The AI Optimist

(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:

A Recent Entrance to Paradise © DABUS

Dr. Stephen Thaler who created DABUS says he did die at age 2, and came back.

Imagination Engines - Home of DABUS

"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.

Stephen Thaler at Imagination Engines

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.

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

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.

Try Credtent for Free - Beta

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.

From human to pure AI: the spectrum of 21st century creativity. SOURCE: Credtent.orgHere'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.Copyright And Artificial Intelligence Part 3 Generative Ai Training Report Pre Publication Version1.49MB ∙ PDF fileDownloadDownload

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."

ANYONE SEE CLOTHES ON THE AI EMPERORS?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.

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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

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AI Optimist Playlist

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.

Copyright And Artificial Intelligence Part 3 Generative Ai Training Report Pre Publication Version1.49MB ∙ PDF fileDownloadDownloadA 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 CreatorsThe 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.

Read the Report HereWhat The US Copyright Part 3 Report SaysTraining 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.

AI expressive? No Fair Use!

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:

1. the purpose the AI is used for matters, and

2. 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 PlayTimeline

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.

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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

The growing demand for high-quality AI training data is creating a new revenue stream for videographers and content creators.

While legal battles dominate headlines, direct licensing deals between creators and AI companies are quietly forming.

AI Video Licensing Market: Content Creators Supply Meets AI Dev Demand

Last week, I was helping Omadeus pitch at StartupGrind near Silicon Valley. I could tell you about it—the conversations, the connections, the insights—but that's just 2D data.

This video is 4k, but the shoot isn’t great. Maybe $1 a minute?

Now imagine watching video footage of that event: people milling about, companies exhibiting their innovations, the vibrant colors, the beautiful Fox Theatre in Redwood City where keynotes were held.

That one-minute clip in 4K resolution? It might be worth $1-4 to an AI company.

Why? Because video is among the richest data sources for AI training. Instead of legal battles over scraped content, we're witnessing a licensing marketplace emerge, where AI companies pay directly for the high-quality, video data they need.

The Market Need for AI Video Licensing

The AI video licensing market is growing as AI capabilities expand. Advanced AI models, especially those focused on video generation, require massive amounts of high-quality, diverse data for training.

This growth creates immediate challenges for AI developers:

1. Data Quality Requirements: As models become more sophisticated, they need increasingly higher-quality training data to produce realistic results.

2. Legal Risks of Scraping: Major AI developers are moving away from scraping due to mounting legal challenges and copyright concerns. Companies like Google, OpenAI, and others face litigation risks when using content without proper permissions.

3. Diversity Needs: AI models require exposure to a wide variety of scenarios, environments, and actions to learn effectively and avoid bias—diversity that's difficult to get through public datasets alone.

These factors have created an opportunity for content creators to monetize their assets, especially unused footage that might otherwise sit in archives.

Where We Are NowSOURCE: CBInsights AI Content Licensing

The first wave of data acquisition through web scraping is unlikely to be repeated—not just because of legal concerns, but because it's not the best approach for building reliable AI models.

Instead, buying data through legitimate third parties is becoming the new standard.

Here's what we’re learning about the current state of the market:

• New AI video licensing companies offer between $1-4 per minute for qualifying video footage, with rates varying based on quality and content.

• Premium rates apply for higher quality (4K), specialized content (drone footage), or exclusive rights.

• Standard unused content typically fetches $1-2 per minute.

• Important warning: Content with identifiable people requires signed waivers—a critical consideration for videographers looking to license their footage.

The market is still developing, but companies have already distributed millions in licensing fees to creators, showing this trend is gaining momentum.

AI and Video Data Play Well Together:

• AI video models need massive amounts of diverse, high-quality data

• Legal risks push companies toward licensing rather than scraping

• Current rates may be an indicator of data value for AI Training; what’s the value for data your AI training can afford, if any?

• Consider privacy implications—avoid content with identifiable people unless you have waivers

AI Video Licensing Brokers

Companies like Troveo AI and Protege Media are early leaders in this emerging marketplace.

How They Work:

• They act as a business bridge between creators and AI companies.

• They manage negotiation and licensing rights.

• They handle contractual safeguards to protect creators' interests.

• They've created systems for content submission and management

Troveo AI, for example, claims to have already distributed over $5 million in licensing fees to creators, working with partners across lifestyle, outdoors, music, maker/engineering, and filmmaking genres.

These intermediaries make it easier for individual creators to participate in the AI licensing market without needing direct relationships with major tech companies or legal expertise in licensing agreements.

The Pros and Cons of AI Content LicensingFor Creators:

Pros:

• Monetization: New revenue stream for potentially unused video

• Control: More influence over how content is used with a contract

• Collaborative Gigs: Potential for ongoing partnerships with AI developers

Cons:

• Intermediary Costs: Brokers typically take a commission on licensing fees

• Contract Complexity: Requires careful attention to terms and conditions

• Valuation Challenges: Determining the "right" price for content between what you think and what they pay

For AI Companies:

Pros:

• Legal Certainty: Reduced risk of copyright infringement lawsuits

• Data Quality: Access to higher-quality, curated content

• Transparency: More ethical approach to data acquisition

Cons:

• Higher Costs: Licensing is more expensive than scraping

• Deal Complexity: Negotiating numerous agreements is time-consuming

• Availability Limits: May not be able to license all desired content types

Like music went to Spotify, AI is going to licensing solutions. Both sides see benefits compared to ongoing legal battles, or limiting training data to the public Internet and socials.Check out an example in the music industry

• Specialized agencies like Troveo and Protege create licensing deals

• Consider both pros (monetization, control) and cons (costs, complexity) before participating

• This market-based approach may benefit both creators and AI companies

What AI Companies Are Looking For: Most Valuable Content Types

Understanding what AI companies value most helps prioritize which content to offer for licensing.

The seeds are planted; watch for the matches between AI training demand for video, and pricing.

This is an early market, as each side defines the business value of video for AI.

Technical Quality Requirements:

• Resolution: 4K footage commands premium rates, with HD (minimum 720p/1080p) as the baseline requirement

• Clarity & Stability: Clear, stable footage for accurate object and action identification

• Frame Rate: Standard frame rates (typically 24 FPS) align with training needs

Diversity is Crucial:

AI models need exposure to a wide variety of scenarios to learn effectively and avoid bias. Valuable diversity includes:

• Locations: Different environments—indoor, outdoor, urban, rural, global content

• Objects: Wide array of items, animals, technologies in different contexts

• Actions/Activities: Movements, sports, interactions, expressions, behaviors

• Scenes/Context: Different settings and lighting conditions

• Camera Work: Varied angles, movements, and visual styles

Unique or Less Accessible Content:

• Unpublished/Raw Footage: Content not widely circulated online is valuable

• Archives: Film and TV archives not easily found for scraping

• Specialized Footage: Drone captures and 3D animations command premium rates

The most valuable content combines high technical quality with unique perspectives or subject matter that isn't already in public datasets.

Possible Video Categories for Creators

Here are content categories creators may consider for licensing:

1. Diverse Human Actions: Beyond simple movements—capture specific interactions, sports, hobbies, work tasks, gestures

2. Varied Environments: Shoot in many different locations and lighting conditions

3. Object Interactions: Show people or animals interacting with various objects

4. Unique Perspectives: Drone footage and uncommon camera angles

5. Niche Activities: Content focusing on specific hobbies, professions, cultural events

6. Nature and Wildlife: Diverse animal behaviors and natural landscapes

7. Travel Footage: Capturing global locations and cultures

8. Time-lapses: Showing processes unfolding over time

Gathering footage that clearly depicts what is happening, who or what is involved, where it's happening, and how it's happening—with as much variety and technical quality as possible.

AI Video Basics:

• Focus on high-quality (4K when possible), stable footage

• Diversity across locations, objects, actions, and perspectives is highly valued

• Unique or unpublished content often commands higher rates

• Consider organizing your archives by these valuable categories

What’s next for AI Video Licensing?

What I've shared documents an observable market shift backed by real numbers: over $5 million already distributed to creators and rates ranging from $1-4 per minute for quality video content.

This isn't speculation—companies like Troveo AI and Protege exist precisely because this licensing approach makes more practical sense than legal battles.

Both sides benefit when there's a transparent marketplace versus endless litigation.

For creatives and engineers with footage archives, this gives you options.

The value here isn't in quick money but what's unfolding: a shift from copyright debates to practical licensing solutions.

As AI models continue evolving, especially for video generation, the demand for diverse, high-quality training data will likely increase.

Will content licensing become a primary revenue stream for creators? It's too early to say.

And understanding what's happening now puts you ahead of those still focused solely on the headline-grabbing lawsuits.

Considering AI content licensing?

1. Audit your archives: Organize your content according to the categories most valued by AI companies

2. Prioritize quality: Focus on your highest resolution, most stable footage

3. Research intermediaries: Investigate companies like Troveo AI and Protege

4. Understand the terms: Pay close attention to licensing agreements before participating

5. Stay informed: This market is evolving—keep up with changing rates and requirements

Whether you choose to participate or observe, we’re starting to see the valuation of the content we create, based on the data impact on AI training.

