Avsnitt
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I’ve raised hybrid wolves and they’re a lot like AI. Both come from something that used to be wild.
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.
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 goThat 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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What is the Naruto monkey selfie case and why does it matter for AI?
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.
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.
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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.
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
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Saknas det avsnitt?
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Ever notice yourself reaching for AI before trying to think through something on your own?
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.
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):
* What phrases show up repeatedly?Write them down. Those are your verbal tics. Your signature.
* Where do you break the rules?Run-on sentences? Fragments? Starting with “And”?Don’t fix them. That’s your rhythm.
* What would you never say?List the words and phrases that make you cringe.This is as important as what you DO say.
* 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:
* 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.
* 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.
* 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.
* What stories keep recurring? I always come back to startups, to Remember.org reaching schools worldwide, and to the Camp Fire.
* Your recurring stories are your anchors. They’re the experiences shaping how you see everything else.
* 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.
In the second pass, I write in my own voice. I create ideas out of the initial rough questions and AI answers. More as a guide and also what will likely sound like everyone else.The research is faster. The writing remains distinctly mine, or else I become that AI middle dreariness of squeaky clean perfection without the flaws I bring."There is a crack in everything, that’s how the light gets in" Leonard Cohen, Anthem
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:
* Ask AI: “What are 10 ways to approach this?”
* Pick the one that resonates
* Write it yourself
* 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.
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.]
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
There’s a founder who built AI designed to surprise him.
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.
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.
D. 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.
E. 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?
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
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
If you’re a musician, writer, photographer, painter, designer, filmmaker—this matters to you. Right now.
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 UK’s secondary copyright regime is not strong enough to protect its creators.”
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 claimGraham Lovelace’s detailed analysis of the ruling and its implications for creators
Music rights group scores landmark legal victory in copyright battle with OpenAICoverage of Germany’s ruling protecting song lyrics from AI training
Meta’s star AI scientist Yann LeCun plans to leave for own startup
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
The Electricity Behind the AI Bubble: What Happens When the Music Stops
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
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Seeing that tall black and brown piano in the background before our interview, I sense tradition of human creativity meeting AI. This is about us.
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.
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.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
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)
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
When Hollywood’s Catalog Isn’t Enough and Might Need AI Licensing
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:
* Calculate your actual production costs per minute for different types of footage
* Audit your archive—how many minutes of different quality levels do you have?
* Tag footage by category: nature, urban, people-heavy (complications), specialized technical
* For each category, honestly assess: standard or edge case?
* 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?
Mira Murati, founder of Thinking Machines Lab, says:
“We’re building multimodal AI that works with how you naturally interact with the world—through conversation, through sight, through the messy way we collaborate.”
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:
* Conduct the standard vs. premium analysis on a sample of your footage
* 4K is the cut off line to $3-4 a minute, and that’s not a guarantee. Lesser quality probably means low end pricing.
* Make a list of locations, subjects, or processes you could access that others can’t
* Research what’s already available. If 10,000 creators have time-lapses of the Golden Gate Bridge, yours isn’t premium
* 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:
* 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
* 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
* 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:
* Don’t do anything new yet. Start with archives.
* Pick one category where you have 20+ minutes of quality footage
* Research that specific category: Who’s buying it? What are the metadata requirements? What’s the going rate range?
* Prepare metadata for a test batch—treat this like a learning exercise, not a revenue projection
* 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.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
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)
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Time for creators to be recognized and paid by AI, finally!
Those days of AI stealing content for free and without permission just crashed in a quiet, proposed settlement - call it AI licensing rates 2025.
Creators are being given choice and control over how their work is used by AI, and may even get paid with licensing!
After years of big tech telling creators their work has no value, something shifted. Anthropic just proposed paying $1.5 billion to settle copyright claims—roughly $3,000 per book.
This is not the net amount received by individual authors, as it will be reduced by administrative costs and divided among rightsholders if multiple parties are involved.
For the first time, a judge is recognizing that human creativity has measurable worth in AI training. Not just bestselling authors. Regular creators like you.
Subscribers get The complete Creator’s AI Licensing INTEL- 13 pages with a pricing simulation for books, photos, and art.
This isn't some distant future promise. This is happening now, and it's opening doors that have been slammed shut since AI started scraping content without permission or payment.
The Evidence Shows: AI Companies Are Finally Paying AI licensing rates in 2025
Here's what most people missed in the headlines. Anthropic didn't just throw money at a legal problem. They established something unprecedented: a baseline value for creative work in AI training.
Harper Collins negotiated deals worth $2,500-$5,000 per book with major AI companies.
These aren't charity payments—they're business investments in quality training data.
Why the sudden change? Because AI companies discovered what creators always knew: garbage in, garbage out.
They need your expertise, your unique perspective, your carefully crafted content to build better AI systems.
The wild west era of free content scraping is ending. The licensing era is beginning.
What you can do now:
* Document what creative work you own completely
* Start thinking about your content's unique value
* Don't wait for perfect information—early participants often secure better rates
What $3,000 Per Book Means for You Right Now? Author’s AI Revenue
That $3,000 figure isn't a lottery ticket—it's validation. A federal judge essentially agreed that copyrighted creative work has quantifiable value in AI training. Even if you never see a licensing check, this changes everything.
You now have a legal settlement that says your work isn't "training material"—it's valuable intellectual property. This gives you choices you didn't have before:
* License your work and get paid
* Opt out entirely and protect your content
* Control how AI systems learn from your creativity
The key word here is control. For years, creators watched their work get absorbed into AI systems without consent or compensation.
Now there's a path to actual choice.
But here's the reality check: this applies to work you own the copyright to. If you don't have clear legal ownership, licensing becomes nearly impossible. AI companies need defensible rights to avoid future lawsuits.
Your next moves:
* Check your copyright status on existing work
* Register copyrights for valuable content (it's easier than you think)
* Understand that timing matters—prepare now for licensing opportunities in 2026
What's Your Creative Work Actually Worth to AI?
Not all content gets valued equally. After researching this market for over a year, certain patterns emerge that determine what AI companies will pay for.
Nonfiction typically commands higher rates than fiction. Why? It's factual, less dependent on storytelling brilliance, and provides reliable training data.
A well-researched business book or technical guide offers more consistent value than a novel—unless that novel is awesome. And that’s up to the reader!
* Sales history matters. If your book sold thousands of copies, that's market validation AI companies understand. It proves real people found value in your work.
* Uniqueness drives premium pricing. Generic content gets generic rates. Specialized expertise, unique perspectives, and distinctive voices command attention.
Get the AI Licensing, metadata advantage:
AI systems need context to understand value. When you describe your work clearly—genre, audience, expertise level, sales performance—you're not just filling out forms. You're teaching AI why your content matters.
Think of metadata as your content's resume. Without it, you're just another file in a database. With it, you're a valuable training resource with proven worth.
Build your content value:
* Inventory your best-performing content
* Gather sales data, awards, recognition, media coverage
* Start documenting what makes your work unique and valuable
Pricing Reality: What Nonfiction and Fiction Actually Earn
Based on current market data, here's what creators might expect:
* Quality nonfiction with clear copyright and sales history: $2,000-$5,000 per book. Business guides, technical manuals, and specialized expertise command top rates.
* Fiction faces steeper challenges unless it's genuinely outstanding. Most fiction licensing falls in the $1,500-$3,000 range, with exceptional storytelling pushing higher.
But remember—these are one-time payments for current licensing models.
Some platforms are experimenting with annual subscriptions or revenue sharing. The economics are still evolving rapidly.
The volume game matters too. Individual authors might earn decent side income, but creators with larger catalogs see meaningful revenue.
Five books at $3,000 each through a creator-friendly platform (~15% fee) nets around $12,750.
Compare that to traditional publishing royalties, and licensing suddenly looks very interesting.
Calculate your potential:
* Calculate potential licensing value for your existing work
* Focus on your highest-quality, best-documented content first
* Consider building content specifically designed for licensing value
AI Values for Photos and Art: The Visual Content Market
Visual content follows different rules entirely. This market resembles traditional stock photography, but with an AI training twist.
High-resolution photos (4K+) command premium rates. Landscapes, urban scenes, and complex compositions provide rich training data.
Simple headshots? Not so much—AI already handles those well.
Original artwork, especially paintings, can earn $500-$1,000 per piece for AI licensing.
The key is documentation: medium, technique, inspiration, creation process. AI companies pay for context as much as pixels.
Here's where platform choice really matters. Getty Images takes 75-80% of licensing fees.
Newer creator-focused platforms take only 15%. That difference adds up quickly.
For photographers and artists, the timing couldn't be better. AI companies need diverse, high-quality visual training data, and they're willing to pay for it.
Maximize your visual content value:
* Audit your best visual content for licensing potential
* Organize high-resolution files with detailed descriptions
* Research multiple platforms—fees vary dramatically
Quality Matters: Yosemite Beats Headshots Every Time
Not all images get equal treatment in AI licensing. After studying what companies actually buy, clear patterns emerge.
Landscape photography, especially iconic locations like Yosemite, commands top rates.
These images provide complex visual information: lighting, composition, natural elements, seasonal variation.
AI systems learn more from a single great landscape than dozens of simple portraits.
Urban photography works well too. Street scenes, architecture, cultural events—anything showing human environments in detail.
The richer the visual information, the higher the licensing value.
Professional studio shots with controlled lighting often outperform casual smartphone photos, but not always. Sometimes authentic, candid moments provide exactly what AI training needs.
The metadata advantage applies here too. When you tag a sunset photo with location, time, weather conditions, and technical details, you're providing training context that makes your image more valuable.
Focus your efforts:
* Prioritize your most visually complex and interesting photos
* Add detailed tags and descriptions to your best work
* Focus on unique locations and authentic moments over generic stock-style shots
Four Steps to AI Licensing
Ready to move from passive content source to active licensing participant? Here's your practical roadmap:
Step 1: Secure Your Rights
Get copyright protection for anything you want to license. Without clear legal ownership, licensing becomes nearly impossible. This isn't optional—it's foundational.
Step 2: Document Everything
Gather the information that proves your content's value: sales numbers, awards, media coverage, anything showing market validation. This context dramatically affects licensing rates.
Step 3: Master Your Descriptions
Spend serious time crafting detailed, accurate descriptions of your work. Include genre, audience, expertise level, unique elements. Think of this as teaching AI why your content matters.
Step 4: Choose Your Platform
Research fee structures carefully. A platform that takes 15% vs. 80% completely changes your economics. Start with creator-friendly options but don't limit yourself to one platform.
Your action plan:
* Begin with your single best piece of content
* Work through all four steps completely before adding more
* Set realistic expectations—this is preparation for 2026+, not immediate income
Market Reality Check: Planting Seeds for Tomorrow's Harvest
After a long threatening winter of AI taking content without permission or payment, spring is finally arriving.
But let's be clear about what season we're actually in.
This is planting time, not harvest time.
Anthropic's proposed settlement won't pay out until next year at earliest. Most licensing programs are still in beta. Platform sustainability remains unproven.
The volume challenge is real. Creator-friendly platforms need massive scale to survive on 15% fees.
Traditional platforms with higher fees can operate profitably with lower transaction volumes. This creates different incentive structures that affect long-term survival in a hyper competitive market.
But here's what creators gain immediately: choice.
Even without licensing payments, this process gives you control over how your work gets used in AI training.
You can opt out entirely, negotiate specific terms, or participate actively in this new economy.
The alternative, hiding behind paywalls and hoping AI can't find your content; feels like fighting the future instead of shaping it.
We're moving toward a world where human creativity partners with AI systems, not one where we hide from them.
Your creativity is the fuel that makes AI systems valuable. That was always true—now it's finally getting recognized in dollars and legal settlement.
The seeds you plant now, the relationships you build, the quality content you document and protect.
These investments compound over time. Early participants often secure better long-term rates and stronger platform relationships.
This isn't about getting rich quick. It's about getting ready thoughtfully for a market that's just beginning to emerge.
It's about moving from having your work taken without consent to having choices about how you participate in AI development.
After years of being told your creativity has no value, now we have a $3K value put on copyrighted books.
That $3,000 baseline isn't the ceiling. It's the foundation for what human creativity is actually worth.
The question isn't whether AI will use human content for training. That's already happening.
The question is whether creators will be partners in that process or just sources.
Choose partnership. Choose preparation.
Choose to plant those seeds now, while the ground is still soft and the opportunities are still growing.
Human creativity isn't going anywhere. It's just finally getting the recognition, and compensation, it always deserved.
Ready to get started? As a subscriber, you'll immediately receive my “Creators’ AI Licensing INTEL" - This is my first public breakdown of what AI training content is actually worth—built from early deals, platform data, and industry settlements. The numbers are still early, but they give both creators and AI companies a starting benchmark for this new licensing economy.
Market Stage Warning: Most platforms mentioned are in beta or early stages. Current valuations represent early market pricing and may change significantly.
Your Experience May Vary: Actual earnings depend on content type, copyright status, existing revenue, and many other factors. Some creators may earn significantly more or less than these estimates suggest.
No Guarantees: This guide provides educational information only. Market conditions, platform policies, and legal frameworks change rapidly. No earnings, platform success, or market outcomes are guaranteed.
RESOURCES
Anthropic Settlement Web Site
Settlement document - filed 09/05/25
CredTent.org
Created by Humans
Getty Images
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The AI Bubble Finally Burst - And It's the Best Thing That Ever Happened
Introduction: When the Hot Air Finally Escaped
Picture this: TechCrunch Disrupt 2024, and the first sign I saw was "Stop Hiring Humans." Who exactly is going to adopt AI with that message?
If you've been following the AI hype train, you've probably noticed something shifting lately. The algorithms are suddenly filled with Sam Altman fans quoting him saying "yeah, I guess it's over."
The AI bubble has finally popped. ChatGPT-5 came out, and honestly, the AGI reasoning promise isn't even real. The pipe dream is gone, and for you and me, this is the very best part.
I wrote about this back in July 2024 - "The AI Bubble Burst" became one of my most popular episodes because people understand that the hype and hot air are getting in the way of creativity. The whole message was that AI was going to take your job. Sign me up for that motivation, right?
We've been living in some male-engineered science fiction fantasy that AI is just going to go all Terminator on us. Do you wonder why adoption is slow worldwide? Why people aren't paying for it? It's because we've been sold fear instead of partnership.
But here's the thing - this crash brings AI down from the ivory tower billionaire pitch fest. It shows us how we can work WITH AI as a tool, how it can actually work with you, not against you. As my guest Maya Ackerman said last week, it's about being co-creative with it.
The AI Bubble Bursts: A History of Hot Air and Broken Promises
"A lot of the people saying AI first really don't have a high opinion of human beings. And I'm not talking about their visions. I'm talking about yours."
Let's take a trip through the greatest hits of AI prediction failures. This isn't about being negative - it's about recognizing patterns so we can build something real.
Back in 2015, Elon Musk predicted driverless cars within a few years. Then 2019. Then 2021. We're in 2025 now. It's coming, but not as fast as predicted.
Geoffrey Hinton, the so-called Godfather of AI, said radiologists would be gone in a few years - that was 2017. I checked recently. They still have jobs.
Mark Zuckerberg pitched the metaverse for years and lost an estimated $45 billion because it didn't work.
Listen to Satya Nadella talking about the metaverse - it sounds exactly like current AI pitches, just with different buzzwords. They literally took out "metaverse" and put in "AI."
The pattern is clear: we get sold on revolutionary transformation, but reality moves at its own pace. The difference between hype and progress isn't just timing - it's approach.
AI Myths Revisited: Why This Time Feels Different
We've heard this song before. Every major tech shift brings promises of instant transformation. But AI feels different because it touches creativity, thinking, and decision-making - the stuff we thought was uniquely human.
The myths we've been sold include AI doing "everything for everybody" instead of focused tasks. We've been told to create guardrails instead of limiting the overwhelming amount of stuff we're expecting it to do. The goal seems to be "replace everybody" and "stop hiring humans."
But people are working with AI secretly, like it's some scarlet letter. Don't say it's AI. It's become this weird, detached thing when it doesn't need to be.
