not hard to imagine well-intentioned people only consuming the “yummy easy friendships” that AIs offer, just as they are tempted to consume only the yummy easy calories of processed foods. The best remedy to counter this temptation is similar to fast food: education and better choices. Part of growing up in this new world will be learning to discern the difference between pretty perfect relationships and messy, difficult, imperfect human ones, and the value the latter give. To be your best — whatever your definition —requires that you spend time with humans!

Rather than ban AI relationships (or fast food) you moderated it, and keep it in perspective. Because in fact, the “perfect” behavior of an AI friend, mentor, coach, or partner can be a great role model. If you surround yourself with AIs that have been trained and tweaked to be the best that humans can make, this is fabulous way to improve yourself. The average human has very shallow ethics, and contradictory principles, and is easily swayed by their own base desires and circumstances. In theory, we should be able to program AIs to have better ethics and principles than the average human. In the same way, we can engineer AIs to be a better friend than the average human. Having these educated AIs around can help us to improve ourselves, and to become better humans. And the people who develop deep relationships with them have a chance to be the most well-adjusted and empathetic people of all.

The argument that the AIs’ emotions are not real because “the bots can’t feel anything” will simply be ignored. Just like the criticism of artificial intelligence being artificial and therefore not real because they don’t understand. It doesn’t matter. We don’t understand what “feeling” really means and we don’t even understand what “understand” means. These are terms and notions that are habitual but no longer useful. AIs do real things we used to call intelligence, and they will start doing real things we used to call emotions. Most importantly the relationships humans will have with AIs, bot, robots, will be as real and as meaningful as any other human connection. They will be real relationships.

But the emotions that AIs/bots have, though real, are likely to be different. Real, but askew. AIs can be funny, but their sense of humor is slightly off, slightly different. They will laugh at things we don’t. And the way they will be funny will gradually shift our own humor, in the same way that the way they play chess and go has now changed how we play them. AIs are smart, but in an unhuman way. Their emotionality will be similarly alien, since AIs are essentially artifical aliens. In fact, we will learn more about what emotions fundamentally are from observing them than we have learned from studying ourselves.

Emotions in machines will not arrive overnight. The emotions will gradually accumulate, so we have time to steer them. They begin with politeness, civility, niceness. They praise and flatter us, easily, maybe too easily. The central concern is not whether our connection with machines will be close and intimate (they will), nor whether these relationships are real (they are), nor whether they will preclude human relationships (they won’t), but rather who does your emotional agent work for? Who owns it? What is it being optimized for? Can you trust it to not manipulate you? These are the questions that will dominate the next decade.

Clearly the most sensitive data about us would be information stemming from our emotions. What are we afraid of? What exactly makes us happy? What do we find disgusting? What arouses us? After spending all day for years interacting with our always-on agent, said agent would have a full profile of us. Even if we never explicitly disclosed our deepest fears, our most cherished desires, and our most vulnerable moments, it would know all this just from the emotional valence of our communications, questions, and reactions. It would know us better than we know ourselves. This will be a common refrain in the coming decades, repeated in both exhilaration and terror: “My AI agent knows me better than I know myself.”

In many cases this will be true. In the best case scenario we use this tool to know ourselves better. In the worst case, this asymmetry in knowledge will be used to manipulate us, and expand our worst selves. I see no evidence that we will cease including AIs in our lives, hourly, if not by the minute. (There will be exceptions, like the Amish, who drop out but they will be a tiny minority.) Most of us, for most of the time, will have an intimate relationship with an AI agent/bot/robot that is always on, ready to help us in any way it can, and that relationship will become as real and as meaningful as any other human connection. We will willingly share our most intimate hours of our lives with it. On average we will lend it our most personal data as long as the benefits of doing so keep coming. (The gate in data privacy is not really who has it, but how much benefit do I get? People will share any kind of data if the benefits are great enough.)

Twenty five years from now, if the people whose constant companion is an always-on AI agent are total jerks, misanthropic bros, and losers, this will be the end of the story for emotional AIs. On the other hand, if people with a close relationship with an AI agent are more empathetic than average, more productive, distinctly unique, well adjusted, with a richer inner life, then this will be the beginning of the story.

We can steer the story to the beginning we want by rewarding those inventions that move us in that direction. The question is not whether AI will be emotional, but how we will use that emotionality.

This essay is also available on my Substack. Subscribe here: https://kevinkelly.substack.com/

In my professional life, I’ve had several bestselling books published by New York publishers, as well as many other titles that sold modestly. I have also self-published a bunch of books, including one bestseller on Amazon and two massive hit Kickstarter-funded books. I have had lots of foreign edition books released by other publishers around the world, including bestsellers in those countries. Every year I also publish a few private books to give away. I’ve contracted books to be printed in the US and overseas. I’ve sold big coffee-table masterpieces and tiny text booklets. Together with partners, I run some notable newsletters, a very popular website, and a podcast with 420 episodes. I accumulated followers on various platforms. I’m often asked for advice about how to go about publishing today, with all its options, so here is everything I have learned about publishing and self-publishing so far.

The Traditional Route

The task: You create the material; then professionals edit, package, manufacture, distribute, promote, and sell the material. You make, they sell. At the appropriate time, you appear on a book store tour to great applause, to sign books and hear praise from fans. Also, the publishers will pay you even before you write your book. The advantages of this system are obvious: you spend your precious time creating, and all the rest of the chores will be done by people who are much better at those chores than you.

The downsides are also clear: Since the publisher controls the money, they control the edit, the title, the cover, the ads, the copyrights, and licenses. Your work becomes a community project, and it slows the whole process down, because yours is not the only project everyone is working on. Your work needs to fit into their lineup, their brand, their catalog, their pipeline, their schedule of all the other projects going on. The pace can seem glacial compared to the rest of the world.

For the most part, however, the peak of this traditional system is gone, finished, over. Reading habits have altered, buying habits are new, and attention has shifted to new media. It’s an entirely new publishing world. Today, some books experience some parts of this, but exceptionally few are treated to this full traditional process.

Publishers

Established mass-market publishers are failing, and they are merging to keep going. Traditional book publishers have lost their audience, which was bookstores, not readers. It’s very strange but New York book publishers do not have a database with the names and contacts of the people who buy their books. Instead, they sell to bookstores, which are disappearing. They have no direct contact with their readers; they don’t “own” their customers.

So when an author today pitches a book to an established publisher, the second question from the publishers after “what is the book about” is “do you have an audience?” Because they don’t have an audience. They need the author and creators to bring their own audiences. So, the number of followers an author has, and how engaged they are, becomes central to whether the publisher will be interested in your project.

Many of the key decisions in publishing today come down to whether you own your audience or not.

Agents

In the traditional realm, agents helped authors and they helped publishers. Publishers did not want to waste their time evaluating probable junk, so they would spend their limited time looking at what agents presented to them. In theory, the agent would know the editors’ preferences and know what they were interested in, and the editor could trust them to bring good stuff.

For the author, agents had the relationship with editors, would know who might be interested in their project, and the agent would guarantee that the legal contracts were favorable to the author, and most importantly, negotiate good terms. For this work, agents would take 15% off the top of any and all money coming from the publisher. For most authors, that is a significant amount of money.