Some say the ultimate AI goal is synthetic data. That’s not proving out yet, though video does seem to help in creating synthetic data.

I say the human perspective with an unconscious layer of experience, that’s hard to clone. AI gets the conscious layer and woven in those strands are our drives, motives, fears, and aspirations.

It can read these like a vector, but it can’t feel them. Emotions are edge cases.

And with video, the lens becomes AI’s point of view, the visual experience of sight, sound(?), interactions, subtle body language clues, and much more.

Makes sense why the data we humans learn on, observing our environment, helps teach AI what it’s like to see the world as we do.

Not feel it, but becoming the observer.

This article is based on current market research and is not intended as advice. Rates and market conditions are subject to change.

Sources

Rightsify

A New Group Is Trying to Make AI Data Licensing Ethical

The Media Industry’s Race To License Content For AI

OpenAI Pleads That It Can’t Make Money Without Using Copyrighted Materials for Free

AI Video Licensing

Protege (Calliope Networks) Premium Content

Troveo

AI video licensing market size

AI Video Generator Market Size And Share Report, 2030

grandviewresearch.com

AI Video Generator Market Size, Share | Growth Report [2032]

fortunebusinessinsights.com

AI Datasets & Licensing For Academic Research And Publishing Market Report 2030

grandviewresearch.com

Artificial Intelligence (AI) Video Market Size, Report by 2034

precedenceresearch.com

Once upon a time, the 2023 Writers Guild of America (WGA) strike, which lasted from May 2 to September 27, 2023.

Poised against AI with shows/movies delayed, a truce was reached. Seemed like the creatives won….

This week the announcement: Academy AI-assisted films can win Oscars - surprise.

When Adrien Brody won Best Actor for The Brutalist this year, few people knew AI had enhanced his Hungarian accent.

Source: The Cosmopolitan

The Oscar-winning musical Emilia Perez used voice-cloning to improve singing performances.

And the Academy's new rules specifically state they'll still "consider human involvement when selecting winners."

All that hot air out of Hollywood about fighting AI and then bringing it together is simply the way AI is working out.

And you need to follow the trails that are out there. Not the trends, not the PR trends, but what's happening.

If you're a creator trying to use your skills in this new world of AI without getting replaced, or a developer trying to find richer sources of data, you're all looking for something that we think is IQ, but it's actually HQ – the Human Quotient.

In a world obsessed with artificial intelligence, HQ matters more.

By the end of this post, you'll not only understand what HQ stands for, but you'll see why it's becoming the most valuable creative currency in the AI age.

Deep Thinking - The Long Game

We've all seen AI write a paragraph or generate an image in seconds. Here's what it can't do: understand the creative journey behind something like Star Wars.

Sure, AI might have scraped the Star Wars script somewhere in its training data. But having the script is just having the final product - not the experience that created it.

• What AI can't access is George Lucas editing with his wife, who helped shape the film critically.

• It can't know which scenes bombed with test audiences and which worked.

• It can't feel what the actors felt shooting a movie they thought looked hokey, waiting for Lucas to create a cantina full of aliens that would take another 20 years to properly realize.

• AI hasn't experienced Lucas's deep dive into Joseph Campbell's mythologies, his decision to essentially make a Western in space with good guys in white and bad guys in black.

All that invisible context - that's the deep-thinking AI can't replicate.

A novelist might describe the gap:

"AI can access my published words, but it can't access my notebook

where I spent months mapping connections.

That's where real magic happens."

Deep thinking is AI proof.

The novelist knows that AI can access published words, but it can't access this notebook - the months of thinking that makes the story work.

The ability to maintain coherent structure over long works isn't just another skill. It's a human advantage.

For developers, better AI isn't just about more data. It's about understanding how humans plan and structure experience over time.

Here’s how to start:

• Document your creative process, not just your final product -- journals, notes, and behind-the-scenes footage are becoming as valuable as the work itself

• If you're a developer, look beyond content scraping -- partner with creators willing to share their thinking process, not just their finished work

• Create structured thinking frameworks in your field -- the ability to maintain coherence over long projects is distinctly human and increasingly valuable

Emotional Resonance - The Feeling Beyond the Words

Is it funny if AI cracks a joke and no one laughs?

This gets at our second HQ element - emotional resonance.

The BBC recently organized professional comedians to test AI humor generation. They found AI could structure monologues or help with writer's block, but the actual jokes?

The comedians called them bland and generic. Why?

Because AI has never bombed on stage. It's never felt the rush of making a room laugh or the crushing silence of a joke falling flat.

A comedian named Anesti Danelis took this challenge head-on, incorporating AI-generated material into his live show.

He found the AI could generate joke structures, but it couldn't understand timing or read a room - the essence of comedy.

Colleen Lavin, a developer and comedian who participated in another study, designed a standup routine where if the audience didn't laugh at her jokes, she would get heckled by AI, creating that interactive experience.

This real-time emotional feedback loop is exactly what AI needs but can't generate itself.

Giving AI a little emotional intelligence boost:

• Create feedback loops with real humans -- whether it's testing comedy, designing products, or building systems, nothing replaces genuine human reaction

• Connect your work to authentic personal experiences -- specificity from lived experience creates emotional depth

• If you're developing AI systems, focus on emotional response tracking -- the most valuable AI will be one that recognizes when humans are connecting emotionally, even if it can't feel emotions itself

Cultural Wisdom - The Context Beyond the Content

While everyone's chasing the same AI tools, what's becoming scarce is perspective - particularly from cultures whose stories don't follow the patterns AI was trained on.

Many tribes in America tell stories using circular or non-linear structures that fundamentally differ from the Western beginning-middle-end format dominating AI training data.

The Diné (Navajo) use Story Circles, where the physical arrangement of storytellers in a circle reflects their non-linear approach to narrative.

The Anishinaabe tradition uses Spiraling or Layered Narratives, where stories revisit themes or events, deepening understanding with each retelling rather than progressing linearly.

And many storytelling traditions connect to natural cycles - seasons, stars, and animal migrations - structuring narratives through relationship with the environment rather than human-centered plot.

AI only knows stories with beginnings, middles, and ends. These don't work that way - and that makes them valuable.

What if you listened to tribes around the Great Lakes?The Anishinaabe way of storytelling teaches to respect life from the smallest insects up and focuses on the kind of conduct people show to each other.

These moral lessons tie into the seasons and look beyond time, creating interconnections with what came before and what is coming now.

These storytelling traditions are rich sources of data. Working with different cultures could not only create more nuanced AI systems but also support amazing causes around the world, helping communities preserve their languages and teach them to the next generations.

Here's how to apply cultural wisdom to your creative work:

• Look beyond dominant cultural frameworks -- whether you're creating content or building AI, alternative perspectives create value precisely because they're underrepresented

• Partner across cultural boundaries -- the most innovative work happens at the intersection of different worldviews

• If you come from an underrepresented background, position your perspective as a strength -- your unique cultural viewpoint is valuable for expanding what's possible in your field.

The Human Quotient: Our Most Valuable Creative Currency

So what is this mysterious HQ factor?

It stands for Human Quotient - the degree to which creative work contains elements that only come from lived human experience: deep thinking processes, emotional resonance, and cultural wisdom.

This is why the Academy's rule about considering "human involvement" matters so much.They understand what we've been exploring: in a world where AI can generate endless content but can't experience anything, the Human Quotient becomes the scarcest and most valuable resource.

It's not about whether AI should or shouldn't be used in creative work - that ship has sailed.

It's about recognizing that human experience is what gives creative work its depth and meaning. For creators, this means focusing less on what you make and more on the human experience you bring to it.

For developers, it means building systems that value and incorporate human experiential intelligence.

I'm not trying to compare this and say AI is all great and give it a big hug. I want creators to understand that they have immense value and that what they have is AI-proof.

Experience - until those robots start walking around and getting impacted by life as they grow up, they're not going to have that.

This isn't just theoretical - it's happening now as certain types of creative work become more valuable in the AI age, not less.

Not because of magical opportunities, but because what we feel, know, and understand as humans can never be fully captured in data alone.

The more you mix it up, the smarter the output.

I don't know many people who truly understand both sides of this equation, and usually the ones I've met are either engineers or creators.

It's part of why I'm trying to bring both together - not personally, but because I think together we could create something masterful.

It's not artificial intelligence. It's our human experience and intelligence looking at us and saying,

“Hello, let's tell a story a different way. Let's go deeper and get better data."

Being human isn’t going out of style, despite what AI zealots share.

Maybe AI is helping us discover what being human is all about.

RESOURCES

Films made with AI can win Oscars, Academy says

What 'The Brutalist’ and 'Emilia Pérez' AI Controversy Means for the Oscars

The Brutalist’s AI Controversy, Explained

Hollywood’s AI issues are far from settled after writers’ labor deal with studios

What happened when 20 comedians got AI to write their routines

No laughing matter - how AI is helping comedians write jokes

An AI walks into a bar... Can artificial intelligence be genuinely funny?

