The AI Optimist

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:

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

* 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.

* 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 Now

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 Licensing

For 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.

* 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:

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

* Varied Environments: Shoot in many different locations and lighting conditions

* Object Interactions: Show people or animals interacting with various objects

* Unique Perspectives: Drone footage and uncommon camera angles

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

* Nature and Wildlife: Diverse animal behaviors and natural landscapes

* Travel Footage: Capturing global locations and cultures

* 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?

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

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

* Research intermediaries: Investigate companies like Troveo AI and Protege

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

* 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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

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, 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 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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

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.

"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."

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.

AI 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.

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.

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.

RESOURCESImagination 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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

When 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.

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

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

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

* 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:

* 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.

* 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.

* 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:

* 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.

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

Which elements have remained consistent throughout your development?

* 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.

* 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.

* 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.)

RESOURCES

My 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.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

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.

"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.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

"I 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.

Search LibGen via The Atlantic

And it reveals something critical 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 innovation and 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 workWhen 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."

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?"

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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

"'I'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.

"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"

The "It's Different This Time" Argument

The Authors Guild lawsuit makes a fascinating distinction:

"ChatGPT and other LLMs are not search engines. They do not lead readers to authors' books; they replace them with automated derivatives that do not compensate the human creators."

This cuts to the heart of what makes the NYT case different too - it's not just about learning from content, but potentially replacing the need for it.

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 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

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.

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:

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

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

* 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:

* Fair compensation systems that recognize when AI benefits from creative work

* Clear attribution when AI builds on specific sources

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

* 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

Every AI Copyright Lawsuit in the US, Visualized

What is Third-Party Audience Data? Examples, Datasets and Providers

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

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

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

That buzz when you discover something that feels right - that's how I felt about the UK's 'Make it Fair' campaign for creators' rights with AI.

Web site © NMA Ltd’ or ‘Courtesy of NMA Ltd’.

Then boom - nothing. Like creators trying to make up for lost time.

And what’s “AI fair” varies depending on the country you are in…

Like chasing a static object dancing in random neural networks.

Or eating over-ripe sourdough bread in a seedy San Francisco bus stop.

Sourdough bread and AI. What do they have in common?

Both begin with something small that keeps growing, learning, and evolving.

Some sourdough starters are over 100 years old, taking in new flour and water each time, growing into something new. AI does the same with creative work.

But while everybody loves the smell of fresh bread, we're less comfortable with AI consuming our creativity without clear boundaries.

At least Japan gives you rules you can understand. Pretty much every other country is sitting on the sidelines, waiting for courts to figure it out.

The battle for creative work isn't tech versus artists.

It's about whether we value both the starter and what grows from it.

Like sourdough requires regular feeding, AI needs continuous training with fresh data—not just now, but 100 years from now.

In the future that won't be the Wild West AI of the past few years.

That's why the UK's "Make It Fair" campaign launched this week, another voice shouting the same message.

Meanwhile in the courts and regulatory agencies, many decisions will be made this year.

Creative industries in many countries are fighting back against rules letting AI use copyrighted work without permission, payment, or involvement from content owners.

They're not anti-tech, they're pro-creator, demanding the obvious: consent and compensation.

Bakers often share their sourdough starters as gifts.

AI companies in many countries took creative work without asking. We're not saying stop.

Act like a baker, not a unicorn.

We're saying think and ask before you act.

This battle between creators and AI isn't just playing out in studios and corporate boardrooms between lawyers.

It's happening at the international level, with two countries taking completely different approaches to the same problem.

"The battle for creative work isn't tech versus artists.

It's about whether we value both the starter and what grows from it."

And whether what is “Fair” in the UK, is the same in the US, or Japan?

Japan vs. US: Two Models For AI's Creative Future

In Japan, all copyrightable data is allowed to be put into AI. That might sound like a free-for-all, but they've established rules, boundaries, and practices that make this work better than you might expect.

Japan's approach to AI is community-focused, viewing creativity as an evolutionary timeline.

What somebody creates now builds on what came before, like software development. They're encouraging learning and training while still providing protection for creators.

The Japanese model doesn't mean creators have no recourse. Even without lawyers, they offer mediation and arbitration so anyone can get their case heard.

