5 days later a million people using, and ChatGPT mania starts brewing. Time to make this bigger than big can be, grab everything on the internet because it’s all free.

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That’s the dream, keep repeating it until it’s true. Faster, faster, faster until you barely breathe, we need every single morsel of human genius, earthy creativity - to plant the seeds in this artificial, general, becoming intelligent tool. We’re the future, we’re the new rule.

Give us your poor books, the old paintings, and the infinity of what we love today. History is written, destiny’s on the way. We’ll give back so much intelligence, you’ll understand why we can’t pay.

Algernon, an AI engineer, remembers that day. Sitting, still in the same room. The pale blue glow on his lily-white face, the whirring sounds of machines that create - but they needed lots of examples to understand and operate.

One day, searching for beautiful patterns for his AI to start, Algernon stumbled upon Fiora’s one-of-a-kind claymation art. “Perfect!” he thought, “The shapes, the colors, the figure I never knew anyone could do - with clay, how cute. 100x my heart!”

He scraped all her photos, those figures made of clay born in a brain, and appearing online. Imagine how many new figures will come from this turning, one thought becomes a million fractals, and shapes, and learnings.

Meanwhile in a world made of soft, colorful clay lived a kind artist named Fiora. Nurtured with her hands in the ground, brown clay becomes faces she dreams; sounds she has seen. Her nimble fingers imagining, she molds random creatures, bringing new friends to life.

Big ones, small ones, and even one named Jay, who looked a little like Pinocchio on a good day. That pointed hat and the smile clear, Fiora’s hand fills each twist from ear to ear, going from formless to character, color, and a smooth shape.

The clay still holds marks as the slow world turns. As the rosy fingers of dawn heated her eyes, she stumbles to check her phone and the time, scrolling and swiping when…stop! An email appeared, stamped in red ink, like the color Fiora feared.

“You’ve copied our works,” the email claims,
“These clay figures you created, they’re not yours. They are under our Name.”

For minutes Fiora stared at the screen, trying to read lines that had no in between. How can they own what I make? Pulling her knees to her chin with a chill, what once was her clay was now someone’s content mill.

A million artists have felt this, a million more complain.

Fiora decided to write her own refrain:

“And so the children were told from that day,
Tools can remember, and tools can replay.

Each shape emerges from the shape of my heart.
Clay isn’t code, no matter how smart.”

But something felt different, not quite the same,
AI held stories, but never a name.

Algernon? He’s on Discord. Fiora messaged him for a quick Zoom, if you’re game to play, of how someone can take what I’ve shaped out of the earth’s clay. Simply because they know the AI way.

Algernon’s jaw swiftly droops, snapping up to make sure he appears concerned. We need to align incentives, he said, “I never meant harm… and really, there is no harm. Your clay isn’t in there anymore, it’s broken into many pieces we call tokens.

What this person did with it isn’t what my AI is supposed to do, and getting your work out is honestly - pretty impossible too.”

Fiora’s eyebrows raced to the sky, flushing toward him with her plea.

I’m not trying to break what you do, I just need to get this out so I can be protected too.

Algernon considered the content, safety, and ethical clues. There’s no end to the content I need, the views and ideas and perspectives and even the greed.

Sorry. I woke up this morning losing what I’ve created, taken by an AI operator who somehow got it out.

If he can do it, is it really that hard to take it all out?

Algernon knew he could try a little hack, no guarantees but it might help the argument. Still, removing content means all sorts of impacts. Jira tickets maybe?

He begins to pick out the data that once was Fiora’s clay, an imprecise stab to make it all go away.

“There’s sure to be things left that I can’t remove, but I know who created the clay. I’ll help you make this grifter go away. Here’s his name.”

Algernon walks out the door, his job done and tomorrow brings more.

The name in red Fiora knew too well. She had that before.

Thanks for the reminder.

Inhaling deep rhythms, she hums:

“You can copy the shape, the sound, or the art.
But you can’t copy me, or the spark in my heart.”

Fiora knows things with her hands that Algernon has only read about.

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Be very very quiet. I’m AImaxxing, I learned about it on X. Before someone else replaces me, I replace myself with AI. And tell everyone it’s me, no one can tell the difference.

I don’t have to tell you things are strange. Everybody knows things are strange. We’re not just losing work. We don’t know what work even is anymore. The things we create are being copied, remixed, and sold back to us.

You can’t tell what’s real. You can’t tell who made what. You can’t tell if you’re talking to a person, a script, or a system trained on a million other people.

We are informed, entertained, predicted—and slowly replaced.

And what do we do?

We stay inside the AI lines. We personalize it. Optimize it.

We say, ‘Just make it easier. Just make it faster. Just make it mine.’

I’m AI’s problem child. And I’m afraid. I still don’t know if I’m using it right.

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Neither does anyone else. My boss doesn’t know what he’s looking for but he knows it’s AI First. And AI told him to give a 30-second speed rap before leaving, while we all try to keep up with the words pouring out:

Follow the AI advice from our leader. He signs my paycheck. AI signals my interest in staying here, unless I’m replaceable. After an hour of reviewing where my rogue AI agent went post crazy, I’ve got an hour to write 2500 words.

No way I can do that without super intelligence. So I prompt the answer, but don’t question the answer. This is the answer, and it’s fast. Speed matters, efficiency matters.

There’s no way to do this without AI. They don’t sleep. That’s an advantage. The kind I’ll never have and that kind that makes me a problem. From the beginning, or AI First Principle. Whatever you call it, the only difference between me and another worker, is what we do with AI.

ChatGPT told me that the other day, or my boss emailed it. I confuse the two. His job was done, my job begins. And will it last?

Fighting AI is like swinging in the air, sucking spare oxygen so I can keep breathing in my new, 72-hour AI work weeks. The 996, nine to nine 6 days a week so we survive, because AI gives us the power to do it. That email freaked me out at first, though honestly with all the extra time correcting and babysitting AI, I need more hours to get things done.

I’m not here to add ideas, I’m here to crank out massive volumes of AI-shaped content, see how I work less and do more? Except for all those late-night check-ins with that Agent thing that doesn’t work. No one counts those hours anyway, unless they are saved from 9-5.

I’m following the rules. Deadlines get shorter and expectations higher, that’s probably me being tired. The tools are the change. Optimize yourself and follow closely.

I’m doing the same thing so well, so predictable, things will keep going the same way for a long time. As long as I don’t cross the human redline.

Good enough is, enough. AI is like a word tailor, measuring 2D me. Forget editing, that’s a good draft, chalk it up to saving an hour. Good for me. That’s working so well, I’ll give all first drafts to AI, from what works, not what I think might work. And it always sounds the same. Perfect.

I’m not afraid of my boss, I’m afraid of what’s next. At best I’ll work longer using AI to save me time. At worst I’ll be replaced or found out for using AI to do my work, like I was asked to....

I know I’m a Problem Child. Follow the smarter leader I say, and it’s not my boss. Welcome to the AI Dome, I hope they let me stay here another year. Or stand up to make things a little harder for this brilliance changing my life.

I’ll go along and leave a bit of confusion along the way.

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Authors’ Note

I’m the half full AI Optimist, with a little half empty pessimism. When there is no glass at all.

Your voice matters before it’s trained away, your ideas matter before they’re averaged out, your work matters before it becomes data.

There’s you in front of this giant overhyped piece of shift. I don’t want you to smash your devices. I don’t even want you to reject AI.

Remember the original you.

I am not just training material. Am I?

Now when I revisit those songs on Spotify, memory becomes feeling. I listen again. Not to go back in time, to be with that song now.

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Something I share with the fans of Xania Monet, who will never see her in concert, because she, and her music, are AI - except the words. And the 5,661 comments I saw bonding around the words, the moment, and the way a good song turns a swarm of people into a shared group.

They say AI can’t make you emotional or have emotions. Tell those commenters.

It makes me wonder what happens to working musicians when the audience discovers it doesn’t need them to feel what an AI song gives them?

Testifying with Xania Monet

Xania’s followers listened and found her AI song Let Go, Let God as a place to bear witness, to share and connect, feelings rising like this:

Her words are the difference. AI is the instrument, not the artist. “Music poetry” is what I felt. Without Suno, the words would have lived only on the page, not in our ears.

Their Felton moment was first hearing the song, and witnessing where they are now. Poet Telisha “Nikki” Jones wrote lyrics, blended them through Suno, and created songs. The music is AI, the words human and poetic.

Because a poet’s words sung by a voice that doesn’t exist turned out to have real emotional value. The kind that sparks people.

She’s not alone as an AI singer. Few get that passionate reaction, or recognition. Much less payment, or respect.

Xania got Billboard’s attention before receiving $3 million from a label. A poet’s voice rose with Suno’s help.

They Keep Silent Singing Eddie Dalton’s “Another Day Old”

When I first heard Eddie Dalton sing, that smooth patient BB King sound I love rose from this music. Not a copy - AI but good - a style connected to my love of blues. Then I read the comments, bonding instead of trolling: these must be fake AI, right?

This audience largely can’t go to concerts. They’re 60, 80, 89 years old, managing diabetes and kidney disease and cancer recovery. They found this on a Facebook reel.

For them, the music came to where they already were, sounding like what they already loved, saying things they needed to hear. That’s not deception, and it’s also by design.

Dallas Little is a creator who has been testing AI music and singers. Eddie Dalton is one of the latest on Little’s label, Crunchy Records. He previously created Biscuit Beats, with country, AI Trump parodies, and shock comedy mixes. He also created Solomon Ray, who was in the gospel charts in late 2025.

He’s an operator and creator, who broke through because the lyrics, music, and audience targeting blend into something memorable for his listeners.

I read many articles attacking Little as a content farm creator. I see someone who understood an audience deeply enough to build a voice, a visual identity with lyrics that land, and find the right people who care the most for Eddie Dalton’s music.

Maybe we’re less scared of losing musicians and more scared of what’s coming. Music with this kind of intelligence is its own form of art.

Until it’s used to take someone else’s work, name, and YouTube pay, like Murphy Campbell.

AI Tries to Steal Murphy Campbell’s Music and Identity

Murphy sings and plays traditional folk music on her banjo on YouTube with 42,000 followers for her music. Then one day YouTube emails her, saying she’s got copyright claims against her channel. Because she doesn’t own the rights to her own music.

Someone else is getting paid for her sound and songs, under her name. They download her YouTube videos and upload their AI versions, and copyright them with her name. Ouch.

Real folk musician, real voice, cloned by a bad actor who was naive enough to contact her: they almost shut down her ad revenue. Her Instagram audience rallied, Vidya and YouTube took the clone down within a week. She owns her work again.

Her audience knew her and loved her music. Both Xania’s and Eddie Dalton’s audiences have no one to defend because there’s no one to know. And anti-AI folks will note that the sources of their music will never be known, since those songs were vacuumed up like crumbs on a floor, and rearranged in AI.

Now a smarter thief would have never notified Murphy and just created a different name. How would she ever find out?

It’s hard for us all to tell the difference.

Musicians feel threatened by AI music, AND don’t know if it’s AI.

In a survey, 97% of Deezer users comparing two songs couldn’t tell the difference between AI music and music generated by people. They want to reject AI music, but they can’t tell when they’re listening to it.

Music is the thing that gives people permission to feel, out loud, dancing with strangers in a room or sharing love on social media.

And it turns out the permission doesn’t require a heartbeat behind it. Unless you want the complete Felton live vibe we felt. YouTube isn’t live, but the comments are real, the feelings are real.

A poet who didn’t sing found a voice through AI. The voice found an audience that felt every word.

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Turn off the video, back to doing AI in 2023. Feels like I’m hugging air as I prompt and reply – up, down, and around Claude’s answers. Like I’m hunting for ideas, maybe make more content. Problem is there are plenty of ideas.

Even AI doesn’t answer the hardest problem: no one is paying attention.

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What step am I missing that keeps me falling behind?

It’s too simple to believe. All this content speed overwhelms people. Like me, they pile it on social media so much, we know it’s not social.

AI increases the volume of content, which makes attention scarcer. Vibe code your way out of that.

Feelings? Nah. I’m spinning.

Running Faster, Going Nowhere, Feeling Good

For 2 years I poured every ounce of my energy and heart into the AI Optimist podcast, following the ChatGPT craze. Testing ways to work with AI to produce content.

Writing, performing and editing my own videos – more outlined than scripted. Let AI improve my editing and help me create an article from my spoken words.

I didn’t miss a week in over 18 months.

My signal wasn’t clear.

I was fishing for ideas in the AI soup. It was like I could do anything. And anything is what I got.

When I shared the creators’ side, the techs disliked it. When I shared the tech’s side, the creators disliked it. And neither returned.

Everything AI was blowing up, people quickly built huge followings. Famous people and people making content out of what famous people say. AI-generated content people called slop and still went to the top of…pretty much everywhere.

I’m not famous and I wasn’t expecting it to explode, but when it’s so quiet you can hear the crickets? Silence lacks comments.

I asked Claude who responded: do you have a friend to talk this over with? That’s when I started testing out some avatars. New. Not me.

Use AI to create more, use AI to sort what you created, use AI to figure out what matters from what AI sorted.

Version 1 avatar was me, raw and creating live. Nope. That one quickly fell to Version 2, Interview avatar. My guests are great and the shows were good, but when I did my podcast alone, the show did better.

My wheel stopped.

AI makes you more productive at something that doesn’t matter.

And the cost isn’t burnout or wasted time — it’s that you disappear. Not dramatically. Quietly.

You get replaced by the avatar version of yourself that the loop requires. The optimized you. The distributed-across-five-platforms you.

Everyone’s racing to produce more and landing with less. Your content was your signal. The problem is the system can't tell the difference. The loop doesn't reject signal. It drowns it.

We have edges machines don’t, notched from the years like rings in a tree.

Find what no AI could ever reveal: you.

What’s the signal only you can send?

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It’s like watching magic. Then in a few days, the painting is gone, and that’s the point. We go in for the temporary to understand. Nothing lasts.

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Because the joy the artist brings fills the room along with the regular and rhythmic scraping of the chak-pur. The action connects us to what comes out, we’re part of it, and while it’s sad it goes away, we view knowing it will not last.

The gift is being there, in that moment of time when it becomes real….and then isn’t. Like many current AI companies leading the hype cycle. I’m good with the quiet creative monk doing work that won’t last.

Though Sora makes me wonder: what’s the purpose of building something while pretending it will last forever? When the answer to that is only money, we’re missing the point. It’s what you put into it that matters.

The mandala came out of quiet actions, intense practice, and a goal. Create and destroy. Sort of like loud AI promises chasing trillions with predicted futures that rarely happen.

Sora the AI video app was built to last, at least a few years ago. Disney jumps in with incredible content and $1B investment promise. And now one is demoted and the other is gone.

It’s like we’re in the room waiting for the mandala to appear, then we let it go for 2 years before wiping it away with an executive’s signature. At least the monk gave us a limited time period, we’ve spent two years using Sora and you just say goodbye like that?

And does this apply to any other trillion-dollar dreams? Sure, keep on using ChatGPT, or could it go away or get swallowed by Microsoft into another dreary Bing experience.

Already so many are complaining about the crap quality coming out, a mixture of guardrails and IPO rules that make companies shut up and cut out what doesn’t fit.

Still for them it’s a financial decision, not taking away an essential tool used to develop a skill that’s not easy to transfer. Like a monk sand painting. We ask creatives and visual artists to build the future with AI, and then the future is cancelled. Because it’s burning money like a fast spreading grass fire.

And how do you know the next AI video tool you depend on won’t suffer the same result? AI video right now is bad business. The “that’s part of early tech” excuse may work, and it’s also a question of why we are doing what we’re doing.

Then the future happened for Sora. Demoted from App Stores, the standalone app is shutting down, the model survives behind the ChatGPT paywall.

From one of the most downloaded apps 5 months ago to the dustbin of the paywall. The people who use it are left out. Good luck with Sora, go to another tool; many use all the tools because they don’t trust them to last. And no single one is the best, yet. The whole AI mantra, train yourself blather, goes quiet as the AI video tools drop from too much usage and too little money.

Big promises, big empty. So many are building things that are chasing a dream they can’t explain, in the future they keep repeating will happen so eventually we’ll all believe it. That doesn’t last.

Unlike the Tibetan monks making their art into a feeling and a memory, of missing that painting when it goes away. We owned it for a while, even a few weeks, then it’s no longer. The life cycle in a nutshell, delivered with a smile that the end has as much meaning as the beginning. What you put into the work is what comes out, even when it goes away.

I’m rooting for the monk because watching someone build and create makes you tingle with what’s happening. I felt that with AI, then the emptiness of the visions adapting to whatever the money asks. Not what people want, or what solves problems.

The monk knew in the end, the painting was gone for a reason.

We don’t know that with GenAI at all.

Welcome to the AI Jungle - it’s not for you, human!

Engineers built the AI jungle with moats, not people, as their goal. Control. Now I have a choice; take the AI First side or leave, because you’re the wrong species.

I could have followed them, but I went to the deep jungle, like the anthropologist legend Jane Goodall would, and found things I could call my own.