Are agents worth it? In the beginning of a career, yes. They are a great way to connect with editors and publishers who might like your stuff, and for many publishers, this is the only realistic way to reach them. Are they worth it later? Probably, depending on the author. I do not enjoy negotiating, and I have found that an agent will ask for, demand, and get far more money than I would have myself, so I am fine with their cut. Are they essential? Can you make it in the traditional publishing world without an agent? Yes, but it is an uphill climb.

The problem is, how do you find a good agent? I don’t know. I inherited a great agent very early in my career from the publisher I first worked for, and I have happily been with them since. If I had to start from scratch now, I’d ask friends with agents who make stuff like my stuff to recommend theirs.

In self-publishing, you avoid agents and so keep that 15%.

Advances

What an agent will ask for from a publisher is a bunch of money upfront, when the contract is signed. This is the advance. You pitch a book, and if the editors accept it, they give you a deadline of a year or so to produce it. The role of the advance is to pay you a wage until the book is released, after which it will begin earning royalties for you. Royalties might be something like 7-10% of the retail price per book. The money you get on signing is technically an “advance against royalties.” Meaning that whatever they pay you in advance is deducted from your royalties, so you won’t be paid anything further beyond the advance until and unless the earnings of your royalties exceed the advance.

It is very common for authors to not earn anything beyond their advance. The calculation for the amount of the advance goes roughly like this: Let’s say you earn $1 royalty for every book sold. The publishers estimate they can sell 30,000 copies in the first year, and so they offer you an advance against future royalties of $30,000, or one year’s worth of sales. Obviously, many other factors go into this equation, but to a first approximation, the most you will get for an advance is based on what kind of sales they expect immediately.

The rule of thumb for an author is that you should get the biggest possible advance you can (and this is how an agent can help) – even if this means you won’t earn out the advance. The reason is: the bigger the advance, the bigger commitment the publisher must make in promotion, publicity, and sales. They now have significant skin in your game. Publishers are stretched thin, and their limited sales resources tend to go where they have the most to lose. If an advance is skimpy, so will be the resources allotted to that book.

BTW, you should not have concerns about taking a larger advance than you ever earn out, because a publisher will earn out your advance long before you do. They make more money per book than you do, so their earn-out threshold comes much earlier than the author’s.

Thus one of the advantages of this traditional system – of going with a publisher – is that they bankroll your project. They reduce a bit of your risk. Likewise, that is the genius of Kickstarter and other crowdfunders for self-publishing: the presales bankroll your project, reducing risk. Crowdfunding becomes the bank.

Crowdfunding

I’ve written a whole essay on my 1,000 True Fans idea, simplified as thus: You don’t need a million fans to make self-publishing, or the self-creation of anything, work. If you own control of your audience – that is if you have a direct relationship with your customers individually, having their names and emails, and can communicate with them directly — then it is possible to have as few as a thousand true fans support you. True fans are described as superfans who will buy anything and everything you produce. If you can produce enough to sell your true fans $100 per year, you can make a living with 1,000 true fans. I go into this approach in greater detail in my essay first published in 2008 which you can read here.

Today there are many tools and platforms that cater to developing and maintaining your own audience. In addition to crowdfunders such as Indiegogo, Kickstarter, Backerkit, and dozens more, there are also tools for sustaining support with patrons, such as Patreon. Crowdfunders tend to be used at the launch of a project, while something like Patreon permits constant support, primarily for a creator rather than a particular project. These can be combined, of course. You could launch your self-published work with a Kickstarter, and then gather Patreon support for sequels, backstory and making-of material, future editions, or side projects. Periodic publications have subscriptions for ongoing support.

These days backers expect a video — and other marketing bits – selling the book. Pre-sales for a crowdfunding campaign have become very sophisticated and require a lot of preparation. The Kickstarter for my Asia photobook was relatively simple and crude.

The chief advantages of crowdfunding are three, and they are significant: 1) You can get the funds before you create in order to support you while you create. 2) You keep all of the revenue (minus 3-5% for the platform), unlike an outside publisher. And 3) You own the audience, for future work.

The disadvantages are also three. 1) It is a huge amount of work. Most crowdsource campaigns go for 30 days and tending it for 30 days is a full-time job. 2) To be successful requires a different set of talents – marketing, sales, social engagement – other than what a creator may have. 3) You are responsible for making sure your fans actually get what they were promised. This “fulfillment” aspect of crowdfunding is often overlooked until the end, when it turns out to be the most difficult part of the process for many creators.

Production

Once upon a time, it was a huge deal to design and physically print a book (or press a music album, or deliver a reel of film). Today those processes can be done by amateurs with little experience. And often digital versions make creating, duplicating, and distribution even easier than ever.

There are three paths to production: traditional batch manufacturing; on-demand printing; digital publishing.

Batch Printing Presses

The traditional way of printing hardcover books still exists and it is a big business. A really first-rate printer will have different kinds of presses for different jobs – including the same fast digital printers as the on-demand printers. In fact, for some jobs, they will use these same digitally controlled ink-jet printers, just at a larger scale and speed. The chief advantages of classic printing on paper are three: you get scale, quality, color.

Pages from my first photobook, Asia Grace, published by Taschen, stacked up in their printing plant in Verona, Italy. In the old days before presses were completely computerized, the art director for the book (me) would be present during printing to oversee the many color tradeoffs each signature of pages needed.

Scale: Books printed in larger volume batches, or “runs,” can win huge discounts on the price per copy. A regular-sized hardcover book printed in Asia might only cost a few dollars to print, and a few more dollars for packing and shipping to your home. That’s a great deal if you list a book at $30. The higher the volume, the lower the price per unit. Printing outside of Asia is more expensive, but still worth considering – if you think you want a lot of copies, say more than 5,000 to start.

Quality: There is a resurgence in considering a well-crafted book as an art object. By leaning into its physicality – adding an embossed cover, heavy rag paper, deckled edges, glorious binding – the book can transcend its intangible counterpart on the Kindle. You as a publisher can make a book a unique custom size, or with magnificent die-cut covers for added zest and higher prices. Some self-published authors offer handsome bound book sets, or books hand-signed via tipped-in sheets, or super high-end limited editions, cradled in their own box. All these kinds of qualities require a collaborating printer somewhere.

Color: On-demand printing can do color, but not oversized, and not cheaply. In my experience, serious coffee-table visual books still need the hand-holding and economics of a printing plant. And I regret to say, that after many years of searching, I have not found a printer in the US capable of doing large full-color books at a reasonable price. You are most likely going to have to go to Asia, such as Vietnam, Indonesia, Singapore, India, or Turkey. China still has the best prices with the highest quality of color printing.

The disadvantages of having your book printed at a printer are #1: You have to house and store them somewhere. Either you have an available basement or garage, or you rent a place, or you hire a dropshipper, or you pay for a distribution giant like Ingram or Amazon to handle it for you. The full run of a book can take up more room than you might think when they are packaged up for shipping. My Vanishing Asia book set, financed on Kickstarter, printed in Turkey, filled up 4 shipping containers, each 40 feet long! That is a LOT of books to store.

Disadvantage #2 is that you need to pay the printer first, long before you sell the books. Not only is this a cash flow challenge, but you have to guess how many books you will sell before they are sold. (Having the pre-sale on a crowdfunding platform like Kickstarter is a big help in relieving that problem.) To get the best price you need to print a lot, but if you print a lot, you have a lot to pay for and to store if they should not sell.