It's not perfect, but it creates a framework where AI development and protection can coexist. And it’s also indicative of the culture of copyright in Japan.

This contrasts with the US approach, where we're still figuring things out case by case, waiting for the courts to establish precedents.

Our uncertainty creates anxiety for both creators and engineers. Both feel a negative outcome would be destructive to their careers and businesses.

Whatever happens, half will feel let down, and possibly left behind.

Japan's copyright rules for AI rest on two key pillars that balance progress with protection:

The Non-Enjoyment Purpose Requirement

Intention matters. Japan allows copyrighted content to train AI systems if the purpose isn't to recreate the original work. You're not bringing in Picasso’s paintings to create more Picassos - you're learning artistic styles, influences, and color theory.

This creates breathing room for AI development while maintaining boundaries around blatant copying. The focus isn't on inputs but outcomes.

Article 34 Proviso: Where Protection Lives

Japan doesn't leave creators without protection. Their Article 34 Proviso acts as a safety net, establishing that if AI output causes actual harm to a creator's career, reputation, or financial status, normal copyright protections kick in.

This practical approach says: let data flow into AI systems, but watch carefully what comes out. If the output creates direct competition or damages creators, that's where the line gets drawn.

“Make it Fair” for who?

Meanwhile, the UK is having its own creator uprising. The "Make It Fair" campaign launched in February 2025 stands in stark contrast to their 2019 rules regarding AI and copyrightable data.

While the 2019 rules allowed AI to use copyrighted data without permission or payment, "Make It Fair" aims to protect creators' rights and ensure fair compensation.

The campaign addresses the broader impact on creative industries, which generate over £120 billion annually for the UK economy.

They're advocating for stronger copyright protection, opposing government proposals that would weaken existing laws.

Unlike the 2019 rules, the campaign emphasizes the need for AI companies to obtain permission and provide payment for using creative content.

"Japan's approach creates breathing room for AI development while maintaining boundaries around blatant copying. The focus isn't on inputs but outcomes."

Fair for creators versus Fair Use for AI is a defining point in legal decisions.

AI as Hero AND Villain: The Joseph Campbell Perspective

I’d love to ask mythologist Joseph Campbell about AI, he'd likely see our collective anxieties as a projection of our shadow selves.

Just as vampires emerged from three people creating horror stories in 1816 and evolved into a cultural phenomenon, AI represents our deepest fears and highest hopes.

Campbell would recognize AI playing dual roles in our modern mythology: hero and threshold guardian.

For AI enthusiasts and engineers, it's the hero's journey personified - overcoming odds, transforming challenges, and bringing about a whole new world.

It represents progress, potential, and possibility.

For creators whose work has been taken without permission, AI represents the opposite - a destroyer, not a builder of worlds.

It's the threshold guardian, that archetypal figure standing at the door between the old world and the new, challenging whether we're ready to proceed.

This tension isn't new. When electronic synthesizers entered music in the 1970s, many musicians feared the end of "real music."

When digital photography emerged in the 1990s, professional photographers predicted the death of their craft (though Kodak was crushed).

When CGI revolutionized filmmaking in the early 2000s, actors worried they'd be replaced by digital avatars (and that’s looking more possible with AI).

Each time, the technology became a tool that expands rather than replacing the human element. The initial fear gave way to integration - but not without establishing boundaries and rules.

Oh yeah, and don’t steal things first without asking.

Silicon Valley finds that a dated practice, and in this case it’s hard to stop the AI train.

That's why creators are so concerned with legacy. If they can't make a living from their work like Dr. Polidori (who wrote one of the first vampire stories), why create at all?

The fear isn't about AI creating - it's about AI regurgitating what humans have already done without adding value or respecting the source.

Our relationship with AI is complex. We’re both drawn to its potential and frightened by its impact. Like all threshold guardians in mythology, it forces us to confront hard questions about what we value and how we define creativity itself.

"AI plays dual roles in our modern mythology - both the hero on a journey to transform the world and the threshold guardian challenging whether we're ready to proceed."

The 3 C's: Creator Control Toolkit

The Creator Control Toolkit is a way to grab back ownership of your creative work in the AI age.

While many creators feel powerless against AI training on their content, there are practical steps you can take right now.