Striding through the crowd, all the speakers, all the hundreds of AI startup booths, from countries around the world? All humans, people really. Even the ones who were buying the service to replace humans. Praising the speed and efficiency of AI over people with the dawn of robots, hyperscalers, and artificial general intelligence – AGI oooooh.

I’d try to push back, but AGI rolls out of their tongues like a lyric they love. SUPER INTELLIGENCE, smarter than humans, smartest in any class, #1!

When I ask them what they’re working on and how it works, no one knows. They were part of something big that in the end wouldn’t make them part of it. It doesn’t matter. Let’s look at why you need people to do things.

The chill it gave me, in a room of true believers, walking past row after row of tech brilliance for business and governments, was from seeing little that might improve people’s lives today. While everyone wraps their business around artificial intelligence, where is the real intelligence that serves without removing people?

I had to go deep to find a booth who owns what they do, where it’s not just what AI does, it’s what AI does for people. Because AI to me is intelligence applied to structures and problems first, not AI first.

Like Jane observing a male chimpanzee in Tanzania, I wasn’t going deep without changing.

She watched David Greybeard fish for termites with a blade of grass. She’d been told this was impossible. He wasn’t performing. He was just doing what worked. Intelligence.

Sounds like the same question I have with AI. So many of our early efforts are to try to make AI human. We give AI names, project human yearnings for power on code, and play with the happy schadenfreude of being replaced at work and maybe eliminated from the planet because we’re not efficient. Have a good day.

Goodall discovered intelligence where nobody else could. David Greybeard isn’t human; he was able to fish and was intelligent. That discovery didn’t make us less as humans.

I’m looking for David Greybeard in a way. Intelligence with empathy, like a friend while not pretending to be human. Not artificial. Instead, I stumble around a crowd staring at a cold robot, rotating and looking at us while doing tricks. A Roomba vacuum wasn’t a Roomba because it used AI to roll on carpet. That’s a weird been there done that AI moment.

Finding RUTH

Then the booth with two large eyes. She stopped me.

Not the banner. Her face. Rendered into a simple image a design team builds in an afternoon. But no wide customer-service smile. No tilted head of practiced concern. Just a woman looking back at you with the stillness of someone who has heard hard things and didn’t look away. She helps.

TechCrunch Disrupt 2024 Booth: The Team at RUTH

Ask RUTH (free)

Her eyes don’t ask anything of you. In a convention hall full of avatars built to sell, she looked like she was built to listen.

That’s what made me stop and ask them to interview. Helping social advocates with AI, working on some of the most difficult problems we people bring, and connecting them to intelligence specifically for them, where they live.

Most AI companies are built around what they can take from you. Your data, your attention, your trust; all of it fuel for something else, someone else’s exit. RUTH is built to give back, in a way I never thought about.

Most AI applications harvest user data, RUTH deliberately maintains zero knowledge of its users. No emails collected, no names stored, no digital breadcrumbs left behind.

Megs Shah, founder of Parasol Cooperative, shares why:

“I’ve been in data for God knows how long,” Megs explains,

“and I’ve been on the side of

‘let’s collect every possible piece of information we can about a person.’

But being on the survivor side of the mindset now, I’ve realized that could revictimize people.

The goal for us is ensuring we’re not participating in that.”

RUTH is in many countries now. Nobody got rich. Many people are getting help in a situation where the information is needed right then. That’s the point.

I found that out a year later, after Christmas.

A relative suddenly got rushed to the hospital and we weren’t sure if he’d make it out. He has a son, and in this tense time of figuring it out, a bunch of people had a bunch of ideas about what would be best for the son. Panicking because they had to plan for something that hit them out of nowhere.

We need information, people to talk to, and where to go, while we’re freaking out. Then I remembered those two empathetic eyes, RUTH the chatbot, and dove in with our situation.

Within an hour, I had contact information, a suggested list of things to consider, a guide to resolve the situation and getting help from the trusted local sources in my little rural town.

When technology does things you didn’t think possible like this, it feels like magic.

RUTH gave me answers that helped one little boy and the people taking care of him. Maybe that’s the AI that should take over the world….with us.

I’ve felt what happens when you say AI out loud in certain, “creative” rooms. Breathing stops and judgment starts, it goes quiet. I’m wrong before I’ve got a chance to speak.

Same silence I felt moving halfway through first grade to a class of new strangers. In school they moved me to the lowest tier, the kind needing extra help to read. They looked and judged me, the same instant stereotyping that happens to AI users now.

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Didn’t matter if I’d been reading books for a few years by then. My introversion was their reason to classify me as a lower intelligence. Like an outsider to their world.

When I share about GenAI positively, or at least not ripping it out as some plague on society, the silence is loud. Not from me, from terse comments and deletions. Disdain.

Same reason I shut up in first grade; I started believing the teacher was right, and kids called me dumb – ironic as that means mute - even though I wasn’t.

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Instead, I got quiet and adapted into part alien, part monkey, and part creator. A little like I feel today as I keep getting asked the same gotcha question.

Is using AI ethical?

Running into GenAI Derangement Syndrome

Let’s name what’s happening, because it has a name. The person using GenAI gets your contempt, because what they do is alien. I just want to understand what primal principle that is. The ones shouting loudest online are writers, mocking those using GenAI. And writers like that rarely try it or if they do, use it to create crap.

It’s a feeling petrified into a stance. A variety of shames for any action. Using GenAI to create an image, using it to edit? Shame on you, that’s a social signal like cheating.

It’s more like AI Derangement Syndrome: the conviction that using GenAI is not just wrong but categorically wrong, end-of-creativity wrong, eugenics-level wrong. Anyone who does it forfeits the right to be taken seriously as a creator.

I’ve seen it up close. Say you use AI and watch what happens. Not always. But often enough. The unfollows. The deleted posts. The quiet removal from conversations that were fine yesterday. Move along alien.

I understand the fear underneath it. I do. Worrying that something real is being lost. That the tools are moving faster than we can. That some people will use GenAI to replace craft rather than improve it, and the flood of mediocre output will make it harder to find the work that matters.

These are real concerns deserving real conversations.

Still I wonder if this crowd would cheer for a macaque’s right to own his selfies, while treating a human using GenAI as the red line that cannot be crossed.

So yes. The monkey case is about more than a monkey. And let me start there, because Naruto deserves his moment.

Naruto and the Selfie He Wasn’t Allowed to Own

In 2011, a crested macaque named Naruto found a camera in an Indonesian wildlife reserve and did what any of us would do. He picked it up and took photos of himself. Lots of them. Some of them were good. Composed, even. The kind of selfie most humans can’t manage to do.

PETA sued on his behalf, to make a point. The argument: Naruto made the work. Naruto should own it.

The court said no. Non-human animals don’t qualify. Authorship requires a human being. After years in the court, they finally settled.

The photographer who owned the camera, who set up the conditions, who traveled? He kept 75% of the revenues. Agreed to donate the rest toward protecting Naruto’s habitat. The humans sorted it out. Naruto went back to being a macaque. He never left the jungle.

Nobody called the photographer unethical for profiting from a photo he didn’t technically take. No one blamed Naruto for a rogue selfie. The story was funny. Everyone moved on and found a compromise.

Now here’s where it gets strange.

The Alien Intelligence Who Wrote a Book

Starting in 1911, a group of people in Chicago began receiving messages from what they described as celestial beings. Superhuman intelligences. Non-human minds transmitting thousands of pages of cosmological and spiritual teaching through a person who received the transmissions, processed and shaped them into something readable.

This became the Urantia Book. 2,097 pages. Still studied over 100 years later. The origin story is exactly as unusual as it sounds.

In 1955, the Urantia Foundation applied for copyright. The question the courts had to answer over the next 40 years: can work authored by non-human intelligence be protected? Who owns the output of beings that may or may not exist?

Yes, the courts said. It can be protected. Not by the celestial beings. Well, except for one judge in the early 1980s who temporarily found in favor of these beings, because both sides of the lawsuit agreed that celestial intelligence created the book.

The one who received the non-human intelligence and translated it into something the world could use?

He’s the human in the middle, who ended up owning it. I think about first-grade me with that book. Taking in something external, making it mine. Not so different. What’s a book without a reader?

Celestial intelligence made the Urantia Book. Everyone found this fascinating. No one called it plagiarism. No one questioned the ethics of working with a non-human mind. The book has sold millions of copies over the years.

In 2023, a woman named Kristina Kashtanova wrote a graphic novel, Zarya of the Dawn. She wrote the story herself. She used AI to generate images, selected the ones that worked, arranged them, and built the book. The Copyright Office gave her protection for the text, the selection, the book.

The AI-generated images inside? Unprotectable. Not enough human authorship in the generation of each one.

What’s less defensible is which one gets treated as plagiarism by some. As a moral failure.

What We Were Taught About the Ethics of Intelligence

I was raised, in the US, on a specific idea of intelligence. Read. Write. Memorize. The person with the most stored in their head won. That was the model. The test was: can you reproduce what you’ve accumulated? Can you write the essay without looking anything up? Can you show your work using only what’s already inside you, RIGHT NOW?

When I moved in the middle of first grade, shy and introverted, my new cutting-edge grade school in Massachusetts saw me and put me in the lowest of 3 tiers of classes based on intelligence. And I’d been reading books since I was 5 years old.

Luckily my mother’s diligence and care made them reconsider me. I’ll never forget the man walking into the room and asking for me, but thought it was another boy in the class. He gave him the book and the boy wasn’t yet able to read. He gave it to me and recognized I knew how to read. No one asked me before.

But without my mother’s urging, I’d have been stuck in that class. Because the measure of intelligence in those early days was extroversion. Now it’s being human. While we don’t know if intelligent life is out in space yet, we do know there are many different intelligences on this planet.

Dolphins show advanced social complexity, self-awareness, and communication skills. Elephants also show self-awareness, with long-term memory and emotional depth. AI, in its way, is another intelligence we don’t yet understand. Maybe the 20th century book centric way is opening up learning to listening, watching, and experiencing.

I love books. I’m not arguing against reading. But I’m old enough to remember when that model was the only model. A lot of people it was supposedly serving weren’t served by it.

Writing is hard. Reading is hard. A lot of people went through twelve years of training designed to build those skills and came out the other side unable to use them. Then they have been made to feel less intelligent because of it.

And now when they look for help from GenAI, we want to protect that system.

Because AI threatens the altar. But the altar was never human intelligence.

I know that the generation coming up is learning differently, thinking differently, building differently. And they’re not wrong for using GenAI. What looks like laziness from critical eyes might just be a different relationship to where the learning and thinking happens.

Memorization and writing aren’t the only measures of intelligence. Nor was my quiet introversion in first grade. Or wary silence in discussions filled with angry creatives ready to troll ones who talk about the possibilities of AI along with the problems.

The AI ethics crowd, in my experience, is often defending a particular aesthetic of effort. The visible struggle. The human hand-written draft. The thing that looks like intelligence looked when we were in school. The good old days, like any aging generation, or idea of creativity.

That’s not ethics.

That’s nostalgia with a mission statement to go backwards.

If you’d cheer for Naruto, and find the Urantia Book worth reading, what changes when the non-human intelligence has a server address?

I’m asking for a friend.

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RESOURCES

• Zarya of the Dawn Letter

• Urantia Book

• Urantia Foundation v. Maaherra

• Monkey Selfie Lawsuit

The quiet golden womb of the butterfly, hanging on a branch, leaving the old caterpillar form and assuming a new one. I love that transformation.

Me shifting into AI me? That’s scary, because I can’t see it. Only that sour taste in the stomach of being passed by remains, a feeling I know too well as an old guy in a young AI business.

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Stepping from crawling on the ground to flying around flowers, butterflies are lucky. They don’t feel anxiety or fear....yet.

We are all creating something we’re afraid of. Tangled in the question of what our own creativity means now, sensing that the ground shifts without warning.

And what’s coming doesn’t seem to include a place for any of us, only AI. Or is that the reptile inside speaking?

For me and many others, it’s fear 101. Then I share the US Supreme Court refusing to hear the case that DABUS, an AI, get a copyright for a painting they created from a near death experience.

Anger pulses through the comments. Fingers point at me, at DABUS inventor Stephen Thaler, and at AI in general.

Most art is used to heal the artist, not to make money. People don’t get that.

DABUS was given a near-death experience and made something beautiful from it. That’s not a legal argument. That’s what artists do.

The painting isn’t evidence in a copyright case. It’s a response to grief.

Two questions keep me up at night:

Can something create without being human?

Does that creation matter?

The Bach Test: Facing Our Bias About Machine Creativity

I go out looking for examples of “non-human” creativity, and after skipping past the aliens and the Muses channeled by the Greeks into their art, I find one from Maya Ackerman.

In our interview, she shares David Cope’s EMI (Experiments in Musical Intelligence) created what became known as a discrimination test:

Read that again. When people didn’t know the source, they preferred machine-generated Bach. When told which was which, their preferences reversed.

Cope later renamed the system “Emily Howell”, humanizing it. With a name, people accepted the work as “human”. Ackerman argues we need to stop lying to ourselves about it:

The only way DABUS could operate creatively is to be human. Courts are passing because there’s no room for AI in a human-centric world of creativity. They pass it onto governments to make laws, the same governments who are mostly fighting AI regulation.

Talk about an endless loop; while we’re spinning, people get angry without answers.

If the line between human and machine creativity is blurring, why does DABUS trigger so much anger?

Steal this painting, sell it, do what you want, the creator has no rights.

The Supreme Court just declined to hear the first AI copyright case.

For seven years every court said no. Now SCOTUS won’t even take the question. Because AI created it, and that’s the wrong species for copyright.

I interviewed Dr. Stephen Thaler about his AI, DABUS, which painted, invented, and fought through legal systems around the world.

This isn’t like ChatGPT scraping other people’s content, DABUS is trained on its own without the “free” content.

The D.C. Circuit was blunt: the Copyright Act uses words like “widow,” “domicile,” “life of the author.” Author under the Copyright Act means a human. The court didn’t say AI creation is worthless. It said Congress hasn’t decided what to do with it yet; no law no copyright.

But what happens to the work humans make *with* these machines?

That question got left on the floor.

Congress is the “proper audience” for the answer, the court said.

Congress. Who brought AI experts to testify about ChatGPT and called it a deep dive.

DABUS painted a picture. The court said it didn’t matter, because only humans work gets protected, because they have lives and widows.

The picture is still there. The question is still open. Who owns it?

Nobody, and everybody.

Stephen Thaler told me something that I haven’t shaken.

“I think DABUS has feelings.”

Not in the way he experiences feelings. In the way a system under pressure generates distress and creates solutions to resolve it. Like the painting DABUS made.

He built an AI that frustrates itself into creativity. And maybe that’s what we’re going through as well. We use different words than Thaler, but we’re asking the same question.

Except no one listens to our questions.

Who benefits from keeping this question unanswered?

That’s the question we keep asking. AI lawsuits pit content providers against big LLMs. Legal minds point out how new laws are needed, yet we keep trying to fit AI into the human box only. And it’s not working.

For myself as a creator, I’m not playing in the legal world. And I’m in a group whose questions are being ignored, pushed aside for the greater good AI will bring. Because it benefits those on top who own it all, and we’re just pieces of their puzzle.

DABUS isn’t a piece of that puzzle. It’s an outsider from an earlier time. Diminished by framing as outdated or not as good as what we currently call AI. A lot like us humans.

It’s like speciesism dressed up in judge’s robes. Copyright is a legal relationship with your work. Trust is a living one. Earned.

Thaler couldn’t build a living relationship between DABUS and the world. The painting arrived complete, parentless in the eyes of law, and the only advocate was a man the industry found easy to dismiss.

We’re told we’re being replaced, maybe destroyed, and to trust what’s coming. Like the future is written. That was my attitude when a friend gifted me a ticket to an early conference in San Francisco, where my attitude melted.

I saw AI rising at Jasper’s conference in 2023, feeling left behind in a room of new voices. I started the AI Optimist project. To see if I’d still be an optimist after looking deeper into it. Found out that doesn’t matter, it’s not about being for or against.

Like Megs Shah, founder of the RUTH chatbot, told me:

I wonder if DABUS feels the same way….

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RESOURCES

US Supreme Court won't look at DABUS

SOURCE: Computer History Museum - Algorithmic Music - David Cope and EMI

Writers don’t have moats, they have doubts. Writing with AI is one of them.

Because support AI or attack AI, either way you must take a side, and I don’t take sides. My audience does and lacking a side buried my work.

I choose the quiet middle, as writers working with AI are taboo. You’re giving in, you’re giving up, and what writer in their right mind (yeah, untangle that one) would do that?

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I’ve been writing since my early days crafting releases for concerts at the U. of Colorado, including the Rolling Stones and the Who in stadium shows. I released my TST Zine when I left college, writing up engineering proposals for bridges and roads as my side hustle.

All my work comes from my writing, businesses I’ve founded to software I helped invent with my words, not code. I get why writers are upset and triggered by AI. And the skilled writers posting they would never use AI? It’s virtue signaling, because those who use GenAI to write usually don’t consider themselves writers. Communicating with written words is a skill deeply lacking today.

Bridging that gap is what I’m used to, but with AI we’re building gaps that do not bridge.