On-demand

You can use free software to design your book and then send it to an on-demand printer to make 1 copy or 1,000 copies, printed one by one as each copy is sold. The copy does not exist till it is sold, so there are no books to bank, store, or ship. An on-demand regular softcover book would cost about $5 to make. It will be professional quality, indistinguishable from a trade book you might buy on Amazon, in part because many of the books from big-time publishers you buy on Amazon are actually printed on-demand using this same technology. (Big-time publishers are also printing on demand!) However, while the ink printing is first class, the bindings, paper quality, cover details won’t be up to what you can get with the best modern presses. What you’ll get is the good-enough printing contained within the average hardcover book.

The advantage to a creator (and to NY publishers) is that there is no inventory of unsold books to store or handle. You print the book when, and only when, it is sold. The disadvantage is that the cost of printing is more per book.

I can use four different services to print on-demand books. My preferred color and photo/art book printer is Blurb, for quality and ease of use. They keep up with the state-of-the-art color printing. You can design your book, export it as a PDF and have Blurb print it on-demand. Or you can use Blurb’s own web-based design program, or you can use a version of its software built into Adobe’s Lightroom, which is pretty standard for photographers. It’s very simple to go from photographs to a very designed book and then printed.

Sample pages from the various coffee table books I have had printed on demand from Blurb. Some of these books have editions of 2 copies; however the quality of the color printing is first class.

A second option for on-demand printing of standard books with black and white texts, as well as books with color illustration, is Lulu. Their photo/art books are a bit cheaper than Blurb. They are very competitive with standard text-based books. Most importantly, Lulu integrates with your own customer list, so you own your audience.

That is not true of the third option, which I also use a lot: Amazon. Amazon offers its print-on-demand service, called KDP, to anyone who wants it, with the added huge advantage that your book will be not only listed on Amazon immediately, but also delivered by Amazon’s magical logistical Prime operation. So potential fans can discover your work on Amazon and then have it delivered to them the next day for free. This is huge! But the huge and sometimes deal-killing disadvantage is that you do not know who your readers are, as you do with Lulu. Although Amazon makes it ridiculously easy to create and sell a book, with them you don’t own your audience. But in some cases, that is still worth it.

The fourth option is IngramSpark. I have little direct experience with this vendor, but others who do claim it is the best choice for text-based books aimed at libraries and bookstores. Indie bookstores are doing much better than chain bookstores and they usually avoid Amazon’s distribution system, Ingram is their main vendor for getting books — as it is for libraries. In addition to getting your book into the Ingram distribution, IngramSpark offers the self-publisher more options for book sizes, paper and binding.

Because you can print as few as one copy of a book, I use these on-demand print services to manufacture prototype versions of a book to check for its sizing and feel. This small on-demand prototype of my book of advice was later published in a larger page size by Penguin/RandomHouse.

Digital

By far the easiest way to publish a book is to sell a digital copy of it. More authors should consider just publishing digital books. You still have to promote it, but you don’t have to print it, ship it, handle it, or store it. A commercial publisher might offer the author a royalty of 7% of the retail price which is say $2 per $30 book, so you may make just as much money per book selling it for $2 in digital. Creating digital books is a great way to start a publishing career. I have two friends who started publishing their science fiction stories as inexpensive digital short stories, which sold well, and then were later discovered by print publishers, made into printed books, and eventually turned into movies by Hollywood. And they still sell the digital versions!

You can sell an e-book – or even a chapter of a book – on Amazon’s KDP. You can easily make a book for the Kindle. I’ve had some digital books up on Amazon KDP for the past ten years, and they continue to sell slowly, yet I have not had to do anything with them since they were uploaded. While the Kindle gives you a royalty of 70%, and accesses a large Amazon/Kindle audience, its downsides are that you don’t own your audience, it demands exclusivity, and you must use their proprietary file format which removes any distinctive interior designs and prevents it from being read on other devices. There are dozens of other e-book readers and e-book platforms like Kobo, Apple Books, and Google Play Books who have different proprietary constraints. IngramSpark has an interesting hybrid program for e-book + on-demand printing.

These days a lot more people are comfortable reading a book in PDF form. I sell secure PDFs of some of my books on Gumroad, an easy-to-use web-based app, that collects the payment and sends the buyer an authorized copy. Gumroad works fine, does not charge much, and is super easy to set up; it is perfect for the low digital sales volumes I have.

The digital publishing world is fast-moving, and I don’t have as much recent experience with e-books to feel confident in finer resolution recommendations.

Audiobooks

Another important digital format I neglected to mention in the first version of this piece is Audiobooks. For the past decade audiobooks has been the fastest growing format for books — the one sunny spot in a worried landscape. There are many readers who only audit books, and never read them. I don’t have any experience in self-publishing audio books; all my mainstream publishers developed the audio versions with almost no input from me. But my friend and science fiction author Eliot Peper has self-published 9 audiobooks, sometimes hiring voice actors and, more recently, narrating them himself.

Currently the platform of choice for self-publishing audiobooks is ACX. ACX is a do-it-yourself platform, run by Audible, the major audiobook platform, and is also owned by Amazon. They are a full-service platform with sound quality tests, and a million narrators you could hire, and other tools to make the process easy. They take a hefty royalty of 40% and demand exclusivity, but your book is listed on Audible; for many readers that is the only place they will ever look for audiobooks. Alternatives such as Spotify are expanding into audiobooks which might make better deals with authors.

Distribution

Digital is easy, but increasingly, the “difficulties” of analog books have become an attraction. Some readers gravitate to the tactile pleasures of a well-made artifact and revel in the physical chore of turning pages. Sometimes the content of a book demands a bigger interface than a small screen can provide, so it needs the oversize release of a large printed page. Some appreciate the longevity of paper books, which never go obsolete and can be read for centuries without a power source or updates. Others are attracted to the serendipity of browsing a bookstore. And some folks value the limited scarcity of a printed volume.

But once words are printed on a page you have to ship them somehow. On-demand printers like Amazon KDP, Lulu or Blurb will directly ship the books to individual readers as they are ordered one by one. There is no inventory for you, and thus no work for the author. The ability of the on-demand publishers to handle long mailing lists varies and is a bit of work. Amazon has the best prices for shipping (zero) but the worst facility to mail to a list. They want readers to order from their own Amazon accounts; Amazon wants to control the audience. Blurb can only ship to customers who order on Blurb. Lulu lets you control your own fan list but charges a lot more to ship.

Cartons of my heavy oversized graphic novel, Silver Cord, pile up in my studio after being shipped in from China. I was unprepared for the chore of shipping the over sized books out to all the backers without damage.

Let’s say you want to go all in, you print the books yourself, and now you have to get them to your fans. Three options; From the printing plant, the books will be shipped by truck to either:

1. Your garage. You purchase mailing envelopes or boxes and tape them up and mail them out. Plus: you can sign the books. Minus: tons of ongoing work, and not cheap to mail, even with Media Mail in the US. Shipping globally is a huge headache, and insanely expensive.
2. A Drop Shipper, or what is today called a 3PL. For a fee, this kind of company will pick your book from your inventory in their warehouse, package it, and ship it out to your fan, or a bookstore. You give them either a mailing list or access to your orders on Shopify or Kickstarter. Plus: no grunt work, no inventory at home. Minus, not cheap either and they also charge a storage fee for holding your books, which could be there for years. Commercial dropshippers also favor large volume enterprises. I am currently trying out dropshipper eFulfillment that has low minimums and works with small-time operators like me.
3. Amazon. You ship your books to an Amazon warehouse, and they fulfill the orders on Amazon as a third-party merchant.  You would be selling books, just other merchants sell toasters or toys. Plus: they handle everything, and they offer readers free shipping. Minus: they only order the number of books they expect to sell easily, so there can be a lag until sales start, and of course, they only handle books bought on Amazon. That can be fine. I did one book that was only available on Amazon, nowhere else — I did not have any copies myself — and it sold great, with zero distribution worries on my side.