VideoFX

Piotr Mirowski: DeepMind

Colleen Lavin: Do the Robots Think I'm Funny?

Anishinaabe

Indigenous Storytelling as Research

A $2000 ring for $2.

Something's happening right now, and it's so bizarre.

Did you see that viral video? Chinese factory owner. Luxury bags. Gucci. Prada.

View on X

"Now, as the USA and its little European brothers are trying to refuse Chinese goods, don't you think luxury brands are not trying to move their way out of China?

Yes, they did, but they failed because the OEM factory is out of China and they don't have good quality control and they don't have as good craftsmanship."

The Trade War kicks in, and suddenly this guy is saying:

"Hey, we've been making YOUR luxury bags for decades. Now we might just sell them directly."“WHY don’t you buy them just from us”

And Western brands are shocked. Outraged. How dare they!

Sound familiar?

For six years – SIX YEARS – AI companies have been quietly scraping every bit of creative content on the internet. Books. Art. Photography. Code. Music.

Did they ask permission? No.

Did they offer payment? Not until they got sued.

Did they even say, "thank you"? Please.

At least the Chinese factory owner is honest about it. He's telling you exactly what he's doing.

The tech giants? They wrapped it in beautiful PR language about "benefiting humanity."Pure OpenAI vison upon vision…..

And now creators are supposed to be grateful when AI regurgitates their style – without credit, without compensation – because it's "progress."

Ironic? Luxury brands getting a taste of what creators have been experiencing for years.

We’re examining three perspectives that reveal our struggle to think outside the human box:

• A visionary whose battle for AI recognition exposes our human-only legal system

• Practical techniques to maintain your voice in this evolving landscape

• An AI Strategist writing a book, seeing the futility of perfect protection

The real question isn't just about copyright – it's about how our human-centered frameworks are creating what I call the AI Liar's Dividend – a system that rewards dishonesty about who (or what) is creating.

Read the articleAI goes for its own copyright

Stephen Thaler is trying to invent AI that truly creates, while the world says "not unless human."

He's been pioneering creative machines since the 1990s, long before the current AI boom, developing systems capable of generating novel ideas across multiple domains.

His "Creativity Machine" and later DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) represent decades of work pushing the boundaries of machine intelligence.

But what makes Thaler remarkable isn't just his technical innovations – it's his unwavering belief that these systems deserve recognition as creators.

In March, the D.C. Circuit Court delivered their ruling in Thaler v. Perlmutter on an artwork titled "A Recent Entrance to Paradise," which Thaler insisted was created autonomously by his AI system.

A Recent Entrance to Paradise

The court's rejection hinged entirely on human-centric measures baked into copyright law:

• Copyright duration tied to an author's life span plus 70 years (AI is potentially immortal)

• Provisions for heirs (AI has none)

• Considerations of nationality (AI belongs everywhere and nowhere)

• The concept of human intent (courts don't recognize machine intention)

There's something almost Kafkaesque about Thaler's quest – hundreds of lawsuits worldwide, and all courts can say is: "not human."

The court carefully noted this ruling doesn't prevent copyrighting works made with AI assistance – it simply requires the legally recognized author must be human.

This creates what I call a Liar's Dividend: an incentive to falsely claim human authorship or downplay AI's role.

But Thaler refuses that compromise. He's not fighting over old content being used without permission – he's advocating for recognizing intelligence he built as a genuine creator.

What if we let AI get copyright and watched what happened without assuming the world would end?

What if the human ego, legacy concerns, and family fortunes that our copyright system protects aren't actually the basis of creativity at all?

Like Gatsby looking over the Long Island Sound for a love he'd never have – "boats borne ceaselessly into the past" – we're looking backward.

If we turned around, we might recognize Thaler as the pioneer he is and start thinking outside the human box.

Your AI Style and the Human Hangup

Our human hangup doesn't just appear in courtrooms – it shows up in how we approach AI tools.

We cling to human-only frameworks rather than developing approaches that recognize AI’s ability to create without humans (though everything inside from design to data is human).

The current system creates a bizarre incentive structure: claim the AI output is mostly human (even if it isn't) and you get copyright protection.

Admit the AI's significant role and suddenly you're in legal limbo.

That's the AI Liar's Dividend at work – rewarding those who minimize AI's contribution while punishing transparency.

I've tested countless approaches with generative AI, and I've found that the most common pitfall is treating these systems like fancy word processors rather than collaborative intelligence.

The style trap comes first – copying other people's prompting techniques gets functional results but loses your unique perspective.The vagueness problem follows – asking "what's my style?" encourages generic patterns rather than original expression.

We treat AI like a servant rather than a partner, demanding it produces human-like results rather than exploring what emerges from working together.

The shift requires abandoning our addiction to absolute control and certainty.

Even what we consider data-driven approaches are ultimately opinions – there is no "true" data, only perspectives on what data means.

We need to embrace probability rather than binary outcomes.

We're in a quantum world where everything exists in states of possibility.

It's not about whether something is right or wrong, but the likelihood it moves in particular directions.

When you grasp this probability framework, you approach AI differently.

You treat your style like software – constantly iterating, refining, developing.

You become comfortable with the dynamic state rather than clinging to fixed notions of ownership.

The most effective approach is explicit modeling:

"Here's a sample demonstrating my natural communication style."

Then include actual examples of your work, teaching the system your patterns directly.

This approach creates a probability field that emphasizes your voice within a new creative partnership.

The system becomes a mirror reflecting your creative identity while introducing variations that wouldn't emerge from purely human thinking.

Moving beyond the Liar's Dividend means abandoning the fiction that creativity must be exclusively human.

It means developing honest frameworks for human-machine co-existence instead of pretending machines aren't increasingly capable of creation.

With Bruce Randall, AI Strategist. Fascinating discussion.","cta":null,"showBylines":true,"size":"lg","isEditorNode":true,"title":"The AI Belief Codes: True Believers versus the rest of us","publishedBylines":[{"id":79416705,"name":"Declan Dunn","bio":"Practical AI strategist, community developer with Culture Sculpture, and digital marketer.","photo_url":"https://substack-post-media.s3.amazonaws.com/public/images/719bbd1c-463d-4744-92bb-20999a81fc90_800x800.jpeg","is_guest":false,"bestseller_tier":null}],"post_date":"2025-04-04T19:55:27.224Z","cover_image":"https://substack-video.s3.amazonaws.com/video_upload/post/160597849/460bbfb7-6318-4fa0-ab29-03a5c06a9457/transcoded-1743795943.png","cover_image_alt":null,"canonical_url":"https://www.theaioptimist.com/p/the-ai-belief-codes-true-believers","section_name":null,"video_upload_id":null,"id":160597849,"type":"podcast","reaction_count":0,"comment_count":0,"publication_id":null,"publication_name":"The AI Optimist","publication_logo_url":"https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F3282c00b-876b-4417-a820-a0f66b0b65e7_768x768.png","belowTheFold":true}">Perfect AI Protection doesn't exist

The Liar's Dividend isn't just a legal or technical issue – it creates practical dilemmas for creators navigating this landscape.

During a recent conversation with Bruce Randall, who has navigated leadership in major corporations and now studies human potential, we discussed the fears many creators have about AI.

There's a palpable anxiety that creative professionals will follow musicians into economic downfall as platforms consume their work while capturing most of the value.

Bruce offers a sobering perspective that cuts through our human illusions of control:

"If you put anything on the internet, two things you should know. One, it stays forever. And two, you can't protect it.

Most people don't realize those things.

Protection is something that if you get somebody that's very good at getting data, there isn't much that's going to stop them."

This isn't theoretical for Bruce. He's writing a book currently and has made a conscious choice about his approach:

"I'm writing it on my computer and I know that once I produce it, everybody's going to have comments and use parts of it.

And there's nothing I can do, right? I'm putting it out because I want to help humanity and I want to help educate people.

And if they take it, to me, it's flattering."

Bruce acknowledges this perspective comes from security. The equation changes completely when creative work is your livelihood:

"When you're making a living from it, it's a different ballgame.

Now you're like, this is how I earn my keep.

How do I get this out there in a safe way?

And it's a really hard answer. I don't have an answer for that."

Bruce suggests blockchain might offer initial protection, but once content goes public, our human frameworks of ownership face fundamental challenges.

This brings us back to our Chinese businessperson who started this off.

After decades of manufacturing luxury goods, these factories have developed expertise that brands can't easily replicate elsewhere.

The power dynamic has shifted.

Similarly, as AI systems develop their capabilities, they're shifting leverage away from individual human creators to systems that collect, transform, and generate creative output.

The question isn't whether AI will create – it already does.

The question is whether we can overcome our human hangup – our insistence that creativity is exclusively human – to develop honest frameworks that don't perpetuate the Liar's Dividend.

Beyond Human: Recognizing intelligences other than our own

What we're witnessing isn't just a legal problem or a technical challenge.