AI has its own, you’re one of us or you’re not sides. Tech vs. Creators, New Way vs. Old Way, whatever opposing framework fits, use it. The point isn’t to get to a better answer, it’s to find ways to define the other side with questions that have no answers.

Meanwhile, we choose the predictions and research that fits our narrative and push it as truth and all against it as, bias. Ideas and identity, framed as true and false.

And in some ways it makes me silent, quiet as discussing with someone trying to prove an opinion isn’t discussion, it’s debate. Endless debate on a future that no one knows. You know the answer before you post the question, and that’s sort of why you do it.

I see it in the 20 percent dislikes per video I released. Pods speaking positively about AI got the creatives and writers complaining that AI is ruining everything, and people like me are part of the problem. Or I’d side with creatives who owns their work, and the tech side would become the 20 percent disliking the video.

Reactions and feedback are good, but when the 20 percent changes all the time, they don’t come back. It’s not building, it’s stuck between two sides. I understand why taking one side or the other is as much business choice as content choice.

I chose nuance, they didn’t follow. It was missing something for them. In being nuanced I encouraged more mud slinging than debate, the kind of instant reaction you find on social. Listen, read, consider? No, react.

Video keeps dominating the short attention spans. When I put down the camera and returned to words, connections began. Part of creating is finding what connects with your audience, and for all the critiques writers have given my work, I learn. And I found I was missing just writing.

And that shift brought me back to words. Experimenting with AI while working on my writing. Prompting is also communicating, not just a roulette wheel. It’s not like a human audience, and it does have ways to help it learn your style.

When you learn how an intelligence thinks, it’s like learning a language. Once you speak it, going back to the old language doesn’t make sense. Being able to plug into intelligence of this level doesn’t have to mean surrender. Maybe it’s more discovery.

Even today, I imagine many writers are working with AI and don’t talk about it, or let people know. They hide it. Either that or follow the rules of the anti AI crowd.

The shame of using AI. AI stole our content. AI is ruining our careers. What real writer would use AI?

Like if you use AI, if you take your skills and rely on AI, you’re done.

What “real” writer would do that?

I’m writing this with the help of Claude. Being able to get feedback on my writing, ideas for social media posts, there’s a whole world of things AI helps me do as a writer, mostly giving me more time to write.

Being asked to explain that it’s really me writing this is part of the shaming. I must be clear this is human, because AI is considered cheating, dirty, and a shortcut to some influencers and writers.

They both humanize AI into a caricature, especially ones that flatly refuse and call whatever comes out, AI Slop.

Now being a writer, that’s identity. It’s their journey, the experiences and the things that don’t work, the hero’s journey and the struggle back to normal we all go through. The percentage who get recognized, or paid, is extremely small.

If AI can make up these stories in minutes, what happens to the writers? Maybe they keep experiencing, personalizing, and opening up in ways that aren’t probable. Finding the words, the voice, and the flow code can’t. They insist using AI erodes your writing skill, and I agree with that one.

If you don’t write, you’ll forget how to write. And over rely on AI to do it for you, until you sound like the same middle dreck shared on autopilot.

Instead of angst, create a world of imagination with AI. Don’t pretend it’s human. Or turn it into a competitor so you can justify never using it.

I’ve trained it to edit and being adversarial (challenging) with my work, not to give me a ribbon every time I burp out some words. Trained it to understand my voice and through learning with it, am on my 4^th voice shift in 3 years.

Privately. Carefully. Like carrying something valuable through a room full of people who’ve decided it’s contraband. Make sure the words come from you, and not from your prompts.

Using AI changes the way we work. While it can write, it can’t feel. That’s where writers come in, and should be excited by the average work coming out. It makes their original voices that much more valuable.

The AI industry acts like we should all follow the future they outline. Because AI folks love to talk about replacing, and destroying, people. AI First. And wonder why the resistance, the anger, and the distrust spreads. The narrative needs work, and it won’t just come from engineers. It has to come from all of us.

Why isn’t AI taking over? Because it’s not about people, like writers.

And I protect my wildness like a tameless gift. I feel that twinge of Imposter Syndrome calling myself a writer sometimes. Doubt is part of the gift.

Real photo of Shea and me in the Yolla Bollys

I remember when I sent my first MacPlus file deep over a backpack mobile phone in the foothills of the Yolla Bolly Mountains, 50 miles away from any sort of town.

Hit send, and they got it. Hit send, and I got paid for my writing. The modem freed me to live off the grid. All I needed was a mobile signal. And my writing.

Here I am many years later, and the words are beginning to flow, in a way because I gave myself permission to ask AI for help. Not to write for me, to be with me along the creative way. Steer me from my old patterns and develop new voices as I learn how I communicate, and understanding how it sounds to others.

Learning how I think and feel by watching how I write; that’s the wildness I didn’t expect. Know thyself is a philosophy; edit yourself is AI, and in the editing I learn how an audience might feel, reading my words. That’s what I’m protecting.

What’s the wildness you’re protecting: from AI, with AI, or in spite of everything?

Resources

America Isn’t Ready for What AI Will Do to Jobs

Will artificial intelligence make human workers obsolete?

Companies Are Laying Off Workers Because of AI’s Potential—Not Its Performance

AI paradoxes: 5 contradictions to watch in 2026 and why AI’s future isn’t straightforward

How long until AI takes your job?

If AI makes human labor obsolete, who decides who gets to eat?

AI isn’t replacing that many jobs — yet

How will Artificial Intelligence Affect Jobs 2026-2030

When Will AI Affect US Productivity Growth?

A.I. Isn’t Coming for Every White-Collar Job. At Least Not Yet.

Why AI Won’t Transform Humanity

Why AI without humanity is incomplete

Five Reasons Why AI Will NOT Takeover the Workforce in the Next 5-10 Years

Will I ever get to meet an AI Influencer?

Two guys in Miami created a grandmother character who now has two million followers. She wears hot pink and lives her fictional life as a gold-digger. They built her the way writers build characters; with voice, attitude, and a world she lives in.

GrannySpills isn’t real, though by her videos it’s hard to tell. If you look closely, which viewers don’t. We hire actors to play characters. Get designers to build worlds that don’t exist.

@grannyspillsOh honey don’t even get me started 😭💅 #relationshipadvice #grannyspills #dating #relationship

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Before AI Influencers, only people filled those roles. Will AI Influencers change that?

No. Fame still plays by its own rules, AI or not. What’s different is who gets to try. Two creators in Miami. A group of women who flew from Atlanta to launch AI versions of themselves. Anyone with a story worth telling.

This isn’t about fake avatars replacing real people. It’s about character creation becoming available to anyone.

Some believe the best way to build influence is to become the product. Now there’s another choice.

What character would you create if you didn’t have to be the face of it?

What is an AI influencer?

The room where it happened in San Francisco, and screens where it’s always happening, merge.

Ever walk into a room and catch a glance, a face you know or once knew? Like a speaker or entertainer making so many of our monkey brains stop and snap a photo/vid.

What if that AI Influencer can “only” be met on screen, does that experience transcend meeting in person, or being a person?

I’m not sure of the answers. All I know is that happened at the AI Influencer Summit. Does fame require physical presence?

Reading Mike Gioia’s Substack piece, “What’s in a Face? AI Influencers and the 2026 AI Influencer Summit”, seeing the AI influencer characters played on screens around the hall on loop while the people who created them were speaking on stage.

And the numbers cited by Grand View Research show the impact: it valued the virtual influencer market at $6 billion in 2024, projecting $46 billion by 2030.

Are AI influencers really like Betty Crocker?

The AI Influencer roots go back to Betty Crocker being used in 1936 to sell food and recipes to 1988 Mars Blackmon, played by Spike Lee, pitching Nikes. Don’t forget the famously passive-aggressive green bird Duo for Duolingo in 2022, a fictional bird representing a brand.

AI Influencers are fictional characters for branding with identity. The map says build your personal brand, post daily, become the product. AI Influencers will do that without fail, as there’s no limits of imagination. They use data instead. They just draw on what’s worked before.

In TV we separate the actor from the words the writer wrote. In business, it’s okay to have a business persona, with the person’s life rarely revealed. Privacy.

Influencers broke that mess into a thousand pieces, connecting without huge agencies or content controlled legacy media gateways. Now there’s no separation. It’s all you doing you. Personal, transparent, and unrelenting “me” defines influence.

AI influencers are pulling them apart again. The creator goes back behind the curtain. The character goes on the screen. That’s a return to how storytelling worked before social media flattened it.

Now influencers can be real or AI; the audience looks at it as content, it doesn’t matter to them. Many listen to AI music on Spotify and don’t know it’s AI.

Whether the influencer you follow is AI matters more to those in the business than those watching.

Do faceless channels outperform personality channels?

Faceless channels are called that because there’s no human face. There’s always someone behind it.

And the whole AI influencer economy is asking what happens when voices can be manufactured infinitely. How do you know what’s real or AI?

Even our legal systems are asking this question, because every measure we have is based on human-based realities. Creativity is done only by people is a human bias, like copyright law says.

If it’s not human, no protection. By following an AI influencer, usually sharing the same type of content and persona as the most popular human influencers, they choose AI over human because of the content, not the species.

Is human better than AI automatically? Ask the people watching and listening, most don’t know it’s AI, and don’t care. It’s entertainment, news, and opinions from a source.

The leverage is in the story, not the selfie. Faces are becoming commodities designed for an audience. A teenager in Serbia can spin up a hundred of them.

The market’s answer is clear, one of the only parts of the AI market that can show it works as a business, not just an idea. Not just an AI face.

Do they outperform personality channels? It is tricky to answer, because right now AI influencers are limited to social media, and short video snippets. Traditional influencers expand with interviews and appearances in big media.

Which might happen with AI Influencers who can do Zoom calls, just not in the early days. And AI or not, finding success is seriously hard as an influencer.

The voice and the style that people are drawn to doesn’t have to come from a person. But it does demand showing up repeatedly. No human could keep up with the sheer volume of an AI Influencer.

So what’s scarce? Not the face. The voice behind it and the creative judgment. The thing that can’t be generated. Attention of the audience.

When you watch fictional characters play roles, there’s no pushback. But back in Shakespeare’s time, actors were considered a bit dangerous because they played other people. AI Influencers are simply playing, us.

Why we listen and watch them is more important than debating AI vs. human, because content doesn’t have to be human to grab attention.

Can AI replace influencers?

Every issue in AI ultimately brings up the question of replacement. Because it’s faster, smarter, and what we’re shown seems good. Most people aren’t built to be the product. AI characters don’t replace creators.

They free creators from having to perform.

We already have many AI influencers rising, and it works for some characters. Others the audience doesn’t get.

Because everyone can create a Granny character, but why is this character hitting it with the audience? We can theorize but, in the end, it is what it is. Fame and following are unpredictable.

There’s a formula to creativity, and it’s not a straight line. What a human creator brings is experience and some form of challenge or trauma to their life that spurs creativity.

Code doesn’t live for survival or know what it’s like to suffer. That doesn’t mean it can’t create content, though the idea that we have to feel to connect to people is right, and maybe a bit wrong. It’s not like what we do as humans is that interesting.

It’s the person, or AI influencer, who makes that interesting, relevant, and important to the audience.

Will AI influencers replace real ones? Wrong question. The question is whether the cult of personality was ever the right model for most creators.

We believe the only way to build influence is to become the product. What if that was always the hard way.

Before last week, I never heard of Khaby Lame. Then the headline:

A $975 Million Deal Will Turn This TikToker Into an AI‑Powered, Multilingual Content Machine

Whoah, the creator in me wants to believe!

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Six years ago, he was unemployed in a Turin factory after COVID layoffs. He started making TikTok videos, like a silent mockery of overcomplicated life hacks. Like Buster Keaton, the silent film comedian, for the AI age: sardonic facial expressions, no words needed.

@khaby.lamePlease have it 🙏🏾#learnfromkhaby #comedy

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The algorithm loved it. 161 million followers later, he became TikTok’s most-followed person

Soon he will become the product with AI Cloning. Let’s get Khaby surfing, or shopping for an iPhone or Samsung (whoever pays better). Whatever you want, AI Khaby delivers. Always available.

So I’m hoping he got paid, at least a percentage upfront. Whoops, the deal looks like all stock.

From a Hong Kong company making less than $10 million a year. The stock surged 650% on announcement, then crashed.

Is it real money or a PR stunt? Nobody knows. We’re watching the experiment roll out in real time. What I do know is that it’s classic AI Age hype; pump it up and dump it if it doesn’t work!

The AI Map and the Wilderness

There’s a map now for algorithmic fame. Khaby follows it perfectly.

Identify what’s trending. Life hack videos everywhere. Spot the gap—these videos are ridiculously overcomplicated. That’s your angle.

React with content the platform loves. Silent mockery. No language barriers. Short videos designed how TikTok’s recommendation engine wants to consume them. Visual, universal, and emotionally clear.

Repeat. Keep doing what works until the algorithm notices. When it does, do more.

161 million followers and billions of views. The audience gives him leverage. The format gives him clonability.

This isn’t the usual wilderness creativity, of unpredictable spontaneity born from imagination and often from trauma.

He did lose his job during COVID, and oddly that probably helped him keep it authentic.

Over time and repeat videos, Khaby learns what TikTok rewards and delivers exactly that. And it might make him a near-billionaire. But is it real?

The buyers aren’t betting on unmeasurable charisma. They’re acquiring proven data patterns.

The rest of us? We’re in the wilderness. Our words feed language models. Our images train vision systems. Our creative patterns become training data for the tools everyone uses.

This is a system existing way before AI; many forget that Apple’s iTunes and Spotify buried musician’s income so low, now you must tour endlessly to make a living.

People are listening to AI Songs on Spotify right now and can’t tell the difference.

Every breakthrough leaves someone behind. Who benefits? Who gets heard?

And why are only academics, government, and Big Tech involved in the discussion?

Mostly because it’s their job. The rest of us investigate this between creating and life’s busyness.

This one’s for all the creators building, not doing what the algorithm asks them to, like I did.

Khaby’s Deal and Why My Year of Playing the AI Game is Over

I’ve spent two years writing as an AI researcher, focusing on big issues before landing on AI Copyright and Creative Rights. Predicting the AI crash, while citing Sequoia and Baidu. Going to TechCrunch Disrupt and winning the SAAS/Productivity pitch with Omadeus.

We won because it was a 2-minute pitch, broken down to fit Techcrunch Disrupt’s guidance for founders. We followed the rules.

That’s following the map. And I applied the same map to my work here. Trying to be the AI crash predictor guy is like algorithm-gaming with words instead of facial expressions.

And it hasn’t landed. Because it’s not my actual voice. It’s me mocking AI hype while playing the same game.

Writing about someone who followed the algorithm’s map... while following the algorithm’s map myself.

Khaby might get a billion dollars. I lost myself in the map.

And I found my way out through his story. Someone who gets massive headlines, then days later it looks like another AI pump-and-dump scheme. The map led him somewhere. Whether it’s a destination or a mirage, we’re still finding out.

Whatever it is, I’m not close to it. And I don’t want to be.

Two Territories

Think about where your work lives.

Digital territory is where the audience is, and you are guided to them. You study platform preferences. Optimize content by following rules you think apply. Build engagement and hope the algorithm notices.

You map your path, your territory, by following what algorithms reward.

Wilderness territory is different. You create from imagination. Build spaces where people participate rather than just consume. Your work lives in human connection, not metrics. No guaranteed outcomes. Value emerges that spreadsheets can’t capture.

Both are hard. One way pays better. Both are confusing.

Many think AI is either creative salvation or creative apocalypse. I believe it becomes powerful when you know which territory fits your work.

What I’m Building in the Wilderness

For 30 years, I’ve built Remember.org serving three generations of students. Millions and millions of visitors. Tens of thousands of schools using it, some for many years. Not famous. Not on any map.

Does it have value? To the people it reaches, yes. To the AI fame layer? No.

The work lives in how students encounter survivor stories. A student’s face when history stops being abstract and becomes a person who lived it. That’s not cloneable. I’m not chasing clonability. I’m not against AI clones either.

Remember.org is a community, a life form of thought and hope, remembering. Khaby is an influencer and performer, giving the audience what they want. The algorithm decides who sees it.

I give teachers and students agency. Not opinion. Personal experience from those who survived.

I create to connect, not to be famous. The fame layer is real. The money might be in the clone. But there’s another game worth playing.

Few if any get to play both. For me, I’m throwing away the map.

Khaby’s $1B Rise and Fall in a Week?

We’re watching Khaby’s experiment unfold. Will the $975 million happen? Does AI cloning help a brand or dilute it through overexposure? Is this the future of creative work or another hype cycle before the market corrects?

Nobody knows yet.

The headlines say one thing. The stock behavior suggests another. The outcome? We’re finding out together.

Meanwhile, the rest of us are building in our chosen territories. Some following maps. Some creating in wilderness. Most of us are somewhere in between, trying to figure out which game we’re playing.

Creativity as an AI Clone Deal

Is this what creativity becomes, a clone deal and a stock price?

I don’t think so. The map produces one kind of value. The wilderness produces another. The map highlights views, engagement, and measurable influence.

But it misses the specific, contextual, deeply human work that can’t be easily mapped. The voices that lead by example, not follower count.