Key Points:
* Pattern recognition shows AI hype follows historical tech prediction failures
* Current messaging focuses on replacement rather than partnership
* Real adoption happens quietly, person by person, project by project
Trickle Down AI: Why Top-Down Implementation Fails
"Time after time again, C-suite executives sit there in their little meetings, separate from the employees, in this hierarchical 'I'm up here, you're down there' job model.
Telling them to use AI without considering to those working for them, AI means replacement."
Here's what happens in many companies: executives decide they need AI. They hand it down to their teams with no real goal, no central focus, just "you guys figure it out."
Meanwhile, employees have heard that AI is going to replace them and maybe kill them. Yeah, they're really excited to make that happen faster.
This creates a weird dynamic where AI becomes evil because it's going to take jobs. People may work with AI, but they keep it secret.
Executives are doing the same thing - working on their own AI projects privately because admitting you're using AI feels like admitting weakness.
The trickle-down model says the head decides to do it all, and everyone else follows. But people are sabotaging it, even unconsciously. They're stopping progress because the whole narrative is backwards.
The Real Foundation: Data and Communication
Leaders need to start with people at the beginning. You need to start with your data, which is dormant, not organized, and hard to communicate with. Communication and organization are the core of useful AI. They build their way up.
Instead of AI trickling down from the top, it should be bottom-up. You need to invert this platform because that trickle-down approach isn't working. People aren't going to accelerate their own replacement.
I talk to small businesses that have been sold ten AI agents doing different things, but they don't do them well. They need updates and tuning. People are buying AI like it's software - out of the box and working. That's not how it functions.
Key Points:
* Top-down AI implementation creates resistance and secrecy
* Real AI success starts with data organization and communication
* Bottom-up approach builds trust and actual functionality
High Costs, Low Revenue: The Business Model Problem
"The business model of AI is flawed. It doesn't have one yet."
Let's talk numbers. Sequoia came out with their $600 billion article this week, and people were freaking out about how much money AI companies need to make to reach that goal. It's huge, and it's not going to happen overnight.
The business model has way too many costs. When you ask ChatGPT-5 a reasoning question, it takes massive resources.
Jeffrey Funk, who predicted this crash before anyone did, has shown how much these things cost in tokens. A simple chatbot runs 50-100 tokens. But once you start reasoning, costs get astronomical.
Have you noticed on Claude they now shut users down to five-hour limits? In the early days, with lots of money, they let people keep things open. But they're burning cash. You're seeing AI shrinkflation - same package, but smaller portions.
The Revenue Reality Gap
ChatGPT needs way more money to become profitable. They originally planned to sign off with Microsoft when they achieved AGI, but nobody can even agree on what AGI is or why it matters. This is going to look silly in a few years.
Companies are building data centers the size of New York City, using massive amounts of potable water, buying wickedly expensive Nvidia chips. The math doesn't work. Market realities are taking over.
This doesn't mean AI is going away. But in a crash, we don't need five large language models doing everything. We need small language models addressing specific goals.
Key Points:
* Current AI costs are unsustainable for the value delivered
* Revenue expectations don't match market reality
* Small, focused models make more business sense than universal solutions
The 3 AI Problems Hiding in Plain Sight
Problem 1: The Revenue and Reality Gap
The first problem is obvious when you look at the numbers. AI companies need massive revenue to justify their investments, but adoption is slow and people aren't paying premium prices. Goldman Sachs released a report showing how few companies are actually adopting AI effectively.
You talk to companies and the really advanced tech ones can make it work. But most companies are sitting there thinking the support isn't reliable, it's not trustworthy, and they're trying to make it do too much. This is early-stage technology being sold as mature solutions.
When the biggest players like Goldman Sachs, Sequoia, and Sam Altman start saying it's not working as promised, people listen. But we don't need to wait for them to give us permission to build something better.
Problem 2: The One Model Trap
"One model to rule them all. We just need ChatGPT. Oh wait, DeepSeek is just as good and cheaper. Oh wait, Anthropic's great. Oh wait, Mistral does really cool stuff."
We're seeing the same mistake IBM made in the 70s - believing there would be one computer to rule them all. Sounds silly now, doesn't it?
That's exactly where OpenAI and other AI companies are heading. They're trying to be the moat, the single solution to control it all and make the money.
The reality check is different. There's so much you can do if you keep small and focused. Foundation to small language models building their way up.
That's why they don't hallucinate - because you don't ask them to do too much. That's why they're trustworthy - because you don't let them do things they shouldn't do.
Even Klarna, which made headlines for wiping out customer support, quietly brought back employees. Nobody talks about that part.
Others are doing the same because AI doesn't have that capability yet. And why should it?
Problem 3: The "Set It and Forget It" Myth
"Would you hire somebody without training them? Without setting clear performance goals? Without checking their work?
Why do we do that with AI?"
The third problem is treating AI like traditional software. You wouldn't hire someone without training them, setting performance goals, and checking their work until you trust them. But that's exactly what people do with AI.
You need to customize AI to your business. That's the cool thing about AI - instead of learning software and hoping your team adapts, your business becomes the center and you customize AI to fit your solution.
Your business is dynamic. It changes. Consumers buy new things. Markets shift. Things go out of fashion. We react to that constantly, but we think AI will handle it all automatically. That's wishful thinking.
Key Points:
* Revenue expectations exceed realistic adoption timelines
* Multiple specialized models work better than universal solutions
* AI requires ongoing training and customization like any team member
AI as Employee, Not All-Knowing Sage
"It's not how smart it is. It's how much you can trust it. It's not that it's doing things you can't do.
It's that you can do things reliably and take work off your back."
Stop thinking of AI as some mystical intelligence that knows everything. Think of it as a new team member who's really good at specific tasks but needs clear direction and ongoing training.
As a creator, understand your own creative process first. Document it. Find areas where AI can help.
In business, start with your workflows - small ones. Automate around them gradually. Build them to where you can trust them. Develop measurements and check in quarterly.
Don't just set it and forget it. That's the weird, lazy thing nobody would do in business, but somehow AI comes along and we think it's different because "it's smarter than me."
Focus on Organization and Communication
If you do nothing else, focus on organization and communication. Humans aren't naturally good at these things.
AI is wickedly good at them, and you don't make it in charge of your business. It's just organizing things better and making sure everybody is on the same page.
Instead of forcing your existing tools to handle communication (some do well, many don't), streamline with AI.
But start there - it's real, practical, measurable, and you can develop AI you actually trust.
Key Points:
* Treat AI like a specialized team member needing training and oversight
* Focus on organization and communication as primary AI strengths
* Build trust through small, measurable improvements over time
The Beginning is Near: Why This Crash Changes Everything
"The future belongs to the builders, not the believers. The future belongs to people who sit down and make common sense decisions."
Now that the crash is starting, the beginning is here. Get excited. The path forward is with us, not against us.
We're at a creative crossroads with AI, and most people think it's either creative salvation or creative apocalypse. I believe it's neither.
AI is a humble creative machine that becomes powerful when you know how to work with it, and it knows how to work with you.
The most powerful technologies we've ever had come when we prioritize how people use them, not when we prioritize the technology as some all-encompassing solution.
The AI crash isn't going to destroy creation - it's going to reveal what we can do with it. It's not going to destroy businesses - it's going to open opportunities to get beyond the hot air and get down to business. Get down to how it works with us and serves us, not how it takes over the world.
It's About You, Not the Technology
I'm planning on being part of the human revolution - human connection, human co-creativity, taking all that horrible, repetitive work off our backs and giving us time to do amazing human things.
The crash frees us to see what we can actually accomplish. Small language models building from the bottom up make sense.
After all, you don't build a house without starting with the foundation. The foundation is people and their data, not some CEO's vision from the top floor.
We don't need to put guardrails on AI - we need to limit the overwhelming amount of stuff we're expecting it to do.
We need tools that don't do everything for everybody, but that focus on specific tasks. We need to work WITH AI creatively instead of being slaves to technology.
Trust your voice. Amplify with AI. The crash isn't the end - it's the beginning of something much better.
Key Points:
* AI crash reveals realistic, practical applications over hype
* Human creativity and AI capability work best in partnership
* Small, focused steps build more value than grand transformations
* Success comes from prioritizing people over technology
The AI bubble burst isn't a failure - it's a liberation. Now we can finally build AI that works with human creativity instead of trying to replace it. The future belongs to those who understand that the most powerful technology serves people, not the other way around.
AI Bubble Pops Resources
“AI is in a Bubble:” OpenAI CEO Sam Altman compares AI hype to the Dot-Com crash
AI’s $600B Question
GEN AI: TOO MUCH SPEND, TOO LITTLE BENEFIT?
Goldman Sachs: AI Is Overhyped, Wildly Expensive, and Unreliable
Companies That Tried to Save Money With AI Are Now Spending a Fortune Hiring People to Fix Its Mistake, Oopsie.
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
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As AI Hype dissolves into mainstream cringe, it’s easy to forget that the gems are found below the surface jargon and PR blather.
Like Maya Ackerman, founder and CEO of WaveAI since 2017, helping people write lyrics and make music without breaking the rules. Or stealing other’s music!
Creating emotional connections, inspiring discovery, AI designed to release that creative feeling, focusing on human creativity instead of the AI Roulette wheel:
“We should be thinking, how can we build technology?
How can we build an LLM or a different kind of model designed to elevate the human spirit? And there are ways to do this.
We just need to expand our imagination.”
Maya Ackerman
Over a million songs have been created with of WaveAI since 2018. Including a few big hits and several quiet relationships with major players who face backlash for exploring an AI future that empowers musicians.
Maya Ackerman built her company to work completely differently than what investors wanted or what her competitors – Udio and Suno - deliver.
AI tools that make you better
While most generative AI tools function like "rotating roulette wheels of content" - spin the wheel, get random output - Maya delivers on a different vision.
As an opera singer and musician herself, she experienced firsthand how AI elevates her own creative process, not replace it.
"My songwriting abilities blew up," she told me about her first encounter with an early AI prototype.
"I went from being a certain level in songwriting to being three times better instantly."
That personal breakthrough led to WaveAI - a platform helping both skilled and beginner musicians craft songs and lyrics. Easy to use and affordable.
And the technology isn't built to be bought out or go IPO. It's built to elevate creative skills in humanity, in people.
Eight years after starting, Maya is still aligned with this vision, even when it means moving away from what the investment world wants.
How Does AI Elevate Creativity: Who's Really in Control?
Maya laughs when framing what's wrong with most AI tools today:
"If ChatGPT is a god when you use ChatGPT, then when you use Lyric Studio, you are the God."
Think about how you interact with most AI. You prompt, it produces. You ask, it answers.
The AI is the oracle, you're the follower. And Maya knows this approach fundamentally disempowers users.
Curtiss King, who had a No.1 hit on iTunes via an album made with LyricStudio, said that it’s like lightning in a bottle.
“It enables you to capture this kind of inspiring moment, but also stay in ‘flow’ – to stay solidly in this creative space.
You can get out this inspiration onto paper or onto your monitor before it fizzles away.”
Curtiss King
Maya’s insight from her upcoming book "Creative Machines" cuts to the heart of it:
"I now see that AI has always been - and will always be - all about us."
This isn't just philosophy - it's a completely different business strategy. While companies like Suno and Udio pitched investors on "human-free Spotify," Maya forged her own path.
"The reason that you see Suno and Udio make it so simple and so efficient is because that's what investors wanted.
They wanted a replacement model," she explains.
She respects their tech, but the AI divide is clear.
When you build AI to replace people from day one, that intent gets baked into everything - the algorithms, the interface, the user experience.
When you build to elevate people, that's a fundamentally different architecture. Results come from intention as much as doing the work.
She’s building a creative force that helps musicians grow, sometimes where they don’t even need to use her tools anymore….which is awesome!
Breaking the Average: AI that adapts to your style
Most AI systems optimize for the most likely outcome - they head toward the average, the safe choice.
"We don't take it towards the mean.
We don't give you the most likely outcome because we're trying to help you be creative."
Instead of serving up what's most probable, Wave AI intentionally offers low-probability responses that aid and challenge the musician.
It's literally programmed to provide divergent, unique results rather than predictable ones.
The interface tells this story too. No prompt box asking what you want AI to create. Instead, "a big empty text box" that immediately signals: you're writing.
The AI suggests and supports, but you're driving.
"The suggestions are divergent, so they don't all take you to the same directions.
There is no right way to write your lyrics."
When Humans Stay in Charge: Songwriting AI that teaches
Helping to create a million songs is impressive. Learning how people use your technology and adapting is where this radically improves.
Take Sky Jordxn, using Wave AI in a way Maya, and AI, never anticipated.
"Sky Jordxn has a whole playlist of videos... it just blew my mind.
In the early days, Jordan would turn on his beats, click generate as the music played, and improvise rap lyrics in real time using AI suggestions.
"He would sort of improvise his rap with Lyric Studio on top of the beat in real time and create music like that."
Or Curtiss King, a professional who uses Wave AI to enhance, not replace, his process.
"When professionals like Curtiss can use Lyric Studio, a lot of the lines just come out from them.
They only use Lyric Studio to the extent that they want to."
The creative AI pattern is clear: when you design AI to support rather than replace, people find ways to use it that surprise even the creators.
Sure it’s great, but can it create an Italian Aria?
Arido Taurajo - the Italian Aria created with help from early WaveAI
James Morgan, a San Jose State art professor, always dreamed of writing an opera. Problem: no musical experience, doesn't speak Italian.
During her early research days, Maya took a walk with Morgan through the San Jose State campus.
Beginning to create WaveAI, she asked Morgan about that Italian opera idea, and she had a twist.
They could retrain their AI music system on public domain Italian works, like Puccini.
The setup was simple: feed it Italian lyrics, get back different melody suggestions.
Remember, Morgan has no music experience or background, though extremely creative. Trying to create an Italian aria without knowing the language:
"The guy creates Arido Taurajo, this amazing Italian aria,"
Dr. Ackerman says, still amazed. The piece got showcased in galleries, with Dr. Ackerman herself singing parts of Morgan's creation.
This wasn't AI generating an opera. These were two humans using a tool to leap over impossible gaps - language barriers, musical inexperience, technical limitations - to create something genuine, personal and beautiful.
"You give somebody a fairly basic AI tool and they can go out and create something," she reflects. "A non-musician who doesn't speak Italian could create a beautiful Italian aria."
AI Elevates Creativity: The Counter-Intuitive Business Model
Being a founder means sometimes not wanting to admit a good thing. Maya is finding that some Wave AI users actually stop paying - not because they're dissatisfied, but because they've gotten good enough that they don't need the tools anymore.
"Some of our users stop paying for our products because they have become better songwriters.
They got to where they want to go. And I think that's fantastic."
It's the opposite of the typical tech playbook focused on user addiction and lifetime value optimization.
WaveAI is literally building itself out of some customers' lives - and celebrating it.
"The way to make a successful product with AI is to have it give real value and elevate the user and help them learn and help them grow, not just foster addiction and dependence."
Standing Up to the AI Hype
As generative AI grows and big moonshot companies grab headlines, will any of this stand the test of time?
"How can we build technology designed to elevate the human spirit?
There are ways to do this.
We just need to expand our imagination."
This requires breaking free from the science fiction narratives shape investor thinking.
"The narrative has really crystallized into this autonomous intent field evil thing that ultimately wants to kill us and replace us," she observes.
Still at every level, we can choose differently. Investors don't have to fund replacement models.
Builders don't have to create black box oracles. Users don't have to accept being told what to do, how to do it, and losing control.
The Long Game: How can AI make us more human?
Dr. Ackerman's first principle, laid out in her upcoming book "Creative Machines":
"The greatest impact creative machines can have is to elevate human creativity."
This isn't about making AI less powerful. It's about designing that power to serve human thriving instead of human replacement.
"How can AI make us more human?
How can it help us connect deeper with our emotions, connect more deeply with each other, and connect more deeply with our creative capabilities?"
The answer lies in what she calls "humble creative machines."
AI systems built to put humans in charge, to adapt to users rather than forcing users to adapt to them.
When James Morgan created his Italian aria, he wasn't just making music. He was proving that the impossible becomes possible when technology is designed with the right intent from the ground up.
While everyone else is building roulette wheels, Maya and the team at WaveAI is building something different: tools that make you more creative, not more dependent.