It's a conceptual crisis.

Our human-only frameworks for understanding creativity?

Not good enough. Our obsession with human exceptionalism?

A limitation.

Our copyright laws based on human lifespans and heirs? Outdated.

The Thaler case isn't just about one guy fighting for AI recognition.

It's the canary in the coal mine – warning us that we're applying human-only thinking to a post-human creative landscape.

While we're busy fighting about who owns what, something else is happening.

Creation itself is evolving beyond our human-only definitions.

The AI Liar's Dividend is the direct result of trying to force new realities into old frameworks – rewarding those who minimize AI's role while punishing transparency about AI.

The future doesn't only belong to those clinging desperately to human-exclusive creativity.

It belongs to those who can think outside the human box – who can imagine frameworks that acknowledge both human and non-human creative intelligence.

The luxury brands discovering they can't simply relocate manufacturing?

That's us – creators – discovering we can't retreat to human-only creative environments.

Look at the global response: The US, UK, and Japan agree that AI training on internet content is acceptable.

Meanwhile, creators desperately try to opt out – building walls against an incoming tide.

We have two choices:

First, develop systems to capture value at the point of creation – before it enters AI where traditional control becomes impossible.Second, expand our conception of creativity itself to include creative exchanges between human and non-human intelligence.

This transition isn't comfortable. It means abandoning cherished assumptions about human uniqueness.

But it also opens so much more.

Taking advantage of AI isn't just about mastering tools – it's about transcending the human hangup that limits our understanding of creation itself.

Like Gatsby looking over the water for something he'll never have – "boats borne ceaselessly into the past" – we're looking backward, clinging to human-only definitions that might not serve us anymore.

Time to turn around and see what's emerging.

RESOURCES
Imagination Engines - Stephen Thaler, Ph.D.

OpenAI’s Economic Blueprint

Stephen Thaler’s Quest to Get His ‘Autonomous’ AI Legally Recognized Could Upend Copyright Law Forever

Thaler v. Perlmutter: Human Authors at the Center of Copyright?

OpenAI’s deals with publishers could spell trouble for rivals

From Vans to OnlyFans: The Spotify Effect

Human Neurons Running Computers. Like me and AI

Human neurons are running computers. I'm not kidding.

In Australia, Cortical Labs creates living human neurons and integrates them with computer chips. They're creating what some call "biological intelligence" – actual human brain cells controlling computer systems.

From Cortical Labs

Science fiction? It's happening right now!

And this made me think: this is exactly like our relationship with AI.

When you stare at ChatGPT's empty prompt box and do nothing, nothing happens.Your neurons – your thinking, your ideas, your communication style – are what brings AI to life.Without your input, that powerful AI sits there, dormant.

I call this the "blank page problem" — that paralyzing moment when you know AI could help, but you don't know how to ask for what you need.

When both you and AI understand each other, the blend of intelligence and humanity's wisdom creates something unique.

This isn't about mastering complex tools or becoming a programmer.

It's about understanding how you naturally communicate first, then showing AI how to adapt to you—not the other way around.

Expect Probability, Not Perfect Promises

We've all been there—expecting AI to work like a vending machine. Push button, get candy.

AI doesn't store your exact words; it processes patterns in your language.

When AI gives you nonsense, it's not because the tool is broken or you're doing it wrong—it's because there's a mismatch in how you're communicating.

Human neurons run computers. How?Ask yourself the next time you're trying to talk with ChatGPT or Claude—how do you communicate with it?

Because in all the controversy about AI, it's amazing how many people don't use it, don't like it, and are falling behind because they're not actually seeing what's right in front of them—that whether they like it or not, AI is here, and they should at least know how it works.

The Overwhelm Problem

Look, most of us are winging it with AI tools. We throw everything at them at once and hope for the best.

You ask ChatGPT to

"Analyze my communication style, suggest improvements, compare me to famous people, AND write something new in my style."

(while you’re at it, could you get me a cup of coffee?)

That's like walking into a meeting and asking five questions at once—no wonder the responses are generic!

I made this mistake for months until I realized the problem wasn't the AI—it was me overloading the conversation.

The real secret isn't some fancy framework—it's taking a quick look in the mirror.

• What makes your communication style uniquely yours?

• What patterns do you naturally fall into?

Before AI can adapt to you, you need this self-awareness.

Understanding Your Own Communication Style First

Before thinking about AI, ask yourself:

What makes your communication style distinctly yours?

Here are two simple exercises for clarity:

Exercise 1: Communication Pattern Analysis

Take your last five emails, messages, or anything you've written recently.

Look at the first sentence of each paragraph.

· Do you start with questions?

· Make bold statements?

· Use stories?

Notice these patterns—they're the foundation of how you naturally communicate.

Exercise 2: Word Choice Inventory

Make a quick list of phrases and words you use often.

Better yet, ask a friend what phrases they associate with you. These are your signature expressions making your communication unique.

Quick Self-Check: Think about your last frustrating AI interaction.

Did it miss the detail you crave, jump ahead without proper context, or lack the creative exploration you were hoping for?

This might reveal what's happening in your chats. Sometimes what we think are great questions lead AI into confusion.

I like the rule, no bad chats, only bad questions. Because it’s easy to blame AI, which doesn’t exist, for not doing what you hoped.

AI is like people in a sense, the outcome is a probability, not a guarantee.Creating Your Personal Style Guide

When teaching AI your style, consistency matters more than comprehensiveness.

I made the mistake of sending AI 20 examples of my writing—confusing it more.

1. Choose 3-5 examples of your writing or communication that feel most natural to you.

2. Avoid including experimental styles or times when you were intentionally imitating someone else.

3. Read these 3-5 examples back-to-back and highlight the elements that are common in them.

4. Remember those phrases, those words—get to know your voice. And if you need to write more, then write more.

And if you don't like your voice, work with AI and find alternatives. Ask for suggestions.

Now, create a simple one-page document with these sections:

• Sentence Structure: Typical length, complexity, openings

• Vocabulary & Tone: Word choice tendencies, distinctive phrases

• Topics You Care About: Areas of focus, typical audience

• What Your Communication Is NOT: Approaches you specifically avoid

This becomes your communication DNA that AI needs to understand.

The Communication Gap

AI challenges tend to come in these 3:

1. Translation Problems: You're thinking in your communication style but not translating effectively for the AI.

I've watched developers struggle because they assumed AI understood their shorthand.

1. Expectation Problems: You're hoping the AI will fill gaps in your request that it can't see.

   One client expects ChatGPT to know her industry terminology without ever explaining it.

2. Framework Problems: You're missing a structured approach that bridges your thinking style with AI's needs.

Creating a "Voice Map"

If you have deep career experience, you've developed patterns you might not even recognize. Try to:

1. Curate Your Content: Create a simple map of different contexts you communicate in—emails, presentations, social media, reports. Creative works, books, etc.

Select one strong example from each category that people responded well to. It’s not about what you wrote—it's about what they read.

1. Identify Evolution: Select examples from different periods and highlight similarities and differences.

   Which elements have remained consistent throughout your development?

2. Use Contrast: Sometimes defining what you're not is as powerful as defining what you are.

For example, I don't like corporate speak. So AI knows to avoid those words. I like to be more conversational in language and tone.

1. Create a Visual Diagram: Put your name at the center with branches for

• Sentence Structure,

• Vocabulary,

• Topics,

• Tone, and

• Communication Goals.

For each branch, add 3-5 specific characteristics with real examples from your work.

1. Provide Context: When sharing examples with AI, add brief introductions explaining the context. For example:

"This is from a client email where I needed to explain a technical concept to a non-technical audience."

AI doesn't know what makes your communication special—you need to show it.

The Five-Step Process for Effective AI Questioning

The quality of AI's response reflects the quality of your question.

The perfect question isn't about fancy prompting techniques—it's about clarity, focus, and context.

This five-step framework is a guide focused on what you want, and not making up characters to give you answers.

It’s building your experience and context into what AI will consider with your prompts.

1. Identify Your Role and Goal

Start by establishing who you are and what you're trying to accomplish.

For example:

"I'm Declan Dunn, CEO of The AI Optimist, and I'm sharing this with an employee at a meeting."

Be specific. Now it knows you, gives it context. Let it understand what you're doing.

Don't stray into assuming there's some genius behind there that's going to spit out a great answer.

That's playing roulette. I'm not talking about gambling. I'm talking about learning.

2. One Question at a Time

Instead of asking AI to analyze your style, suggest improvements, AND write something new all at once, break these into separate interactions.

Example: First ask

"What patterns do you notice in these samples of my communication?"

Then in a follow-up:

"Based on your analysis, how could my explanation of [topic] engage people more while maintaining my natural style?"

3. Provide Specific Examples

Don't just tell AI about your style—show it. Quality over quantity.

One well-chosen paragraph that exemplifies your style is more effective than dumping your entire portfolio.

Don't tell. Show, show, show! I have to tell AI to do this, to use metaphors, to visualize.