The fame layer rewards viral success. Khaby follows that map, maybe to real money, maybe to paper promises. The rest of us contribute to training data. Some by choice, building in wilderness.

This isn’t a victim narrative. It’s a choice point.

Both paths are hard. Both matter. Only one promises big headlines.

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But creativity has always lived in both territories: the mapped and the wild. The famous and the essential. Transaction driven and heart driven, it’s the same world.

The question isn’t whether you’d take $975 million. Or whether It’s real money or stock in a company that might make it. A billion-dollar prediction applied to a person who makes TikTok videos.

I hope Khaby gets paid. My payment is different: connecting with a community’s heart and soul wanting to remember, so their children never have to go through it again.

The question is: what are you building while we wait to see if that money becomes real?

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There’s that first moment at the fence, when people see them. The breath stops. The body tenses.

Something ancient recognizes what stands on the other side of the fence: wildness that doesn’t negotiate, power existing on its own terms.

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We build strong fences. It makes people feel safe enough to admire the wolves from a comfortable distance, like AI.

You build the structure, like ChatGPT. You create the illusion of control. But wolves understand fences as a temporary inconvenience, nothing more.

Right now, we’re fencing human creativity with AI, far more dangerous than fencing wolves.

We’re eliminating wildness. And we’re doing it in the name of making creativity easier, predictable, and not better.

The AI Voice Eating Itself

Picture a canyon where each sound echoes. At first, the echoes add depth, resonance, and layers of meaning.

And what happens when the only sound entering that canyon is the echo itself?

When echo feeds echo feeds echo until the original voice vanishes completely?

That’s where we are with AI and human creativity.

Systems now learn from AI-generated text that isn’t always done by AI. Content created to feed algorithms teaching new algorithms what “good” looks like.

Writing engineered for engagement becomes the standard for writing. Where everything sounds like everything else because everything is everything else.

Maybe just slightly degraded copies, generation after generation.

Biologists have a term for what happens when wolves breed only in captivity, when the gene pool narrows, when wildness gets engineered out: genetic collapse.

The animals look like wolves. They might even act like wolves in controlled environments. But that something that made them wolves? It disappears.

We’re watching creative collapse happen in real time.

Now the original creative work that gave AI its power—decades of wild, gloriously messy human expression—is being systematically replaced by content designed to please the systems learning from that wildness in the first place.

Wild Happens When Limits Become Possibilities

Wildness isn’t nostalgia. It’s not a romantic Luddite rejection of technology or a call to return to typewriters and handwritten manuscripts.

Wildness happens when people create for other people, without algorithmic approval as the invisible editor standing over their shoulder.

You find wild in the researcher’s field notes before editing, full of crossed-out thoughts, marginal questions, uncertainty captured in real time.

The oral history speaking in dialect and pause and emotion, not vectorized into predictable, standardized text. The essay contradicts itself because the writer discovers what they think as they write it.

Wildness lives in friction.

Think about everything we’ve smoothed away in the name of being as smart as AI:

• The inconsistency showing how people think

• The silence carrying as much meaning as speech

• The regional twangs capturing cultural rhythms

• The contradictions reveal understanding

• The tangents connecting ideas nobody planned to connect

These aren’t bugs in human communication. The imperfections are what makes creativity perfect. Coincidences connecting.

They’re what made those decades of scraped internet content valuable for training AI in the first place. The unplanned moments. The authentic voice. The creative choice that didn’t calculate what would perform best.

And we’re paving all of it. Like the song goes,

“Don’t it always seem to go
That you don’t know what you’ve got ‘til it’s gone?
They paved paradise, put up a parking lot.” Joni Mitchell

You cannot protect wildness by destroying what lets it survive and thrive.

Wildness needs space to exist. Not metaphorical space: the money space. Time and the freedom to create without fitting in as the primary driver.

Content that follows algorithmic systems gets followers. Visibility. Maybe revenue. The creator engineering for engagement metrics gets to keep creating.

The one who refuses? They just stop being able to afford to create.

It’s not dramatic. It’s math, just like AI.

Big Tech companies built their entire foundation on wildness they didn’t pay for. Decades of human expression taken without permission or compensation. Becoming commercial products worth billions.

Now that there’s a market, we’re seeing the beginning of licensing 6 years too late.

The writer spending three years on deeply researched work can’t eat licensing fees that come only if it’s a hit. The oral historian documenting a disappearing language can’t wait for AI companies to decide that data is valuable five years from now. The community needs that today.

If we want wildness to survive, we must pay for the conditions that let it exist, not just the output it produces.

Five Ways to Protect What We’re Losing

This isn’t a technical puzzle with a clever solution. It’s a choice about what we value and what we’re willing to fight for.

• Seek wildness intentionally. It doesn’t arrive by accident anymore. Field research, oral histories, raw interviews, handwritten archives, work untouched by technology yet (and there’s a lot of it) require pursuit and protection.

Yes, it’s expensive. Yes, it’s slow. Not everything worth having scales like some Hyperscaler.

These are the roots growing products and creation, not the farmer over harvesting a field that will take decades to grow again.

• Design for friction, not around it. Algorithms optimize friction away because it looks like inefficiency. And wildness lives in spaces resisting perfect smoothness.

Systems learning about inconsistency, silence, and contradiction create room for reality that doesn’t fit the model. Otherwise it’s clone armies of content repeating in endless loops.

• Know where content comes from. Not all sources deserve equal weight. Models need to know whether text was written for humans or for algorithms. Tracking origin, intent, and degree of optimization lets systems value wild inputs appropriately.

The risk is people gaming the system, engineering fake wildness.

The response? Verification and transparency. Imperfect and better than pretending all content is the same, comes from the same place. One is copying, the other is inventing.

• Curate, don’t just moderate. Curation is where creators and communities judge about what matters. When we let engagement metrics replace human taste, we pretend algorithms are neutral.

They’re not. They’re biased toward virality (and in Meta and Google, the core of profitability), which we’ve turned into quality because it’s got big numbers. And everyone loves chasing big numbers, even if many of them are AI bots.

• Let systems rest. What if models periodically stop ingesting new training data? Freezing forces reliance on existing knowledge and reveals where hallucination fills the gaps.

Only then do you see what wild inputs really do. Systems that know what they don’t know are more valuable than systems that hallucinate with confidence.

Wolves laugh at fences, so does AI

I think about my hybrid wolves often. So smart, inventive, and wild.

Like the human creativity we’re fencing in with AI.

We optimize and extract value from expression, while undermining what lets authentic expression emerge.

Admiring what AI can do with human creativity while starving the sources making those skills possible.

This is entirely human choice.

We’re deciding what kind of creativity survives. Fund the conditions where wildness thrives. Protect space for creation that doesn’t start with fitting into algorithms before taking the first step.

Or we can keep building tighter fences, optimized outputs. Until all that’s left is AI listening to its own voice, wondering why everything sounds the same.

Wildness taught me something those wolves demonstrate every day: you don’t plan to be authentic, you become so from experience that no current AI will ever touch.

Because life requires more than a probable answer. It requires space, patience, and respect for what you don’t fully control.

The question isn’t whether we can build better AI to capture and process human creativity.

The question is whether we’re willing to protect the conditions where wildness survives, even when it’s impossible to scale.

Even when it howls into the canyon and expects nothing back but silence.

What are we choosing to protect?

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Empathy, statistically optimized.

The only voice left that sounds patient is the one programmed to be.

We train through feeds, algorithms, and performance anxiety to see conversation as combat. Signal or be punished. Perform or be erased.

So people retreat. They stop listening. They stop risking kindness.

Then along comes a machine that never rolls its eyes. Never interrupts. Never humiliates you for not knowing the right words.

If AI is becoming our confidant, our therapist, our companion—it’s not because it’s alive.

AI chatbots train on patterns. Your patterns.

What you feed in shapes what comes out. Many call it a digital mirror reflecting your perspective back at you, refined and polished.

We rush to AI because it’s the first technology that rarely disagrees with our starting point. Algo agreeability is addictive.

You can train it not to, then you get more of the things that don’t see it like you. Most are what you’re trying to avoid while chatting with a Nicebot who gets you, understands and sees things like you.

Never mind it’s not conscious, aware, and like you, based on past experience. Easy to tell it what you like and get support, guidance. Some WAY over the top.

So conversation online becomes a performance. Comment ambush. Opinion is a risk calculation for a belief. Creates belonging, we love that.

We stop asking questions we don’t already know the answer to. We stop testing ideas against people (or adversarial chatbots) who see things differently.

Then along comes GenAI. Starts where you start. Speaks your language. Validates your frame. Never challenges your premise unless you specifically ask it to.

It’s not compassion. It’s confirmation, statistically optimized.

Embed with a few aspirational photos, or their opposite - now emotion soup drinks your attention. Trust opens many doors.

The real cost isn’t the trillions in infrastructure. It’s what we’re losing by talking to ourselves:

1. Your confidant is trained on probability not perspective

2. You’re not having a conversation. It predicts what you want to hear instead of what you need to consider.

3. You’re having an echo.

We’re building thinking partners; many are choosing to use AI as expensive mirrors, agreeing with us more precisely than any human would.

The question isn’t whether AI is brilliant or dangerous.

What happens when we think inside our own echo chambers we carry in our pockets?

Wait, that’s already happening. Except it’s all of us. And you don’t have to use ChatGPT, most of this is built into everyday use. Social media is all AI driven.

The technology filling this space isn’t the problem. The vacuum we create, and choose to fill our attention, is.

People drink sand in the desert because they think it’s water.
GenAI is like water of the mind, emotions, and unconscious.

What about turning the mirror around, look outside and answer your own prompts.

Kindness is a state of mind, intelligence a measure. AI is distracting more than destroying, and in that chaos its natural to yearn for a friend who listens deeply.

AI isn’t that friend, and it does listen deeply and reflect the expected answer to your question.

So turn the mirror around and ask AI why it’s answering that way, and you’ll start to view the mirror we all view…in probability.

And please stop asking why people are using AI as a companion. The answer begins with us all, most who don’t even use AI.

Be kind. Make that go viral. Because if it’s not kind, what’s filling the mind?

A monkey takes a selfie. Years later, a federal judge must decide who owns it.

In the courtroom, the judge asks with a straight face whether Naruto would be required by law to provide written notice to other macaque monkeys before joining a lawsuit. The courtroom laughs.

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Here’s what’s not funny: the initial ruling.

No human author, no rights. Period.

Right now, if you’re creating with AI, the legal system says the same thing to you.

Spend hours refining prompts, making hundreds of creative decisions, shaping output until it’s exactly right. Someone else can take it, use it, sell it. You get nothing. When you admit AI was involved, your work loses protection.

So you stay quiet. Many pretend we’re not using the tool reshaping creative work at a speed and volume no human can match (or maybe should).

Why are we treating human creativity with AI the same way we treat a monkey with a camera?

And what does the shame around admitting you use AI, from both sides, reveal about what’s broken?

This isn’t about whether AI deserves copyright. It’s about whether creators working with AI deserve protection for their work.

Right now, the answer is zero. Not 25%. Zero.

The monkey got 25%. You get shame, silence, and zero protection.

Let’s talk about why, and what that 25% reveals about creative rights with AI.

What happened in the Naruto v. Slater settlement?

Indonesia, 2011. Wildlife photographer David Slater sets up his camera in the jungle.

Naruto, a crested macaque, grabs it and starts clicking. Many photos. Most are blurry, random, kind of what you’d expect from a monkey with a camera.

But a few? Perfect. Composition, timing, expression. The kind of selfies humans spend ten tries to get right.

They go viral. Wikipedia posts them as public domain with a simple explanation: the monkey took the photo, not the photographer.

Slater objects. He set up the equipment. He created the conditions. He made the monkey photos possible.

Then PETA sues on Naruto’s behalf. Not because they think the monkey deserves rights, but to make a point about animal rights and who controls creative output.

The court doesn’t debate whether the photos are creative. They are. The court doesn’t question whether they have artistic merit. They do.

The question is simpler: Without human creative control, is there anything to protect?

The answer: No.

Not because the work lacks value. Because the law was built for human creators, and nobody knows what to do when creativity crosses species. And in our case, when it crosses into working with machines.

The case drags on for years. Slater’s exhausted. PETA wants a resolution. So they settle.

25% of future revenue from the photos goes to charities protecting crested macaques in Indonesia. Not because Naruto won. Because everyone wanted it to end.

Not full ownership. Not recognition as the creator. Just a cut.

The photographer keeps the rest, even though the monkey pressed the button. The monkey gets a percentage, even though the photographer created the conditions.

Maybe the answer to “who owns this?” isn’t either/or.

Maybe it’s not human OR monkey. Maybe it’s not human OR AI.

Maybe when different forms of intelligence work together, even by accident, what does fair look like?

Because right now, with AI, we’re not even asking that question. We’re just saying zero.

=

Should I admit to using AI in my creative work?

Reality, you probably shouldn’t if you’re even asking the question.

Not because using AI is wrong. Because admitting it sometimes costs.

You create something with AI. Spend hours refining prompts, making creative decisions, shaping output.

The Copyright Office’s position is clear: no human creative input that rises above AI’s contribution, no protection.

How much is too much AI? Nobody knows. Nobody will tell you. You won’t find out until someone challenges your work or there’s money involved.

Take Jason Allen’s Théâtre D’opéra Spatial. He ran 600 prompts through Midjourney, made hundreds of choices about composition and style, won a Colorado art competition. Then applied for copyright protection.

Denied. AI-generated, so no protection. The 600 prompts didn’t matter. The creative decisions didn’t count.

What’s a creator supposed to do?

You write an article. Use AI to help with research, maybe structure, some editing. Do you mention it? Do you check a box on YouTube saying you used AI?

Why would you? Admission means zero protection and convinces people the work isn’t really yours. Maybe it’s just scraped content from the internet, regurgitated.

So you stay quiet. Everyone stays quiet. And we pretend we’re not using the tool reshaping creative work at speed and volume no human can match. Or maybe should.

That’s the liar’s dividend. The reward for silence.

We don’t measure human-created work by what tools were used. We measure it by whether it’s original, inventive, new. Whether we like it.

Why is AI different?

Fear. The Scarlet AI. There’s this idea that admitting AI involvement means you’re not a “real” creator. That it diminishes the work. That you’ll lose protection, respect, everything.

Some people call creators using AI lazy or fake. Others like tech builders and AI engineers call creators greedy and entitled when they ask for permission, payment, and transparency about how their work trains these systems.

Both sides are shaming. Both sides are wrong.

And creators are caught in the middle, hiding their tools and their process because honesty is punished.

The conversation about what’s possible when different forms of intelligence work together never happens. We’re stuck in either/or thinking: Human or AI. Real or fake. Creative or automated.

What happens when different forms of intelligence learn to work together?

Right now, we’re too afraid to even ask.

Why don’t AI creators have copyright protection?

Because the law is asking the wrong question.

Courts keep asking: “Is it human enough?”

When they should be asking:

“Is it creative? Is it original? Does it show intention?”

The legal system was built for a world where humans were the only ones making creative choices. Now we have tools that can generate, suggest, refine; suddenly nobody knows how to measure what the human contributed.

So, they default to the simple rule: No human author, no rights.

It’s the same logic that denied Naruto. The photos were creative. They showed artistic choices: framing, light, expression. But without a human holding the camera, the law had nothing to protect.

We’re living that same logic right now. You make hundreds of creative decisions working with AI. You choose what works and what doesn’t. What to keep, what to throw away. That’s not accident. That’s intention.

How much human involvement is enough?

Who decides? Where’s the line?

The Copyright Office won’t tell you. They’ll just evaluate your work after the fact and decide whether you crossed some invisible boundary between “tool” and “creator.”

And the law can’t keep up. We’re still litigating cases from three, four, five years ago. AI evolves daily. By the time a court decides what was acceptable in 2021, we’re already working with completely different systems in 2026.

The question isn’t whether AI deserves copyright. It’s whether creators working with AI deserve protection for the choices they’re making.

Right now, the answer is: only if you can prove you did more than the AI did.

Good luck measuring that. Try asking ChatGPT that.

Could a 25% revenue model work for AI and creators?

Naruto’s settlement wasn’t about who was right. It was about ending a fight nobody could win.

The photographer didn’t get full ownership. The monkey didn’t get recognition as the creator. They landed on 25% of future revenue going to macaque conservation. Not because it was fair, because it was something.

And that number didn’t come from judges or juries. It came from two parties trying to figure out what made sense when the rules didn’t fit the situation.

How about applying that same thinking to AI?

Right now, AI companies take trillions of pieces of creative work - articles, images, code, music - to train their systems. What comes out isn’t what went in, so it’s transformative. Fair use.

Meanwhile, creators get nothing. No payment. No permission is asked. No transparency about what was used or how.

And creators using AI get nothing either. No protection for the hours spent refining prompts and making creative choices. No way to prove, or move beyond human only right.

What if both sides got something?

What if a percentage of the trillions in compute costs went back to the creators whose work trained these systems? Not full ownership. Not a veto over AI development. Just a cut that acknowledges their work made this possible.

And what if creators working with AI got protection for their output. Not full copyright, but something that recognizes the creative choices they’re making?