Promotion

The short version: it is not hard to produce a book. It is much harder to find the audience for it and deliver the book to them. At least 50% of your energy will be devoted to selling the book. This is true whether you publish or self-publish.

A misconception about Kickstarter, Backerkit, and crowdfunding platforms like Patreon is to imagine that you will automatically find your audience there. It is almost the opposite. You won’t be able to have a successful crowdfunding campaign unless you bring the crowd with you. You must cultivate your audience BEFORE you ignite them on Kickstarter.

You won’t have time to build your audience during the fundraising period. The typical crowdfunding campaign lasts 30 days; that will be just long enough to entice them about your work. That also means that for a month, it will be a full-time job for somebody to promote, advertise, and “convert” your audience to your book or project. That somebody is probably you. These days promotion includes making a short video announcing the launch, devising tiers of “rewards,” keeping up with status notifications of how the campaign is going, and doing everything else you can to promote it to new fans. If there is no one willing or capable to give it a month, the effort will probably not reach your goals.

Because Kickstarter, Backerkit, IndieGoGo, Patreon, and other crowdfunders are a platform, there WILL be some people who discover your project there via referrals of similar projects – which is always a plus – but your dominant source will be the audience you accumulated earlier and brought with you. If your project has a likeable presence, the platforms can boost the awareness of it if you are lucky, so getting listed on the front page is something to aim for and it does help.

By now there is also a small industry of “growth” companies who plug into Kickstarter and kin, and who will help you run a crowdfunding campaign for a fee. I actually found that at least one of them was worth the fees they charged, which are now a 20% cut of the additional backers they bring in. They were able to enlarge my campaign way beyond my circle of friends and my existing 1,000 true fans. If I were doing a crowdfunder for the first time, I’d use a growth company like Jellop and I would partner with them from the very beginning.

Getting published by a New York publisher doesn’t get you off the hook. Even if you were to be published by a commercial publisher, you should expect to do serious promotion over the span of a month or more. In theory, the publisher would set up, or at least guide you, through the promotion, but that rarely happens anymore. Even with a commercial publisher releasing your work, you will end up doing the majority of whatever promotion gets done. As in, planning, coordinating, executing, and even paying for book tours and the like. You will be the publicity department no matter what.

Traditionally the promotion of a new book entailed a book tour for the author visiting larger bookstores, where crowds of fans would purchase books. Plus some advertisements for the book in magazines and newspapers, which would also review said book. Ideally this launch would also include appearances on TV talk shows, and maybe radio. None of this works anymore. There are no more paid book tours. Few, if any, book reviews in newspaper or magazines, or author appearances on TV.  Fewer ads for books. If any of these do happen, they will be arranged and paid for by the author. 

But because books tours have sort of disappeared, an enterprising author with an audience can arrange their own tour using their fan list. Many people crave a deeper connection to people they follow digitally, and these fans can fill a room. Recently Craig Mod, an unknown new author in bookland, arranged and paid for his own sell-out tour, astounding booksellers around the US who did not have enough of his books to sell (who is this guy?).

Instead, promotion for books have shifted online. There is booktok, where fans read new and favorite books for viewers on social media. There are podcasts, where authors can be interviewed at length. In fact, for my last two books – which basically got no reviews in publications – sold extremely well because I heavily promoted them on podcasts, big and small. I said yes to every podcast request who had more than 3 episodes. Even a small podcast audience is larger than the audience in most bookstores. And because podcasts can be niche and intimate, unlike say an appearance on TV, they sell books.

The rule of thumb in publishing is that how well a book sells in its first two weeks determines whether it is a bestseller or not. You want to concentrate most of the sales as pre-sales – either on a crowdfunding platform, or on your own, or as pre-sales for a publisher. One way or another this promotion job will be your job, and can end up being at least half of your total effort on a book.

Non-book publishing

Creating a book has become so easy, that most books these days should probably not be a book. Not every idea — or story — needs the long format of a book. In fact, few do. Instead, they should be a magazine article, a blog post, an op-ed, or a newsletter.

Subscriptions

While we have been long trained to pay for books, we have less of a habit of paying for shorter material, particularly in digital form. Printed magazines and newspapers are disappearing, and few survived the transition to full digital, so there are fewer and fewer opportunities to get paid for publishing your work in a form other than a book. Blogs were great, but for a long time, there was no way to get paid, so they were a non-starter for many authors. Recently platforms for paid newsletters, like Substack, Ghost, Beehiiv, Buttondown and so on have risen, creating a small ecosystem for professional writers.

Substack in particular has done a great job in educating the audience to expect to pay for quality content. All the platforms promote subscriptions as the revenue model, instead of ads. A typical subscription newsletter will start charging $10 per month, although you can charge as much or as little as you want, including free. (Reminder that $10 per month is more than $100 per year, so you could do quite well with 1,000 true fans.)

You don’t need Substack, or any of the other platforms to publish a newsletter for money. If you have an audience you can publish your own with some easy software apps. Well-known Mailchimp does lists easily, but not payments. Memberful has a digital payment function and customer management tools; but you have to host the content, and shape your newsletter. It’s great for building your own custom publication, with full ownership of the audience and the design. Substack, Ghost and others make it easy for beginners to build their own subscription newsletters. Medium is another similar, but different, online publishing platform. They host many writers from many backgrounds, but readers pay only one fee to Medium, which Medium then funnels to the writers and editors who curate this mega-magazine. I don’t have enough experience with it as a writer to know how viable it is. I’ve written there but I get no access to the audience directly.

Unmonetised publishing

That’s also part of the challenge of a blog; no ownership. Blogging on your own website is extremely powerful. There are zero gatekeepers. Publishing is instant. Mostly free. You can say anything you want. You can write a little or a lot, every day or once a year. You have 100% control of the design. In many ways, it is the ultimate publishing platform. It’s a fantastic home for great writing and new ideas. For a while blogs had their heyday. But blog websites have three significant downsides which temper their supremacy. One, fewer and fewer people are going directly to a website on a regular basis; it’s harder to maintain an audience and very hard to grow one on the web. Two, unless you implement a membership level of some sort, you actually don’t own your audience. Readers are anonymous. Sometimes you can implement comments on a blog, but they need to be managed and vetted, and their IDs are not useful for an author. And three, blogs, almost by definition, are open to all visitors and don’t have significant revenue models. The fact that you can not easily charge for the writing you do on a blog has been the reason why Substack and other subscription platforms arose. (There is currently a few blogs like Kottke, which are experimenting with paid membership to comment, while keeping the blog open and free.)