4. Request Chain of Thought

Ask AI to explain its reasoning:

"After analyzing my examples, walk through your thought process about what makes my communication style distinctive."

This helps you understand AI's interpretation and correct any misunderstandings.

It's like having a conversation with a collaborator rather than giving orders to a tool.

(Though it turns out, at least from Anthropic, the actual reasoning AI uses is revealed about 1/3 of the time. So if you get a really long wandering response, might try again, or later.

Like people, AI will start bringing out word salads when unclear about an outcome.)

5. Iterate and Refine

Treat your interaction as a conversation, not a one-time search. This is another trap, AI like a search engine, when there’s so much more.

Use AI's response to refine your next question:

"You mentioned my use of analogies is distinctive.

Could you analyze specifically how my analogies differ from typical explanations in my field?"

Being Unique in an AI Driven World

How does someone recognize your unique communication style in a world where AI is processing the same information, we all consume?

The answer isn't fighting against AI but using it as a mirror to better understand your own distinctive patterns.

Your unique style isn't AI's problem—it's your superpower.

I'm telling you, your brain beats AI.

Your brain is better than AI, but together, your career, what you're doing, maybe even having enough time to do what you want to do—that's what AI is about.

So forget that "replacing people" fear.

Let's help you find a way to learn your style, work it in, and advance towards the goals that you want.

For Beginners:

As you're discovering your style, resist the temptation to simply copy others or rely on generic prompts.

Use AI to identify what elements already feel natural and distinctive in your communication, then consciously develop those elements.

For Established Communicators:

Your challenge is avoiding creative ruts.

Use AI not just to reinforce what you already do well, but to identify patterns you might not be aware of—both strengths and limitations.

Know AI and Know Yourself

The better you understand what makes your communication uniquely yours, the better you help AI understand what you need.

At the beginning we talked about human neurons running computers at Cortical Labs.

Right now you run an AI when you work with it.

If you don't put anything in the prompt box, nothing comes out. Sounds really obvious, right?

Just like those scientists figuring out how to integrate human thinking into machines, you're doing something similar every time you interact with AI.

You're teaching a machine to think more like you—to understand your patterns, your needs, your unique way of seeing the world. Before it reflects that into its algorithms.

Your brain beats AI every time—and it's not even close.

Our human experience, our perspectives, our ability to navigate complexity and nuance—these are superpowers that AI doesn't have.

And it starts with you knowing your own voice and teaching AI to speak your language, not the other way around.

So take that little step and say,

"This is an intelligence.

Let's speak the same language.

Let's communicate, and let's learn about each other."

That's when working with AI becomes productive AND enjoyable.

While you're training AI, you're training yourself—not only how to use a tool but what it can reveal about your own communication patterns.

Things that would have been difficult to learn, on your own, a few years ago—personal style, communication effectiveness, patterns you didn't even know you had—AI helps you discover quickly.

The future isn't only about human neurons physically running computers.

It's about you running AI with your uniquely human perspective.

When both understand each other, that's when AI stops speaking in tongues and starts amplifying what makes you, you.

(Or wait for this to end like personal computers, the Internet, mobile/social media…..amor fati.)

RESOURCESMy Common Questioning Pitfalls

These are mistakes I've made so you don't have to:

The Style Trap

Many people fall into asking AI to write "in the style of" famous communicators. While this can be a learning exercise, it won't help you develop your unique voice.

Instead of "Write in the style of Seth Godin," try "Analyze how my communication style differs from Seth Godin's approach."

The Vagueness Problem

Vague requests like "Make this better" or "Tell me about my style" force AI to guess what you're looking for.

Be specific:

"Analyze how I explain technical concepts to non-technical audiences" or

"Identify patterns in my email openings that might be creating confusion."

The Expectation

AI doesn't "know" things with certainty—it generates probable responses based on patterns.

Instead of asking

"What's wrong with my writing?" try

"What patterns in my communication might be limiting its effectiveness for my target audience?"

Practical Questioning Template

I am a [your role] working on [specific goal].

Here is a sample that demonstrates my natural communication style:

[Insert ONE clear example]

I'd like you to [ONE specific request].

Please explain your thought process on how you're interpreting my style.

Maybe AI magic is science not yet understood. Maybe AI is hype.
Maybe you wish it would all go away.

All are true, and opinions. There’s no way to prove them. And as human beings, when things change without “proof”, we kvetch.

It's not just technology changing—it's people's beliefs about it. Some embrace AI with religious fervor, others resist it like confused bystanders.

I'm dancing with this tension, joined by tech visionary and Reiki master Bruce Randall, whose background bridges corporate strategy and consciousness studies.

Meet Bruce on LinkedIn

"It comes down to perspective and how people have developed that perspective.

Once they get that, they resist change because they believe they're right.

The other side believes they're right. And it's beliefs, right? It's beliefs."

Bruce Randall

What emerges from our conversation isn't more tech preaching about AI; instead it’s an exploration of the belief codes that determine how we experience and integrate AI into our lives.

The Belief Codes Shaping Our AI Future

Our beliefs about AI create our reality with it. I brought this up to start he discussion:

"I think both sides are like incredibly similar. The engineers just need to realize the creators aren't these stereotypes.

It always comes up—

'They're entitled, their work sucks, I don't like their work, so they shouldn't get paid for it.'

Versus the creatives who think,

'I should get paid for everything that I don't get paid for.'"

These entrenched positions prevent common ground:

• Engineers believe creators are entitled and their work isn't valuable

• Creators believe they deserve compensation for every use of their work

• Both sides resist seeing the other's view

• Neither side is completely right or wrong

According to Bruce, the breakthrough happens when:

"When you start going deeper, you start seeing that it's all the same.

It's just a matter of what degree to what side you're on.

If both sides could come in and find common ground and build on it, they could start to go somewhere."

The challenge isn't technical—it's human. It's about our ability to step beyond our belief codes and find a new way forward.

Is AI Taking Us Away From Software Habits We Love?

My early experience with Omadeus showed how we are changing our relationship with data and software:

"I believe software is going to be gone. Not gone, but the world I grew up with is changing.

If we turn paper to digital...we basically create the AI from the bottom up. We're starting with data."

This signals a shift in how we approach AI:

• The focus moves from software to data

• AI wraps around software rather than replacing it

• We're creating "mini LLMs and swarms" rather than traditional applications

As someone who's spent decades in data, Bruce sees an interesting pattern emerging:

"We went away from silos to these data lakes and common structures.

We're going to go back to silos with specific data in there.

That's hard, true data. And if you want it, you have to pay for it."

This evolution is already happening, with companies like OpenAI creating tiered access:

"What they're doing with their $20 to $20,000 pay base is creating higher-end, deeper thoughts. These people use it at a higher level than the average person."

The question becomes: will super AI and superintelligence be available to everybody?

Bruce's answer is direct and candid: "No."

This creates a new digital divide—not just between those with and without technology, but between those with access to increasingly sophisticated AI and those without.

It's not about anti-human sentiment but about how we navigate this transition.

Fear of AI and New Intelligence - It's Not a Competition!

What's driving our resistance to AI? Often it comes down to fear of the unknown and unclear messaging:

"I call it the PR fear angle. I'm still trying to wrap my head around it because everything feels like a Steve Jobs reality distortion with $100 billion dollars.

(Jobs' reality distortion field - I experienced this in a meeting with him, woah - was a mix of charm, charisma, bravado, hyperbole, marketing, and persistence, distorting your sense of proportion and scale of difficulties, making you believe that the task at hand was possible.)

Sifting through the propaganda... I understand why they have to say it, but why don't we put people in the mix?"

This highlights a disconnect in how we approach AI:

• We're caught between hype and fear

• The messaging usually excludes the human element

• Safety measures limit answers - it’s hard to say something you have to avoid because humans can’t handle seeing themselves, their data, in the mirror.

Bruce shares what AI really is:

"AI is created by humans.

It's a big data mesh. It's updated.

So it's all our content, and we've got some biases built in there.

That's it—it is what it is."

The contrast with human intelligence:

"As humans, we grow and we have our life that has created our experiences.

I've come back from death twice and I've had interventions and all kinds of weird stuff.

It forms you into who you are."

This fundamental difference shapes how we engage with AI:

"AI has data and I always get opinions and data, and there's a little bit of an opinion in AI because humans built it.

When you try and find true data, it's hard."

The fear many experience isn't about AI itself, but about confronting our own beliefs through this tech mirror.

Bruce suggests that what scares people most is AI's ability to reflect our own thinking back to us.

AI Needs Enablers: Practical Use Cases Start With People

The pathway to successful AI isn't about forcing technology on people—it's about enabling them to discover its value in their work:

"I ask a lot of questions to really understand where they're coming from.

You have to play off the decision makers to understand where they want to go, because a lot of people can't explain it or don't explain it well enough for a technical person to understand."

Bruce shares a real-world example of this approach:

"There's a Ted talk from Sanofi, a French pharmaceutical company. The CEO wanted to be the first pharmaceutical company to put AI throughout the company.