Control - Take Back Your Creative Power

Up until recently, creators have had virtually no control over how their work is used online. People can take, sample, and repurpose your creative materials with little recourse. But that's changing.

* Time/Date Stamp Everything - Whatever you create, get an official timestamp. Services like CredTent can provide certificates or blockchain records that can be updated.

* Set Up Robots.txt - This simple file on your website can block search engines and some AI crawlers from scraping your content. While not foolproof (many large language models ignore it), it establishes your intent.

* Update Your Copyright Notice - Ensure your website explicitly addresses AI training in your privacy policy and terms of service. (I've posted a template on my Substack that you can use - I got it from ChatGPT, it's that easy!)

ChatGPT No AI Training language example:

Not a legal contract, run this by an attorney. I am not one.

No AI Training Clause

* Prohibition on AI Training UseYou may not use, reproduce, or distribute any content from this website, including but not limited to text, images, videos, or other materials, for the purpose of training, fine-tuning, or enhancing any artificial intelligence (AI) models or machine learning algorithms without prior written consent from [Your Company Name].

* Licensing RequestsWe recognize that there may be legitimate uses of our content for AI development. If you are interested in licensing our content for such purposes, you must contact us directly at [Contact Email or Form] to discuss terms and obtain explicit permission.

* Breach and ConsequencesUnauthorized use of our content for AI training or related purposes is a violation of these Terms of Service and may result in legal action, including but not limited to injunctive relief and claims for damages.

* Monitoring and EnforcementWe reserve the right to monitor usage of our content and take necessary action to protect our intellectual property. This includes contacting organizations or platforms that may misuse our content.

* Reservation of RightsAll rights not expressly granted are reserved. This includes the right to refuse any licensing requests at our sole discretion.

If you have questions about this policy or wish to request a license, please reach out to [Contact Information].

* Specify Your AI Training Policy - Clearly state whether you allow AI training on your work. If you do allow it, under what conditions? With licensing? For free? Make this clear.

* Run anything legal by an attorney if you can. I’m not an attorney and this isn’t legal advice. These are simply actions to take to protect yourself.

Consent - Set Clear Boundaries

Consent means having a say in how your work is used. Without establishing boundaries, you're giving permission for your content to be used however AI companies see fit.

* Be Specific About Permissions - Don't leave it to interpretation. State clearly what is and isn't allowed with your content.

* Watch For Services That Assume Consent - Some platforms' terms of service include language that grants them rights to use your content for AI training. Read the fine print!

* Join Collective Licensing - There's strength in numbers. Publishers like Penguin are bringing authors together for collective AI licensing arrangements. Smaller networks are popping up for each creative niche.

* Document Everything - Keep records of your policies and when they were implemented. Screen captures and a Google Drive helps, and similar ones.

Compensation - Get Paid For Your Value

If your work is valuable enough to train AI on, it's valuable enough to be compensated for.

* Explore Licensing Options - Smart contracts on blockchain platforms can automate licensing for your work.

* Consider Collective Bargaining with Providers who may scale - If there are thousands or millions of creators together, you have more leverage.

* Evaluate Compensation Models - Not all payment structures are equal. Consider whether the compensation offered is worth permanently allowing AI to learn from your work.

* Remember: You Can't Turn Back Time - Once an AI has trained on your work, you can't undo it. Be intentional about your decisions.

It's easy to find reasons things won't work, but finding reasons they will begins with protection and understanding the assets you have as a creator.

They say AI output is just a roulette wheel of randomness. That's only true if you let it be random.

Maybe AI gives us an ability to control the roulette - but only if we first control our relationship with AI.

Establish your presence and protection now, before regulations start adding up. As AI gets better, you'll be protected early.

"The three C's for creators aren't just nice ideas - they're your lifeline in the AI era: Control, Consent, and Compensation."

Owning your creative future: making AI Sourdough with engineers

Ever taste 100-year-old AI? How about sourdough bread from Boudin Bakery in San Francisco with a starter from the Gold Rush Era?

Both sourdough and AI rose during a gold rush, are dynamic evolving entities, and need to be refreshed regularly - one with water and flour, the other with data.

AI has a lot in common with sourdough bread.

Sourdough has a starter that can be 100 years old, each time adding more as it keeps growing, learning, and evolving.