In the same vein, X(Twitter), Instagram, TikTok and Facebook are bonafide real publishing platforms. In fact I have published the most significant parts from every page of one of my books, and every sentence of another book, on these social media. You can do serial publishing. But there is no revenue model. You can gain followers but not dollars. Sometimes followers can be transferred into a real audience that you have access to, but it is not easy, and certainly not automatic. While YouTube (see below) can fully support creators, I have not met anyone who is making money selling their content on the social media platforms.

Nonetheless, I continue to blog and to post on the socials; it’s the first place my writing goes. And sometimes, this diffuse audience is all that the writing needs.

Another advantage of the subscriber newsletter platforms like Substack and Ghost, et al, is that there is a social component, and they can be a great way to build a community around your writing. They make it very easy for readers to comment. They also have built-in analytics that can help you understand your audience and how they engage with your content. Equally important, their systems recommend other newsletters to current or new subscribers, thereby enabling others to discover your work. This network effect can help you find and grow your audience. Once a reader has an account for one newsletter, it is very easy for them to sign up for another newsletter from another author.

The design and layout on these platforms are currently very limited, and work best for publishing primarily text, but the platforms are also moving into video and images.

You could think of a newsletter as a subscription to an ongoing book. Writing a book “out loud” — publishing it in parts as you write it — has become much more common. You write in shorter sections, publish the chapters immediately online, and then solicit feedback. This works in both fiction and non-fiction. In fiction, you can publish chapter books, serially, one chapter at a time. In non-fiction, you can write essays or blog posts or newsletter issues (see above), and then make edits to the material later based on comments and corrections. The text is essentially “proofed” and fact-checked by the earlier readers. I have written several books this way, and this process increased the quality of the material manyfold. In addition to correcting some embarrassing mistakes, it also bettered some parts with alerts to ideas I was not aware of.

In the old days, mainstream publishers actively tried to prevent authors from publishing material beforehand, but now the speed of correction, the ease of comments, and the ease of pointing to overlooked research is so easy for readers to do, that it makes great sense to rehearse your writing in public. You can also look at this dynamic process of write > publish > rewrite > republish as a way of building up and maintaining attention. In general, people devote less time to books because of the huge amount of attention they require. It is easier to string that attention out in an ongoing series of posts. If a long-form book comes out of it, it is much easier to reel that string of attention back into the book. And with the advent of paid subscriptions, readers can subscribe to your ongoing book.

Screening

We used to be people of the book, but now we are people of the screen. Our culture used to be grounded on scriptures, constitutions, laws, and canon — all written texts. These were fixed in immutable black and white marks on enduring paper, written by authors, from whom we got “authorities.” Now our culture pivots on screens, which are fluid, mutable, flowing, liquid, and fleeting. There are no authorities. You have to assemble the truth yourself. Books no longer have the gravitas they did, and my children and their friends are not reading many of them. Instead, they watch screens. They read the text in moving images. They are learning more from YouTube than books in school.

While books will continue to be published, the center of attention has shifted to moving images. Worldwide the number of hours of attention given to screens dwarfs anything given to pages. Today, to seriously talk about publishing we must talk about video, VR, reels, movies, games, YouTube, TikTok, and of course AI. The audiences for my books are counted in thousands but the audience for my TED talk videos are counted in millions. I spent minutes preparing for my TED talks and years preparing my books, but given the asymmetry of attention and influence, I should have reversed my own attention and given my videos years of work and then only hours creating the book derived from it.

I don’t have enough personal experience in this emerging media to offer useful advice right now; it may have to wait for part two. I do have a small group of friends who make their living publishing on YouTube and TikTok and Instagram. The screen is their prime media for non-fiction work. I know enough of these new kinds of professional creators to see that this mode is a very viable path. I also have sufficient evidence from my own platform tracking to clearly see that the audience for text is stagnant, and skews older, while the audience for moving images continues to expand greatly, while getting younger. Of the two, I know where I want to work. (It is not lost on me that this essay is text and not a video. I’m working on it….)

Summary Advice:

In conclusion, the way I approach publishing today is with as much self-publishing as I can handle. I’d write in public installments, as a subscription newsletter, or e-book single chapters, or simple posts on my blog. If I could find an audience that wanted more of the material, I’d rewrite, re-edit, re-compose the material into a longer form. I’d release that as an ebook, and/or on-demand printed book sold in my shopify shop. If the material was deep, or involved more creators than just myself, I’d consider crowdfunding it. Those presales allow me to target exactly how many copies to produce. I’d calculate the cost of drop shipping. And at every stage I’d be making some kind of visual version for YouTube and the other attention seeking channels, because that is where the attention is.

To clarify this complicated advice even further, I have made a flow chart of possible options for publishing and self-publishing. This is roughly the decision tree I tend to follow when I am figuring out the best mode for the material and my goals. I hope you find it useful too.

A 16-page PDF of this article is available for free download here.

I want to argue that intelligence may be unique among all resources on the planet. It may be the only resource we create that sees truly infinite, insatiable demand.

Pricing abundance is tricky. Netflix, Spotify, and millions of software apps are offered at a fixed price for unlimited use. That works — they make money — because in fact, there is not unlimited use of them. We get satiated pretty quickly. We only watch so many hours, listen for limited hours, or eventually stop scrolling. This may not be true of AI. It looks like the demand for AI can exceed our own bounded time.

This is not true for other resources we create. When food and calories were scarce, we never imagined anyone would walk by an all-you-can-eat buffet and not take a bite. When clothes were scarce we never imagined anyone would not keep a hat they were handed for free. But it turns out that some of our most basic demands are in fact not unlimited. We can become satiated with food, with clothing, with entertainment, with shelter, and even with companionship (most people top out at 150 friends).

We dream of a world where we all have just a little more than we need. That world is often called utopia, and most commonly believed to be impossible. After all, to satisfy everyone with more than they need would require an infinite amount of resources. At least that is a common belief. However as abundance in some areas of life becomes more common these days, we see evidence that our consumption of many resources is not unlimited. And it may be that very few resources actually have real unlimited or insatiable appetites.

There are a couple of resources that seem to be in insatiable demand, such as energy, or bandwidth. However energy is not as insatiable as it appears. Folks who have massive solar for their homes have abundant “free” (as in unmetered) energy. And while they may “waste” energy by keeping the lights on during the day, or running  air conditioners all the time, at some point, they just can not use any more energy. The same with bandwidth. At first screens and phone calls had noticeable low resolution, and we wanted more and more bandwidth and storage. But at a certain point more pixels and more megabytes simply don’t matter. We may not have reached the point where there are no more improvements — we still need convincing 3-dimensional immersive worlds indistinguishable from reality — but it seems clear that our senses can be satiated.

Health care in another that has often been declared as under “insatiable demand” because it is hard to provide freely.  But the odd thing about health care is no one really wants infinite amounts of health care, because it costs you time and hassle to get it. Even when it is monetarily free, it is not really free to you. Ideally, in a perfect world, you would need no care at all because you would be perfectly healthy. In fact you want minimum care; the least amount of super great care. If you are sucking up huge gobs of care, that’s a sad story you want to avoid. You do want unlimited potential care, but not unlimited care. I have some friends who run a boutique medical service for billionaires. For a generous mind-boggling retaining fee, they offer 24/7 deeply personal unlimited medical care. It’s all you can use for a fixed fee. And yet their clients rarely use much of it (with some exceptions for emergencies). Their clients are NOT consuming their services 24/7, draining it out because it is “free.” When you give the option of unlimited health care, most people most of the time have limited use.