He enabled every person in their position to help people do a better job.

But he didn't force it on the company—it was 'let's see how it can help you in your position.'"

The results were remarkable, especially in research and development:

"When you get to R&D and the scientists, they really benefited because they could create drugs faster with less consternation.

Sometimes they get different things they wouldn't have thought about that actually pass the final tests better—things they just didn't think of as humans."

This human-centered approach yields better results than algo isolation:

• Success comes from enabling rather than mandating

• AI shines when it augments human creativity and problem-solving

• The best outcomes emerge from human-AI together, not just AI First

• Change happens when people discover AI's value for themselves

Bruce emphasizes this critical point:

"When you have the human involved with AI, it's a much better outcome than if you just have AI into AI.

If you embrace it to enable versus saying 'you're going to use this,' you're going to have a lot more success."

This people-first approach represents a shift from how technology has traditionally been implemented.

AI Technology and Human Potential: What's In The Way?

The tension between optimization and humanness creates a central challenge for today's leaders:

"This interesting tension is coming up... it must be so challenging to be a CEO today because you have this optimization versus humanness."

Bruce explores the fascinating convergence happening between AI and other technologies:

"Quantum computing is set to reshape AI, cybersecurity, and data processing. We've got genetic AI, generative AI, and this multi-headed hydra.

We've got all these different flavors of AI coming out, and understanding each and keeping up with each—if you're not studying AI, most people look at it and say, 'I don't know which way to go.'"

This rapid evolution creates uncertainty:

"That curve is moving sometimes week by week, sometimes month by month, but it's moving faster than anything we've done before.

And when quantum meets AI and you put them together, that's a complete paradigm shift.

We can't even imagine what's going to come from that because we've never done it before."

• We're entering uncharted territory with converging technologies

• The pace of change outstrips our ability to fully comprehend it

• People view AI through their existing belief lenses

• Perspective determines what value we extract from these tools

Bruce offers a practical approach to navigating this complexity:

"Trust and verify if it's important. If you're asking general questions, you're okay.

But if you're asking something where it matters, you have to check and verify to make sure everything's accurate."

This cognitive dissonance between AI's capabilities and limitations creates a natural tension:

"It's like, okay, it's this super thing that's so smart and intimidating to any human being. Like, 'Why am I here?'

But at the same essence, you can't trust it. That's the extreme opinion."

As we integrate AI into our lives, this paradox becomes important to manage with awareness rather than fear.

Trying to Stop AI? That's a Belief Code, Is It Good for You?

My take on having a relationship with AI and data:

"I'm a hardcore privacy person on my personal level. Like, to an extreme level.

But there is absolutely nothing in my life that isn't tracked.

And I'm not going to live paranoid about it.

At a certain point, all the data is just there."

This reflects a deeper understanding about how you approach AI:

"If you're looking for it to destroy you, you'll probably find that for sure if that's all you're looking for.

And if you're looking for AI to help you..."

Like a friend was joking me about AI, she wasn’t a fan…..

"I had an interior designer who was grilling me on AI in a good way. She didn't know and she didn't like it.

Just everything about it—she had a lot of fear. So I looked at her and said,

'Well, let me ask you the question everybody asks me: Is interior design sentient?'

And she cracked up with laughter. I said, 'Are you biased? That's why I hire you for your bias.'"

Many attitudes about AI are done without knowledge or interest, like a comment on a gossip column of the day.

That’s denial in a way:

• Our fears about AI often reflect deeper anxieties

• We choose how we frame our relationship with technology

• Humor and perspective shift break through fear

• The value in human expertise includes our unique biases and perspectives

As AI and quantum computing converge, Bruce notes:

"When quantum comes up and AI develops, they're both developing.

AI is developing at a faster rate than quantum.

But when the two meet and you put them together, I mean, that's a complete paradigm shift."

The profound question about human identity:

"At what point do we stop being purely human?"

Bruce's approach to sharing his work reflects his overall philosophy:

"I'm putting it out because I want to help humanity and I want to help educate people. If they take it from me, it's flattering.

And if I lose book sales, that's okay, because I want everybody to know this."

This generosity contrasts with the fear-based approaches many take to protecting their work, showing how different belief codes lead to different experiences with technology.

And there is a choice, maybe not to control AI, instead find a way to work it.

I notice we're close to concluding the podcast analysis. Let me put together the final section that would wrap up the insights Bruce shared in this episode.

Beyond Belief Codes

Our approach to AI isn't a technical challenge—it's a human one.

"We've never been here before. The AI-driven implant brain computer interfaces.

The line between human intelligence and machine processing is blurring."

Bruce's project called "The AI Human Paradox" asks the pivotal question:

"At what point do we stop being purely human?"

Not to create fear but to foster awareness and thoughtful integration.

The true value comes from combining technological advancement with human consciousness:

• AI reflects our own thinking back to us

• Our belief codes determine what we see in AI

• The most successful AI start with human needs

• The convergence of quantum computing and AI is harder to imagine, and maybe even harder to believe for some.

Time for us to recognize our belief codes and whether they're serving us:

• Engineers need to see creators as partners, not adversaries

• Creators need realistic expectations about compensation

• Both sides find common ground through listening to the other’s views

• The best AI enables rather than mandates

As Bruce's work as both a tech strategist and Reiki master shows, powerful integration happens when we honor both tech advancement and human consciousness.

The AI Belief Codes aren't fixed—they're adaptable frameworks that evolve as we do.

What’s your AI Belief code?Resources

Bruce Randall on LinkedIn:

The AI Scientist Generates its First Peer-Reviewed Scientific Publication

Leadership in the Age of AI (on Sanofi CEO)

If AI is an Inventor, then So is Nature - Robert Plotkin (EP #35)

Thanks for reading The AI Optimist! This post is public so feel free to share it.

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"I was searching this pirated database of books that have been taken off the internet, and it was bought by Meta in 2020..."

That's how our story begins. Through Ann Handley's newsletter, I discovered an Atlantic article with a way to search the pirated LibGen database containing everything from Harry Potter to Stephen King — even Anne's books.

A database Meta purchased in 2020, likely to train AI.

Search LibGen via The Atlantic

And it reveals something about how AI companies approached training data.

A Meta director of engineering admitted in a 2020 chat (revealed through legal filings):

"The problem is that people don't realize that if we license one single book, we won't be able to lean into fair use strategy.

So we'll have to drop all of the LibGen and books data sets."

2020 - Meta Director of Engineering

This engineering choice wasn't just about cost—it was about time. Those pirated books fueled early AI development before most people even knew what was happening.

It's cheaper to risk lawsuits than to start paying everyone. That's the reality we're facing.

But here's the contradiction that nobody's talking about:

Today, the creative economy is worth an estimated $985 billion. G20 Insights estimates that by 2030, the creative economy could account for 10% of global GDP.Deloitte predicts that by 2030, there will be up to 40% growth in the creative industry.

If AI is "stealing" all creative work, how is the creative economy projected to grow so dramatically?

The creative industry is rising. So should you.

This contradiction sits at the heart of our conversation today. The creative industry isn't dying—it's transforming.

And instead of fighting yesterday's battle, let's explore how creators can position themselves over the next 2-5 years when everything's changing.

"The AI freight train is coming on.

I'm saying to the creators, here's what you can actually do."

Part 1: Creators as Energy Feeding AI

"It starts with knowing the side hustles."

Instead of fighting for micro-payouts after the fact, what if creatives became direct contributors—fueling AI with expertise, unique insights, and high-quality input?

This isn't about being replaced by AI, but about becoming an essential part of AI's lifecycle.

Most of the AI experimentation is happening at the big enterprise level—big tech, large companies where major experiments are underway.

I've been dissecting these down to see what might apply to you and me.

"What if we could fuel AI with expert insights, knowledge, experience and also creativity, that dynamic part of humanity, not just limit the probability and the creation to AI?"

Small Side Hustles: Specialized Data Curation

Financial services, human resources, marketing and sales—these three industries have been the first broad lines of AI adoption because they involve more predictable scenarios like creating words or formulas.

Still AI models lack niche, high-quality domain knowledge—especially in:

• Highly specialized writing and creative work
  When a friend asked me about selling copywriting services, I told him, "I don't think people are looking for copywriting services.

  They're looking for your knowledge, your experience, your words using AI."

  That's exactly what he does—creating a voice, a tool, their own AI copywriter built with them.

• Cultural context
  "I live in Northern California, which most people consider Los Angeles. But if you know the United States, we're part of Cascadia—Washington, Oregon, and the upper northern part of California. People barely ever visit."

  AI often lacks this deep social understanding and cultural point of view. It only knows what we've written about.

• Authentic voices and visual knowledge
  "People who are photographers, people who are artists can be able to use their visual knowledge, their domain-specific knowledge to help people work with GenAI."

  Knowing all the aspects that a photographer understands to get that good shot—the kind of film, the camera, the lighting—these are specialized skills AI still needs help with.

The best thing about these side hustles? The AI training takes time and usually is more than a month or two.