AI is similar, but while we all like sourdough bread (at least the smell of it), we don't like what AI's doing.

Part of the problem is we can't see, taste, feel, or touch AI - and we're told that our content fed into it is gone. The way it happened was such a slap in the face to creators.

What I like about Japan's model is they stated their approach clearly. I'm no lawyer and I'm not giving legal advice, but they took an interesting and intelligent approach that protects both AI and creators.

That's what I want to work on with you this year. I think both sides are heading toward the same goal and should work together.

This isn't about stopping AI - it's about making clear boundaries when AI is taking things, when it's replicating, and when it's intended to replace people.

The one thing Japan recognized that Silicon Valley missed is that AI isn't just about technology - it's about community. If you hurt individuals, you hurt the community.

What goes in is what comes out. Garbage in, garbage out. A relationship with your community is what will come out in your AI.

How can we find that copyright middle ground where creators and engineers work together?

Creators bring perspectives you don't see - they're the edge cases personified.

Engineers bring structure, coding, logic - everything. Together?

Smart software companies always rely on their users as a major source of growth, and AI won't be different.

They need creators. The challenge is acknowledging this opens the floodgates to lawsuits and compensation for everyone who's created content online.

It’s the copyright version of mutually assured destruction, zero sum games.

If only one side wins, both sides lose.

What happens when countries take completely different approaches?

Japan is moving forward, allowing AI access to copyrightable content but with clear guidelines that make things easier to navigate than the US approach.

Currently, the US is still figuring out whether AI-generated content is copyrightable and what happens when copyrighted content goes into AI.

This is the core business practice bumbled by scraping, buying, and avoiding copyright until the lawyers come….which only happens when the money comes.

As we grow, each country thinks their AI approach is the right one, but Japan's model recognizes the community – via its AI - needs to learn while protecting individual rights.

The US is making major copyright decisions around this time next year.

Do you think taking copyrighted content without permission should be allowed?

How do we handle permission at the scale of billions of documents?

Japan's model does something smart - it stands up for both AI's ability to use copyrighted materials to learn AND for the people who own the IP and content.

They're doing this case by case, and I think that's where the US is heading, just moving slower.

I don't want us to become exactly like Japan, but I'm keeping an eye on their approach. A lot of what engineers say makes sense - these models can get smarter and do things for us.

Not eliminating people, but handling tasks that aren't worthy of our time, freeing us to focus on what really matters.

Allowing people to do the work, not the busy work.

Lawsuits over IP scraped, bought, and ingested?Busy work trying to avoid the inevitable and costly retrofit until the next round.

"This isn't about stopping AI - it's about making clear boundaries when AI is taking things, when it's replicating, and when it's intended to replace people."

RESOURCES

Make it fair: The government is siding with big tech over British creativity.

Report on AI and Copyright Issues by Japanese Government

NO&T IP Law Update

Artists release silent album in protest against AI using their work

Chinese Court Issues First Decision on AI Copyright Infringement

China’s First Case on AIGC Output Infringement--Ultraman

AI Copyright Tools

Pixsy - Fight Image Theft (founded 2014)

Created by Humans: Jan 2025

AI Rights Licensing Platform for books

Bernstein.io

Web3 solution to secure and leverage IP assets

MUSIC

SongSecure

https://songsecure.com/

CONTENT AND ART

Credtent.org

=============================

AI Optimist Playlist

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Ask about getting content back out of AI, I keep hearing,

"AI is a black box. NO one understands."

And...you accept that. After all, I’m not an engineer.

Stuck in this AI Black Box myth - and told I wouldn't ever understand. Until a listener shared a comment that made me triple take. Dang.

Now with the AI tools to detect and protect copyright, it's time to turn around and stop looking in the black box.

Wow, crack 1 – the AI black box hype may just be that, hype.

Some companies are finding their content inside an AI system with just a few tweaks - content tech companies say is "transformed beyond recognition" and impossible to track.

I've been repeating what the big AI companies told us - once your content turns into "tokens," it's like mixing paint.

You can't unmix it, you can't identify it, you can't remove it.

I bought it. All of it.

And why wouldn't I?

These companies kept saying the same thing - it's all math and statistics, it's all transformed, it's fair use. The black box defense.