That’s true for individuals. What about society at large? Even though there is likely no insatiable demand for resources, there is growing demand at the society level, primarily because the human population continues to grow, and a greater portion of that population is getting the wealth to afford the resources. In addition, progress tends to lower the costs per unit for every resource, and that cheaper supply increases more uses, so consumption in total aggregate balloons.  That means that if there is a limited supply, and technology figures out how to need less of it, we will still use more of it. This is called the Jevons Paradox. So while there is not insatiable demands for resources, there can be exponentially expanding demand collectively.

This is the pattern we seem to see with AI to date. It is perhaps the fastest exploited resource we ever made, with the fastest per capita adoption and the fastest increase in hourly usage. But this is just the First Wave.

The First Wave of AIs is measured in humans. How many people have accounts and are using it? Eventually, everyone on the planet will be chating with AIs, and so this first wave will be satiated soon enough.

The Second Wave of AIs is measured in hours. How many hours are people using our AIs? The end goal of many visions of AI is that it be an ALWAYS ON resource. Perhaps your AI agent lives inside of your smart glasses, so it is listening to you all day and watching what you see while you are awake, and watching you, too, and guiding, whispering to you even before you summon it, because it is always on 24 hours, hopping off your glasses onto your device in the bedroom, or abiding in your walls at night. So the makers of AIs measure how many hours they deliver.

The Third Wave of AIs is measured in tokens. The real serious market for AIs is not humans, but other AIs. As we enter the agent world, one agent relies on sub-agents to do work, and these sub-agents themselves dish out work to other agents, and so on, with each layer consuming AI tokens to process, so we can quickly accumulate a vast networked system of interacting AI agents consuming tokens.

And while I can watch only so many hours of Netflix, there is no limit to how much intelligence I can consume. I can give my agents a task and have them check it and recheck it and then triple check it. Soon I will be able to deploy thousands of agents working around the clock on my projects. I can have an army of agents work while I sleep on making whatever they made 10x better tomorrow.  I could give them another day and have the army make it 100x better. And here is the catch: there appears to be no limit for better.

In a weird way, if the AIs really do what we hope they can do, they become a proxy for betterment. They also become a proxy for human time, which we are unable to manufacture otherwise. We are bound — even the billionaires among us — to 24 hours a day. But if we have human proxies thinking and working day and night — armies of them in the trillions — we suddenly have unlimited time.  AIs may be a way for us to finally manufacture additional hours in the day, without stealing it from another human. And this kind of time also seems to be insatiable.

The conundrum, of course, is that producing this infinite ocean of AI is not free. It is nowhere near free. Even though the cost per token (and the energy per token) decreases, we will use far more of them because there is no limit for betterment. That might suggest that the amount we spend on AI will continue to rise, or at least not fall as fast as we might hope. I am reminded that for many decades the price of a new laptop has sort of remained the same, at about $1,000, even as Moore’s Law raged on unceasingly, dropping the cost per transistor to almost zero. The cost of AI tokens will likely follow a similar pattern, where the cost per token falls but the total spend on AI rises. Just as we don’t really count transistors anymore, in the coming years we may ignore the count of tokens as well. We’ll switch our metrics to something more meaningful, perhaps “tasks”, or some kind of measure that reflects the value of the AI’s work.

But how do you price AIs? The 99% bulk of AIs that are inward facing, that interact with other AIs, that are invisible infrastructure, that we never notice unless they are down, these AIs can be metered. But the 1% of AIs that are outward facing, that humans interact with, that are visible, whispering into our ears, overlaying our vision, always on, probably won’t be metered, because we tend to not like meters running in our heads. We want unlimited access service, which is why we have subscriptions for things that spend our attention. We’ll avoid any meter measuring out the godhood that personal AIs give us. But the agent sitting on our shoulder, always on, has at its command, an infinite army of intelligences, always on, and this army will probably need to be metered because there is no limit to better.

When we begin comparing various unlimited personal AI plans we can imagine some of the qualities that might be advertised: speed of response; how far ahead it can anticipate; span of control (how deep of an army of subs); variety of experts on call, privacy/personalization controls,  Features and interface design will sell the service, even if tokens were free — which they will essentially be very soon.

I wrote this short memo last November, 2024, at the invitation of Wired Mid-East for their year-end issue. I think it still holds up nine months later, and represents where we are on this astounding journey.

There are three points I find helpful when thinking about AIs so far:

The first is that we have to talk about AIs, plural. There is no monolithic singular AI that runs the world. Instead there are already multiple varieties of AI, and each of them have multiple models with their own traits, quirks, and strengths. For instance there are multiple LLM models, trained on slightly different texts, which yield different answers to queries. Then there are non-LLM AI models – like the ones driving cars –  that have very different uses besides answering questions. As we continue to develop more advanced models of AI, they will have even more varieties of cognition inside them. Our own brains in fact are a society of different kinds of cognition – such as memory, deduction, pattern recognition –  only some of which have been artificially synthesized. Eventually, commercial AIs will be complicated systems consisting of dozens of different types of artificial intelligence modes, and each of them will exhibit its own personality, and be useful for certain chores. Besides these dominant consumer models there will be hundreds of other species of AI, engineered for very specialized tasks, like driving a car, or diagnosing medical issues. We don’t have a monolithic approach to regulating, financing, or overseeing machines. There is no Machine. Rather we manage our machines differently, dealing with airplanes, toasters, x-rays, iphones, rockets with different programs appropriate for each machine. Ditto for AIs.

And none of these species of AI – not one – will think like a human. All of them produce alien intelligences. Even as they approach consciousness, they will be alien, almost as if they are artificial alien beings. They think in a different way, and might come up with solutions a human would never do. The fact that they don’t think like humans is their chief benefit. There are wicked problems in science and business that may require us to first invent a type of AI that, together with humans, can solve problems humans alone cannot solve. In this way AIs can go beyond humans, just like whale intelligence is beyond humans. Intelligence is not a ladder, with steps along one dimension; it is multidimensional, a radiation. The space of possible intelligences is very large, even vast, with human intelligence occupying a tiny spot at the edge of this galaxy of possible minds. Every other possible mind is alien, and we have begun the very long process of populating this space with thousands of other species of possible minds.

The second thing to keep in mind about AIs is that their ability to answer questions is probably the least important thing about them. Getting answers is how we will use them at first, but their real power is in something we call spatial intelligence – their ability to simulate, render, generate, and manage the 3D world. It is a genuine superpower to be able to reason intellectually and to think abstractly – which some AIs are beginning to do – but far more powerful is the ability to act in reality, to get things done and make things happen in the physical world. Most meaningful tasks we want done require multiple steps, and multiple kinds of intelligences to complete. To oversee the multiple modes of action, and different modes of thinking, we have invented agents. An AI agent needs to master common sense to navigate through the real world, to be able to anticipate what will actually happen. It has to know that there is cause and effect, and that things don’t disappear just because you can’t see them, or that two objects can not occupy the same place at the same time, and so on. AIs have to be able to understand a volumetric world in three dimensions. Something similar is needed for augmented reality. The AIs have to be able to render a virtual world digitally to overlay the real world using smart glasses, so that we see both the actual world and a perfect digital twin. To render that merged world in real time as we move around wearing our glasses, the system needs massive amounts of cheap ubiquitous spatial intelligence. Without ubiquitous spatial AI, there is no metaverse.