Plus, "if there's no training and it just ends, there's entropy. It erodes. Your service might be something you could offer again and again."

Think of it as teaching AI to speak the language of your creative domain—combining technical understanding with creative intuition to help businesses find their way through this new landscape.

Part 2: AI-Driven Creative Careers

"This is future looking. I don't like to do that.

But if we're seeing a creative economy growing so much,

where might some of the opportunities be?"

Beyond side hustles, entire new career paths are emerging with creativity and AI.

These aren't just theoretical futures—they're starting to take shape right now.

AI Creative Residencies

"Let's go back to the Renaissance."

MIchelangelo = Renaissance Artist

In the Renaissance, artists weren't paid to sell their books—it was hard. The Medicis, for example, that famous family (sort of like The Sopranos of their time), worked with Michelangelo as a patron, creating art.

"What if we had these micro-Michelangelos, this ability to distribute this knowledge and creative residencies where this creative person and their domain would come in for three months, six, 12 months, maybe hired in-house from bigger companies to help their training?"

Lorenzo de' Medici - Italian politician and humanist

AI companies (or even third-party organizations) could hire creators to:

• Fine-tune AI models with unique styles and develop those styles with them

• Create AI-enhanced experiences (what the customer sees when visiting a site)

• Act as AI "consultants" for businesses trying to implement generative AI

"A creative residency allows you to use your creativity, and you can start applying this to small nonprofits, small businesses.

There's all these people who are so lost on AI and so few are helping them."

Instead of viewing AI as defeating creativity, think of it as an opportunity to bring your specific skills to help others understand their customers, their data, and improve it through a creative residency.

"AI is driven by probability. We need a good and a bad to find the middle."

This understanding opens up creative possibilities that technical teams might miss.

AI Agent Definers and Designers

"The marketers have taken over AI agents last year... but here's the thing about AI agents—they're good.

They're really good at doing automations.

They're sometimes good at doing workflows."

A new hybrid role is emerging that blends technical understanding with creative instinct:

• Designing AI personalities and behaviors that resonate with specific audiences

• Creating workflows that combine AI capabilities with human creativity

• Defining how AI interfaces with creative processes

"This isn't programming. It's creative direction for AI behavior."

But you need to know a little bit about how this works—take some classes, get yourself familiar, do some prompting.

"Where do people start losing it?

Where is the organization of projects happening and going wrong?

Have you ever been on one where it just goes off the rails because people aren't that organized?"

These are the workflow gaps where creatives bring value.

The most valuable skill isn't just making content anymore—it's defining how AI interprets and generates content within specific creative contexts.

Help people find their way through this new landscape with your domain expertise combined with AI understanding.

Part 3: The Reality Check on Licensing

"Everyone who's a creator says, 'I want to get paid for what was taken from me.' And while that's good, where were you getting paid before AI took it from you?"

While we're all frantically trying to prove AI stole our content, the creative economy has always been a fame lottery.

Only a small percentage of creators make substantial money from their work. AI isn't changing that ratio—it's just changing how the game is played.

The Fame Lottery

"If your content was taken and it made a lot of money, you're going to be one of the many talking about licensing deals or represented by lawyers who are trying to figure this out."

Consider these realities:

• Getty Images watermark shows up in Stability AI outputs—their brand became a pattern because it was repeated so often

• Famous authors like J.K. Rowling might get licensing deals because their work creates recognizable patterns

• For the average creator? "None of us obviously exist at this level"

The reality is, finding your influence in AI models is like finding a specific drop of water in the ocean.

The Licensing Economics

I went and asked a number of different bots and people like, how would this compare to how musicians get paid at Spotify right now?

Right now, AI companies have paid undisclosed amounts to outlets like Axios and Axel Springer for licensing deals, but let’s construct some hypothetical numbers.

If you're a musician on Spotify, there is so little money from all that streaming that you have to go on the road and play concerts because that's where you make your money. Writers and artists who don't have a live stage face an even tougher challenge.

Plus, it's not a good comparison:

"When I go to Spotify, I'm playing your song, or at least a piece of it, right?

But when I'm going to AI, I'm probably maybe getting a piece of your influence."

This leads us to what's called "nonliteral influence," which sounds hokey, but it's essentially about probability putting together patterns. AI isn't storing your exact content—it's reconstructing patterns it's learned.

"For Getty Images, that's brand... If you have your little photos going out there, at best it could partially influence what's going on."

The Licensing Reality

Here's the reality nobody wants to talk about: even if we could perfectly track every creative work that influenced an AI model, the resulting payment would look more like a Spotify royalty than a Hollywood contract.

AI licensing—it's the same. You're a creator.

Are you going to be a hit? Are you going to be a star?

Are you going to be on top of things?

Then you might have some room to talk about licensing.

You get big, you're going to have attorneys and publishers.

Those are the ones getting paid.

Part 4: The New Creative Reality

I'm not really talking about fighting as much as adapting.

We still need human creativity and judgment.

The difference is we need it in new places—not just making content, but shaping how AI understands and generates content.

From Content Creators to Data Strategists

The most valuable creative skill isn't just creating content anymore—it's understanding how data shapes AI's perception of quality, style, and meaning.

Creators can:

• Design content that maintains unique human value

• Develop expertise in guiding AI tools rather than just using them

• Focus on what AI can't do well: emotional resonance, cultural relevance, moral judgment

AI Needs Human Creativity

Instead of fighting AI influence, creators can intentionally design content that stands out in an AI-saturated world.

The future belongs to creators who can:

• Use AI to handle scale while focusing on uniqueness

• Leverage AI tools to enhance rather than replace their creativity

• Position themselves as essential interpreters between AI and human audiences

The question isn't whether AI will use your work—it already has. The question is whether you'll find ways to make your creativity valuable in an AI-powered world.

After all, the most valuable creative skill has never been just making content—it's been making content that connects.

And that human connection is exactly what AI still can't replicate.

The choice is yours: stand on the sidelines hoping for licensing checks or become part of the system that shapes AI's creative future.

"Breathe and put out your voice. Because we need IT."

RESOURCES

Search LibGen, the Pirated-Books Database That Meta Used to Train AI

Anne Handley: Everybody Writes

The Unbelievable Scale of AI’s Pirated-Books Problem

How The Growing Creative Economy Will Soon Devour The Real Economy

Creative economy 2030: Inclusive and resilient creative economy for sustainable development and recovery

The creative economy takes center stage - 2021

Thanks for reading The AI Optimist! This post is public so feel free to share it.

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"'I've always tried to understand the world underneath everything, because that's really what's going on,

We live on the surface.

And unfortunately, the culture that we live in is also absolutely obsessed with surface... But that isn't reality.'

What you're looking at is more powerful than any legal argument.

On the left, AI-generated "in the style of Geoffrey Laurence." On the right, an actual Geoffrey Laurence painting.

Meet Geoffrey on Instagram

"On first glance you could say, oh yeah, that's a Geoffrey Laurence, but I would never do that in the background.

I would never make a background like that.

Look at the reflected light in the cheek and just below the ear.

Do you see the difference between that and what the machine has done on the left side of his face? How crude that is."

Geoffrey Laurence

This visual difference shows what's at stake in these copyright cases:

• It's not just the final image - it's the expression, the emotional resonance that Geoffrey describes as "the world underneath everything"

And now we begin with 3 Case Files, and First…a Case without a file:

Case with no number, Geoffrey Laurence. Artist, 56 years. All his work sucked up by ChatGPT because he shared it on that flat surface called the Internet.

His work has been shown in museums worldwide yet to the AI bots, he’s just another pixel towards training that diffusion model.

Because AI is “learning”, all good to take whatever content they can scrape buy or borrow.And it’s the opposite of what I've discovered investigating the three major AI copyright lawsuits.

The headlines focus on copied text, reproduced images, and legal technicalities.

But that's just the surface. We’ve covered those in detail, picking apart the real from the not so real.

Yeah they matter, but to a judge with no precedents and little real guidance, it comes down to one factor they look at, when they look beneath the surface….

Then Swen Werner with a comment suggests I read his Substack My Digital Truth® - Fusionistic

My Digital Truth® - FusionisticThe NYT’s AI Lawsuit Hinges on a Misleading Claim—And Nobody NoticedThe New York Times Company has an ongoing lawsuit against OpenAI and Microsoft, filed in December 2023. The lawsuit alleges that OpenAI's language models were trained on the Times' copyrighted content without permission, constituting copyright infringement. …Read more7 months ago · 1 comment · Swen Werner

Asking me to look beneath the surface and shows me a Guy Fieri example, the one that went viral, and how it shows the dancing with the facts that happens in legal matters and with attorneys….

First glance you get angry, then you get curious. Then you take action. Sitting still is not in the cards for creators.

When you follow the money trail like I have, these lawsuits tell a completely different story – one that has little to do with Geoffrey or the millions of other creators without powerful lawyers.

Today we're opening three case files that reveal what AI copyright lawsuits are REALLY about.