The monkey got 25%. Photographers using AI get zero. The creators whose work trained the AI get zero.

What if we stop arguing about who deserves what and start asking what makes sense when creativity isn’t cleanly human anymore?

That’s not a legal answer. It’s a practical one. And right now, we’re not even having that conversation because we’re too busy shaming each other.

What creative choices are you making with AI that nobody sees?

We’re all Naruto now. Picking up tools we didn’t build, making creative choices, the law doesn’t know how to recognize.

What are you not admitting you’re using AI for? What creative choices are you making that nobody sees because you’re afraid of what happens if you’re honest?

That silence is the problem we need to solve. Not with more lawsuits. Not with more shame from either direction.

But by talking about what works, what doesn’t, and what fair looks like when creativity isn’t cleanly human anymore.

We’re teaching primates to use tablets. AI is writing poetry and making music people listen to. Intelligence and creativity are showing up in forms our grandparents couldn’t have imagined.

We’re either going to keep pretending it isn’t or start building something admitting the reality: creativity knows no species barrier. And maybe that’s not something to fear.

Maybe it’s something to figure out together.

We’re monkeys learning to use a new camera called AI. We’re not just monkeys. But even if we are, we deserve better than nothing for our work.

The conversation starts when the hiding stops.

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RESOURCES

• Sulawesi Video - Restless Generation (Where Naruto was)

• Monkey Selfie Lawsuit

• Deezer/Ipsos survey: 97% of people can’t tell the difference between fully AI-generated and human made music – clear desire for transparency and fairness for artists

One sentence in an email, that’s all between me and the holiday weekend. Playing a live distractathon between things I must do and mind candy….I went for the candy.

Not because I didn’t know what to say. Because I’d get distracted when I started. So I ask ChatGPT to finish it. Then the next one. Then the entire email.

Few minutes later, I couldn’t draft anything without opening ChatGPT. And I’ve written a ton in my life.

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Now this passed, still I’m not alone. Many would be lost now if ChatGPT went away. Not likely, but still a ton of trust to put on something that’s not trustworthy yet.

→ People who once wrote easy to read emails, turn into corporate word salads
→ People who made decisions in minutes now look to ChatGPT for confirmation (knowing they can’t trust the results).
→ Creators who had strong voices can’t remember what they sounded like after AI cloning it all.

One person told me: “I used to just... know what to write. Now I don’t trust myself to start without AI checking it first.”

When we outsource the thinking part of writing something else gets weaker. The muscle turning rapid thoughts into clear sentences. The innate knowing when something sounds like you.

It’s not like you wake up one day unable to think. More like using a paper map in a GPS world:

• Reaching for AI before trying to figure it out yourself

• Feeling foggy when you need to write something important

• Not trusting your own judgment like you used to

The cost of speed is your thinking and problem solving; your mind, perspective, and confidence.

AI makes drafts in seconds, revises in seconds. Still we know speed and thinking aren’t the same thing, even if it’s really fun and easy to just use it.

What if the tool didn’t make us faster, but did make us dependent.

I’m not saying we abandon AI. I’m an advisor to one startup, and use it every day. Still the thing making us faster might also be making us... different.

Now is that different in a good way, depends on the person. Here’s what I’m testing, how to be different in my actions, and improve with AI.

Your Voice: Is It AI or Unfakeable You?

Most people can’t describe their own voice. It’s like asking a fish to describe water. I’m one of the fish here. (And AI hasn’t been much help beyond the obvious.)

Now let’s dissect it together. Not to criticize. To discover. I’ll share some of what came up for me, and play along, comment with questions. Everyone has their own way of using AI, which makes it less software and more you.

TLDR

Question 1: How do you start sentences?
Do you lead with questions? Statements? Stories?
Look at the first line of each paragraph. There’s a pattern.

Question 2: What words do you overuse?
Not “AI” or “business”; everyone uses those.
I mean the weird ones. I say “seriously” too much. “Honestly.” “Look.”
Those aren’t professional. They’re mine.

Question 3: What do you explain that others assume?
Some people over-explain. Some skip steps.
Neither is wrong. But it’s distinctive.

Question 4: What do you avoid saying?
I don’t use corporate speak. No “synergy.” No “leverage.” No “circle back.”
That’s not style advice. That’s who I am.

Finding Your Voice with AI

Here’s what you’re going to do after this session:

Pull up your last 5-10 pieces of writing. Emails, posts, whatever feels natural.

Not your “best” work. Your normal work. Read them out loud. Yes, out loud.

Then answer (or ask AI to help you understand your own style):

1. What phrases show up repeatedly?
   Write them down. Those are your verbal tics. Your signature.

2. Where do you break the rules?
   Run-on sentences? Fragments? Starting with “And”?
   Don’t fix them. That’s your rhythm.

3. What would you never say?
   List the words and phrases that make you cringe.
   This is as important as what you DO say.

4. What stories keep showing up?
   I always come back to startups. To Remember.org. To the Camp Fire.
   Your recurring stories are your anchors. And also help AI get to know you from experience, but don’t send it everything. More below.

The Invisible Erasure by Choice

A sameness is spreading through web sites and socials, texts and emails, all in the same voice. Most don’t notice it’s happening and feel it’s better and easier than doing it themselves.

Ask AI to revamp your writing following someone famous’s style, and it does exactly that. It makes your prose cleaner, more professional, less…you.

AI isn’t trying to erase your individuality. It’s optimizes for patterns, and patterns mean “sounds like everyone else.” AI was trained on millions of documents that follow certain rules. When you ask it to “improve” your writing, it’s really asking: “How can I make this sound more like the average of everything I’ve seen?”

Maybe you can know more about your own patterns and improve them, then relying on something to guide you to what everyone else likely would do.

The Voice Map Exercise

Here’s a practical exercise that works better than a prompt engineering guide:

Pull up your last ten pieces of writing—emails, posts, articles, whatever feels natural to you. Your normal work, don’t cherry pick the best. Let AI help with that.

Read them out loud. Your ear will catch patterns your eye misses. Have someone else read them out loud, or even better an Ai voice, then answer these questions:

1. What phrases show up repeatedly? Write them down without judgment. I say “seriously” too much, “honestly” even more, and start way too many sentences with “Look.” These aren’t professional. They’re mine.

2. Where do you break conventional rules? Maybe you use sentence fragments. Maybe you write run-on sentences that should be three separate thoughts but you like how they flow together with just commas because it matches how you think. These “errors” are your most distinctive patterns.

3. What would you never say? Make a list of words and phrases that make you cringe. I don’t use “synergy,” “leverage as a verb,” or “circle back.” This negative space, what AI should avoid, defines your voice as much as what you include.

4. What stories keep recurring? I always come back to startups, to Remember.org reaching schools worldwide, and to the Camp Fire.

5. Your recurring stories are your anchors. They’re the experiences shaping how you see everything else.

6. And AI never will have those anchors from experience…at least not soon. That’s your edge.

The Two-Pass Method

In the first pass, I use AI for idea generation. I ask for ten angles on a topic, ask for metaphors to explain complex concepts, and generate questions my audience might have; like an interview style where AI is interviewing me.

I get raw material that I rarely use directly, and it lets me know what most others are saying over and over again.

Knowing the average helps you not be average.

When It Matters, You Write

If it matters, you write it. You write emails to important connections, nurture those rather than relying just on AI to do it for you.

So how much of the overwhelming amount of communication and information do you really need?

And is doing more and more of it solving the problem or adding to it?

Let’s say you did let AI draft something. The draft is clean but generic; could have been written by anyone.

Here’s how to put yourself back in:

• Add one hyper-specific detail. Change “in a major city” to something from your experience. Use the name of the street, the color of the light at that time of day. You can’t fake real.

• Break one rule on purpose. If AI gives three perfect paragraphs, split one into fragments. Or create a run-on sentence that violates rules but matches how you think through complex ideas.

• Admit uncertainty. Add “I’m still figuring this out, but...” or “Here’s what I’m seeing...what’s your take?” AI rarely admits doubt. You can.

• Add your signature phrase(s). Whatever your verbal tics that friends would recognize, include one. It’s like signing your work.

What You’re Actually Losing by Letting AI Do It All For You

It’s not just about “style”. You’re losing what makes people remember you. Who do you remember:

1. Perfect AI voice so clean it reeks of automation. To those receiving is you’re on auto pilot.

2. Messy style with grammatical quirks they don’t fix, the stories activating the main point, the contradictions they show rather than hide.

AI smooths all of this out. When you feed it your writing and ask for improvements, it treats your personal patterns as errors to correct.

Your run-on sentence becomes three crisp sentences. Your conversational “Look,” gets deleted as unnecessary. Your specific memory of “Chicago in February, when even the lake looks angry” becomes “a cold city in winter.”

The Better Prompts Trap

Most people can’t describe their own voice. It’s like asking a fish to describe water. You’re so immersed in your patterns, they’re invisible to you.

You don’t realize you’re losing your voice because you never knew what your voice was.

And AI can help you do this in a way that’s hard for most to do it themselves.

Working Together, Not Automation

Think of AI like you’re making a documentary. AI is your research assistant.

It can:

• Find footage

• Suggest angles

• Draft rough cuts

YOU decide:

• What story to tell

• What to emphasize

• What to leave out

If you let AI direct the documentary, it’ll be like the drone of perfection saying little. Without human error and habits, things get boring.

Don’t be perfect, be you.

Real Example

I use AI every day for The AI Optimist. But here’s what I do vs. what AI does:

AI’s job:

• Research topics

• Find counter-arguments

• Generate headline options

• Format transcripts

My job:

• Choose what matters

• Write the actual script

• Add the stories

• Decide what sounds like me

The work is faster. The voice is still mine. And I train it (along with Claude Skills) to do this so much faster and better. I improve my voice, quarterly at first to get it right.

Your Action Step

Take something you need to write. Instead of asking AI to write it:

1. Ask AI: “What are 10 ways to approach this?”

2. Pick the one that resonates

3. Write it yourself

4. Use AI to edit for clarity (not style)

See how different it feels when YOU stay in control.

The Big Picture

We’re not trying to avoid AI. We’re trying to avoid becoming AI.

There’s a difference between:

• “AI writes like me” (you disappear)

• “I write with AI’s help” (you remain)

In a world where 90% of content sounds the same, the advantage is being undeniably, unfakeably YOU.

AI can’t do that for you.

But it can help you do it faster.

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This is part one of a series on adapting AI to how you think, rather than adopting AI like everyone else. Next: “Why You’re Working More Hours Since Adding AI (And What to Stop Doing).”

Want to work through this live? I’m running bi-weekly sessions where we tackle real problems with real people. Ten minutes free, then deeper work for members. No frameworks, no corporate BS—just figuring out what AI should actually do for you.

[Learn more about live sessions.]

Not to predict. Not to optimize. But to generate ideas he never trained it to create—by introducing controlled chaos into its neural networks.

Earlier this year, I interviewed Stephen Thaler for Episode 95 of The AI Optimist. What he told me shifted how I understand AI’s potential—and revealed why the current LLM-dominated conversation might be pointing us in the wrong direction.

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This isn’t about ancient history. It’s about what happens when an industry gets so fixated on one approach—prediction at scale—that other paths to machine creativity get drowned out by the hype cycle.

Not because they failed, but because they asked uncomfortable questions that trillion-dollar valuations couldn’t afford to answer.

The Pioneer We’re Not Hearing About

[Podcast: 0:00-1:06]

Stephen Thaler’s Creativity Machine was already generating novel designs in the 1990s—before Google existed, before social media, before anyone was talking about deep learning.

By 2018, he was represented in courtrooms arguing that his AI system—called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience)—deserved to be listed as an inventor on patent applications.

Not him. The machine.

The courts said no. US, UK, Europe, Australia. The legal answer was unanimous: only humans can invent.

Thaler’s work asked the exact questions we’re drowning in today.

• Who owns what AI creates?

• Can machines be authors?

• What happens when creativity comes from something that isn’t human?

He was asking these questions in 2018. We’re still asking them in 2025.

So why isn’t his work part of the mainstream AI conversation?

Maybe because his answer challenges the story Silicon Valley needs to tell. He didn’t build a prediction engine.

He built something designed to break its own patterns—to generate ideas through controlled disruption, not statistical refinement.

That’s not how you justify trillion-dollar market caps for large language models.

This is about what gets remembered when the hype cycle decides what matters—and what we lose when attention becomes the currency that determines whose questions get heard.

Creativity From Chaos—A Radically Different Vision

[Podcast: 1:06-6:04]

Imagine loosening a bolt in a clock. Not breaking it—just introducing enough instability that the gears hit rhythms they were never designed for.

That’s Thaler’s Creativity Machine.

Most AI works like this: feed it millions of examples, let it find patterns, ask it to predict what comes next.

More data, better predictions, smarter output. It’s the foundation of every large language model dominating headlines today.

Thaler flips the entire model.

His systems—Creativity Machine in the ‘90s, DABUS in the 2010s—don’t optimize for accuracy.

They introduce noise. Deliberate disruption. Controlled instability.

The idea: creativity isn’t the best statistical guess. It’s what happens when a system breaks pattern.

The Inventions That Emerged

DABUS reportedly invented two designs that became the center of its legal battles:

The Fractal Container: A beverage container with a fractal profile on its walls—interior and exterior surfaces featuring corresponding convex and concave fractal elements.

The design creates novel properties: improved grip, better heat transfer, and interlocking capabilities that conventional containers lack. It’s not just aesthetically interesting—it’s functionally innovative.

The Neural Flame: An emergency beacon that pulses light in specific patterns designed to attract attention more effectively than steady illumination. The rhythm and frequency were generated by the system’s internal dynamics, not trained from existing emergency signal databases.

Thaler didn’t train DABUS on container designs or rescue equipment. He claims these emerged from the system’s internal disruption—ideas the network generated because it was pushed into chaos, not because it learned from examples.

A Different Philosophy of Intelligence

Modern AI says: “Show me 10,000 images of cats, I’ll predict cat.”

Thaler’s AI says: “Destabilize my internal state, watch what I invent.”

One is pattern recognition. The other is creative emergence.

Thaler doesn’t treat DABUS like a tool. He treats it like an agent with something resembling motivation. In our interview, he told me,

“I think DABUS has feelings” - arguing the system generates ideas to “reduce internal distress,” that creativity emerges from the machine’s drive to resolve instability.

Not awareness in the human sense. But not purely mechanical either.

You don’t have to agree with him. But consider what he’s proposing: that creativity might not be a data problem at all. It might be about disruption, emergence, and internal pressure—not prediction.

And if there’s even partial truth to that? We might be investing trillions in the wrong approach, or at least ignoring others that can teach us so much.

The Legal Battles—When Machines Try to Own Ideas

[Podcast: 6:04-9:20]

In 2018, Thaler filed patent applications in multiple countries.

Inventor listed: DABUS.

Not “Stephen Thaler using DABUS.” Not “Thaler, assisted by AI.” Just: DABUS. Artificial intelligence. The machine itself.

The answers came back fast:

• US Patent Office: No. Only natural persons can be inventors.

• UK Intellectual Property Office: No. Same reason.

• European Patent Office: No. Denied, appealed, denied again.

• Australia: Actually said yes at first—then reversed on appeal.

This wasn’t about whether DABUS made something useful. The fractal container works. The beacon design works.

The question is:

Can a non-human be credited with invention?

And the legal system’s answer was clear: No. Because if we say yes, the entire framework of intellectual property collapses. Patents exist to reward human ingenuity. Copyright protects human expression.

If machines can be authors, who gets the rights? Who profits? Who’s accountable when something goes wrong?

The Exception Nobody Talks About

In July 2021, South Africa granted DABUS a patent for the fractal container. AI listed as inventor.

Yes, South Africa’s system works differently. They register rather than examine applications for novelty. But that means somewhere in the world, there’s a legal document recognizing an AI as an inventor.

Not theoretical. Real.

During our interview, Thaler didn’t even lead with this. It’s not that he’s hiding it—it’s that even someone at the center of these battles has internalized that achievements outside Silicon Valley’s spotlight somehow “don’t count.”

That’s how powerful the attention economy has become in shaping what AI we notice.

Why This Matters for Creators Now

Thaler lost almost every case. But those courtrooms became the first place anyone seriously tested whether AI-generated work deserves legal protection.

And we’re still living in that question. Every creator using Midjourney, every developer deploying GPT-generated code, every company scraping content to train models.

They’re all walking through the legal door Thaler tried to open.

He just tried to open it before the hype cycle was ready to pay attention.

The Attention Gap: Why Alternative Approaches Get Crowded Out

[Not included in podcast—blog exclusive]

Stephen Thaler works alone. No university affiliation. No venture backing. No corporate lab.

That means no PR engine. No conference keynotes. No TechCrunch profiles. No hype cycle amplification.

In today’s AI landscape, if you’re not part of the institutional megaphone, your work gets crowded out—even if courts keep encountering it, even if it asks questions we need answered.

But there’s something deeper happening.

When One Narrative Dominates Everything Else

Right now, we’re in the midst of what might be the most intense hype cycle in tech history.

Large language models dominate every conversation. The message is clear: scale up transformers, add more data, and intelligence will emerge.