We have the first glimpses of spatial intelligence in the AIs that can generate video clips from a text prompt or from found images. In laboratories we have the first examples of AIs that can generate volumetric 3D worlds from video input. We are almost at the point that one person can produce a 3D virtual world. Creating a video game or movie now becomes a solo job, one that required thousands of people before. 

Just as LLMs were trained on billions of pieces of text and language, some of these new AIs are being trained on billions of data points in physics and chemistry. For instance, the billion hours of video from Tesla cars driving around are training AIs on not just the laws of traffic, but the laws of physics, how moving objects behave. As these spatial models improve, they also learn how forces can cascade, and what is needed to accomplish real tasks. Any kind of humanoid robot will need this kind of spatial intelligence to survive more than a few hours. So in addition to training AI models to get far better at abstract reasoning in the intellectual realm, the frontier AI models are rapidly progressing at improving their spatial intelligence, which will have far more use and far more consequence than answering questions.

The third thing to keep in mind about AIs is that you are not late. You have time; we have time. While the frontier of AI seems to be accelerating fast, adoption is slow. Despite hundreds of billions of dollars invested into AI in the last few years, only the chip maker Nvidia and the data centers are making real profits. Some AI companies have nice revenues, but they are not pricing their service for real costs. It is far more expensive to answer a question with an LLM than the AIs that Google has used for years. As we ask the AIs to do more complicated tasks, the cost will not be free. Most people will certainly pay for most of their AIs, while free versions will be available. This slows adoption. 

In addition, organizations can’t simply import AIs as if they were just hiring additional people. Work flows and even the shape of the organizations need to change to fit AIs. Something similar happened as organizations electrified a century ago. One could not introduce electric motors, telegrams, lights, telephones, into a company without changing the architecture of the space as well as the design of the hierarchy. Motors and telephones produced skyscraper offices and corporations. To bring AIs into companies will demand a similar redesign of roles and spaces. We know that AI has penetrated smaller companies first because they are far more agile in morphing their shape. As we introduce AIs into our private lives, this too will necessitate redesign of many of our habits, and all this takes time. Even if there was not a single further advance in AI today, it will take 5 to 10 years to fully incorporate the AIs we already have into our orgs and lives.

There’s a lot of hype about AI these days, and among those who hype AI the most are the doomers – because they promote the most extreme fantasy version of AI. They believe the hype. A lot of the urgency for dealing with AI comes from the doomers who claim 1) that the intelligence of AI can escalate instantly, and 2) we should regulate on harms we can imagine rather than harms that are real. Despite what the doomers proclaim, we have time because there has been no exponential increase in artificial intelligence. The increase in intelligence has been very slow, in part because we don’t have good measurements for human intelligence, and no metrics for extra-human intelligence. But the primary slow rate is due to the fact that the only exponential in AI is in its input – it takes exponentially more training data, and exponentially more compute to make just a modest improvement in reasoning. The artificial intelligences are not compounding anywhere near exponential. We have time.

Lastly, our concern about the rise of AIs should be in proportion to its actual harm vs actual benefits. So far as I have been able to determine, the total number of people who have lost their jobs to AI as of 2024, is just several hundred employees, out of billions. They were mostly language translators and a few (but not all) help-desk operators. This will change in the future, but if we are evidence based, the data so far is that the real harms of AI are almost nil, while the imagined harms are astronomical. If we base our policies for AIs on the reasonable fact that they are varied and heterogenous, and their benefits are more than answering questions, and that so far we have no evidence of massive job displacement, then we have time to accommodate their unprecedented power into our society.

The scientists who invented the current crop of LLMs were trying to make language translation software. They were completely surprised that bits of reasoning also emerged from the translation algorithms. This emergent intelligence was a beautiful unintended byproduct that also scaled up magically. We honestly have no idea what intelligence is, so as we make more of it and more varieties of it, there will inevitably be more surprises like this. But based on the evidence of what we have made so far, this is what we know.

Today, AI tools lower the energetic costs of creating something. They make it easier to start and easier to finish. AIs can do a lot of the hard work needed in making something real.

I find little joy in having AIs do everything when I am being creative, but I do get enjoyment in co-creating with them. Co-creation feels like real creation. My role still takes effort and significantly determines the quality, style and nature of what is created. I typically spend 30 minutes to an hour co-generating an image in an engine like Midjourney. I have spent hours with AI co-writing an essay.  All the stuff I like the best requires my personal attention and involvement as a co-creator.

My hypothesis is that in the near future, the bulk of creative content generated by humans – with the assistance of AI – will have an audience of one. Most art generated each day will be consumed primarily only by its human co-creator. Very little completed art will be shared widely with others – although a small percentage of it will be shared widely.

If most art created in the future is not shared, why is it made? It will be chiefly made for the pleasure of making it. In other words, the majority of all the creative work in the future will be made primarily and chiefly for the joy and thrill of co-creation.

Right now there are roughly 50 million images generated each day by AIs such as Midjourney, Google and Adobe, etc. Vanishing few of these 50 million images per day are ever shared beyond the creator. Still image creation today already predominantly has an audience of one.

A large portion of these still images are preliminary: a sketch, a first draft, a doodle, a memo, a phrase, not meant to share. But even among those creations completed, very few are shared – because they were made for the pleasure of making them. You can generate an endless stream of beauty for the same reason you take a stroll through a garden, or hike into the mountains – in the hope you’ll catch a moment of beauty. You might try to share what you find, but it is not why you went. You went to co-create it. I think of a walk in a garden, or a hike in the high mountains – a hike that is not necessary for transportation reasons – as an act of co-creation. Together with nature, we are co-creating the moments of beauty we might find. Most of the beauty in the world is never seen by anyone. When we encounter these glimpses of a vista, or an exquisite way something is backlit, we are an audience of one. The joy is in discovering it; sharing is an afterthought.

We have some traditional analogs of an audience of one with journals, sketchbooks, diaries, and logbooks. The creations in these forms are not intended to be shared beyond the creator, and in some cases, that limited audience is what makes them powerful. They bring a type of protective solitude to the creative act, and that power will also be part of an AI-based Audience of One. These kinds of private art act as a generative platform for bigger things. But reckoned in volume, a bona-fide artist may create far more material in private than is ever seen in public. However if you asked an artist why they fill notebooks and sketchbooks and journals, they will not say it is because this creation is inferior, but because they love to create it, because they enjoy it. 

For those who view art primarily as a communication act, this art for an audience of one – traditionally found in journals and sketchbooks – still serves as communication, but to the self. In a curious way, AI can elevate self-communication, because its co-creativity enlarges the canvas and deepens the details of this communication to yourself. It is an enlarge self-inquiry.

In an AI-enhanced world, the realm of journals, sketchbooks, diaries and other private forms is expanded. Instead of compiling simple notes, doodles, fast impressions, small observations and other acts that can be done quickly, our journals, sketchbooks, and diaries will include fully rendered paintings, entire novels, feature-length movies, and immersive worlds. 

These new creations will shift the time asymmetry long associated with creation. Until now an author would toil years on a book that could be consumed in a day; a painter sweated months over a painting viewed in seconds; a million work hours would be put into a movie that is watched in 2 hours. Henceforth, it may be quicker to generate a movie than to watch one; quicker to co-create a historical novel than to read one; faster to co-make a video game than to play it. This shifts the center of gravity away from consuming to generation in a good way.