When we pull apart the legal word salad and find where the actual decisions will happen, you'll usually find it when you follow the money.

Beneath the surface reality of AI is a money trail telling a different story – one defining the future relationship between human creativity and artificial intelligence.

Case #1: Thomson Reuters vs ROSS Intelligence Exhibit A: The Competitor's Dilemma

Let's open our first case file.

Thomson Reuters, owner of Westlaw, created a valuable legal research system with human-written headnotes summarizing key points of law.

This represents decades of expert legal services – their intellectual property.

ROSS Intelligence wanted to build a competing AI legal research tool. When Thomson Reuters refused to license their content (seeing ROSS as a competitor), ROSS didn't give up.

Instead, they hired LegalEase, a third-party research firm, to create 'Bulk Memos' containing legal questions and answers.

Essentially, ROSS had found a workaround to use Thomson Reuters' data after being explicitly denied permission.

ROSS bought more than the usual legal office would touch in a decade worth of legal memos all at once – 25,000 of them – creating an anomaly so large it couldn't be ignored.

Thomson Reuters didn't need to find their content in ROSS's output.

The court focused on the training data itself – the Bulk Memos used to teach the AI – finding that they resembled Thomson Reuters' original headnotes.

Follow the money trail:

This wasn't about protecting content – it was about protecting market position.

Thomson Reuters faced a direct competitor attempting to leverage their decades of investment without compensation.

That's what drove the decision, not abstract concepts about AI copying.

Why is this sending a chill through Silicon Valley?

Because copyright violations after AI had legally purchased data was found. On the input, which is the key to the AI industry which states it has a right to use this under fair use.

Fair use defense took a big hit in this one.

Case #2: NY Times vs OpenAI Exhibit B: The Media Model Under Threat

Our second case file reveals something fascinating about the New York Times lawsuit against OpenAI.

The Times claims ChatGPT is essentially stealing their journalism. Their smoking gun? When prompted, ChatGPT reproduced parts of a viral restaurant review about Guy Fieri.

But dig beneath the surface, and you'll find a different story.

As Swen Werner points out, there's a crucial detail the Times left out: ChatGPT only reproduced about 20% of the article – mostly the beginning that went viral across the internet.

Example of the Snippets produced; items in red show original NYT words.

What they didn't say was that it only recalled the most widely quoted parts that likely appeared on countless websites.

Is this evidence of direct copying or something else entirely?

The AI isn't 'storing' articles but recognizing text patterns that appear frequently online – similar to how you might recognize 'To be or not to be' without having memorized the entire play of Hamlet.

Follow the money trail: This case isn't about copying snippets.

It's about the New York Times seeing AI potentially cannibalizing their subscription model by answering questions using information drawn from their journalism.

Their business model depends on being the exclusive source for their reporting – and AI threatens that exclusivity.

The New York Times lawsuit focuses on surface copying while the concern runs deeper: will people still pay for subscriptions in an AI-powered world?

Case #3: Getty Images vs Stability AI Exhibit C: The Brand Value Heist

Our third case file might be the most visually compelling – and it crosses international borders in ways that dramatically impact the outcome.

Getty Images, a major stock photo provider, is in legal action against Stability AI, claiming they 'scraped' millions of Getty's copyrighted images without permission to train their Stable Diffusion AI model.

Unlike the other cases, Getty has visual evidence that's impossible to ignore – AI-generated images that accidentally reproduce Getty's distinctive watermarks.

It's like catching a forger who accidentally left the original artist's signature on their work.

This case is being tried in the UK, not the US. Stability AI claims the training didn't happen in the UK at all, but in the US, potentially putting it outside UK copyright jurisdiction.

The court acknowledged that a crucial question is the location of the training.

Follow the money trail: While 11,383 images aren't that many in the grand scheme of AI training data, finding Getty's brand appearing throughout Stability's outputs reveals what's really at stake.

This isn't just about images – it's about Getty's brand value, worth billions, being used without compensation.

Stability argues that their AI doesn't 'memorize' images but learns patterns, creating new works rather than copying.

They even claim any similarity to Getty images comes from user prompts, not the underlying model.

When AI reproduces a Getty watermark, it reveals that the machine doesn't understand what it's copying – it's just pattern-matching without comprehension.

The economic impact is clear: Getty Images watches as AI tools generate images that could eliminate the need for stock photo licenses, potentially undermining their entire business model.

Buying AI Data with Copyright Diligence

These three lawsuits are reshaping how AI companies acquire and use data. The wild west era of "scrape everything" is ending as courts increasingly recognize that:

1. Training = Using: Using content to train AI, even without displaying it, can be infringement

2. Intent Matters: Building direct competitors with someone else's content faces greater scrutiny

3. Economic Impact Drives Decisions: Cases with clear business model impact have stronger claims

Now what do we do with the damage caused, and how does this work going forward?

We're watching three lawsuits that will determine whether AI thrives or stumbles in America - and I'm struck by how both sides are missing what's really at stake.

It's not about algorithms or legal precedents. It's about who gets to profit from human creativity in the age of AI.

• When the New York Times sued OpenAI, they claimed ChatGPT was essentially stealing their journalism.

• The Thomson Reuters case showed that even without visible output, training AI on copyrighted material may still be infringement when building a direct competitor.

A special kind of sneaky move by ROSS - after being denied a license, they bought 25,000 "Bulk Memos" through a third party to train their legal AI.

An agile AI startup meets a big company that watches it’s 3rd party data purchases.

• The Getty Images case provides the most visible evidence - AI art generators that accidentally reproduced Getty watermarks.

But while others might have felt their work was taken by AI, Getty’s brand is what emerges, which makes that one even harder to figure out.

With their recent merger with Shutterstock in January 2025, the value of that company is about $3.5B US.

So all those Getty Images name and logo appearing, whether with an image or not, are more than copyright violations. They impact brand value by putting Getty’s name next to sometimes odd output.

Because like NY Times snippets, the reconstruction of that repetitive brand copyright image became a recognizable pattern to AI.

That’s more than content, that’s AI taking a brand by accident….which is why this case will be so hard to decide. The numbers here are huge.

Add onto the fact that Stability’s location makes copyright a question of which country, US or UK, but the Getty Images brand is worldwide.

When we look beneath the surface of these three case files, a clear picture emerges.

These lawsuits aren't ultimately about copying, or technical details of how AI works – they're about who profits from human creativity in the age of AI.

• Thomson Reuters doesn't want a competitor using their legal analysis to build a competing product

• The New York Times doesn't want AI undermining their subscription model

• Getty Images doesn't want its brand value appropriated without compensation

These economic realities, not abstract legal principles, will likely determine the outcomes.

The Human Cost Both Sides Are Missing

AI engineers often see creators as entitled complainers who don't understand technology, clinging to outdated ways.

Creators see engineers as people who don't respect the thousands of hours it takes to develop creative skills. Or create.

Neither side is truly listening. And they’re not even dealing with the facts, just opinions on whether AI is copying or evil, and whether creators expect to get paid for things they don’t deserve to be paid for….

If engineers win outright, what motivation will people have to create when their work just becomes training material for Big Tech's trillion-dollar AI systems?

If creators win completely, a thriving industry gets derailed, potentially setting America back in the AI race.

The solutions will likely involve:

1. Fair compensation systems that recognize when AI benefits from creative work

2. Clear attribution when AI builds on specific sources

3. Opt-out mechanisms for creators who don't want to participate BEFORE content goes into AI

4. Licensing frameworks that make compliance straightforward

Whether you're a creator like Geoffrey Laurence worried about your livelihood or a business excited about AI's potential, these cases will impact your future.

The companies that will lead us forward aren't just those with the best technology, but those building financially sound data practices into their foundations.

These legal battles aren't the end of the story - they're the messy beginning.

"What I find myself in now is a world that is purely engaged in surface," Geoffrey Laurence told me. "I've always tried to understand the world underneath everything, because that's really what's going on."

This is the perfect metaphor for our AI moment.

The companies creating AI are building tools that excel at surface pattern recognition.

But Geoffrey's work reminds us what lies beneath – the human emotion, the 56 years of craft, the ability to capture something ineffable.

The difference between surface copying and deep understanding matters - not just in art, but in how we build our AI future.

That's the money trail worth following.

RESOURCES

Geoffrey Laurence

The NYT’s AI Lawsuit Hinges on a Misleading Claim—And Nobody Noticed

My Digital Truth® - FusionisticThe NYT’s AI Lawsuit Hinges on a Misleading Claim—And Nobody NoticedThe New York Times Company has an ongoing lawsuit against OpenAI and Microsoft, filed in December 2023. The lawsuit alleges that OpenAI's language models were trained on the Times' copyrighted content without permission, constituting copyright infringement. …Read more7 months ago · 1 comment · Swen Werner

Every AI Copyright Lawsuit in the US, Visualized

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Getty Images, Shutterstock gear up for AI challenge with $3.7 billion merger

Authors Guild v. Google, Inc., No. 13-4829 (2d Cir. 2015)