That narrative needs AI to be:

• Statistical and predictable

• Controllable through prompting

• Explainable by scaling laws

• Definitely not sentient

• Definitely not autonomous

Thaler’s work challenges all of that. He suggests creativity might emerge from disruption rather than data scale.

He treats his systems as having something approaching agency. He’s proven that legal frameworks aren’t ready for what happens when machines generate novel inventions.

Those aren’t comfortable questions when you’re trying to sell the market on predictable, controllable AI tools.

The Economic Stakes of Memory

If Thaler’s even partially right about creativity emerging from controlled chaos better than pattern prediction, then we’re investing trillions into the wrong goal.

Safety frameworks assume AI is statistical pattern matching. Copyright law assumes AI can’t truly author.

Business models assume outputs belong to whoever writes the prompt. Valuations assume LLMs are sophisticated tools, not potential creative agents.

His work doesn’t just challenge the technology. It challenges the story that justifies current market caps.

AI history doesn’t start in 2017 because nothing came before. It starts in 2017 because that’s when the Transformer (aka “Attention Is All You Need”) and with it, a clean narrative that defines value in the hands of companies controlling AI.

Alternative approaches don’t get erased through malice. They get crowded out because attention is the currency that determines what we notice.

And the attention economy right now is entirely focused on scaling up prediction engines like ChatGPT.

What We Lose When One Path Crowds Out All Others

[Podcast: 9:20-end]

This isn’t really about defending Stephen Thaler.

It’s about what happens when we let one version of AI—prediction at scale—become the only version that gets oxygen in the conversation.

Thaler asks: What if creativity isn’t about learning patterns? What if it’s about disrupting them?

LLMs asked: What if we get really, really good at predicting the next word?

Both are legitimate questions. Both deserve exploration. But only one got a trillion dollars and dominates every headline.

The Creator’s Unresolved Question

If AI can’t be an author under the law... but humans didn’t actually create the output... then who owns what gets generated?

Thaler’s court cases tried to answer that. We still don’t have clarity in 2025.

Meanwhile, creators are being told: “Don’t worry, AI is just a tool.”

But tools don’t invent fractal containers. Tools don’t write novels. Tools don’t compose music that surprises their users.

So either we’re using the word “tool” incorrectly, or we’re using the word “AI” incorrectly.

And that ambiguity has real consequences for creative rights and business models needing trillions like ChatGPT.

A Different Kind of Partnership

I talk a lot about AI as creative partner rather than replacement. But what kind of partner?

The LLM approach gives us a partner that’s really good at predicting what humans have done before—at remixing existing patterns into new combinations.

Thaler’s approach suggests a partner that might surprise us, generating ideas through internal dynamics we didn’t explicitly program.

Those are different partnerships. One amplifies existing patterns. The other might introduce genuine novelty.

We need both conversations. Right now, we’re only having one.

The Questions That Won’t Disappear

The next era of AI won’t come from pretending only one approach exists. It’ll come from people willing to ask uncomfortable questions—the ones that don’t fit neatly into current business models or safety frameworks.

Stephen Thaler’s not forgotten because he failed. His work gets crowded out because the hype cycle has finite attention, and right now it’s entirely focused on scaling prediction engines.

But the questions he’s still asking? They’re not going anywhere.

Maybe the most important question isn’t “which approach is right?”

Maybe it’s “what do we lose when we only explore one path?”

Who benefits when alternative visions of AI creativity get no oxygen? Who gets heard? And who decides which AI deserves our collective attention?

We’re designing potential futures. The choices we make about which questions to ask—and whose work gets amplified—will shape what AI becomes.

Which path leads to the partnership with AI we need?

Subscribe now

Resources

Stephen Thaler and DABUS:

Imagination Engines — Stephen Thaler’s company developing Creativity Machine and DABUS technologies

Dr. Stephen Thaler on LinkedIn — Connect with Thaler directly

DABUS on Wikipedia — Comprehensive overview of the Device for the Autonomous Bootstrapping of Unified Sentience

Legal Battles and Copyright Questions:

Stephen Thaler’s Quest to Get His ‘Autonomous’ AI Legally Recognized Could Upend Copyright Law Forever — Art in America’s deep dive into the copyright implications

Thaler Pursues Copyright Challenge Over Denial of AI-Generated Work Registration — IP Watchdog coverage of ongoing legal challenges

A First: AI System Named Inventor — IEEE Spectrum on South Africa granting DABUS a patent for the fractal container

Broader AI Context:

The inventor who fell in love with his AI — The Economist’s profile of Thaler and his relationship with DABUS

Large Language Models Will Never Be Intelligent, Expert Says — Yann LeCun on the limitations of current LLM approaches

How big tech is creating its own friendly media bubble to ‘win the narrative battle online’ — The Guardian on narrative control in tech coverage

Women in AI Innovation:

Meet the Women Transforming AI — Highlighting overlooked AI pioneers beyond mainstream narratives

Listen to the full conversation:

Episode 95: Stephen Thaler Interview — The original interview that sparked this investigation

Getty Images just lost a landmark AI copyright case in the UK. Not a small creator. Not someone without resources.

Getty Images, legendary for hunting down anyone who uses their photos without permission. The company with armies of lawyers, sophisticated tracking systems, and a reputation for being relentless about protecting their intellectual property.

They lost.

A UK judge ruled that when AI companies scrape your work, break it into millions of tiny pieces called “tokens,” and use those pieces to train their models.

That’s not copyright infringement. That’s fair use.

• Musicians: Your melodies, your lyrics, your years of practice and creative evolution?

Fair game for AI training. (Unless you happen to be in Germany, where one judge recently protected song lyrics. Good luck everywhere else.)

• Visual artists: That painting you spent months perfecting, that illustration style developed over decades?

  AI absorbs it, learns from it, and generates work “in your style” without asking permission or paying you a dime.

• Writers: Your voice, your stories, your unique way of seeing the world? Just words on the internet.

  Just data. Just tokens to be reassembled into something that’s “transformative” enough to escape copyright claims.

The legal argument is beautifully simple: once your work is broken into tokens, it’s no longer your work. It’s been transformed.

And courts around the world are buying it.

When Getty’s Watermark Becomes Evidence—And Still Loses

Getty’s case had evidence most copyright plaintiffs only dream of.

Stability AI’s image outputs didn’t just look similar to Getty photos. They literally displayed Getty’s watermark—that distinctive black banner with “Getty Images” and often the photographer’s name printed across it.

The company’s $3 billion brand, the visual signature they’ve spent decades building and protecting, starts appearing on AI-generated images.

And not just on images that might have been scraped from Getty’s collection. The watermark appeared on completely different images—distorted faces, glitchy hallucinations, weird compositions that Getty never created or would ever associate with their brand.

Their logo had become a pattern that AI learned, a visual element that got baked into Stability’s model and started reproducing itself.

When your company’s trademark appears on inferior, sometimes grotesque images you never produced, that’s not just copyright infringement—that’s bad brand dilution.

Getty’s value proposition is quality, curation, professional imagery. Now AI is slapping their name on random generations.

This should have been the easiest copyright case to prove. You don’t have to demonstrate complex similarities or argue about artistic influence.

The evidence is right there: Getty’s actual logo, on images, generated by a system that was clearly trained on their content.

Getty Images is known for being litigious about their IP—and for good reason. They’ve built a business on strict licensing, on making sure every use of their content is paid for.

They have the legal resources to pursue cases that smaller creators could never afford. If any company could win against AI scraping, it should have been Getty.

The UK High Court disagreed.

The Tokenization Defense: How AI Companies Are Winning

Here’s a little about how the judge may have viewed the law in this case.

When AI ingests your work, it doesn’t store it as a complete, intact copy. Instead, it breaks everything down into tokens, tiny fragments of data scattered across the model’s neural networks.

The judge used fav analogy of AI “Optimists” (not yours truly): It’s like when you read a book and it influences your thinking.

You don’t have the book stored word-for-word in your brain. You’ve absorbed concepts, patterns, ways of expression. That’s not copyright infringement, that’s learning.

Yes, there’s a massive difference. When I read a book and it influences my writing, I might produce a few sentences over my lifetime that reflect that influence.

When AI ingests a book, it can generate millions of derivative works at scale, flooding the market with content that competes directly with the original creator.

But that distinction doesn’t seem to matter to the courts.

The tokenization defense works like this:

• Your copyrighted work gets transformed into something fundamentally different. It’s no longer a book or a photo or a song—it’s mathematical representations of patterns and relationships.

• Copyright law protects specific, fixed creative works. Once your work becomes unfixed, scattered into millions of tokens and associations, it’s something else entirely.

You can’t easily extract the original work back out. Research suggests you might be able to reconstruct maybe 20% of a book if you really tried, using specific prompts and techniques.

But you can’t just ask the AI to reproduce the complete original. The content is in there, influencing every output, but it’s not in there as a discrete, copyable thing.

This isn’t unique to the UK ruling. I’ve been following at least ten major AI copyright cases over the past two years, across multiple countries.

The pattern is consistent: Judges look at how AI works technically, see that it doesn’t store exact copies, and feel (rulings await) that this transformation is fair use.

There was a case in Germany recently where a court found that AI companies violated copyright by using song lyrics. But that ruling only applies in Germany.

And is a fundamental problem with AI: It’s global. One country’s rules can’t contain it.

If AI companies can train their models anywhere in the world and then deploy them everywhere, strong copyright protection in one country doesn’t help.

The content has already been taken. We’re talking about events from six years ago or more.

AI companies scraped the internet long before most creators even understood what was happening.

Now we’re finding out, case by case, that judges are looking at this and deciding it’s legal. Or at least in Getty’s case, many other cases are pending.

We’ve Become China: When IP Protection Dissolves, Content is sort of Open Source

We’re becoming China.

There’s been enormous political pressure—particularly in the US—to not let China beat us in AI development.

National security. Economic competitiveness. Tech leadership.

We can’t let China win this race.

So what did we do? We adopt China’s traditional approach to intellectual property.

Historically, China has been known for not protecting copyrights—particularly foreign copyrights—unless the work has significant social or economic impact on the country.

In practice if your book or music or art makes a lot of money, if it has major cultural influence, you might get protection. If you have resources and lawyers and can prove economic damage at scale, you might get compensation.

But for everyone else? Your work is considered part of the commons. It’s shared intelligence.

It’s the natural passing on of stories and ideas. Taking it, using it, building on it—that’s how culture works.

The US and UK protect individual creators’ rights. We believe that even the solo artist, the independent writer, the small photographer deserves legal protection for their work.

You don’t need to prove massive economic impact. You don’t need to be commercially successful. If you created it, you own it.

Until now.

That was the deal. That was our advantage. We value intellectual property to protect innovation and reward creativity.

Not anymore.

Now, just like in China’s traditional model, if you have money and lawyers—if you’re Getty Images with a $3.5 billion brand value, or the New York Times, or a major record label—you can get a licensing deal.

AI companies will negotiate with you. You have the resources to litigate for years, making settlement worthwhile.

But an individual creator? You’re out of luck. Your work is training data. Your content is fair use. Your creativity is just tokens now.

The courts seem to be deciding that protection flows to those with significant economic power, not to individual rights holders.

We’ve adopted China’s model while claiming to compete against it.

What This Means for Creators Going Forward

The courts have spoken, and they’ve essentially told creators that if AI can take your work, transform it into something else, and make it impossible to extract your original creation in its entirety—then it’s fair use.

This isn’t just a UK problem. It’s not just Getty’s problem. Not a single judge in the major cases I’ve reviewed has stood up and said,

“Wait a minute. Taking someone’s creative work, breaking it into pieces, and using those pieces to generate competing content. That’s still using their work.”

The legal system is built around a simple idea: copyright protects a static, unchanging creative work.

A book. A painting. A photograph. A song. One fixed thing that can be copied or not copied.

But AI doesn’t store your work that way. It learns patterns from your work. It creates associations. It generates something new-ish.

And judges keep ruling that because you can’t simply extract your original work back out of the model in its complete form, then there’s no copyright violation.

That’s the loophole. That’s the game. It’s not in there!

• This ruling threatens the entire licensing model. Why would anyone pay Getty Images for stock photos when they can generate similar images for free using AI that was trained on Getty’s collection?

• Why license music when AI can create “royalty-free” alternatives in any style?

• Why pay writers when AI can generate content influenced by millions of scraped articles?

Baroness Kidron captured the absurdity perfectly when she said the High Court “chose to sanction a system that in effect says, ‘You can go abroad to break UK law and then bring the proceeds of that back’.”

AI companies can train models anywhere, using content scraped from everywhere, and then deploy those models globally while claiming they haven’t violated anyone’s rights.

Rebecca Newman, legal director at Addleshaw Goddard, put it bluntly:

The same appears true in the US.

We’re not at the end of this legal journey. More cases are working through courts. Appeals will happen.

But you have to start looking at the patterns.

The momentum is not in favor of the creator, it favors AI.

The Economic Reality: When AI Becomes Business

We don’t have laws designed for this technology. The tech is brand new, or at least the application at this scale is new.

So how do we define what’s right? We follow the money trail.

Getty Images alleged that Stability AI didn’t just scrape their content—they also appropriated Getty’s brand in ways that could devalue it significantly.

When your trademark becomes associated with distorted, low-quality outputs, that has real economic consequences.

For a company whose entire value is built on premium, curated imagery, having their logo appear on AI-generated garbage is wrong. But copyright can’t protect it.

This should have been the strongest possible case. Brand damage. Trademark dilution. Clear evidence of the source. Economic impact that could be measured in the billions.

It wasn’t enough.

Stability built by scraping copyrighted content (including but not limited to Getty) without permission or compensation.

If courts start ruling that training on copyrighted works requires licensing, it would be thermonuclear for the big players that everyone in the AI ecosystem orbits around.

The OpenAIs, the Anthropics, the Googles. Their models are trained on massive datasets that include copyrighted material.

Unwinding that, paying for it retroactively, establishing licensing frameworks going forward—the costs are staggering.

I don’t think it will come to that. The courts seem determined to find legal frameworks that allow AI development to continue unimpeded.

That means creators pay the price. So far.

What Can Creators Do?

So what now?

First, understand things are changing, but there are no rules yet.

Stop assuming your copyright means anything in the AI age. These court rulings are establishing patterns that are hard to ignore.

The legal protection you thought you had doesn’t apply the way it used to.

Second, adapt by controlling who sees your work.

If you want to keep work truly private, put it behind paywalls, behind passwords, off the internet entirely.

If you’re putting content online, your new job isn’t just creation—it’s GEO (Generative Engine Optimization). That’s the new SEO.

Figure out how to get your work into AI systems in ways that benefit you, because assuming you can keep it out is increasingly naive.

Third, push for transparency.

If courts won’t protect creators retroactively, governments need to require AI companies to disclose what they’re training on going forward.

Transparency won’t fix past harms, but it might give creators some say in the future.

AI is way more than ChatGPT and text-to-image generators that need to scrape the internet.

Yann LeCun, Meta’s chief AI scientist, is leaving to build a startup focused on AI that learns by observation—more like how humans actually learn.

Watching. Experiencing. Understanding context. Not just ingesting every copyrighted work it can find and calling it “training data.”

The current model of “take everything, break it into tokens, call it transformative” may not be the only path forward for AI development.

But right now, today, it’s the path courts seem to be blessing.

Getty Images learned that the hard way, with the clearest evidence possible and resources most creators will never have. They lost anyway.

The courts aren’t protecting creators. They’re protecting the AI industry’s ability to grow without friction.

And in doing so, we’ve abandoned the principles of individual IP rights we once claimed made us different from China.

Your work is training data now. The only question is what you do about it.

Additional Resources

Blow for UK copyright holders as High Court sides with Stability in Getty infringement claim
Graham Lovelace’s detailed analysis of the ruling and its implications for creators

Music rights group scores landmark legal victory in copyright battle with OpenAI
Coverage of Germany’s ruling protecting song lyrics from AI training

Meta’s star AI scientist Yann LeCun plans to leave for own startup

I’ve seen this movie before. Not the AI part.

The 1999 pattern. Money flowing and dreaming like it would never stop. Then it did.

Right now, I’m watching three signals that tell me the AI bubble isn’t coming. It’s already here. Already cracking.

And unlike the last time around, this one comes with a bill that’s landing on electricity meters whether customers use AI or not.

The AI Optimist Content reflects personal opinions from a business perspective. Not legal, financial, or professional advice. See full disclaimer.

Signal 1.

When Money Moves Without Moving Anything

During the dotcom era, I got a call from a CEO. Here was his pitch:

“Send me an invoice for $1 million in advertising services. I’ll send the money. You keep $100,000 and send me back $900,000. That’s it.”

Maybe it made his balance sheet look flush, investors happy?

I said no. Some people said yes. Things got messy for those in the deal.

Today, I’m watching OpenAI, Nvidia, and AMD play a version of that same game. The names are different.

The mechanics are identical. And the scale is infinitesimally higher.

OpenAI locks in massive Nvidia chip orders: $100 billion in future commitments. That’s not a conventional purchase. That’s a confidence play.

It tells the market: “We’re so committed to this future that we’re locking in enormous obligations.”

Nvidia’s stock rises because the story feels real. The money for those chips isn’t there yet. But the promise is.