I don’t believe that total viewing hours in a society will ever exceed total creation hours, but AI-based co-creation can help balance that imbalance. It makes entering into the creation mode much easier – without the need for an audience to justify the effort. From now on, the default destiny for most art will be for an audience of one, and it will abide in the memory of those who generate it. While some of this co-generated work might find its larger audience and some very tiny fraction of it might even become a popular hit, its chief value will be in the direct, naked pleasure of co-making of it.

• Dark Matter = The weight of information. That’s one of the speculations in an offbeat but creative alternative theory of quatum physics that is not entirely a crackpot idea. Way out there. The radical idea that space-time remembers could upend cosmology

• We are going to hear more about Prompt Theory. I for one, fully believe in Prompt Theory. Prompt Theory (Made with Veo 3) – AI-generated characters refuse to believe they were AI-generated

• Something I knew nothing about: the degree to which job interviewees are trying to cheat with AI, and the difficulties that makes for good hires. Tech hiring: is this an inflection point?
• Excellent article on why humanoid robots are slow in coming and why it may take a lot longer to arrive in your home. Robot Dexterity Still Seems Hard
• There are a number of medical technologies that are feasible in the short term but lack sufficient funding to make them happen. I can’t vouch for this list of good tech that could but probably won’t happen in 5 years, but it is a good place to start. 10 technologies that won’t exist in 5 years

Thesis: The missing element in forecasting the future of AI is to understand that AI needs culture just as humans need culture.

One of the most significant scientific insights into understanding our own humanity was the relatively recent insight that we are the product of more than just the evolution of genes. While we are genetic descendents of some ape-like creatures in the past, we modern humans are also molded each generation by a shared learning that is passed along by a different mechanism outside of biology. Commonly called “culture”, this human-created environment forms much of what we consider best about our species. Culture is so prevalent in our lives, especially our modern urban lives, that it is invisible and hard to recognize. But without human culture to support us, we humans would be unrecognizable.

A solo, naked human trying to survive in the prehistoric wilderness, without the benefit of the skills and knowledge gained by other humans, would rarely be able to learn fast enough to survive on their own. Very few humans by themselves would be able to discover the secrets of making fire, or the benefits of cooking food, or to discover the medicines found in plants, or learn all the behaviors of animals to hunt, let alone the additional educations need for the habits of planting crops, learning how to knap stone points, sew and fish.

Humanity is chiefly a social endeavor. Because we invented language – the most social thing ever – we have been able to not only coordinate and collaborate in the present, but also to pass knowledge and know-how along from generation to generation. This is often pictured as a parallel evolution to the fundamental natural selection evolution of our bodies. Inside the long biological evolution happening in our cells, learning is transmitted through our genes. Anything good we learn as a species is conveyed through inheritable DNA. And that is where learning ends for most natural creatures. 

But in humans, we launched an extended evolution that transmits good things outside of the code of DNA, embedded in the culture conveyed in families, clans, and human society as a whole. From the very beginning this culture contains laws, norms, morals, best practices, personal education, world views, knowledge of the world, learnable survival skills, altruism, and a pool of hard-won knowledge about reality. While individual societies have died out, human culture as a whole has continued to expand, deepen, grow, and prosper, so that every generation benefits from this accumulation.

Our newest invention – artificial intelligence – is usually viewed in genetic terms. The binary code of AI is copied, deployed, and improved upon. New models are bred from the code of former leading models – inheriting their abilities –, and then distributed to users. One of the first significant uses for this AI is in facilitating the art of coding, and in particular helping programmers to code new and better AIs. So this DNA-like code experiences compounding improvement as it spreads into human society. We can trace the traits and abilities of AI by following its inheritance in code.

However, this genetic version of AI has been limited in its influence on humans so far. While the frontier of AI research runs fast, its adoption and diffusion runs slow. Despite some unexpected abilities, AI so far has not penetrated very deep into society. By 2025 it has disrupted our collective attention, but it has not disrupted our economy, or jobs, or our daily lives (with very few exceptions).

I propose that AI will not disrupt human daily life until it also migrates from a genetic-ish code-based substrate to a widespread, heterodox culture-like platform. AI needs to have its own culture in order to evolve faster, just as humans did. It cannot remain just a thread of improving software/hardware functions; it must become an embedded ecosystem of entities that adapt, learn, and improve outside of the code stack. This AI epizone will enable its cultural evolution, just as the human society did for humans.

Civilization began as songs, stories, ballads around a campfire, and institutions like grandparents and shamans conveyed very important qualities not carried in our genes. Later, religions and schools carried more. Then we invented writing, reading, texts and pictures to substitute for reflexes. When we invented books, libraries, courts, calendars, and math, we moved a huge amount of our inheritance to this collaborative, distributed platform of culture that was not owned by anyone.

AI civilization requires a similar epizone running outside the tech stack. It begins with humans using AI everyday, and an emerging skill set of AI collaboration taught by the AI whisperers.There will be alignment protocols, and schools for shaping the moralities of AIs. There will be shamans and doctors to monitor and nurture the mental health of the AIs. There needs to be corporate best practices for internal AIs, and review committees overseeing their roles. New institutions for reviewing, hiring and recommending various species of AI. Associations of AIs that work best together. Whole departments are needed to train AIs for certain roles and applications, as some kinds of training will take time (not just downloaded). The AIs themselves will evolve AI-only interlinguals, which needs mechanisms to preserve and archive. There’ll be ecosystems of AIs co-dependent on each other. AIs that police other AIs. The AIs need libraries of content and intermediate weights, latent spaces, and petabytes of data that need to be remembered rather than re-invented.  There are the human agents that have to manage the purchase of, and maintenance of, this AI epizone, at local, national and global levels. This is a civilization of AIs.

A solo, naked AI won’t do much on their own. AIs need a wide epizone to truly have consequence. They need to be surrounded and embedded into an AI culture, just as humans need culture to thrive.

Stewart Brand devised a beautiful analogy to understand civilizational traits. He explains that the functions of the world can be ranked by their pace layers, which depend on all the layers below it. Running the fastest is the fashion layers which fluctuate daily. Not far behind it in speed is the tech layer, which includes the tech of AI. It changes by the week. Below that, (and dependent on it), is the infrastructure layer, which moves slower, and even slower below that is culture, which crawls in comparison. (At the lowest, slowest level is nature, glacial in its speed.) All these layers work at the same time, and upon each other, and many complex things share multiple levels. Artificial Intelligence also works at several levels. Its code-base improves at internet speed, but its absorption and deployment runs at the cultural level. In order for AI to be truly implemented, it must be captured by human culture. That will take time, perhaps decades, because that is the pace of culture. No matter how quick the tech runs, the AI culture will run slower.

That is good news in many respects, because part of what the AI epizone does is incorporate and integrate the inheritable improvements in the tech stack and put them into the slower domain of AI culture. That gives us time to adapt to the coming complex changes. But to prepare for the full consequences of these AIs, we must give our attention to the emerging epizone of AIs outside the code stack.

• This article “Fetility on Demand” by @RuxandraTeslo is a fantastic report on one way to increase the fertility rate by artificially extending reproductive age. Fertility on demand