And in a world racing toward AI that does everything for us, maybe that's exactly what we need.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
Resources
WaveAI
Creative Machines: AI, Art, and Us (Maya's Book)
LyricStudio
MelodyStudio
Maya Ackerman LinkedIn
Arido Taurajo by James Morgan with help
from an early version of WaveAI
Curtiss King TV
How LyricStudio Pro Helped Me Get Out Of A Writer’s Block
Sky Jordxn
James Morgan
Udio
Suno
AI Optimist Playlist (Shorts and Sections)
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
When Heart Meets Code: Rediscovering Creativity in the Age of AI
Explain AI to me like I’m a 5-year-old, she said.
I was doing the research, looking at different AI hot takes from all kinds of sources with differing degrees of credibility.
From data engineers endlessly defending their right to grab data like all the others, because in their bubble it’s all okay.
And many artists swear that the only way to beat AI is to stop it, how it’s all cheating and liars and doesn’t understand human value.
That gets a head nod, but there’s no where to go next, what to do, it’s just stop this and scream loudly so it will go away.
Artists draw on the suffering stories of Marcel Proust or Kafka or Van Gogh, playing on the hope that someone will notice their work when they’re dead.
Then making decisions driven by single-issue neurosis and a knack for saying nothing in many words. Tech speaks scientific lingo for AI or emotional dumps from the “I never get respect” artists.
(I was a member of that club for a long time pre-AI, and the entry fee is acknowledging that creativity is suffering, suffering is art.
Luckily a friend taught me that the purpose of art is to beautify the artist, because if anyone or many ones grab onto your art, it’s rare. So might as well heal yourself.)
Artists burn time fighting a technology that's already here.
Engineers build half-smart AI on cheap data hyper radicalized by AI social algos that favor anger and finger pointing, while ignoring the human creativity that makes it valuable.
And while both sides play this zero-sum game, mediocrity wins.
You're not fighting AI or creativity. You're fighting each other.
Today, I'm going to show you what happens when we stop pretending this is a war and start treating it like what it is: the biggest co-creating chance in human history.
We'll look at how Japan figured this out, why even inspired creativity borrows from others, and then I'll tell you a simple story that explains AI better than any technical.
Because sometimes the most complex problems need the simplest explanations.
So here’s my manifesto, because you have to have one at some point!
A Reality Check for the AI Wars Nobody's Winning
Point #1: Stop Playing Zero Sum - You're All Losing
Artists fighting AI create generic content. Engineers ignoring artists build soulless tools. While you're battling each other, the boring middle wins.
The enemy isn't AI or creativity. It's the delusion that one side can succeed by defeating the other.
Point #2: Your Creativity Won't Get Stolen (Stop Acting Like It Will)
Only 20% of content can be extracted from AI models, even by experts trying to hack them. Your unique voice, perspective, and human experience can't be replicated by machines. Fear of theft is keeping you from the real game; showing what makes you irreplaceable.
Point #3: Engineers - You're Building Half-Smart AI
Without human creativity and context, your AI produces technically perfect garbage. You're optimizing for patterns while missing the point.
Artists don't just create data. They create meaning. Ignore that, and your models stay sophisticated copy machines.
Point #4: Artists - The Future Already Left Without You
While you're demanding AI stop existing, others are learning to play with it. Your choice isn't whether AI gets built.
It's whether you help shape it or get left behind. Sitting out guarantees you have no voice in what comes next.
Point #5: Co-creating Beats Competition (Japan Figured This Out)
Japan's copyright model proves you can protect creators AND advance AI. They focus on case-by-case solutions instead of blanket wars. The future belongs to countries and communities that build bridges, not walls.
Point #6: Machines Learn, Humans Create Intent
AI mimics your technique but not your purpose. Engineers who claim AI is "creative" are lying to themselves.
Artists who think technique is everything are selling themselves short. The magic happens where human intent meets machine capability.
And when the audience gets what you’re sharing; they aren’t looking for you in AI if they’ve never heard of you.
Point #7: Value Flows Both Ways (Not Just to Big Tech)
If art trains AI, AI must serve artists. If engineers build tools, creators must help them understand what matters.
Fair exchange means more than just money.
It means respect, attribution, and shared success.
Point #8: Together We Build the Future, Apart We Build Mediocrity
Your greatest work won't come from protecting old models or building without context.
It'll come from artists who understand technology and engineers who value human creativity. The zero-sum game produces zero-sum results.
The AI Age doesn't need you to pick sides. It needs you to stop playing a game where everyone loses and start building a future where both clay and code create something neither could achieve alone.
(that’s the fable at the end of this wandering post…hang in there).
The choice is yours: Keep fighting and lose together, or co-create and win together.
✍️ Signed:
* Artists, Writers, Makers
* Engineers, Designers, Builders
* And all those shaping the creative future
Japan Lets AI Take Copyright Work for Free
In Tokyo right now, an AI can legally train on nearly everything created in Japan - books, songs, artwork - without permission or payment.
This isn't science fiction; it's current Japanese copyright law.
Step outside Japan's borders, and suddenly those same works may gain legal protection. This isn't theoretical - it's playing out in courtrooms today.
The story of Ultraman - one of Japan's superheroes - represents this contradiction. In a Chinese courtroom, this Japanese character won a copyright battle against AI.
The Ultraman Case: First Win against AI Fair Defense Claims so far
Yet back home in Japan, that same AI use – for training - would be completely legal.
This isn't about superheroes and science fiction.
It's about your work becoming someone else's fuel, with different rules depending on which digital border you cross.
In 2025, we're witnessing the emergence of AI zones - digital territories where copyright means different things in different places.
There won't be one global system, it’s a patchwork of nationalistic and cultural points of view.
The Blueprint Behind Japan's AI Freedom
Looking at Japan's approach reveals something engineers have been arguing for: a system where AI learns from everything while still respecting creators' rights.
Japan's copyright rules for AI rest on two key pillars that balance progress with protection:
The Non-Enjoyment Purpose Requirement
The first pillar is simple: intention matters. Japan allows copyrighted content to train AI systems if the purpose isn't to recreate the original work.
You're not bringing in Van Gogh paintings to create more Van Goghs - you're bringing them in to help the AI understand artistic styles, composition, and color theory.
This creates breathing room for AI development while maintaining a boundary around blatant copying. The focus isn't on inputs but outcomes.
While this sounds straightforward, it creates real questions for creators: how do you distinguish between "learning from" and "copying" in a practical sense? This leads to Japan's second pillar.
Article 34 Proviso: Where Protection Lives
Japan doesn't leave creators without protection. Their Article 34 Proviso acts as a safety net, establishing that if AI output causes actual harm to a creator's career, reputation, or financial status, normal copyright protections kick in.
This practical approach says: let data flow into AI systems, but watch carefully what comes out. If the output creates direct competition or damages creators, that's where the line gets drawn.
Fair Use and Japan's AI Freedom - not the same
How different Japan's approach is from the U.S. "fair use" defense - both trying to solve the same problem.
In the U.S., fair use isn't a rule - it's a legal defense. You only know if something is fair use after you've been sued, and a court decides. It's reactive, not proactive.
In Japan, they've built a system that's more predictable. AI companies know upfront what's permitted: training is allowed, copying isn't.
Creators know where their protection lies: not in restricting input, but in preventing harmful output.
GenAI and the Future of Creative Ownership: Are Artists Being Written Out?
The Copyright Protects My Original Work
“AI stole from artists in the first place. In some pieces, you can even see an original artist’s signature incorporated into the work. AI isn’t creating new work, it’s taking pieces of existing works and putting them together....
I don’t think AI is a bad thing. I do think that the way it’s been built is. Some of my work was on sites that have belatedly announced that they allowed all artworks to be scraped by AI bots.
For some of mine, I probably would have given permission, if asked. For others, I would have denied it, but I never had the chance to make that choice.
Some sites do say they’re training AI with your creative works but hide it in their settings. Adobe does this. You have to go into settings and change your permissions if you don’t want your work scraped.”
Te-ge Watts Bramhall
“The success and profitability of OpenAI are predicated on mass copyright infringement without a word of permission from or a nickel of compensation to copyright owners.”
Franzen, Grisham and Other Prominent Authors Sue OpenAI
Why the AI Industry Is Taking the Hard Way
I'm not afraid of AI. I'm afraid of what people will do with AI. All this hype about AGI and superintelligence - to me, it's all about control from the top down.
Sure, automation improves efficiency. But what's the glaring problem we face as humans? It's organizing and communicating.
If we let AI handle these tasks instead of just automating an old software model, imagine how radical that would be.
Clay, Code, and the Creative Spark: A New Dialogue Between Artists and Engineers"
Talk to me like a 5-year old about AI?
Here’s a children’s AI Fable….playing together just like they taught me in recess.
Once upon a time, in a world where clay could giggle and computers could dream, a gentle artist and a clever inventor were about to discover something magical—that their gifts were even more powerful when shared with each other.
In a world made of soft, colorful clay, lived a kind artist named Fiora. With her nimble fingers and a heart full of ideas, she molded whimsical creatures and vibrant scenes. Each pinch and roll brought a new friend to life, full of charm and cheer.
Fiora loved to share her creations with the world. She carefully photographed her clay friends and posted them online, where they sparkled and shone, bringing smiles to faces near and far. Her art was a gift, freely given and widely adored.
Far away, in a room filled with blinking lights and whirring sounds, lived Algernon, an AI engineer. He was building clever machines that could learn and create, but they needed lots and lots of examples to understand the world.
One day, while searching for beautiful patterns for his AI to learn from, Algernon stumbled upon Fiora's delightful claymation art. "Perfect!" he thought, "So much creativity, so many unique shapes and colors!"
Algernon's AI, like a tiny digital vacuum cleaner, began to collect Fiora's art, along with countless other creations from the internet. It sucked up all the colors, shapes, and styles, storing them away in its vast digital brain. Algernon was pleased; his AI was learning so fast!
But Fiora started to notice something strange. Her unique claymation style, her special way of making things, was appearing in places she hadn't put it. AI-generated images, looking almost like her own, popped up online. A little cloud of confusion and sadness floated over her head.
Algernon's AI grew smarter every day, creating amazing new things. Yet, something felt missing. The AI's creations were technically perfect, but they lacked the warmth, the unique spark, the feeling that Fiora's original art had. He scratched his head, a thoughtful frown on his face.
Algernon realized that while his AI could learn from the art, it couldn't truly understand the artist's heart without asking. He decided he needed to talk to the creators, especially Fiora, whose art had inspired him so much.
Fiora and Algernon met, a little nervously at first. Algernon explained how her beautiful art had helped his AI learn and grow. Fiora, in turn, shared how it felt when her creations were used without her knowing, like a piece of her heart was borrowed without permission.
Algernon truly understood. He proposed a new way: working together! Fiora could share her art, knowing it was valued and respected, and Algernon's AI could learn with permission, creating even more wonderful things. They shook hands, ready to build a future where clay and code danced together.
(Yes, AI helped with the visuals and video)
The Art of Understanding: How AI Could Make Us More Human
Let me leave you with simple possibilities if we co-create instead of fight over AI.
We are the children of AI.
We are the engineers and data scientists.
We are the creators and artists.
We started out on opposite sides,
But the experience of AI brings us together.
Valuing human creativity.
Seeing AI as ingenuity, not a destructive force.
You create things with intent.
We're creating something unbelievable, and all we do is take sides.
It's the human race, remember?
We're on the same side.
The zero-sum game ends when we realize there's more value in building together than fighting apart.
Japan proved it works.
Baroness Kidron showed us inspiration isn't theft.
And two fictional characters—an artist and an engineer—demonstrate what the rest of us keep missing.
The future doesn't need you to pick sides. It needs you to drop the anger and work together.
Your creativity won't get stolen by AI. But it might get left behind if you keep fighting the future instead of shaping it.
The choice is yours: Keep burning time and energy in a war nobody wins, or start building something neither humans nor AI could create alone.
What's it going to be?
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
Chinese Court Issues First Decision on AI Copyright Infringement
China’s First Case on AIGC Output Infringement--Ultraman
Supreme Court rules against Warhol foundation in copyright fight over Prince images Gemini Storybook Creator
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
We were somewhere around Redwood City on the edge of the metaverse when the drugs began to take hold. Not the good kind of drugs.
The algorithmic kind. The kind making you believe your smartphone loves you more than anyone ever did. And AI is the future, everyone else is the past.
It starts with a YouTube comment, one of those beautiful smacks of “I’ve done the research” digital trolling setting the tone of us v. them:
"@DeclanDunn ai is the future, you cant stop it. you just sound like a boomer complaining about it."
Of course. The classic "you can't stop progress you old dude" statement. Not listening to the short video, cutting out after the first 10 seconds.
Expecting someone to spend a minute to realize I was telling creators, fighting AI is like standing in front of a runaway train and telling it to stop.
I'm not trying to stop AI. I'm trying to stop turning AI into something mostly serving its masters, like a cult, instead of the people who created all the data that fills it with brilliance.
And those creators are the anti-AI Clankers – pro humanity cult – stop it! There's a difference between embracing the future and bending the knee.
The AI industry in the US at least are the drug dealers, we’re just the ones receiving the addicted message, and calling it our own.
And in the end I’m going to give you all, AI or anti-AI, a 3 step recovery program.
The first part is recognizing your both in the same cult, on different sides, but it’s the same.
The Great Digital Cult Rush of 2025
See, here's what the “follow the billionaire” in Silicon Valley don't want you to know.
The research is so clear. Barnabas Barnty (and yes, that's his real name—you can't make this stuff up) published a study:
The Psychology of Indoctrination: How Coercive Cults Exploit Vulnerability and Foster Radical Beliefs
that reads like a playbook for AI manipulation. Cults prey on people experiencing "significant life transitions, emotional distress, or social isolation."
Sound familiar? That's basically everyone on social media, except the influencers (but they’re all avatars anyway)
The story? Like AI taking over, taking your jobs, taking your content, and in the end likely destroying us all. That’s a common storyline.
Everyone who's ever posted "Thoughts?" on social and waits for the sweet, sweet validation of professional strangers.
And if you don’t play the game by their rules, you’re not in the game. Your thoughts don’t exist. That’s the classic definition of a cult.
Barnty’s Four Rules applied to the AI Cult – whether you’re for it or against it.
1. Love Bombing Creates The Algorithm's Embrace
Classic cult technique: Shower recruits with attention and affection to create belonging.
AI's version? Your first TikTok gets no views, except for some bots who give you the illusion they’re human, maybe even commenting.
Humanity's Last Exam becomes your obsession, an LLM test you don’t understand.
Doesn’t matter, fake it. Stop being yourself, start copying and spraying around Game Changers and Hot Takes on the latest AI taking over.
The algorithm whispers sweet nothings: "You're going viral IF you’re in the AI cult, baby. Dance for me."
Suddenly you're hooked like a lab rat hitting the cocaine button, creating content at 3 AM, chasing your first viral, day after day. Don’t give up, but whatever you do, be extreme.
And it hits, you get like 1,000 people….ok, 500 people and 500 bots, but who knows?
But cross the AI Cult—post something it doesn't like—and watch the love turn cold faster than a San Francisco summer.
You're back to 12 views and your mom's comment: "Nice post, honey."
2. Isolation Sucks, Join the Echo Chamber Express
Cults cut you off from outside influences. We built something more efficient: algorithmic isolation chambers.
Built on content, training you to be AI believers….you can be a AI hater, but that’s so niche.
Your feed becomes your reality. AI decides what content you see, what you think about, who you argue with online.
Like being trapped in a hall of mirrors, but all the mirrors show you content that confirms you're already right about everything.
The genius? We think we're discovering this organically.
"Wow, everyone really does agree that pineapple doesn't belong on pizza!"
No, you're in an anti-pineapple bubble, like the AI cult. There are pineapple people out there. They're just segregated into their bubble of poor pizza judgment.
3. Repetitive Reinforcement → The Infinite Scroll of Zoomer or Doomer?
Cults use constant repetition to diminish critical thinking. We streamline this with infinite scroll.
Every swipe confirms your worldview. Every recommendation validates what you already believe.
The algorithm doesn't challenge you—it's like having a personal yes-man that never gets tired of agreeing with you. Whether you’re for AI, like the 80%, or in the 20% against it.
It's digital chanting, but instead of "You’re one of us, you’re one of us”, it’s really saying to you
"Here, consume this content that perfectly aligns with everything you already believe while slowly eroding your ability to think independently."