Then reality hit me.

The New York Times, in their legal filing, shows how ChatGPT spit out their articles almost word-for-word, possibly by tweaking a "randomizer" setting.

It's like finding a knob on your blender that suddenly unblends your smoothie back into whole fruits.

I found out about Patronus AI and their CopyrightCatcher. They ran a test with ChatGPT and found that 44% of what it produced was copyable content.

Not "inspired by" or "in the style of" - actual copyable content that could land someone in legal hot water.

If AI isn't really a black box after all, all those company valuations built on the idea of "we transformed it, so we own it" come crashing down.

If your copyrighted content (or something close enough) can be found inside these systems, the whole black box story changes.

Now creators gain control and protect their work if they take a few steps.

No engineering degree required. Often for free.

Recreating Copyright Content: Does AI Remember?

The idea that AI as an impenetrable black box? The evidence is hard to ignore:

* Patronus AI's Discovery: 44% of ChatGPT outputs were copyable content in their early test (2024)

* The NY Times Prompt Hack: How adjusting a simple "randomizer" setting revealed much of the original articles.

* The Visual Evidence: Researchers Gary Marcus and Reid Southen showed Midjourney could create images displaying copyrighted characters from Marvel, Star Wars, Sonic, The Simpsons, and other major franchises—proving the same issue exists across text AND visual AI

* The Key Insight: Thomson Reuters, via its Westlaw legal business, won against a Fair Use Defense by Ross Intelligence, who legally purchased data from a 3rd party to train their AI.

* Ross are finding it and removing it from their AI. Copyright fair use defeat.

Turn down the randomness, and out comes content that looks an awful lot like what went in.

This matters because it may mean your work isn't being "transformed beyond recognition" as we've been told—it's being stored in patterns that can be pulled back out. At least in these examples.

Patronus AI's approach worked because they focused on outputs rather than trying to peer inside the box.

Instead of trying to catalog all copyrighted material in training data (impossible), these tools focus on clear ownership of specific works and detecting when those works appear in AI outputs.

It's a bit like how Shazam works. Shazam doesn't need to understand music theory or know every song ever recorded.

It creates unique fingerprints of songs and matches them against what it hears. The same principle applies here - create a unique fingerprint of your creative work, then search for matches.

So if these systems remember and reproduce specific content, shouldn't creators have some say in how their work is used?

That's what the next generation of tools is helping to address.

3 AI Copyright Tools for Creators

You don't need to be a tech expert to protect your creative work in the AI era. A new wave of simple tools is making copyright protection accessible, regardless of tech background.

* Pixsy: How photographers and visual artists can track and protect their work

* CreatedByHumans.ai: A simple way for writers to timestamp their unique style

* Credtent.org: The multi-media platform that takes minutes to register your work

* The Process: Upload → Set preferences → Get verification → Monitor → Take action

All three of these tools help establish clear ownership of your work, set preferences for usage, and join networks of creators with similar concerns.

While individual creators have limited leverage, emerging networks like these may negotiate effectively—just like ASCAP for musicians.

What's powerful about this approach is that you're preparing for the future now.

With California's DELETE Act and the EU AI Act pushing for more transparency, having your creative work properly documented and protected puts you ahead of the curve.

It's not about stopping all use of your work—it's about having a say in how it's used and getting compensated if possible

This isn't about being anti-AI.

It's about finding a balanced approach where AI advances while respecting the rights of creators. The tools exist.

They're generally affordable—often free.

They're easy to use.

For creators looking to protect their work and potentially benefit from AI usage, they're worth exploring.

"There is a crack in everything, that's how the light gets in." Leonard Cohen

Cracks are appearing in AI's Black Box, revealing copyrightable material just as Patronus AI, the New York Times, and Gary Marcus found.

Two distinct cases emerging:

* The "input" case—alleging that OpenAI illegally hoovered up New York Times articles to train ChatGPT without compensation.

* And the "output" case—arguing that when prompted right, ChatGPT may spit out a New York Times article that readers would pay a subscription for.

Why does this matter? Three simple reasons:

First, it makes copyright claims stronger. If AI spits back similar, specific works, the whole "we're just learning patterns" defense falls apart.

Second, we need to watch what goes in AND what comes out of AI. Most tools focus on spotting AI-generated content.