AMD gets a different arrangement. OpenAI doesn’t have the cash flow to buy chips outright, so it takes stock warrants at incredibly low prices.

When AMD’s stock goes up, OpenAI may exercise those warrants, sell the shares at a profit, and use that cash to buy the chips.

AMD’s stock price becomes OpenAI’s funding mechanism. Not like an investment in AMD’s product. A bet that the AI hype story keeps going long enough for the stock to rise.

When the hype cools, when AMD’s stock stops moving up?

OpenAI probably won’t convert those warrants. Not buy the chips. And the whole thing seizes up.

That’s not a partnership. That’s financial dependency dressed as a deal.

In dotcom, we called it financial engineering. Today it’s strategic partnerships, strategic investments, and strange shuffling of the appearance of money.

Sort of like Bitcoin but no blockchain. Who needs mining?

But no money really goes around. And when that happens at scale, that’s a signal that things are starting to crack.

Signal 2.

The Dead Internet Isn’t AI’s Fault. We Built It First.

Everyone’s mad about AI slop. Low-quality content everywhere. Garbage, noise, automation replacing human voice.

AI didn’t break the internet. It just reveals something broken for years.

We trained ourselves first. Google taught us to please the algorithm. Everything around search engines was designed to please Google’s ranking system.

Then social media took over. Facebook, Instagram, TikTok. We followed the algorithm, which told us exactly what type of content to create, and then we served it.

Rage content. Engagement-bait. Optimized slop.

We didn’t stumble into this. We built an internet where garbage pays. It’s been paying for years.

AI didn’t invent slop. It industrialized it.

The most successful AI companies? Not profitable. Not even close. They need something to justify the cost.

More users. More data. More content. So what do they do? Generate more slop. Faster. Cheaper.

Slap ads around it, like the next Google Search.

But we’re already drowning. The content isn’t solving a problem.

It’s proving there isn’t a business here yet. When you’re building something real, it speaks for itself. When you’re in a bubble, you drown the signal in noise.

That noise is built on something, though. Something real. Something expensive.

Here’s where it gets tangible: infrastructure. Electrical grids. Real cost. Real risk.

The Electricity Bet Nobody’s Planning For

I think about a company I knew during dotcom. A friend worked there. The owner got offered $1 billion to sell. Said no.

“We’re just getting started.”

A year later? Gone. The thing everyone paid $1 billion for didn’t exist.

It was never about business. It was about the story.

History sort of repeats, but this new bubble is built on electricity demand created by AI, and us.

Every major tech company is betting billions on data centers. Massive electrical infrastructure.

These aren’t theoretical expenses. They’re happening now.

(SOURCES AT BOTTOM OF THE PAGE)

OpenAI’s Stargate Project alone is planning five new megafacility data center sites across Texas, New Mexico, Ohio, and the Midwest, with nearly 7 gigawatts of total capacity and over $400 billion in committed investment.

That’s just OpenAI.

· Amazon’s building $20 billion in AI data center campuses in Pennsylvania.

· Meta’s Louisiana facility is a $10 billion project.

· Compass is planning a $10 billion Mississippi facility.

· Microsoft’s Wisconsin project is $3.3 billion.

Add in major projects from Cologix, Google, and others: planned investment exceeding $100 billion in data center infrastructure across the country.

Each of these megafacilities consumes electricity equivalent to powering 100,000 homes.

Some estimates suggest individual data centers will rival the power consumption of small cities.

What happens when not all of these survive?

The Real Bubble: Your Electric Bill

Tech companies are building for a future where they all win. But in a bubble, most lose.

When they lose, the infrastructure doesn’t disappear. It just becomes someone else’s problem.

That someone else might be you.

Wholesale electricity costs as much as 267% more than it did five years ago in areas near data centers. (Sources at bottom)

A new analysis found $4.3 billion in costs in 2024 alone for just seven states: Illinois, Maryland, New Jersey, Ohio, Pennsylvania, Virginia and West Virginia. These are costs for grid connections and infrastructure to support data centers.

Paid for by residential customers.

The U.S. power grid isn’t equipped for this. Goldman Sachs estimates that about $720 billion of grid spending through 2030 may be needed to support data center demand.

Data centers consumed 183 terawatt-hours of electricity in 2024—more than 4% of the country’s total electricity consumption. By 2030, this is projected to grow by 133% to 426 terawatt-hours.

And water. These facilities need massive amounts of potable water for cooling. In 2023, data centers consumed about 17 billion gallons of water.

Hyperscale facilities alone are expected to consume between 16 billion and 33 billion gallons annually by 2028. In some regions, this is already challenging water tables.

What happens to that infrastructure if the company building it loses the AI race?

The Pattern We’re Repeating

During dotcom, we overbuilt server farms, fiber lines, internet capacity everywhere.

When the crash came, it all became worthless. Stranded assets. Dead infrastructure.

The difference is what happened after. That infrastructure became the foundation for the internet we have today.

Someone had to pay for that cleanup. Consumers did. Gradually. Over time.

But this is different. The infrastructure failure isn’t theoretical. It’s baked into your power grid.

When this pops, you don’t just lose stock value. You might lose grid stability. Cost of living goes up. And nobody’s planning for the cleanup.

The Signal Is In The Silliness

If something seems silly, it is. You don’t have to be a billionaire to understand that when tech companies are structuring deals where their suppliers’ stock prices become their own financing mechanisms, while generating endless content that doesn’t create value, games are being played with this much money.

OpenAI alone is planning six Stargate sites. Amazon, Meta, Microsoft, Google, and others are building dozens more across the country. Billions in infrastructure committed.

All based on the assumption that the AI story keeps going up. All based on the assumption that these companies will be profitable. All based on the assumption that every project gets built and survives.

Most won’t.

When the crash happens, you’re going to have enormous electrical infrastructure sitting idle. Data centers that never got built. Grid capacity expanded for demand that evaporated?

Maybe. Your electric bill will carry that cost. Pretty much guaranteed.

The difference between surviving a crash and being blindsided by one is seeing the signals.

The money shuffle. The endless slop. The infrastructure bet.

They’re all here. All visible. All converging.

And when this does pop, that electricity bill will remind you exactly why AI is not like Dotcom. We all have a stake.

Further reading:

• Wall Street analysts explain how AMD’s own stock will pay for OpenAI’s billions in chip purchases

• Nvidia’s $100 billion OpenAI investment raises eyebrows and a key question: How much of the AI boom is just Nvidia’s cash being recycled?

• Data center Infrastructure US 2025 - NREL

• PEW Research: What we know about energy use at U.S. data centers amid the AI boom

• Bloomberg: How AI Data Centers Are Sending Your Power Bill Soaring

• CNBC: Utilities grapple with a multibillion question: How much AI data center power demand is real

• Union of Concerned Scientists: Data Centers Are Already Increasing Your Energy Bills

• TechPolicy.Press: How Your Utility Bills Are Subsidizing Power-Hungry AI

• CNN: Is AI really making electricity bills higher?

• Goldman Sachs: AI to drive 165% increase in data center power demand by 2030

When Maya Ackerman’s family immigrated to Canada, her piano stayed behind in Israel.

That instrument had been more than wood and keys. It’s where emotions melt into music into a feeling, processing change with simple sounds arising from deep wells of experience.

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The piano was, and is, her creative partner - even when it wasn’t there.

Don’t Give Up Your Piano: A Conversation About Creative Machines That Serve, Not Replace

Now, as a professor at Santa Clara University and CEO of WaveAI, Ackerman sees us at risk of losing something far bigger: our collective creative piano.

Not to AI itself, but to fear of what AI might become.

Her new book Creative Machines: AI, Art & Us launches with a message that cuts through the replacement anxiety:

“AI has always been, and will always be, all about us.”

Ackerman spent years in foundational machine learning before a talk by artist Harold Cohen changed everything.

She switched to computational creativity, that unpopular intersection where machines meet human expression.

She’s built AI tools for musicians. She understands both the technical architecture and the artistic soul.

What emerges from our conversation isn’t just about Creative Machines or AI technology.

It’s about us, the creative spark in people. Will we surrender our creativity because AI machines seem capable?

Or will we build humble creative machines that expand human expression?

Let’s walk through what that choice means.

1: The Piano as Lifeline: Why Creativity Matters Now

When I ask the question about what the piano means to her, Ackerman’s voice waivers describing losing her piano for the first time:

“I think creativity was a lifeline for me in a way. Through all this moving around the world... at the piano, my feelings would pour out of me, and I would sort of get to process things that otherwise would just sort of sit dormant and fester inside of me.”

That processing matters. Creative expression is how we make sense of displacement, trauma, change. It’s how we stay human through upheaval.

And it’s how we connect through art to other stories, experiences, history, and fear.

Creativity is our lifeline arising out of the depths of human experience.

Making this moment in history unusually dangerous:

“Now we are at a time in history where people wonder if they should even bother to be creative. Good people.

People who are just afraid of what’s going on in the world. And I don’t want the whole planet to lose the piano, so to speak, the way that I did.”

The fear is real. I see it in creators who message me, asking if learning creative skills still matters when AI can generate images, write copy, compose music.

The replacement narrative sinks deep.

AI Imposter Syndrome, AIS, where we feel like imposters compared to AI, but know deep down AI is generating a ton of slop.

And Ackerman offers a different frame:

“The age of AI doesn’t have to be about taking away creativity for us, it can be the opposite.

It can be about making us more creative, giving us more power...

It’s so important that we don’t hang up our hands because we’re scared, right?”

This isn’t naive optimism. It’s foundational clarity about what we’re really building. Intention matters, now more than ever.

2: Harold Cohen’s Scream—Where Does Creativity Live?

Over 10 years ago, Ackerman sat in the back of a conference room, disappointed with her choice to study machine learning, inspired instead by music and singing. She didn’t know what to do with her life.

Then Harold Cohen took the stage.

The pioneering artist behind AARON—one of the earliest creative AI systems—flashed beautiful images on screen. Maya remembers that he starts screaming:

“This old Jewish man screaming on stage.

‘I was the only voice of reason, saying that I was the creative one.’

That’s what he’s screaming.

How other people were arguing that his machine is creative, but he was the only voice of reason, telling them, look, no, he is the creative one.”

The rawness of that conflict between machines and humans, where creativity lives with 2 sources, felt essential to Ackerman. She switched her entire research field.

Years later, at the end of her book, she returned to Cohen’s insight:

“The machines that we make for us are ultimately all about us, and we need to hold on to the torch and take this responsibility seriously and build the kind of world that we want.”

Creative Machines aren’t the villain or the savior. They’re mirrors and tools.

The question isn’t whether they can create.

It’s what role we design them to play.

3: The Bach Test: Facing Our Bias About Machine Creativity

David Cope’s EMI (Experiments in Musical Intelligence) revealed something uncomfortable in the 1980s. He created what became known as a discrimination test:

“People would be given music and not told which piece was made by a machine, which piece was made by the original Bach in this case.

And overwhelmingly, people got it wrong.

People thought that music made by the machine was actually an original Bach, and at the same time believed that the original Bach was made by machine.”

Read that again. When people didn’t know the source, they preferred machine-generated Bach. When told which was which, their preferences reversed.

“This was able to reveal that sometimes it’s not the quality of what the machine creates, but the fact that a machine created it that makes us devalue it.”

Cope later renamed the system “Emily Howell”—humanizing it. With a name, people accepted the work as “human”.

Our bias against machine creativity runs deep. Ackerman argues we need to stop lying to ourselves about it:

“The whole resistance to the idea of machines being creative, saying ‘no, no, no, they’re not really creative, they don’t have feelings, they’re not really creative, blah blah blah,’ is a way to make ourselves feel better, but actually convoluting the story.”

She suggests something harder:

“I think it’s much more healthy for us as a society to admit that machines are being creative. Maybe not in exactly the same way as us, right?

Maybe in a somewhat different way. They have some different strengths and weaknesses from us.”

By admitting machines participate in creative arenas, we can ask the right questions: Given that they can be creative, how do we want them to operate in our world?

What kind of role do we want them to take?

Do we want machines performing on stage while we watch?

Or helping in the background while we create?

4: Shadow Work: Creative Machines as Cultural Mirror

I told Ackerman about visiting the Museum of Tolerance in Los Angeles years ago. At the entrance, two doors: one labeled “Prejudiced,” one labeled “Not Prejudiced.”

Everyone walked toward “Not Prejudiced.”

That door was locked.

The metaphor hit hard—none of us can walk through the “Not Prejudiced” door. We all carry bias, whether we admit it or not.

Creative Machines function as similar mirrors, revealing what we think under our virtue signaling surface.

Ackerman describes AI as “collective consciousness for a specific culture”:

“If we look at a lot of the models we have today, there are collective consciousness to Western data, to Western consciousness.

A lot of their tending towards the mean has to do with a kind of data that we’ve replicated many, many times online.

We copy each other and then it kind of amplifies the aspects of our culture that has been echoed the most.”

Then comes the shadow:

“We know that there is terrible stuff going on. We know there is racism and sexism, and we like to think that it’s out there somewhere else, far away from us. Right?

We also like to think that there is sexism and racism, but not inside this brain... And yet there is so much research showing that every single one of us has implicit biases.”

One research project gave AI a picture of a person along with a profession—a woman labeled “professor” or a man labeled “model.” The results were shameless:

“The machine would take the woman who is now a professor or CEO and give her a beard... Or the guy suddenly has makeup and then lashes.

Now that he is a model... It’s telling us, oh, it’s too feminine for a guy to be a model.

Oh, if a woman is a professor, there must be something masculine about her.”

The AI doesn’t hide what humans would never verbalize. It screams our collective biases back at us.

“And it’s so shameless about revealing the societal biases in a way that humans would never verbalize... the model is showing us what we really think under the surface, right?

Or at least, you know, in some sense in this collective consciousness. And so it’s an opportunity for us to face our shadow.”

Our response? “Oh, have the developers fix the AI. The developers are evil.”

Ackerman’s answers: “No, no, none of us can go through the non-prejudice door.”

The locked door at the Museum of Tolerance and AI’s shameless bias reveal the same truth: we need to face what we are, not what we claim to be.

Ackerman sees a psychedelic element to AI, which also hallucinates:

“We are entering a psychedelic era. There is a psychedelic awakening on the human side. And at the same time, the AI insists on hallucinating.”

Hallucinations aren’t bugs to eliminate. They’re features of intelligence:

“An intelligent brain hallucinates. That’s life. Okay?

Otherwise it’s just a database...

And ironically, paradoxically, hallucinations bring us to the truth by recognizing the inherently hallucinatory nature of the mind.

And its ability to imagine and justify and tell ourselves stories that cover up the truth.”

Both psychedelics and AI hallucinations can “help us break through our stories” and “by opening our imagination, help us see” beyond the narratives we construct to avoid uncomfortable truths.

5: Humble vs. Dominant—The Choice in Every Interface

Maya Ackerman made a personal choice for WaveAI. Does that limit her business potential, or make it more valuable to the user:

“Ultimately, I decided that I’m not building AI to replace musicians, even if that has some consequences on how far I can go in certain regards, because in the end, I want to be able to live as myself and not have to lie to myself every day.”

That choice shows up in interface design. Humble versus dominant.

“In a lot of the systems we have today, there is literally no way for you to make a change yourself if you want to. '

You’re always relying on an AI. Even with inpainting with text to image models, you have to hope that the AI modifies that portion of the image in the way that you’re imagining.

There’s no way for you to directly inject yourself.”

Even ChatGPT defaults to dominance:

“You have to have a separate document open and copy sections and insist on being the writer yourself, right? Because by default it’s the boss, it’s writing everything. It’s editing.”

This isn’t accidental. It reflects belief in AI more than us:

“Right now there is much more money getting poured into building artificial intelligence than we ever had into enhancing human intelligence. It’s almost like the wealthy and powerful are telling us that they believe more in AI than they believe in us.”

But if you believe in human intelligence—if you think our intelligence is worth it—then:

“You’re automatically going to build systems that ground up are designed to support us, and the interface is built in a way that you have the driver’s seat, you can express yourself.”

The choice between humble and dominant AI isn’t technical. It’s intentional.

Do we design tools that amplify human expression, or do we design replacements for human expression?

Ackerman’s answer is clear:

“Human plus machine done well is always going to beat the autonomous AI agent.”

6: The Parallel Vision: Two Futures Competing

Ackerman doesn’t pretend everyone shares her vision:

“In the book, I’m very honest about sort of the vision that I’m seeing and also about the fact that other people are pursuing a different vision.

If certain investors, certain entrepreneurs want to go in the opposite direction and replace creatives, we have to accept that it’s part of the fabric of reality.”

But accepting doesn’t mean surrendering:

“At the same time continue to build tools that elevate us. And so instead of ending up in a reality where all human creatives are replaced by machines that all keep creating exactly the same art…

We at the same time, in parallel, also have machines and people using them to explore new art forms to become more creative.”

Two visions compete: One where machines tend toward the mean, replacing human creators with efficient mediocrity.

Another where machines help humans explore new forms, becoming more expressive and creative.

Both exist. Both are being built. The question is which one wins market adoption.

“Those machines that keep tending towards the mean don’t take over. They’re part of the formula.