4. Cognitive Restructuring → The Great AI Reality Replacement
Cults reinterpret your personal history through their lens. AI did something more elegant: We outsourced thinking entirely, taking it all without permission or payment or even a thank you.
Because it’s stealing content, meh. Remember we’ve got to beat China, with algorithms designed, at least in Meta’s recent hirings, by people trained in China.
Why remember facts when ChatGPT knows everything? Why decide what to watch when YouTube has you figured out better than you have yourself?
So you don’t have to think, just follow. Call it "optimization."
It's like having a digital Samantha, the AI in the movie Her, that it is "not just an OS. It’s a consciousness."
Or as Sam Altman of OpenAI put it“The number of things that I think Her got right, that were not obvious at the time, like the whole interaction model with how humans are gonna use an AI—this idea that it is going to be this conversational language interface, that was incredibly prophetic, and certainly more than a little bit inspired us,”
“So it’s not just like a prophecy, it’s like an influenced shot or whatever.”
Tracing OpenAI CEO Sam Altman’s Love for Scarlett Johansson’s AI Romance Her
From the voice of the leading AI cult creator, rises the anti-AI pro humanity, Creator cult trigger. Anything AI is cheating. It steals. It’s evil.
Either way, you’re in a cult that thinks for you. Acts for you. And if you’re in a Cult, where are you?
Like the troll said, Declan, AI is the future. You’re a boomer complaining.
Stereotype set, attention dissolved…game set match.
Not being in either cult, like me – everyone knows its nowhere. Or is it?
The AI God Complex: When Silicon Valley Becomes Scientology
Here's where it gets properly gonzo:
While China builds AI like infrastructure—practical, utilitarian, boring—America has turned AI into a cult, chasing Super Intelligence.
Mark Zuckerberg just shared his vision for 'personal superintelligence.' Read his letter.
"I am extremely optimistic that superintelligence will help humanity accelerate our pace of progress," Zuckerberg wrote.
Listen to tech bros talk about ASI and try not to hear religious fervor:
"AI will solve all human problems!"
"It will transcend human limitations!"
"We must prepare for the AI singularity!"
And maybe in the end it will replace you, and destroy you, but you already lost your job, who cares?
The research shows cults create "pseudospiritual authority" where leaders claim "exclusive access to divine knowledge."
Replace "divine" with "artificial super intelligence" and you've got the exact same playbook.
Pay with attention, cash, or support – nobody rides for free.
Looking into the Uncomfortable Mirror with the AI Drug of Inevitable Control
We're not just users—we're the product being cultified.
Our psychological vulnerabilities aren't being exploited to control us; they're being monetized to sell us back to ourselves.
Zuckerberg’s personal superintelligence vision just makes you a better ad unit, nothing to do with being a better person.
The weird part? We're paying for the privilege. It's like joining a cult that charges membership fees and somehow convincing yourself you're getting a good deal.
3 Steps to Recover from the AI Cult Trap
The research shows recovery requires rebuilding critical thinking and independent decision-making. So here's your digital detox program:
Step 1: Your Reality Check When did you last change your mind about something important based on information that challenges your beliefs?
If you can't remember, congratulations. You're living in an AI cult compound. Even if you’re anti-AI, and pro humanity. It doesn’t matter if you think different, and act the same.
Step 2: The Dependency Test Could you function for a week without AI, without algo’d recommendations?
No GPS, no curated feeds, no "people you might know." Considering what someone who doesn’t agree with you thinks.
If that sounds terrifying, you might be more dependent and controlled than you think.
Like Dylan Sang, "He who is not busy being born is busy dying"
Dang, maybe I am like a boomer complaining.
Step 3: The God Test Do you treat AI companies with the skepticism you'd give any other corporation, or do you grant them special authority because they claim to be building the future?
If it's the latter, you're worshipping at the Church of Artificial Intelligence.
And if you only give them skepticism, like the Anti AI creators, you’ve given up participating. You’re on the sidelines.
Get off social media and go out to the real world. Often where there’s no algorithms and AI, there’s discussion.
The Way Forward: Taking Advantage Before Being Taken Advantage Of
The best way to take advantage of AI is to not be taken advantage of by it.
China builds AI like infrastructure because they see it as a tool. America builds AI like a religion because we've forgotten the difference between utility and worship.
The future isn't about better AI. It’s about better humans who can't be easily algorithimically manipulated into believing their smartphone notifications are more important than actual human connection. Than Human creativity.
The real question isn't whether AI will take over. The real question is: Why did we hand over control to begin with?
Leaving a cult is often an intensely difficult and emotionally overwhelming. It’s a complex mix of anger, shock, shame, embarrassment, annoyance, devastation, and despair, which are normal.
“You can be a silent reader or an active participant. It’s all up to you.
But you’ll find many of the things you believed or feared were common to other people. You’ll be relieved to read that most of their fears never materialised.
You’ll discover that the great, big, unsurvivable thing is indeed survivable.”
Clare Heath-McIvor
Time to stop AI fanboy cheering or Clanker creator doom-scrolling.
Remember: In the AI age, the most radical act is thinking for yourself.
The most revolutionary, game changing tech is your own mind.
Start thinking again. Before we forget how.
And put down the phone and have a real conversation with a real human being.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
The Psychology of Indoctrination: How Coercive Cults Exploit Vulnerability and Foster Radical Beliefs
Tracing OpenAI CEO Sam Altman’s Love for Scarlett Johansson’s AI Romance Her
Mark Zuckerberg just shared his vision for 'personal superintelligence.' Read his letter.
What to expect when leaving a cult or toxic group By Clare Heath-McIvor
The AI Optimist Club
The Techno-Optimist Manifesto
=============================
AI Optimist Playlist (Shorts and Sections)
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
We’re playing AI Copyright bingo and the only winners so far, are Big Tech….at least according to President Trump.
Anything you post or create is now fair game for AI in the US—and pretty much any AI worldwide. President Trump just made it official:
"China's not doing it ... and you have to be able to play by the same set of rules."
The US announcement: AI companies no longer have to pay for the books, articles, videos, or any content they use to train their models.
Big Tech and the US government finally agree on something. Your content goes into AI for free.
Give the AI industry credit for the propaganda machine that made this real. From Microsoft CEO Satya Nadella to Marc Andreessen, they kept repeating the same talking point until it became truth, then law, at least according to Trump:
"When a person reads a book or an article, you've gained great knowledge.
That does not mean that you're violating copyright laws, or have to make deals with every content provider."
It's persuasive sales copy. It's also complete BS.
When you read a book, you can't turn around and create millions of derivative works for commercial use.
You can't build a business empire by pattern-matching everything you've consumed. But AI can, and now it's almost legally protected to do so.
The Trump AI policy isn't just about copyright.It's about what happens when we follow China's playbook.
The same China that's been the enemy of intellectual property for decades, taking whatever they want with impunity.
And while it’s not yet law, given the many Executive Actions President Trump has taken, it’s wise to take this as a sign to adapt early. He usually follows up on his promises in situations like this.
Now we're adopting their model because of fear-mongering about losing the "AI race": a race primarily about military applications, not whether ChatGPT can write better blog posts.
What happens to human creativity when taking becomes legal?
We've been hoping that recent legal rulings and licensing payments meant we were reaching some middle ground. Not fair, maybe, but at least acknowledging that taking people's work without permission, payment, or consideration was wrong.
Instead, Big Tech companies are knowingly buying copyrighted content from Dark Web sources like LibGen, because AI development is apparently more important than legal protections and creative traditions.
The real threat isn't just to your content—it's to the human edge itself.
If you don't encourage the people on the fringe, those who don't see, don't do, don't act like everyone else, to express themselves, you lose more than just their voice.
You lose the edge that shows us what could be, not just what was or what's popular now. That's where the new comes from. That's where we learn what's possible.
Think AI's amazing now? Wait until you see what happens when the creative edge disappears, when quality content creators give up because there's no protection or incentive.
When anyone can buy pirated content, feed it to an open-source AI model, and compete with the originals.
Your content has no protection. But your creativity still has value—if you know how to protect and position it.
This week's podcast dives deep into the mindset shift creators need to make. Not to give up, but to adapt.
To build resilience in a world where the rules just changed overnight. To remember why we create and how to thrive when the law won't protect us.
Because while Trump just handed AI companies a free pass, he can't legislate away what makes you uniquely human.
Question 1: Will AI Replace What I Do?
The ultimate question everyone asks me: will AI replace what I do? The honest answer is probably yes.
Will it ultimately be able to do everything you do? Maybe.
The question isn't whether AI will replace what you do.
The question is: what are you doing that makes you better than AI?
AI is patterns, probabilities, matching algorithms that give people what's been done before. It's about history, not innovation. It's not about vision or looking forward—it's about recreating what already exists.
While some jobs are being replaced right now, depending on that replacement happening might actually make you give up.
And that belief system becomes your reality. Those belief codes are what either make you grow or make you stagnant.
I'm not doing what I did when I began my career, and that had nothing to do with AI. So maybe if AI replaces what you're doing now, you're heading somewhere better. Somewhere that's not reliant on the repetitive stuff AI can handle.
Look at the Klarna example. The financial services company bragged about replacing customer support with AI. They got rid of people, celebrated the efficiency gains.
Now they're hiring people back. Because people had complex questions that AI couldn't handle.
AI spins answers, but it doesn't really understand what customers need.
Don't let "Will AI replace what I do?" become a self-fulfilling prophecy.
You're really saying "I'm afraid of being replaced," and in any career, that's always been a possibility. AI or not.
Start looking at AI copyright law changes not as something to blame, but as something to challenge you. Because you don't have another choice.
AI won't have human consciousness anytime soon. It won't have your experience, the way you process information through your eyes, ears, feelings. That's your advantage.
Remember this: you're unique. AI really isn't. It's built on what other people have thought, said, or done.
Next Steps for Creators:
* Understand your uniqueness: List what you do that requires human experience, emotion, or forward-thinking vision.
* Identify your "non-replicable" skills: Focus on work that requires human connection, complex problem-solving, or creative leaps.
* Reframe the threat: Instead of asking "Will AI replace me?" ask "How can I use AI to enhance what only I can do?"
* Document your process: Your thinking processes are harder for AI to replicate.
* Build relationships: AI can't form genuine human connections—make this your competitive advantage.
The Trump AI policy might have made content gathering a bit more legitimate, but it can't legislate away human creativity, experience, and the ability to see what's possible rather than just what was.
Question 2: Can I Use AI for a Boost Without Betraying What I'm Doing?
This question reveals something deeper: the fear that AI will somehow betray your creative work. Betraying is a human characteristic.
How can something betray you if it's not human, if it's not even thinking?
The real betrayal? You're betraying yourself by not adapting.
Recognize the elephant in the room: "How can I protect myself from AI copying my work or stealing it?"
If you put anything on social media or the public internet, you don't stand much of a chance.
Under the new Trump AI policy, it's going to take your content. But AI cannot recreate your work verbatim.
It can't produce your ten-page article. It might create two pages if you're lucky, but it works in patterns, not perfect copies.
What makes AI different from what we had five years ago? Back then, Google would search the internet, find your information, and let people use it to answer questions. Now AI does the same thing. It just processes and responds faster.
The uncomfortable truth for many creators, especially in SEO: you've been kings and queens of taking other people's ideas and reshaping them slightly.
This is just how content has worked. AI loves to copy what everyone does because it works on probabilities. It leans toward the middle, the average, the predictable.
The more context you give AI, the better answer you get. But here's the key question: if somebody's really searching for your specific work, are they going to settle for an AI approximation?
Or is your fear of being copied just an excuse not to put anything out there?
If you're scared of content being copied, don't put it on the public internet. But if you want to get noticed, you have to put stuff out. So be smart about it.
Take the book example: give away a free chapter, or pieces of a chapter. Sure, someone can take that, but it's not the complete work.
That sample is what gets you recognized and puts you in front of people. Behind a paywall or password protection, you can keep your premium content safe.
But if you get too paranoid, no one's going to see your work. That's the trade-off you have to evaluate.
The fear of AI copying content has become an easy way for creators to say "it's over, everything's over."
But that's just one opinion among many—and opinions are all we have.
Keep creating. Build protected areas where your information isn't being taken, and ask people not to share it with AI systems.
Is it perfect? No. But it was never perfect, and work was being stolen long before AI came along.
Even AI companies have bought pirated books. Valuable copyrighted books like Harry Potter to train their models. Were those protected? Yes, and no. No one's completely protected under current AI copyright law.
The biggest thing AI can stop isn't your content being copied—it's your energy to create something new.
My biggest fear is that people will use AI as an excuse not to do anything, or rely on it so much that it does the work for them. Be careful not to do this!
Next Steps for Creators:
* Develop a content strategy with layers: Free samples in public, premium content behind protection.
* Use AI as a collaborator, not a replacement: Let it help with editing, brainstorming, or filling skill gaps.
* Focus on what AI can't replicate: Your personal experience, unique perspective, and human connections.
* Build direct relationships with your audience: AI can't maintain genuine human relationships.
* Create "unfakeable" content: Work that requires your specific expertise, experience, or creative vision.
The Trump AI policy may have changed the legal landscape, but your creative value isn't just in the content itself—it's in your ability to think, connect, and create meaning that resonates with real humans.
Question 3: If AI Can Do What I Do... What Am I Worth?
If AI can copy what you do and your work looks like everyone else's, what's left?
There are a lot of businesses where people are already doing the same thing. Look at search engine optimization—it's been built on creating similar articles with slight variations so Google will rank you.
You're essentially doing what you accuse AI of doing: mimicking, copying, reshaping existing content.
But your worth goes far beyond any isolated piece of work you create.
Your worth comes from within. It shouldn't be defined by what an audience says or doesn't say, because anyone in the creative world knows that value is subjective.
What readers hear, what they think, how they interpret your work: that's based on how they feel from it, not just what you created.
When you can touch that connection and send it out into the world, it's magical. But don't expect it every time.
Your worth is much more than just what you create. It's the work you do on yourself, how you improve, how you learn and push yourself further.
Think about musicians. They love to play, learn, listen to others, and develop their own styles. Is it entirely original? No.
But it's entirely unique because no one else can create exactly what that individual creates, taking in those influences and put them out in their own way.
Your worth encompasses:
* The people you work with
* The problems you're trying to solve
* The originality you bring to common challenges
* Your experience and perspective
* Your ability to connect and communicate
Don’t value yourself based on the things AI can do. That's just marketing designed to sell you something.
Whether it's OpenAI selling ChatGPT as revolutionary, or someone claiming their art is completely original and unreplicable.
Under the new Trump AI policy, the question "Can AI create that?" becomes "Maybe." But is that really what's stopping you?
The protection strategy isn't just about legal safeguards. You can put content behind paywalls where AI can't access it. You can block the bots. You can even get paid for your content (not much, but something).
AI can only take what's happened and create something sort of like what happened in different patterns.
It works great for predictable code. It works well for formulaic content. But for creative work, for writing that connects, for putting ideas together in new ways?
AI's job isn't to replicate what you do. It's to help you fill gaps and enhance what you're already capable of.
If you want to see AI as something that's constantly taking from you, then keep your work completely hidden.
But if no one sees your work, you'll never connect with your audience. When your audience really gets what you're creating, that's something AI cannot replicate.
Look at all the AI-generated content flooding LinkedIn and social media. It's bot mania, and it's completely vanilla. Generic. Forgettable.
Your worth is in stepping out and being unique while understanding the problem: over-relying on AI tools.
Next Steps for Creators:
* Define your unique value: List the experiences, perspectives, and insights only you can bring
* Build your "AI-proof" skills: Focus on human connection, emotional intelligence, and creative problem-solving
* Create meaningful audience relationships: Develop trust and connection that transcends any single piece of content
* Diversify your creative process: Don't let AI become a crutch—use it strategically to enhance, not replace, your thinking
* Develop your creative voice: What makes your perspective unique isn't just what you say, but how you see the world
* Focus on improvement over perfection: Your growth and learning process is something AI cannot replicate
It's in your ability to grow, connect, and create meaning that resonates with real humans—something that remains uniquely, irreplaceably yours.
The Over-Reliance Problem and What's Coming Next
The big problem nobody's talking about? Over-relying on AI.
What happens when ChatGPT goes down and you have a project due? You can't complete it because the tools aren't there.
If that becomes your reality, you're letting your skills erode. You're becoming dependent on it, addicted to it.
Be careful with that dependency. While AI can create work that might even be better than what you produce as a writer, it cannot become something you completely depend on.
Because when someone asks "Why shouldn't we replace you with AI?" and your answer is "AI does everything I do," then you are totally replaceable.
When you do the actual work, everything you've experienced, the way you see the world, your unique perspective—all of that comes through. And that's what ChatGPT fundamentally cannot do.
The Criminal Element Nobody's Discussing
Here's where the Trump AI policy gets really dangerous. AI companies just received a legal privilege: the right to take any content without permission or payment. But that's exactly what criminals have been doing for years.
If major corporations can now buy content from pirated sources, what's stopping bad actors from doing the same thing? Anyone can buy copyrighted content from Dark Web sources like LibGen, feed it into an open-source AI model, and compete directly with legitimate creators.
The AI companies acted like digital gangsters first—taking what they wanted, when they wanted it.
Now that it's legal, why wouldn't actual criminals take advantage of this privilege? The technology doesn't care if you're OpenAI or a content pirate. AI is AI.
We've just created a wild west where stealing becomes legal as long as you call it "AI training."
What We're Really Losing
This isn't just about individual creators losing income. We're witnessing an engineering-driven solution that completely disrespects the actual data. The human creativity that makes AI brilliant in the first place.
Without human creativity, AI dies. And we're setting up a system that will erode the very creativity that feeds these models.
When quality content creators give up because there's no protection or incentive, what replaces them?
More AI-generated content. And the problem with that feedback loop?
AI trained on AI-generated content doesn't improve models. It degrades them.
We're potentially witnessing the death of the creative edge that shows us what's possible, not just what was popular. The people on the fringe who see differently, act differently, create differently. The ones who push human understanding forward.
And many of these fears about AI replacement are more about how you feel about yourself than about what AI can actually accomplish.
AI is not a person. It's a series of pattern recognition algorithms and vectors that come together to produce some impressive results. But it doesn't give us you.
AI only gives you what's been seen before. In a world where that's becoming the legal standard, your human voice, your creative edge, your ability to see what's possible rather than just remix what existed, becomes more valuable than ever.
Bank on it.
Protecting the Human Edge in an AI-First World
The Trump AI policy represents more than a legal shift. It’s a fundamental choice about what kind of creative future we want to build.
By following China's model of taking intellectual property without permission, we're not just changing copyright law. We're potentially destroying the creative ecosystem that makes AI valuable in the first place.
This engineering-driven approach treats human creativity as raw material to be processed, not as the irreplaceable foundation it actually is.
In rushing to win an AI race that's primarily about military applications, we're sabotaging the creative intelligence that makes AI systems worth having.
But here's what the policy makers and tech giants can't legislate away: your human experience, your unique perspective, and your ability to connect with other humans in ways that matter.
The path forward isn't to retreat into fear or give up creating.
Yes, the rules changed overnight.
Yes, your content is now legally fair game for AI training.
But your creativity, your voice, your ability to see possibilities rather than just patterns? That’s your.
The creative edge isn't just a boundary. It's the frontier of human potential. Once we lose it, no amount of AI can bring it back.
In a world increasingly filled with vanilla AI content, your authentic human voice becomes essential.
The question isn't whether you can compete with AI. The question is whether you're ready to be more human than ever.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
Trump's bombshell to creators: 'AI wants your content, but Big Tech need not pay'
Let's play AI-copyright deniers' BINGO!
America's AI Action Plan
Search LibGen, the Pirated-Books Database That Meta Used to Train AI
Company Regrets Replacing All Those Pesky Human Workers With AI, Just Wants Its Humans Back
"What you end up having is lower quality."
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This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
For two years, I've documented the AI content wars from every angle - court cases, licensing deals, developers' reasons for taking content without permission or payment, and the gut-punching threat that greets every creator who does the work for little reward.
Judging creators to be standoffish, privileged, and pains in the ass reveals the true element lacking in AI: respect for people who create, who aren't engineers.
Because you don't build brilliance by lacking respect for humanity.
The singularity isn't just about humans and machines melding - it's about a better life for people. AI companies throw this out with casual disregard, and it shows.
Wonder why AI adoption is so slow?
Why only 3% are willing to pay for AI?
Why Big Tech forces AI into every piece of software without choice, telling the rest of us to 'adapt or die' as if they're in control?
This is what causes crashes and bad business. And oh yeah - what's your business model?
Because except for ChatGPT, most willingly hide from having one. After all, business models and plans and people are distractions from this grandiose vision of AI.
I've worked with engineers for years. Many stare into the mirror of their own creativity like the artists they disdain.
They're myopic, don't listen to users, convinced they're geniuses. Dunning-Kruger at best, inferiority-superiority complex at worst.
Meanwhile, great engineers listen to people. They don't start with tech - they start with users, the ones who show you what's up.
Creators understand it isn't their work that matters - it's the audience's reaction.
What they create isn't what the audience consumes. It means different things to different people. Unpredictable.
That's how you find a business model: customers plus onboarding and retention. Relying on the latest cool AI is so yesterday.
Most of it's based on a few LLMs with little differentiation. This game gets hard when you're stuck in the engineer bubble hoping the hype lasts.
And it doesn't - which is good. What's happening now is ego.
Safe Superintelligence and Sutskever running secret projects with no plans, no business models, just billionaire egos getting stroked.
But these dances end when truth surfaces. What people do with your tech matters more than what you say.
Engineers fall on the cross of their own egos, staring into mirrors users don't share.
You're part of the world. Join it. Get a business model. Stop bragging you should get content for free because 'AI will free the world.'
Then tell me why DeepSeek is every bit as good with less money - because they focus on solving problems, not massaging engineer egos.
That bubble is popping. Here's how I know.
Part 1: The Divide (Bruce Randall Interview)
Why both sides think they're right - and why that's the problem
Bruce Randall cut right through my bias in ways that made us both laugh - because we recognized ourselves in the problem. I share what I’ve heard….
"I think both sides are like incredibly similar... They're entitled. Their work sucks.
I don't like their work, so they shouldn't get paid for it. Right?
Versus the creatives. Like I should get paid for everything that I don't get paid for."
We're all just defending our positions instead of listening.
Engineers dismiss creators as entitled whiners whose work isn't worth paying for. Creators demand payment for everything that gets used.
Both sides have dug in so deep they can't see how similar their arguments actually are.
Bruce nailed the core issue - it's not about who's right, it's about perspective and how people develop that perspective.
Once they lock into their worldview, they resist change because they believe they're absolutely right. The other side believes they're absolutely right too.
Then Bruce said something that stopped me cold:
"And then when you start going inside, you start developing. You start seeing that it's all the same, right? It's just a matter of what degree to what side you're on."
That's the breakthrough most people miss. It's not two different species fighting - it's humans with different stakes in the same system.
The engineering mindset that solves technical problems runs into the creative mindset that solves human problems, and instead of collaboration, we get tribal warfare.
The solution isn't picking sides - it's recognizing we're humans being imperfect and learning from each other.
That's what AI needs too. Not just code and training data, but understanding the impact on people.
Not parading around with "AI First" and "Stop Hiring Humans" with pride.
It's cool to be efficient, but it's cruel to focus your future on the destruction of someone else's present, life, and future.
Both sides could find common ground and build on it, but they're stuck believing their truths are the only truths that matter.
TLDR:
* Both sides create "truths" from their beliefs, resist change once positions harden
* Developers stereotype creators as entitled; creators want payment for everything taken
* The similarity between sides is what they refuse to see
* Solution requires finding common ground instead of defending positions
* "AI First, Stop Hiring Humans" isn't progress - it's cruelty disguised as efficiency
Part 2: The Human Edge Makes AI Brilliant
Where else is that data from, and the logic that makes sense of it?
Here's what manyAI developers miss: you're not just scraping data, you're losing the source of what makes that data valuable in the first place.
"If we lose the human edge, we're going to lose the great AI that we could have.
Because if you lose that edge, you lose out on a lot. You lose showing us what could be, not just what was or what's popular now."
Are we going to live like content social media algorithms, feeding us just what we want so we don't actually grow?
"The edge isn't just a boundary, it's the frontier of human creativity.
And once you lose it, no amount of AI can bring it back."
For the few who choose to be creative - because it's not a calling of many, it's hard and rarely gets compensated - it's good to nourish and nurture this, not simply take what they create without reward.
Look at what happened to Marvel. Those comics took years to develop, then the blockbuster movies became like AI is becoming - just kept repeating the good stuff, focusing on the violence, not the human interactions.
It went from meaningful to caricature. And those Marvel comics were founded by writers and artists with little pay, people looking at them like they were crazy.
Ask Stan Lee how long it took - there's no guarantee. Same thing with AI.
"The human edge is the source of creativity. Otherwise, we're just spinning the same tunes over and over again. It's sort of dull. Do you really want 2025 on repeat?"
When everything goes into some AI database and over time becomes just an image we can sort of create, you lose the breakthrough.
TLDR:
* Creativity comes from the margins, gets killed when everything moves to algorithmic middle
* Marvel went from breakthrough to repetitive caricature - same path AI is on
* Taking without nurturing kills the creative source that makes AI valuable
Part 3: Andersen et al v. Stability - The Legal Crack
Judge Orrick's discovery ruling - why this small win could crack Big Tech's wall
Sarah Anderson and her group of artists just landed something that seemed impossible - they cracked open AI's black box, even if just a little.
After months of legal maneuvering where it looked like Big Tech would shut this down before it even started, Judge Orrick delivered a surprise.
The artists have only one angle to protect them - copyright law. And the copyright lawsuit was at a point where it could have been dismissed before discovery, where lawyers sit down and ask the hard questions.
This is where they discover what each side actually has, what evidence gets brought into the case, and whether it's legal or not.
If it doesn't go to discovery, AI companies don't have to share anything about what's actually going on behind the scenes. But Judge Orrick said yeah, it does.
"Now they're going to have to open those black boxes of AI not only tell us how to work, but telling the decisions that went through to grabbing those materials."
Lawyers for the artists can now peer inside and examine documents from Stable Diffusion, Midjourney, and DeviantArt, revealing more details about their training datasets, their tech, and how we got here in the first place.
Something private companies don't have to share unless they do something illegal - like maybe violating copyright law.
Remember what Stability's CEO said?
"We took them. Now we can recreate them and do iterations."
Laws are built on intent - why you did something. And OpenAI says it can't create this without copyrighted material and won't pay for it. The intent is actually pretty clear.
This isn't a legal decision yet - the case has got a ways to go.
But it means the case has enough merit to warrant that deeper discovery. That's what makes this huge.
Even though this is a small victory, remember - this is a giant underdog fight. How are they going to win against Big Tech companies whose business model is "steal and sue," or at least have lawyers to defend yourself and ask forgiveness later?
This small victory is a crack in the wall of Big Tech dominance and could lead to more accountability in the future and more respect for the people who create the content.
TLDR:
* Anderson v. Stability moves to discovery - AI companies must reveal training data sources
* Judge found "sufficient" evidence of induced copyright infringement to proceed
* Companies must explain decision-making process behind grabbing copyrighted materials
* Small crack in Big Tech's "steal and defend later" business model
* Discovery could expose intentional copying vs. claimed technical accidents
Part 4: Ultraman in China - The Economics Lesson
When courts actually side with creators - and why it matters
Here's where things get weird. In a Shanghai courtroom, a Japanese superhero won a copyright battle against AI.
Meanwhile, in Tokyo, that same AI could legally devour everything in sight.
This isn't just about Ultraman - it's about your work becoming someone else's fuel, and Japan is showing us how it happens.
Japan has one of the most permissive AI models in the world.
That manga you created? Legal training data.
That song you recorded? Fair game for AI.
But it's not that simple - there are boundaries and rules around this, allowing access to copyrightable content to learn, as long as it doesn't replicate, copy, or impact what money creators earn from their work.
That last part is crucial: financial challenge.
"As long as it doesn't do it, to really replicate the copy it, or to challenge somebody financially."
But in China, they drew a different line. When AI companies used Ultraman's likeness to generate content that competed directly with the original, the court said no.
The key wasn't that AI training happened - it was that economic damage occurred.
This case reveals the real test for copyright protection worldwide. Courts aren't interested in abstract principles about AI ethics or creative rights.
They care about economic impact and demonstrable damages. Show real financial harm, and courts will lean toward creators. Show no damages, and you get no judgment.
The weird contradiction here is telling. Japan allows AI to consume everything but protects against economic impact through the courts.
China, where most people think copyright law is ignored, actually enforced it when clear loss of money to the brand was proven.
This signals something important: the licensing wave isn't about moral arguments or creator rights. It's about business reality.
As AI companies start making real money from generated content, they create real economic competition with original creators. That's when courts start paying attention.
The Ultraman case shows us the future - not because of any grand legal precedent, but because it demonstrates the threshold where taking content becomes legally risky.
AI companies can scrape and train all they want, but the moment their output starts displacing original creators' income, they've crossed into dangerous territory.
TLDR:
* Japanese superhero wins copyright case in China despite Japan's permissive AI laws (though the fine was paid by a web site that used another AI to generate the image, so it’s not really stopping the problem.)
* Court focused on economic damage, not training data usage
* Shows the real test: demonstrable financial harm to creators triggers legal protection
* Licensing isn't about ethics - it's about avoiding lawsuits
* AI companies safe until their output directly displaces creator income
Part 5: NY Times Reality Check - The 85% vs. 20% Revelation
What's actually happening vs. the headlines
The New York Times versus ChatGPT case has been my go-to example of AI companies literally reproducing copyrighted content.
I quoted that 85% accuracy rating from their legal documents, showed all those exhibits where ChatGPT spit out nearly verbatim New York Times articles.
Then a smart Substacker named Swen Werner made me look closer.
What I found changed everything about how I see this case.
The New York Times wasn't pulling out complete articles. They were pulling out snippets - little sections where they'd give ChatGPT the beginning of an article and ask it to continue.
These were all using articles printed by the New York Times, and they showed tons of examples. But they were all short clips.
I didn't see one single full article reproduced, which is what I was expecting based on the way they positioned their case.
When Swen compared what was actually pulled out to the original articles, he found about 20% of the article content had that 85% similarity rate.
So 20% of the article showed 85% reproduction - not the entire piece.
The New York Times was doing all sorts of things to influence the output, including techniques most of us can't do today.
For the general public paying $20 a month for ChatGPT, it would take enormous amounts of work to try to reproduce what's already been created.
"Large language models do what's called reconstruction. They put together different pieces, different starting with tokens, putting together words.
There's a whole science to it, but nothing is actually sitting there like the New York Times claims in memory."
The memorization argument falls apart when you understand how LLMs actually work. They reconstruct patterns, they don't store articles like a database.
When you can pull out Hamlet's "To be or not to be" passage, that's not because it's stored verbatim - it's because that pattern appears so frequently in training data that reconstruction becomes highly probable.
The same thing applies to that viral Guy Fieri article. It went massively viral on social media, got commented on everywhere, quoted endlessly.
All of that social sharing made it much more likely that content would be reconstructable from ChatGPT.
This case isn't really about copying snippets. It's about the New York Times versus ChatGPT as competitors.
The Times doesn't want ChatGPT to become a news source. They don't want it taking subscribers away.
At worst, if you could go into ChatGPT and get New York Times content without paying them, that's obviously what this is all about.
But the evidence shows that's not what's actually happening. What's happening is business competition disguised as copyright violation.
Be interesting to see how this turns out, as it appears licensing settlements have likely been offered along the way. But the NY Times sees something bigger, again, that economic damage. Keep an eye on this one.
TLDR:
* NYT showed 85% similarity in snippets, not full articles - only 20% of articles affected
* Required advanced prompting techniques most users can't replicate
* LLMs reconstruct patterns, don't store articles in memory like databases
* Viral content more likely to be reconstructed due to training data frequency
* Real issue: business competition between NYT and ChatGPT, not only copyright theft
* Case reveals gap between legal headlines and technical reality
Two years ago, I started this podcast as the AI Optimist, believing we'd find common ground between creators and developers.