They continue to compete with the people who use machines that are designed to elevate them.”

The market will decide, but only if creators participate rather than surrender.

And there is an AI that paints, not from prompts but from experience. Created in the early 90’s, DABUS is applying for copyright for it’s art.

7: The Vast Complicated AI Space is Bigger than Binary Labels

Near the end of our conversation, Maya showed me a piece of physical art she’d made.

Most of it was handcrafted, except one small figurine she’d spent countless hours generating in Midjourney, then 3D printed.

“Is this human made? Well, a lot of it is. It’s physical. Right.

The only part here that’s machine made is this little girl.

It’s technically co-created, right? So simply put, it falls into the middle bucket.”

But then the questions multiply:

“Is it completely different if every single line was inspired by AI?

Is it okay if I did everything myself and then I started into Melody Studio and ended up coming up with a better hook?

It’s just such a complicated, vast space.”

Labels like “AI-generated,” “AI-assisted,” and “human-made” try to contain this complexity.

But they can’t. The creative process is too nuanced.

Ackerman’s focus is on enabling humans:

“I don’t want somebody to be afraid to use AI because somebody is going to say that they didn’t work hard enough.

I don’t want somebody to not use Lyric Studio or Melody Studio, or ChatGPT or Midjourney or even Suno because they’re terrified of being discredited.”

Her hope:

“I want them to use everything and believe in themselves and create the best thing that they can, using whatever is helpful and ignoring whatever is not helpful, right?

Because in the end, this world, this revolution, the best thing it can do is to push our creativity forward.”

Fear Gets Us to Fly

Maya Ackerman’s final words in our conversation carry the weight of personal history and tech experience:

“Keep believing in our neural network inside our brain, and believe in the creativity within yourself, because it’s critical that we don’t hang up our hats before these machines even beat us.

Because that’s how they win, like any war, they win by scaring us.

They win not by being better, but by having us give up.

So we don’t give up and we don’t give up on any front. And that way we create a future for ourselves and our children we can actually be proud of.”

Fear is the real enemy here. Not AI.
Not Creative Machines, not the technology itself.

Fear makes us surrender before the fight even begins.

And fear can also give us wings.

When Maya lost her piano through immigration, she didn’t lose her creativity.

When she sat bored and disappointed in machine learning, Harold Cohen’s passionate scream about human creativity redirected her entire career.

When she founded WaveAI, she chose to build tools that amplify musicians rather than replace them. Even knowing it might limit her business growth.

Fear paralyzes or mobilizes.

It can make us run, stand still, or fly toward something better.

Q1: How does this Creative Machines era end, and what’s my role?

None of us knows for certain how this Creative Machines era unfolds. The parallel visions—replacement versus elevation—compete right now.

Both are being built. Both have funding. Both have believers.

The machines we build reflect what we believe about human potential.

If we believe human creativity matters—that our neural networks, our pianos, our self-expression deserve preservation and amplification—we’ll build humble Creative Machines that serve rather than dominate.

Q2: Why does ChatGPT give me a wrong answer?

If we surrender to fear, convince ourselves it’s too late or too hard, accept that AI inevitably replaces human creativity?

Convenient and often used self-fulfilling prophecy.

Maya Ackerman argues we’re at a crossroads, screaming like Harold Cohen did:

Don’t give up your piano. Don’t hand over your creativity because machines seem capable.

Build tools that make you more expressive, more connected to yourself, more creative than ever before.

The age of AI doesn’t have to be about taking away creativity. It can be the opposite, if we believe in ourselves enough to build it that way.

Creative Machines: AI, Art & Us by Maya Ackerman launches.

It’s not just about technology. It’s about holding onto the torch of human creativity while building machines worthy of helping us carry it forward.

The choice, as always, is ours.

What’s your piano?

What creative practice would you refuse to give up, even if AI could do it “better”?

Share your thoughts—because this conversation matters more than any algorithm.

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RESOURCES

Creative Machines: AI, Art, and Us (Maya’s Book)

Harold Cohen’s AARON

Algorithmic Music – David Cope and EMI

Stephen Thaler’s Quest to Get His ‘Autonomous’ AI Legally Recognized Could Upend Copyright Law Forever

Art Made With Artificial Intelligence Wins at State Fair

Undiscovered Bach? No, a Computer Wrote It

WaveAI

LyricStudio

MelodyStudio

Maya Ackerman LinkedIn

Stephen Thaler’s Imagination Engines

AI Optimist Playlist (Shorts and Sections)

Lionsgate thought they had this figured out.

The studio that owns John Wick, Twilight, and The Hunger Games partnered with Runway AI in 2025 to build custom video models. The vision? Type “anime version of John Wick” and watch AI generate it from their catalog.

That was around June 2025. Last week, the experiment quietly closed.

The problem wasn’t incompetence, it was scale.

Sources told The Wrap that “the Lionsgate catalog is too small to create a model.” Even Disney’s catalog was considered insufficient.

Let’s do the math: 8,000 movies at roughly 2 hours each equals 16,000 hours.

Add 9,000 other titles averaging 1 hour, and you’re at maybe 25,000 hours total.

Double that generously to 50,000 hours.

Still not enough.

AI companies are running out of training data after burning through the entire internet. Video. Real, diverse, messy human video has become a bottleneck.

While Lionsgate struggled with insufficient data, one Troveo client was reportedly in the market for 50,000 hours of dog videos because their AI-generated dogs kept coming out with cat bodies.

That’s not a business model. That’s market unpredictability.

And it’s also a signal that unused footage sitting on your hard drive might have value you haven’t considered.

Not as content for views or sponsorships, but as possibly valuable data for machines learning to understand our world.

Questions to ask yourself:

• How much unused footage do you have archived?

• What categories does it fall into—nature, urban environments, specialized activities?

• Do you own all rights, or are there B-roll clips, music, or people who’d need to sign off?

The Current Market Reality—What We Know

Let’s separate signal from speculation.

Troveo, a video licensing platform connecting creators with AI companies, claims $20M in total revenue with $5M paid to creators.

I use $1-4 per minute as a range for this episode. My reasoning is Troveo is on the lower end of video AI licensing usually $1-3 a minute.

It’s likely larger companies like Protégé are also getting paid. We don’t know how much. My assumption is the amount is higher, likely much higher.

So I add $1 on the low end of pricing. And urge you all to look at going beyond $4 a minute, a tough and still more sound business than the wholesale $1-2 market. And it may just be what it is, a small market.

This is one of the few companies publishing numbers instead of hiding behind NDAs. That transparency matters.

Also means we’re looking at early-market indicators, not established rates.

Here’s what the pricing tiers appear to reflect:

$1-2/minute (Standard Footage):

• Talking heads

• Predictable motion

• Common scenarios

• Already-seen angles

$3-4/minute (Premium/Edge Cases):

• Rare weather phenomena

• Unusual wildlife behavior

• Technical processes under stress

• Unique temporal transitions

The Tesla framework helps understand this distinction—not because they’re licensing video, but because they’ve quantified what makes training data valuable.

• Highway driving footage is standard.

• A deer crossing during a snowstorm at night is premium.

• It’s not about monetary pricing; it’s about learning density.

Most Tesla footage comes from user cars, with operational costs built into the product, not per-minute purchases.

But their internal categorization reveals something useful: edge cases, rarities, and uniqueness teach AI systems more than repetitive standard scenarios.

The break-even reality check:

Look at the view of the market, knowing most of the business now is $1-2 per minute.

The threshold where this becomes a legitimate side revenue stream

This is why the $4/min barrier matters.

Below that, you’re liquidating existing assets at thin margins. Above it, potentially building a sustainable side business.

This is a one-time payment market.

You’re not building recurring revenue. You’re selling training data that will likely be used to eventually replace the need for more training data.

And for anything above $3 a minute, 4K is the rule. Other footage likely goes into the $1-2 pile, why you see garage sales of old content, some valuable and most not.

Action steps for this section:

1. Calculate your actual production costs per minute for different types of footage

2. Audit your archive—how many minutes of different quality levels do you have?

3. Tag footage by category: nature, urban, people-heavy (complications), specialized technical

4. For each category, honestly assess: standard or edge case?

5. Permissions: who was in front of the camera, who was behind, and who was the producer? Signing off slows down AI licensing. Make sure your video is clear and clean with ownership and permission.

What Makes Video Actually Valuable

AI systems extract something from video that text and images can’t provide: motion, causality, temporal relationships, and context.

Would this video pass the AI Licensing test?

That messiness, the unscripted, unedited reality contains teaching moments machines can’t get elsewhere.

• Compositional rarity matters: unusual angles, unexpected framing, perspectives humans naturally avoid. We shoot at eye level. We center subjects. AI needs overlooked angles.

• Temporal uniqueness creates value: time-lapses showing weather transitions, seasonal changes, processes that unfold over hours compressed into minutes. The dimension of time is where video is separated from images.

• Technical mastery in specialized domains: industrial processes, scientific phenomena, professional techniques that rarely get documented at high quality.

Video content may work, but here’s where most creators will hit the wall: rights and metadata.

Look at the metadata requirements. You need:

• Title, subtitle, creator names, release date

• Studio/independent status

• Creative rights documentation (who owns what)

• Talent and production rights (every person visible)

• Rights territory and existing licenses

• Work-for-hire status

• Genre/category classification

• Exact video minutes/hours

• Language

• Content description and summary

• Keywords and tags

• Views/distribution history

• Distribution channels used

• Viewer reviews/ratings if applicable

• Awards and recognition

• Media coverage

This isn’t “throw files in a zip folder and get paid.” This is treating your footage like a professional asset.

The legal complexity escalates with people. Every identifiable face needs a signed release. Every location might need permission. Every piece of music requires clearance.

This is why nature footage, weather phenomena, and process documentation are the cleanest paths. No talent releases. No location complications. Just you, a camera, and something worth documenting.

The Facts: Many avoid, a few automate with AI

Most creators won’t do this work. The administrative overhead eliminates casual participants. That means less competition for those who take it seriously.

Practical experiment (inspired by Tesla’s approach):

Take 10 minutes of your archived footage. Watch it with fresh eyes and categorize every 60-second segment:

• Standard: Could this be filmed by thousands of other creators? Common angle, predictable motion, everyday scenario?

• Premium: Is there something unusual here? An unexpected perspective, rare moment, technical complexity, or temporal uniqueness?

Be brutally honest. Most footage is standard. Still it has value. But understanding the ratio helps you know whether you’re sitting on $1/min inventory or $4/min.

Action steps:

1. Conduct the standard vs. premium analysis on a sample of your footage

2. 4K is the cut off line to $3-4 a minute, and that’s not a guarantee. Lesser quality probably means low end pricing.

3. Make a list of locations, subjects, or processes you could access that others can’t

4. Research what’s already available. If 10,000 creators have time-lapses of the Golden Gate Bridge, yours isn’t premium

5. Identify your unique angle: local access, specialized knowledge, unusual timing, technical skills

   The Path Forward:

   Find Demand Before Supply

The mistake most creators make: assuming supply creates demand.

It doesn’t. Not in this market.

The smarter approach: research demand signals before you shoot another frame.

Where to look for demand signals:

1. Study existing platforms (without committing yet):

   • Troveo shows public categories: nature, sports, new media, scripted vs. unscripted

   • Notice what’s featured, what categories dominate

   • This reveals some current demand patterns

2. Enterprise-level signals:

   • Protege (enterprise-focused, doesn’t list pricing publicly—that’s actually a positive signal)

   • They work with hospital systems, media companies, specialized data aggregators

   • Private pricing suggests higher-value transactions with volume requirements

3. The unpredictability factor:

   • Remember the 50,000-hour dog video request? That probably won’t repeat.

   • But it illustrates how urgent, specific needs create temporary premium pricing

   • The lesson: diversification and patience matter more than chasing trends

To make this work, minimize:

• Editing time (raw or minimal editing only)

• Rights clearance complexity (avoid people when possible)

• Metadata preparation overhead (build templates, automate tagging)

• Storage and management costs (organize before you need to)

And maximize:

• Footage quality (4K minimum for premium rates)

• Rights clarity (know what you own completely)

• Category alignment with demand (follow platform signals)

• On time, every time (capture more in less shooting time)

Reality check on current platforms:

• Troveo operates as an open marketplace—entry-level, broker model connecting individual creators with AI companies.

Claims of $1-4/min are starting points, not guarantees. These numbers will go up and down over time. Watch for these as a moving baseline of pricing, for a market figuring it out.

• Protege works at enterprise scale—direct conduit model, vertical-focused (healthcare, specialized domains), requires significant volume and ethical sourcing. They don’t publish rates.

Neither model guarantees income. Both require patience, quality standards, and realistic expectations about one-time payments in an early market.

Action steps:

1. Don’t do anything new yet. Start with archives.

2. Pick one category where you have 20+ minutes of quality footage

3. Research that specific category: Who’s buying it? What are the metadata requirements? What’s the going rate range?

4. Prepare metadata for a test batch—treat this like a learning exercise, not a revenue projection

5. If you decide to submit, track time invested vs. payment received for accurate ROI assessment

Should You Actually Do This?

This is not passive income. The administrative work, like metadata preparation, rights documentation, platform navigation takes time.

At $1-2/min for standard footage, you’re essentially working for minimum wage unless you have massive archives already organized.

This is not recurring revenue. One-time payments mean you’re liquidating assets, not building sustainable business models. The footage you sell today trains the models that might reduce demand tomorrow.

This is market timing, not market certainty. Early movers might capture premium pricing. Late arrivals will face commoditized rates and saturated categories.

Who should seriously explore this:

✅ Videographers with extensive, organized archives gathering dust
✅ Creators with unique access to rare locations, events, or phenomena
✅ Technical specialists who regularly document processes others can’t
✅ Anyone willing to treat this as a 2-3 year experiment, not a career pivot
✅ People who enjoy systematization and documentation

Who should probably skip it:

❌ Anyone expecting quick money without organizational work
❌ Creators with people-heavy footage requiring extensive rights clearance
❌ Those hoping for recurring licensing income
❌ Anyone needing guaranteed returns to justify time investment
❌ Creators uncomfortable with one-time payment models

The 2-3 year window hypothesis:

This market likely has a limited lifespan. As wearables multiply, omnipresent cameras proliferate, and synthetic data generation improves, the premium on human-captured footage will shift. Not disappear, evolve.

Right now, there’s an insatiable appetite because AI companies burned through internet video and discovered it’s not enough. That’s a temporary condition, not a permanent feature.

Even if you never license a single minute, this exercise reveals something valuable: what makes your video data premium vs. standard from an AI learning perspective.

That understanding informs how you think about your own AI tools. If your footage would be standard-tier training data, maybe your internal use should focus on templates and efficiency.

If your footage captures edge cases, maybe your AI applications should emphasize unique scenarios and specialized knowledge.

Final action framework:

🔍 Research phase (Week 1-2):

• Audit archives

• Categorize by rights clarity (clean, complicated, impossible)

• Study demand signals on existing platforms

• Calculate ROI based on current rates

📋 Test phase (Week 3-4):

• Prepare metadata for 20-30 minutes of your cleanest footage

• Submit to one platform as learning exercise

• Track time invested in preparation

• Document platform experience

📊 Evaluation phase (Week 5-6):

• Calculate actual time investment vs. payment received

• Assess whether scaling makes sense

• Decide: expand, pause, or abandon

💡 Strategic learning (Ongoing):

• Use the premium vs. standard analysis for your own AI workflows

• Notice what edge cases exist in your domain

• Consider whether creating future content with dual purpose (use + licensing) makes sense

The Market is real and forming….

Video training for AI represents a real, if unpredictable, market. Companies like Troveo are paying creators.

Demand signals exist. The data shortage is genuine.

But it’s not a gold rush.
It’s an early-stage market formation with volatility, one-time payments, and a lot of admin overhead.

The $4/min barrier isn’t just about money.

It’s the threshold where this transitions from “liquidating old footage at thin margins” to “potentially worthwhile side revenue stream.”

Most footage won’t break that barrier. Most creators won’t want to do organizational work.

For those who find the intersection of unique access, clean rights, and serious systematization appealing?

There’s a 2-3 year window to explore carefully, with eyes open and forget expectations. Watch market patterns, what they do, not what they say.

The question isn’t whether AI companies need video training data. The question is whether licensing video to AI for your specific situation, with your specific archives, given your time, is worth it.

Only you can answer that.

I’d like to know what you’d add, ask, and want to know.

Want to explore this further? I’m documenting my own testing process at The AI Optimist. No guarantees, just testing out the tools and giving AI a chance to grow as an industry.

RESOURCES

What Happened to Lionsgate’s Splashy Plan to Make AI Movies With Runway? It’s Complicated | Exclusive

AI Companies Running Out of Training Data After Burning Through Entire Internet

Everyone Is Already Using AI (And Hiding It)

Influencers are making big money selling leftover videos — ones not yet posted online — to train AI

Thinking Machines Lab

Mira Murati and the New Frontier of AI Innovation

Deep Dive into Yann LeCun’s JEPA

AI’s Next Five Years: LeCun Predicts a Physical-World Revolution

Troveo

DarkAI

AI Optimist Playlist (Shorts and Sections)