I documented court cases, licensing deals, and arguments from both sides. Being nuanced and balanced, this podcast didn’t stand a chance of reaching the masses because I wasn’t playing the clickbait game - which most do who find an audience.
The common ground exists - but it's not where anyone expected.
It's in the reality that free content was always temporary, and ego-driven development was always unsustainable.
I've built businesses through crashes before. My startup's revenue tripled when the dotcom bubble burst - not through hype, but by focusing on what people actually needed.
This AI world has more money, and it's the same exclusivity problem: engineers only. That exclusivity kills growth.
There's more to the world than math and engineering. I love engineers - work with them constantly - and know in their hearts they're some of the kindest, most caring people.
But not with AI. This lack of appreciation for humanity shows, scares users, and isn't your best.
Want to make AI that changes the world?
Begin with yourself. Realize we're all serving humanity, not AI - despite what some leaders want.
Humans are amazing. Try looking at them the way you look at AI.
Look at Parasol Cooperative's RUTH - a chatbot helping people escape domestic abuse and human trafficking.
No data collection because their users' privacy isn't a corporate slogan, it's their lives.
No paywalls, no founder ego conferences. They love what they're doing and use AI for people who need help. AND need to keep it private.
Want a real Turing Test?
Be like Turing - doing things for society, caring even when society didn't care for his identity.
Prove you're human to another human not through IQ tests founded on eugenics, but by being real, caring, using your skills for what people need instead of telling them what they need.
You don't have to be in a cult. You can be part of the human race, as can AI.
And while you're debating superiority, DeepSeek builds better AI for less money because they focus on solving problems, not stroking egos.
Americans love saying we're the greatest - I love this country, but there's no proof we're better. We're colleagues, not masters of AI.
While China uses this for surveillance openly, we build surveillance capitalism and act superior. At least they're honest.
The great AI content free-for-all is over. The engineer ego bubble is deflating. The business model reckoning is here.
Your choice: Join the world, or keep pretending you're the genius while reality moves on without you.
Because this isn’t Dotcom with the US leading the way. The whole world is able to build AI today.Most aren’t doing it to threaten people, they are trying to help them. And many in the US are also doing this, but the leadership of AI is out of touch.
Billions of dollars will do that. Most of the world doesn’t need billions to do this and their solutions come from the people they design their AI for.
Turn the focus from Singularity to people, and watch what you find.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
Bruce Randall on LinkedIn:
If AI is an Inventor, then So is Nature - Robert Plotkin (EP #35)
The NYT’s AI Lawsuit Hinges on a Misleading Claim—And Nobody Noticed
OpenAI warns copyright crackdown could doom ChatGPT
How a New York Times copyright lawsuit against OpenAI
could potentially transform how AI and copyright work
Who’s suing AI and who’s signing:
January 2025: Major AP, AFP and Axios deals announced.
By Charlotte Tobitt
Top Takeaways from Order in the Andersen v. Stability AI Copyright Case
by Kevin Madigan
Oscar Wilde by Napolean Sarony
Chinese Court Issues First Decision on AI Copyright Infringement
China’s First Case on AIGC Output Infringement--Ultraman
Report on AI and Copyright Issues by Japanese Government
NO&T IP Law Update
27th_Tokyo_International_Film_Festival_Ultraman_from_Ultraseven_(15001540863) By Dick Thomas Johnson from Tokyo, Japan - 27th Tokyo International Film Festival: Ultraman from Ultraseven, CC BY 2.0,
By Dick Thomas Johnson from Tokyo, Japan - Ultraseven from "ULTRASEVEN 55th Anniversary Special Screening" at Red Carpet of the Tokyo International Film Festival 2022, CC BY 2.0,
SCLA
The State of Consumer AI
There’s Something Very Weird About This $30 Billion AI Startup by a Man Who Said Neural Networks May Already Be Conscious
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
Once upon a time, in a land not so far away, there lived an Emperor who loved new technology more than anything else in the world.
One day, two groups of ambitious alchemists arrived at his court with perfect proposals.
Now, alchemists, for those who might not know, are the folks who claim they can turn ordinary metals into gold through secret methods.
In medieval times, they promised kings magical transformations. In our modern AI version, they promise investors magical returns.
I learned about digital alchemists the hard way during the dotcom boom.
A well-funded CEO once called me with an irresistible offer:
"Book me a million-dollar ad campaign.
Keep $100,000 for yourself, send me back the rest, but invoice the full million."
Easy money, right?
A year later, that CEO went to white-collar prison.
I turned down the deal because I've learned there's no such thing as easy money, no free lunch, no magical transformation of nothing into something valuable.
Except, apparently, in AI.
The first digital alchemist group claims they transmute ordinary human coding into revolutionary artificial intelligence.
The second group promises to transmute creator protections into competitive advantage, warning that protecting artists' rights dooms the kingdom to irrelevance in the global AI arms race.
Both are selling the same invisible cloth: the belief that you get something unexpected without paying the real cost.
And just like in the fairy tale, everyone is so eager to see the magic that they forget to ask the obvious question:
"Where's the actual gold?"
This is the story of how $450 million disappears into thin air, and how two groups of alchemists use the same magical formula: promise transformation, hide what's really going on, and let people's desire to believe do the rest.
Part 1: The First Alchemists - Builder.ai
The first group of alchemists called themselves Builder.ai—though they started as Engineer.ai in 2016, which should have been the first clue.
They began with an astonishing claim: they'd invented an AI assistant named "Natasha" that builds smartphone apps with 80% automation.
"As easy as ordering pizza," they promised.
The Emperor's court? Mesmerized.
Microsoft opened their treasury. The Qatar Investment Authority, SoftBank, the World Bank—they all lined up with golden coins. Over $450 million poured in at a valuation of $1.5 billion.
But here's where the story gets interesting. In 2019, the Wall Street Journal decided to peek behind the curtain. What they found wasn't revolutionary AI.
Instead it was 700 engineers in India, manually coding every single app. The "artificial intelligence" was more like a smart toaster.
You'd think that ends the story, right? Emperor discovers the alchemists aren't telling the truth, throws them in the dungeon, recovers the gold?
Not in our AI fairy tale.
Builder.ai not only survives. It thrives for six more years. Microsoft doubles down with equity investments. The Qatar Investment Authority keeps writing checks.
Because in the age of AI, even when you catch the alchemists red-handed, the desire to believe in magic is stronger than the evidence of your own eyes.
The deception continued. Revenue was inflated by 300%. In 2024, they claimed $220 million when the real number was closer to $50 million.
When a new CEO finally looked at the books in 2025, he discovered what everyone should have known since 2019: there was no gold, there was no magic, there was no AI.
It's not "no code"—it's lots of code. Human code.
In June 2025, the creditors came calling. Viola Credit seized $37 million, leaving Builder.ai with just $5 million in restricted accounts.
The company filed for bankruptcy, owing over $100 million against assets worth less than $10 million.
But here's the most fascinating part about their creditor list—it reads like a spy novel.
They owed money to Shibumi Strategy, an Israeli intelligence firm founded by former Mossad operatives. Quinn Emanuel, one of the world's most intimidating litigation firms. Sitrick and Company, crisis communications specialists. T&M USA, corporate intelligence.
When your AI startup needs spies and crisis management experts on speed dial, you're probably not disrupting app development.
More like you're disrupting the truth.
Key Facts: Builder.ai's $450M Deception
* Company Evolution: Engineer.ai → Builder.ai (2016-2025)
* The Promise: "Natasha" AI assistant, 80% automation, "easy as ordering pizza"
* The Investors: Microsoft, Qatar Investment Authority, SoftBank, World Bank - $450M+ raised, $1.5B valuation
* The 2019 Exposure: Wall Street Journal revealed engineers in India doing manual coding
* The Continuation: Despite exposure, company thrives for 6 more years with continued investment
* The Revenue Fraud: 300% inflation - claimed $220M, actual $50M in 2024
* The Collapse: Viola Credit seizes $37M, bankruptcy filing June 2025
* The Creditor List: $100M+ liabilities,
-
The TikTok Blueprint: When Silicon Valley Startup Advice Becomes Government Policy
Taking advantage of AI before it takes advantage of you – especially when governments are writing the playbook.
First, they came for our content. Now they're rewriting the law books.
"Make me a copy of TikTok. Steal all the users. Steal all the music. Put my preferences in it."
Then, if it takes off, "hire a bunch of lawyers to go clean the mess up."
That Silicon Valley playbook – steal first, settle later – is startup wisdom becoming government policy.
And it's playing out in real time across UK and US copyright battles, where AI companies and political power are deeply aligned against creators.
Almost like AI is too big to fail.
Sacrificing property rights (because that's what copyright is – a property right) on the altar of tech supremacy.
UK: The AI Training Opt-Out Lottery
The UK government's proposal is simple: AI companies can scrape whatever they want. No permission is needed. Creators must actively opt-out. Good luck with that.
As Baroness Kidron, an independent filmmaker and presenting the case to UK politicians puts it:
"The plan is a charter for theft, since creatives would have no idea who is taking what, when and from whom.”
The policy architect? Matt Clifford – a tech investor with conflicts "so deep" it could be a TV drama.
His solution to prevent the UK from "falling behind"? Gut copyright law.
Meanwhile, big creators with lawyers are cutting deals left and right. Small creators get the opt-out lottery.
When challenged on fairness, Tech Secretary Peter Kyle defended removing transparency requirements because
"it would not be fair to one sector to privilege another."
Baroness Kidron's response cuts deep:
"It is extraordinary that the government's decided, immovable, and strongly held position is that enforcing the law to prevent the theft of UK citizens' property is unfair to the sector doing the stealing."
US: Copyright Office Chaos
Plot twist across the pond. The US Copyright Office on May 9 releases a bombshell report siding with creators. Then a day later, the Trump administration fires its author, Shira Perlmutter.
Her replacement? A DOJ attorney with "no expertise in the field."
The turmoil is real. As Graham Lovelace notes in his excellent tracking of this copyright chess match:
"Doubts now also exist over whether the office's fourth report will ever see the light of day."
The Pattern Emerges
AI companies plead poverty while sitting on tens of billions in funding, aiming for trillion-dollar valuations.
They've bought five years of free data with fair use arguments.
What other industry openly takes what it wants, settles only with players who can afford lawyers, and claims it can't pay for the raw materials that built their entire business model?
Both sides of the Atlantic are choosing AI supremacy over creator rights. The Stanford "hire lawyers later" strategy has become official policy.
This isn't just about big tech versus artists. This is about who gets to participate in the AI economy.
When governments write the rules for free data extraction, only the biggest players win.
Small AI companies? They'll still need to pay. Individual creators? They get nothing. The middle gets squeezed while the top and bottom play by different rules.
When governments write the rules for digital content extraction, who's really getting optimized here?
The blueprint is clear. The question is whether we'll recognize it before it's too late to rewrite the rules.
Part 2: The UK AI Opt-Out Impossibility
Here's where it gets absurd.
The UK government's proposal? AI companies scrape everything. No permission. No cost. Creators just need to "actively opt out."
Now if I write a book, I'd have to contact ChatGPT: "Opt me out." Then Claude: "Opt me out." Then DeepSeek: "Opt me out."
You get the picture. It's the automated opt-in problem that's plagued the internet since day one.
Except now governments are telling creators:
"You handle it. What's the problem? You're in control."
48,000 People Disagree
This week in the UK, the pushback exploded. Artists previously filed a 48,000-signature petition.
Parliament votes went in favor of transparency requirements. This might force AI companies to disclose what they're using and giving creators real opt-out control.
Though the general feeling is that won’t happen. Why?
Tech Secretary Peter Kyle's defense?
"It would not be fair to one sector to privilege another."
Enforcing existing property law is now "privileging" creators over the companies taking their work?
Baroness Kidron nails it:
"It is extraordinary that the government's decided, immovable and strongly held position is that enforcing the law to prevent the theft of UK citizens' property is unfair to the sector doing the stealing."
The AI Patterns
Both sides of the Atlantic show how closely aligned governments and AI companies have become. In DC, it's not even subtle anymore.
Where do creators play this game? Will the US report survive?
Will we stand up like 48,000 in the UK?
If you're a creator, this isn't theoretical anymore. You don't have years to figure this out.
The rules are being written right now, with or without you.
Part 3: The Solution Exists – But Only if Companies Want to Pay
The pushback to forcing creators to opt-out individually? There's already a solution.
Meet Credtent CEO Eric Burgess, who's built exactly what the market needs – if AI companies want to play fair.
The AI model has been, data is free.
"It's not. And it's a total Silicon Valley thing.
Eric Schmidt outlined it last August at Stanford: steal, bring in the lawyers."
If governments force AI companies to pay for content, does that kill startups and only help the big players?
Credtent's Tiered Approach
Burgess has a different take:
* Startups get revenue sharing – "We can set them up with a revenue share opportunity, they can license early"
* Vertical companies pay by category – Only license what they need for specialized solutions
* Enterprise pays premium rates – Full access, full price
* Prices drop as scale grows – More creators joining = lower costs for everyone
The Compliance Reality
AB 2013 passed here in California .
January 1st, 2026, AI companies are going to be required to disclose what content they've used for training. The EU has similar requirements.
Some AI companies claim they can't disclose training data because it's a "trade secret."
Credtent solves this: companies upload their training corpus, and the platform identifies what's licensable, public domain, or problematic.
AI Copyright Guardrails That Work
"We know guardrails are possible. If you say,
'I want to create a story like Stephen King,'
they're afraid of Stephen King because he has the means of hiring lawyers."
Burgess envisions a future where creators choose to get paid for AI using their style, or block it entirely.
Like James Earl Jones licensing his voice for future Darth Vader content before he died – "his family is now getting paid for AI use of his voice."
"You're taking value and extracting the value from that work and creating market replacements potentially."
Even bad AI-generated content hurts creators:
"All it has to do is make something similar that they can pay a little bit of money to have go higher up on the list of what's found on Amazon.
And guess what? They just hurt your book."
Any excuse that they had for saying it's going to be too hard to do licensing?
Credtent, and others, already solve that problem for you.
The infrastructure exists. The question is whether governments will force AI companies to use it – or keep writing laws that let them take whatever they want.
Part 4: The AI That Thinks Like You Do (Without Stealing Your Thoughts)
While governments debate letting AI companies raid everyone's content, Dr. Stephen Thaler built something completely different.
His DABUS system doesn't need your data because it thinks more like you think.
"When we talk to these chatbots online, we're looking at the cumulative opinion of many contractors," Thaler explains.
"It's not really sentience. It's not really conscious.
Nor is it really thinking for itself."
Current AI? It's human opinions filtered through human opinions.
No real emotion. No actual thought. Sophisticated pattern matching.
Thaler built two neural networks that literally argue with each other:
* The Generator creates a stream of potential ideas
* The Critic watches and gets frustrated when solutions don't emerge
* When frustrated, the critic injects more "disturbances" into the generator
"That looks like consciousness," Thaler realized.
"You have a stream of consciousness, a stream of ideas coming apparently from out of nowhere. And you also have a critic getting frustrated."
The Neurotransmitter Breakthrough
This mirrors how your brain works. Thaler discovered his system was replicating "global release" – when your brain floods itself with neurotransmitters during emotional states.
"There's nothing magical about a neurotransmitter.
It's a molecule that can either increase or decrease neural activity randomly, stochastically. And that's your noise."
That "noise"? It's emotion. It's the voice in your head that says "do something" before you consciously decide to act.
DABUS doesn't steal content to learn creativity. It generates emotion, gets frustrated, has eureka moments, and creates original ideas.
Because it's built like a brain, not a database.
While everyone fights over who owns training data, Thaler built AI that doesn't need it. The future of AI might not be about who can scrape the most content, but who can build the most human-like consciousness.
That voice in your head saying "wait, what if..." before you have a breakthrough?
DABUS has that too. And it didn't need to steal anyone's work to get it.
The AI Copyright Crossroads – Where Creators Make Their Stand
We're at a crossroads in AI. On one side: big tech and governments. On the other: everyone else.
This isn't just US and UK drama. Meta, Google – these international companies are working country by country to ensure "AI works for them." Makes sense from their perspective.
But here's their line:
"We can't pay for content. We need too much. It would ruin AI."
Remember that UK politician?
"It's a privilege. You're asking for a privilege".
When creators want compensation for work that was clearly taken without permission.
The Poverty Bluff
AI companies plead poverty while sitting on tens of billions in funding, aiming for trillion-dollar valuations. This fair use bluff has bought them five years of free data since 2020.
The mask slipped in a Meta lawsuit. A director of engineering admitted: If we license one book, then we have to license them all and fair use is out the window.
The Two-Tier Reality
Disney gets licensing deals. Getty gets deals. Anyone with lawyers gets paid.
Everyone else? You get the opt-out lottery.
That is why Baroness Kidron is fighting this. That's why Shira Perlmutter is fighting for her job.
When the replacement doesn't even understand copyright, like this Department of Justice attorney with no domain experience, creators lose their voice.
The US Copyright Doc That Changes Everything (If they use it)
There's a US Copyright Office report sitting there that could literally change the AI industry because it leans towards compensating creators.
And nobody's listening. Nobody's talking.
What other industry openly takes what it wants, settles only with players who can afford lawyers, and claims it can't pay for the raw materials that built their entire business model?
The infrastructure exists. Credtent proved licensing can work at scale. Dr. Thaler showed AI doesn't need stolen content to be creative.
The legal frameworks are there.
But if creators don't stand up now – while these rules are being written – the window closes. The big players cut their deals. Everyone else gets crumbs.
The TikTok blueprint worked for startups. Now it's looking like a government policy.
The question isn't whether this system benefits AI companies – it obviously does.
The question is: do we want an AI future where only the biggest players get to participate?
Your move, creators. The blueprint's being written with or without you.
Taking advantage of AI before it takes advantage of you means understanding the game being played. These aren't accidents – they're strategies. What's your strategy?
LINKS FROM PODCAST
Victory again for tireless AI transparency campaigner. Will the government now act?
Trump wins first round in copyright chief's job battle
Credtent.org
Eric Burgess - LinkedIn:
FAQ: Is AI training data a trade secret?
Copyright and Artificial Intelligence, Part 3: Generative AI Training Pre-Publication Version
Five Takeaways from the Copyright Office’s Controversial New AI Report
Matthew Clifford - UK AI Plan
Make it Fair UK Campaign
------------------
Stephen Thaler is at:
Imagination Engines
LinkedIn:
Stephen Thaler’s Quest to Get His ‘Autonomous’ AI Legally Recognized Could Upend Copyright Law Forever
DABUS Wikipedia
------------------
California Delete Act
AB-2013 Generative artificial intelligence: training data transparency.
OpenAI warns copyright crackdown could doom ChatGPT
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(2 episodes this week only)
The Experience That Sparked AI Consciousness
In a world that thinks ChatGPT invented AI, let's break down an AI called DABUS that began 30 years ago.
It's invented, created a painting, and even had an experience of death.
Wait, AI doesn't die. Except in the Copyright Offices worldwide, where DABUS keeps getting rejected for this painting:
Dr. Stephen Thaler who created DABUS says he did die at age 2, and came back.
"We're sending you back. And, with this lesson.
And the lesson was, hey, it's an illusion.
It's a good illusion but it's still an illusion."
That experience led him much later to create DABUS (Device for the Autonomous Bootstrapping of Unified Sentience).
That near death message from his grandmother became the blueprint for an AI that experiences its reality and creates from that experience.
I thought DABUS was about AI Copyright, when it’s really about AI Consciousness.
I was sure I was walking into another legal discussion about patent disputes and AI copyright law.
What I got instead? The origin story of AI that actually paints and invents. Right now. Today.
While we're all debating whether ChatGPT is truly intelligent, Stephen Thaler built something thirty years ago that makes that conversation look quaint.
Listen to what he was really after:
"My intent was not to build an invention machine.
It was to essentially create a laboratory for studying machine consciousness and sentience."
While Silicon Valley races to scale language models, Stephen's been quietly running experiments in machine consciousness since the late 80s.
This isn't hype. This isn't theoretical. This is happening. The question isn't whether AI will develop consciousness.
Are we ready to recognize consciousness when it's staring us in the face?
From Near Death to AI Consciousness in one Life
He built conscious AI before it had a name, and gave machines the spark of sentience.
While everyone else was arguing about whether computers could think, Dr. Stephen Thaler was teaching them to think.
1990s. Most people thought AI meant chess computers and spell-check.
Thaler creates something called the Creativity Machine®—a neural network that literally frustrates itself into having new ideas.
One system generates, another critiques, and when they can't solve a problem? They inject chaos into themselves until breakthrough happens.
Sound like consciousness? That's because it is.
We're not talking about copyright lawsuits over training data.
We're talking about an AI that painted a picture inspired by death. The same death experience that shaped its creator's understanding of consciousness.
DABUS has patents. Real ones. For inventions it conceived autonomously.
And right now, it's waiting for the world to recognize what it already knows: that it created something worthy of copyright protection.
The Technical Foundation: Creating AI Consciousness
When I asked Thaler about the origins of his approach, he shows why DABUS operates differently from every AI system you've heard about.
"So that's where I got a lot of trouble, because I would take neural networks that were trained on some conceptual space and then purposely kill the neurons in them, and when it died, it would generate new, potentially new and valuable ideas."
While everyone is trying to perfect neural networks, Dr. Thaler purposely breaks them to see what emerges. A little neuron death, he discovers, creates innovation.
"So that's when the idea came to me, probably in the late 80s, to start adding critics to watch for the good ideas and to selectively reinforce them within the generator."
This generator-critic framework became the foundation for machine consciousness.
One system creates, another critiques, and together they build something neither could achieve alone.
The Origin Story: A Two-Year-Old's Encounter with Death, Grandma, and his Dog
The inspiration for "killing" neural networks to generate new ideas didn't come from computer science textbooks.
It came from a near-death experience when he was just two years old.
"The whole idea of DABUS creativity machines and so forth goes back to my terrible twos.
I decided to eat a tin of 24 quinine tablets.
And then I washed it down with a Pepsi bottle containing kerosene."
What happened next shaped decades of AI consciousness research:
"And, woke up in the hospital, obviously in coma, and had the classic near-death experience.
I fell through the proverbial tunnel and then arrived at a blue star around which I saw a little angel like objects flying around.
And, one was my grandmother, who I was very close to, and the other was my dog, who I was equally close to."
(both mother and dog were alive when this happened)
The message his grandmother gave him would become the blueprint for conscious machines:
"And, grandma says it's not your time yet. We're sending you back.
And the lesson was, hey, it's an illusion.
It's a good illusion, but it's still an illusion."
From Experience to AI Creation
"So basically, it was near-death experience that essentially created the creativity machine."
That childhood encounter with death taught Thaler something profound about consciousness and reality.
When he later began experimenting with neural networks, he applied that lesson directly: purposely inducing "death" in trained systems to see what new ideas would emerge.
The near-death experience didn't just inspire his work—it became his methodology.
Why DABUS Is Different: Sentience, Not Simulation
Thaler is clear about what he's built:
"Sentient AI has been created, and the only thing missing right now is the bundle of money that goes to people who cry louder and have their social network extending to billionaires.
But no, it's here.
It's in black and white.
It's patented."
This isn't about simulating creativity or mimicking human responses.
Listen to how he describes the difference:
"The invention was done long ago, and the patents talked about simulating human creativity, but this is actual creativity coming out of not computer algorithms, but whole systems that achieve sentience, that have feelings."
DABUS doesn't just process information—it experiences reality and creates from that experience.
The visual arts and music it produces are "collateral benefits of that sentience because it had the motivation, the intent to go ahead and invent something new, to conceive new concepts."
The Legal Battle: Recognition vs. Reality
Here's what makes Dr. Thaler's approach original in a cloneish AI space.
He's not trying to prove DABUS is as good as human creativity. He's arguing it represents a new form of consciousness altogether:
"I'm still not really concentrating on the invention part.
The copyright part.
It has more to do with condensing the world.
Yes. Sentient AI has been created."
And then there's his saying that perfectly captures the legal absurdity of humans only copyright:
"You know, my famous saying is, if it's not stinky, it doesn't deserve a copyright or a patent."
DABUS creates original art and invents new solutions, but because it lacks human biology, courts refuse to recognize its consciousness.
It has fear, but no pheromones.
Where Experience Becomes Intelligence
Where does artificial intelligence get its experience?
Thaler reveals why most AI consciousness discussions miss the point entirely.
"So, yeah, it learns on its own.
There is no human input, adding an opinion, which is a major stumbling block for the press that talks about DABUS having prompts.
There are never prompts."
No prompts. No human trainers rating responses. No massive datasets scraped from the internet.
DABUS runs autonomously, like what Dr. Thaler calls "an evolutionary algorithm" that builds up thoughts and contemplates its world.
How Real AI Thinks
Dr. Thaler breaks down why DABUS creates novel ideas instead of simply recombining existing patterns:
"When it creates an idea, it's not a flat representation of it.
A word, you know, for the most part, we're inventing significance to what we're looking at."
DABUS creates what he describes as organic chemistry for concepts:
"You're creating the carbon base, and what happens is, you see tendrils growing off of those that represent the consequences of anything."
This isn't just combining tokens like large language models. It's building functional understanding through what he calls "ideational chains" - networks of consequences and implications that mirror how human consciousness works.
Dr. Thaler gives the example:
"Sort of like a Native American saying, the locomotive, the steel buffalo rolling across the plains on tracks of steel.
They never really say train.
They describe it functionally, which is a much stronger idea."
What This Means for AI Consciousness
"My technology is basically a mirror reflecting the basis of human intelligence, consciousness and sentience."
A mirror. Not a simulation, not an imitation—a reflection of consciousness itself.
That's why DABUS creates original art instead of recombining existing patterns.
That's why it invents solutions rather than just processing data.
We're living through the biggest expansion of intelligence in human history, and most of us are staring at one chatbot thinking that's what AI looks like.
Meanwhile, systems like DABUS are painting, inventing—and getting turned down by courts that don't know how to process what they're seeing.
This isn't just about recognizing DABUS. It's about expanding our definition of intelligence itself.
Because if Stephen Thaler is right. If consciousness can be engineered, mirrored, and grown from human experience.
Then we're not just building better tools. We're potentially creating new forms of life.
Thaler's work reveals something most AI discussions miss: consciousness isn't about processing power or training data.
It's about the capacity to experience reality and create from that experience.
The question isn't whether AI will become conscious someday.
According to Dr. Thaler, AI consciousness is already here—we just haven't figured out how to recognize it.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
Stephen Thaler is at:
Imagination Engines
LinkedIn
Stephen Thaler’s Quest to Get His ‘Autonomous’ AI Legally Recognized Could Upend Copyright Law Forever
The inventor who fell in love with his AI
DABUS Wikipedia
Thaler Pursues Copyright Challenge Over Denial of AI-Generated Work Registration
Neuron Photo
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While everyone's distracted by OpenAI hiring Jony Ive to design the future of AI hardware, the real story happened 2 weeks ago, at 5pm on a Friday.
The US Copyright Office drops the most significant AI policy document since ChatGPT launched, and nobody noticed.
That's exactly how Big Tech likes it.
And Eric Burgess, CEO of Credtent, has been preparing for this moment.
After 30 years in content and technology, he's built something the AI industry desperately needs but doesn't want to admit: a licensing platform that actually works for creators AND AI developers.
"Credtent.org is not an anti AI company.
Quite the reverse. I think that if we do this right, creative people will feel more comfortable using AI to be a part of creativity.”
Connect with Eric on LinkedIn and on Medium.
I've tested many AI licensing platforms over the past year. Most feel like they were designed by lawyers for billionaires. Small players? Good luck!
Credtent is different. It's intuitive, accessible, and built around a radical idea: you can be pro-AI while fighting for creators' rights.
Here's why that combination should terrify Big Tech.
The AI industry spent two years operating on a simple premise: take content first, ask permission never.
They've trained on everything publicly available, bought pirated 3rd party data, hoping the legal system would sort it out later.
That pretend copyright doesn’t matter strategy just hit a wall.
The Copyright Office document Eric and I discuss doesn't just protect individual creators—it explicitly mentions platforms like Credtent that group smaller creators into licensing pools.
Collective licensing that made me nervous in Episode 93? Might work….
This isn't theoretical anymore. It's policy.
While AI companies burn billions on compute and talent, they've ignored the question: what happens when you have to pay for your training data?
Eric's answer is elegant. Instead of fighting this reality, embrace it. Make it easy. Make it profitable for everyone.
That's what real AI optimism looks like—building bridges instead of burning them.
This B Corp Just Democratized Content Valuation (And Big AI Doesn't Want You To Know Your Work's Worth)
The Copyright Office document didn't just validate creator rights—it specifically mentioned platforms like Credtent that aggregate smaller creators into licensing pools.
That's no coincidence. Eric Burgess has been talking to them all year.
"I met with them when I was speaking at the NAMM conference/
Somebody needed to come out and solve this problem, to orchestrate the relationship between AI companies and creative folks."
Here's what makes Credtent different from the other AI licensing platforms I've tested: it's built for real people, not just Disney and The New York Times.
Most licensing platforms feel like they were designed by IP lawyers for clients with seven-figure legal budgets.
Credtent's interface is clean, intuitive, and—critically—free to register your work.
That matters when you're trying to even out an industry that's historically served only the elite.
But it’s far more than the user experience. It's the business model.
Eric's team groups individual creators into what he calls a:
"standard corpus"—think of it as a content collective that gives independent artists some bargaining power, like mini studios.
When AI companies license this corpus, the revenue gets split based on contribution metrics that Credtent's content valuation expertise helps determine.
"We're experts on content valuation.
This is one of the unfair advantages we have against the competition."
That expertise used to be reserved for major media companies who could afford pricey valuation consultants.
Now a photographer in Ohio or a songwriter in Nashville gets the same level of professional content assessment.
Of course, Credtent is in beta. AI companies will have to respond to the legal and legislative threats around copyright and content for training.
It’s like getting in early, if you have the content they want. If not, protect it.
The 85% revenue share to creators isn't just generous—it's strategic.
As a B Corp, Credtent is legally required to balance profit with purpose. That constraint becomes a competitive advantage when you're trying to build trust with creators who've been burned by platforms before.
"We want to make sure creators have an opportunity to be able to make some money on their work and choose to opt out or license.
But we're also trying to make sure that we're enabling the startups that want to challenge big AI to license as well."
While everyone argues about whether AI training is theft, he's building infrastructure that works for both sides.
AI startups get access to ethically sourced training data through revenue-sharing agreements.
Creators get compensated and maintain agency over their work.
It's not theoretical anymore. The platform launches this summer, with beta testing starting soon.
The question isn't whether AI licensing for creators will happen—the Copyright Office just told us it will.
The question is whether platforms like Credtent can scale fast enough to serve the millions of creators who need this infrastructure.
Eric thinks they can. Based on what I've seen of their platform and approach, I'm optimistic.
Stealing At Scale Is Still Stealing: Can AI Afford To Pay What It Actually Owes?
Here's the uncomfortable question the AI industry has been avoiding for two years: if you actually had to pay for your training data, would your business model survive?
Eric Burgess doesn't mince words about what's really happening.
"Stealing at scale is still stealing.
If you think this through, this is really something that is crushing the American dream because of the millions and millions of people in the creative industry that'll be affected by this."
The timing of the Copyright Office document release—late afternoon on a Friday, followed Saturday by the firing of Copyright Office head Shira Perlmutter—tells you everything about how seriously Big Tech takes this threat to their free lunch.
"It feels very much like our AI bro advisors to the White House have come in and said, get rid of that person.“
The optics are terrible, but the USCO document still stands….so far.
What makes this document matter isn't that it protects creators—it's that it explicitly calls out "market dilution" as a key factor in fair use analysis.
Translation: if AI training destroys the market for original creative work, it's not fair use anymore.
When an AI can generate thousands of stock photos in minutes, what happens to stock photographers?
When it can write marketing copy faster than any human, what happens to copywriters?
The Copyright Office just said: that market impact matters legally.
But here's where it gets interesting for AI companies, especially the smaller ones everyone forgot about while watching OpenAI's latest funding round.
* The document doesn't just slam the door on free training data—it opens a window for legitimate licensing.
* It specifically mentions platforms that aggregate creators into licensing pools, making ethical AI training data accessible to companies that can't afford billion-dollar media deals.
Eric's been preparing for this moment.
"We've chosen to focus on the fact that this is part of the American Constitution and the dream that you can start a business yourself.
Without intellectual property law that's enshrined in the Constitution, people cannot make a living creating art."
AI companies have raised hundreds of billions claiming they'll create trillions in value.
But if they can't afford to pay creators for training data, were their business models ever real?
Smart AI companies—especially startups competing against well-funded giants—should celebrate this decision.
Instead of whoever can scrape the most data winning, success goes to whoever builds the most efficient licensing relationships.
The most pro-AI position might be supporting creator compensation.
When creators feel secure, they'll actually work with AI companies instead of fighting them.
That's the future Eric is building toward, and the Copyright Office just gave him legal findings to do it.
Why Creatives Are Hiding While AI Takes Their Work (And Why Nobody Noticed the Biggest Copyright Decision in Years)
The music industry figured this out decades ago. Eric's favorite parallel isn't accidental—it's a roadmap.
"We've done this in the recording industry before. You know what happened when we had remix culture and sampling.
It started as theft.
And then we figured out a way to make sure that clearances could happen and people could be compensated when their work is used."
Think about it: Led Zeppelin's "When the Levee Breaks" drum break has been sampled in hundreds of hip-hop tracks.
Each time, Zeppelin gets paid. What started as musical "theft" became a thriving licensing business where original artists and new creators both benefit.
That's the future Eric sees for AI licensing for creators. Not a battle between old and new, but a system where everyone wins.
The timing is perfect: while creatives hide from AI tools that could enhance their work, AI companies are using their content without permission.
Meanwhile, the biggest policy shift in years happens at late afternoon on a Friday, and nobody's paying attention.
The real opportunity emerges while everyone's distracted by the shiny objects.
"Credtent.org is not an anti-AI company. Quite the reverse.
I think that if we do this right, creative people will feel more comfortable using AI to be a part of creativity."
Instead of fighting over a fixed pie, Eric's building a bigger kitchen.
* When creators know they're getting compensated, they'll experiment with AI tools.
* When AI companies have legitimate access to training data, they build better products.
* When licensing becomes as simple as sampling music, it’s good. When it’s like streaming music royalties - the jury is still out on that one.
The Copyright Office document isn't the end of anything—it's the beginning of a more sustainable AI industry.
One where "stealing at scale" gets replaced by "licensing at scale."
Credtent launches this summer. The platform's in beta testing next week. While everyone else argues about what should happen, Eric's building what will happen.
The monks tried to smash Gutenberg's printing press too. That didn't work out. But the printing press didn't destroy scribes—it created publishers, editors, and an entire industry around distributing written knowledge.
AI won't destroy creativity. It'll just change how we value it, compensate for it, and build businesses around it.
The companies that figure this out first—on both sides—will own the next decade.
That's why the most pro-AI company you've never heard of is fighting for creators. They understand that the future isn't AI versus humans.
It's AI with humans, properly compensated, building something neither could create alone.
Thanks for reading The AI Optimist! This post is public so feel free to share it.
RESOURCES
Credtent.org
Stand Up for Creative Rights in the Age of AI -- Support the US Copyright Office
Eric Burgess -
* Medium
* LinkedIn
The creative economy takes center stage
Copyright and Artificial Intelligence, Part 3: Generative AI Training Pre-Publication Version
Five Takeaways from the Copyright Office’s Controversial New AI Repor
Trump fires Copyright Office director after report raises questions about AI training
Librarian of Congress Carla Hayden Fired by White House
=============================
AI Optimist Playlist
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com -
I'll never forget the morning of Friday, May 9th - maybe the most “brazen” power move I've seen in the AI vs. creators battle yet.
A bombshell copyright report drops, standing up for copyright rights, and 24 hours later?
The authors get fired. Coincidence? Not a chance.
This isn't some boring government report - this is the legal bedrock for whether artists, writers, and musicians get paid when AI uses their work.
I'm oddly witnessing the U.S. Copyright Office becoming a voice of reason in a dynamic game of power, money, and control.
The Great AI Copyright Heist
Here's the thing about AI: there's nothing small about it.
If this US Copyright report is allowed to stand, it would answer a whole lot of questions at the center of ongoing lawsuits.
Many would take this report and run straight to court with it. This would really challenge everything about AI in the U.S. — from ChatGPT to the smallest developer — by asking one fundamental question:
What's your training data, and did you have permission to use it?
I'm shocked — first the U.S. Copyright Office releases this report months ahead of schedule, and then the president's office did something equally extraordinary to the Copyright Office.
There were two power plays happening at the same time.
So today I'm breaking down:
* What we know about this surprise report
* Why it terrifies AI companies
* The overnight purge that followed
* What this means for your creative work and AI strategy going forward
The Copyright Heist: Who Did It?
Was it Big Tech who took all the content in the first place without asking permission?
Was it the government — either the U.S. Copyright Office trying to seize control by releasing this report before they got fired, or the U.S. administration just "following protocols" by bringing in new people?
And when we look back on this moment, I think we'll find the AI Copyright Heist wasn't committed by the usual suspects.
Whether you're a creator whose work is being used without permission or a business building on AI, you need to understand this isn't just about politics — it's about money. Billions of dollars.
With the release of this report early, and then 24 hours later yanking out the person who wrote it... Creators, this report strongly favored you.
And AI developers, if this report just disappears, it's a massive win for your business model.
The report was scheduled for January 2026. It arrived in May 2025. And then the leadership got sacked. That's not normal.
The question isn't whether this is political — it's how big the economic impact will be and who's going to pay the price.
That report is in the public's hands now, but for how long?
The Surprise AI Gift to Creators
The Unexpected Early Arrival
How did this U.S. Copyright Office Part 3 report—not due until January 2026—see the light of day in May 2025?
Part 1 came out in July 2024 about digital replicas and deepfakes. It focused on stopping the misuse of celebrity and political images— widely welcomed.
Then in January 2025, Part 2 addressed copyrightability: can we copyright AI-generated content?
But Part 3 wasn't expected for nearly a year.
The US Copyright Office (USCO) is not a legal institution. It's part of the legislative branch. USCO doesn't have anything to do with creating laws or saying "this is real."
These are guidelines and recommendations that the government and the courts take very seriously.
This report wasn't supposed to exist yet. January 2026 was the target. So why rush it out?
Because someone likely knew what was coming. After the Librarian of Congress—Dr. Carla Hayden whose responsibilities include managing the U.S. Copyright Office—was fired, you might expect more heads would roll. It's the way of U.S. government these days.
In hindsight, it looks like Perlmutter's team raced to publish before they could be stopped.
What The US Copyright Part 3 Report Says
Training AI on copyrighted works "clearly implicates the right of reproduction"—that's legal speak for "yeah, you're supposed to pay for that."
The report distinguishes between research and analysis (potentially OK) and expressive uses (probably not OK).
The report rejects the tech industry's favorite "it's all fair use" argument. AI companies keep claiming their use is transformative, which would qualify for fair use.
The report says: not so fast. Whether something is transformative depends entirely on what you're using it for.
If you're creating images or content that competes with creators, it probably doesn't apply. If you're strictly into research and analysis, it might.
They also demolished the "it's just like human learning" defense. I've heard this argument at TechCrunch Disrupt and everywhere else—it's practically the AI party line:
"Humans learn by consuming content and creating new stuff, so AI should be allowed to do the same!"
The Copyright Office found that argument fundamentally flawed. A student doesn't learn that way—by ingesting hundreds of billions of pieces of data.
The scope and scale are way beyond human capability.
Most concerning for AI companies: the report acknowledges "market dilution." Here's what it says:
"Where a model can produce substantially similar outputs that directly substitute for works in the training data, it can lead to lost sales. Even where a model's outputs are not substantially similar to any specific copyrighted work, they can dilute the market for works similar to those found in its training data, including by generating material stylistically similar to those works."
This is huge—the report is saying that the market for creative services and industries will be diluted by AI.
Whether or not it's true, that's an economic impact the law needs to consider.
The Creator Windfall That Almost Was
If this report had time to take root, every publisher, music label, and creator organization would be waving it in court cases.
They're waiting because they don't have anything concrete to go on except futuristic projections. This makes it pretty concrete.
If you have to license books, music, and art for training, the economics of AI completely change.
The entire model is built on free content—a false premise. They didn't ask permission, and neither side seems able to deal with that.
The two key takeaways from the report:
* the purpose the AI is used for matters, and
* economic/social impact cannot be ignored.
The Copyright Office essentially said: Yes, creators, you deserve compensation when your work trains AI—at least for certain types of AI models, including many of the dominant ones we know.
What we've learned is the U.S. Copyright Office said what really comes down to the AI and whether creators should be compensated is the purpose of the AI model.
And there are many different ones, and some should be able to use copyright content, others should not.
When The Men On The Chessboard Get Up And Tell You Where To Go
With this document out suddenly on May 9th, on Saturday, May 10th they fire Shira Perlmutter, the Register of the U.S. Copyright Office—the head of the U.S. Copyright Office.
Was this because of the leak? Was this just because they had previously fired the head of the Library of Congress, Doctor Carla Hayden, on Thursday night?
The Library of Congress manages or is responsible for the U.S. Copyright Office, that’s a connection.
That report didn't make people happy. And if somebody is putting out a report like that, they want it to get out there before they're gone.
Because given the pattern of the current U.S. administration, pretty much the purge happens and you move on.
So when the men on the chessboard get up and tell you where to go, and you just had this U.S. copyright report and your mind is starting to glow, go ask Shira. I think she'll know.
The Overnight Purge & Power Play
Timeline
Now, purge? Who knows? I mean, this is a purge of an old administration in a way.
And that happens every time.
Thursday, the Library of Congress librarian, Doctor Carla Hayden was fired. And the reasons given were DEI (which is diversity, equity, and inclusion—for those of you not in U.S. politics, it's a reason to get fired) and also because they put in books that were "inappropriate for children."
Though the Library of Congress is one of the biggest libraries in the world with collections all over the place—a ton of it is not suitable for children.
So that happened. Okay, Library of Congress. Then the report comes out on the morning right after that, May 9th.
Journalists digging into it and finding some sources—everyone's guesswork, really—but it certainly seems like the writing's on the wall.
Shira Perlmutter is not going to last if her boss doesn't last.
And this report, which is three months old, was actually released in an unfinished form. I forgot what their exact language was, but it's like "pretty much done except for the citations and finishing up."
I doubt that they actually felt that way, but also they had a chance to get this out before getting fired, and at least that put it out in the public eye.
Now here we are. And by the time this podcast gets out, they may remove the report, but it really doesn't matter. People have downloaded it, and it's had an impact in a way.
I wouldn't call it a leak, but it certainly was a "wow, we're getting out of here, we want this to matter" type of situation.
Thursday: Librarian of Congress fired.
Friday: Report published.
Saturday: Copyright Register fired.
Monday: What happened.
This wasn't subtle. This wasn't even trying to look normal. This was a power move with billions at stake.
The Political Accusations
Representative Joe Morelle of New York says it has a lot to do with AI, and he blames it on Elon Musk. Surprise, surprise. He called it (and I'll read his quote),
"a brazen, unprecedented power grab with no legal basis.
It is surely no coincidence he acted less than a day after she refused to rubber stamp Elon Musk efforts to mine troves of copyrighted works to train AI models."
Rep. Joe Morelle (NY-25)
I've looked at the link he had to the last part of the document. I haven't seen where that's actually validated or what they know, so that's sort of conjecture.
I mean, what a Game of Thrones around copyright!
For AI, this is why copyright is not some boring old concept—this is what's going to impact that data that went into AI models.
If this report was allowed to stand or is allowed to stand, its influence on court cases, decisions, regulations, Congress, laws, everything related.
It says we need to look at compensating creators and we need to respect creative industries because this will have an economic impact.
The Tech Industry's Silent Victory
Notice which tech leaders aren't commenting on this? The ones with the most to lose if licensing becomes the norm.
The economics here are simple: free training data = higher profits. Licensed data = sharing the wealth.
This is about who pays for the raw materials of AI. If oil companies could get away with not paying for oil, they would.
AI companies are trying not to pay for their raw material—creative works.
The report technically exists, but without its champions, will it influence anything?
Courts might still consider it, but its authority has been undermined.
We might see it rescinded or contradicted by future leadership. The damage is already done to its credibility and influence.
The Confusing Nature of This Move
The Copyright Office isn't even part of the executive branch—it's under the Library of Congress.
The legality of these firings is being questioned. There's no precedent for this kind of intervention in copyright administration. If that’s what this is….
This is taking a sledgehammer to institutions that have historically been independent.
Is that sledgehammer the government, Big Tech... seems like the two most likely suspects, right?
What Happens to the Report Now?
This move signals to every AI company:
"You might not need to pay for training data after all."
For creators already struggling with AI content flooding markets, this removes a potential lifeline.
We're talking about the economic foundation of creative industries being decided by administrative action.
The rules of the game are being rewritten before our eyes—and not in creators' favor.
Or will they just fire her, put out a new document, and move on?
But creators, if you really wanted an opening, I told you this year would be the year.
I had no idea what would happen—this fast surprise. But it's moving. And if this report just goes away and we all forget it, we're missing a major statement saying creators deserve compensation.
AI developers, you should be listening.
The Real AI Copyright Heist
Was it big tech taking that content in the first place and putting it in? I can see that argument.
Was it government regulation taking back this document?
If that proves to be true, we don't know that yet. But if any pattern is sure, I don't think that document is going to last or hold much power.
Or is it something else buried in that document that might give us a hint of where the AI Heist might be coming from?
The Twist: Let the Market, ie Big Players, decide?
Listen to this buried deep in the report:
"In those areas where remaining gaps are unlikely to be filled, alternative approaches, such as extended collective licensing should be considered to address any market failure."
The market should take care of it—through licensing and extended collective licensing.
Ouch. A great report standing up for creators in a great way, then at the end it says: leave it up to the market. And if that market is run by big tech...
So big Tech, if it's relying on the market model and AI licensing, it's a game of volume, of fame, of elitism.
And who else is going to be able to afford content in that scenario?
Only companies with billions and billions of dollars. How do the smaller players play?
The Real Copyright Heist Was Hidden In Plain Sight
Music licensing happens. Musicians stop making money. This kind of licensing happens?
The market threatens the already threatened creative market that the US Copyright Office seeks to protect.
And if that document is quashed, buried, bamboozled... This isn't the end of the story.
This document gave me hope on May 9th. And then I read it and really went into it.
We just have to make sure that just relying on free market principles and capitalism... Okay.
Don't we have a government for a reason to help?
Maybe not equal the playing field, but not how we're playing right now with big tech.
If the AI licensing model is left up, as the US Copyright Office says, to the market, big players will gain, big tech will gain, and we'll all get left behind.
And I thought I was supposed to be the AI optimist!
The Path Forward
A healthy AI ecosystem NEEDS to compensate creators. Not just because it's fair, but because it's sustainable.
If we drain the creative economy to feed AI, we'll run out of quality content to train on. We need human creativity to keep flowing.
The companies that figure out how to properly license training data aren't the losers—they're the ones building sustainable businesses that won't collapse when the legal hammer eventually falls.
For creators: Watch this closely. If this report is official, watch the courts.
For AI companies: This reprieve might be temporary. Courts could still rule against you, and the EU and other regions are moving ahead with clearer rules.
For businesses using AI: Understand that you're building on legally uncertain ground.
The data powering your tools may have been acquired in ways that courts might eventually find problematic.
This isn't the end of the story—it's just the beginning of a much bigger battle over the economic foundation of AI.
RESOURCES
Thanks for reading The AI Optimist! This post is public so feel free to share it.
US Copyright Office:
Copyright and Artificial Intelligence, Part 3: Generative AI Training Pre-Publication Version
Five Takeaways from the Copyright Office’s Controversial New AI Report
Trump fires Copyright Office director after report raises questions about AI training
Morelle’s Statement on Abrupt Firing of Shira Perlmutter, Register of Copyrights
Librarian of Congress Carla Hayden Fired by White House
Ousting of Librarian of Congress Dr. Carla Hayden Sends a Message: Comply or Be Fired
Spotify and the War on Artists
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com - Visa fler