Knowing when your content was used for training in the first place might be more important.

Third, it shows these companies can track specific content if they want to. And if they can track it, they can credit it and pay for it. The technology exists.

Some AI companies are already building systems that tell you where information came from.

When you ask a question, you get both an answer AND the sources it used. Kind of like footnotes in a book, but for AI.

AI isn't a magical black box. It's more like a really advanced library with a randomizer switch.

Sometimes it mixes books together to create new stories, but it can also just read you pages from specific books it remembers.

The "we don't know what's in there" excuse is running out of steam. These systems remember, they store, and with the right settings, they reproduce.

Because creativity isn't contained in black boxes, nor is AI. It's more open than we think, less powerful than we believe, and...

Hungry for human creativity because without it, the data becomes stale.

The output becomes another copy of another copy, copied from someone.

Want an AI that works for you?

These tools are just the beginning. Add to it an audience you build over the next 2-5 years, and you've got something AI can't replicate:

🚀 direct connection with people who value what only you can create.

Protection is becoming easier. Compensation is tricky, but now you've got proof of what's yours. And that proof is the first step toward everything that follows.

AI is more beginning than ending, and despite the headlines, it’s still super early.

And we're beginning to discover what it really means to be human, and the value of creative life in a world where we now know—the black box has cracks after all.

The light getting in? Human creativity more dynamic than AI.

RESOURCES

How a New York Times copyright lawsuit against OpenAI

Generative AI Has a Visual Plagiarism Problem

HarperCollins Faces Backlash Over $2,500 AI Training Deal with Authors

Penguin Random House protects its books from AI training use

Penguin Random House underscores copyright protection in AI rebuff

Thomson Reuters wins AI copyright 'fair use' ruling against one-time competitor

California Delete Act

Authors strike back against AI stealing their books as licensing startup

How does Shazam work? Music Recognition Algorithms, Fingerprinting, and Processing

AI Copyright Tools

Pixsy - Fight Image Theft (founded 2014)

Created by Humans: Jan 2025

Patronus AI (2024)

CopyrightCatcher by Patronus

MUSIC

Open Music Initiative - using APIs

SongSecure

CONTENT AND ART

Credtent.org

Artrights

Verisart - Protect your Artwork

Leonard Cohen photo

By Rama - Commons file, CC BY-SA 2.0 fr

https://commons.wikimedia.org/w/index.php?curid=53046764

AI Optimist Playlist

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

What's Immortal, Always Hungry, and Sucking the Life Out of Creativity?

I’ve been swimming with tech fear cycles for years. The internet destroys privacy (it did). Mobile phones make us antisocial – yup. Social media may wreck democracy maybe.

Each wave brings fear, adapting, and moving forward. But something about AI fears feels different.

"AI is a vampire," a friend complained over coffee.

"It lives forever. It consumes everything in its way. And that's the source of its immortality."

His words hit harder than my dark black coffee. Because it connects dots I've been staring at for months.

People fear AI, like a digital vampire. It lives forever. It owns what it consumes. It turns our work into its power source.

And this isn't the first time we've feared something feeding on human…ideas.

The links between vampire stories and AI fears go deeper than tech worry. They touch on basic questions.

* Who owns what AI creates?

* Who gets credit when ideas change form?

* What happens when creations outlive creators?

When we say AI "feeds" on data instead of "learns" from it, we're using vampire terms.

When we worry about AI outlasting us or changing our work beyond recognition, we're telling scary stories with tech words.

And this feeling that something is taking our work without asking? That's as old as the stories themselves.

AI isn't scary because it's alien. It's scary because it plays two roles at once.

It's both our explorer into new territory and the guardian at the gate testing if we're ready to move forward.

AI makes us face ourselves by creating something that's like us but not us.

The vampire comparison makes sense.

Both vampires and AI show us versions of ourselves we don't like seeing. Both have origin stories about disputed ownership and mixed-up credit.

WAIT, vampires aren’t real?When’s the last time you had coffee with AI…..

Let's go back to the coldest winter Europe ever faced. It's 1816, at Villa Diodati in Switzerland.

This is where both Frankenstein and the modern vampire were born during a storm of creativity – and disputed authorship.

When Lord Byron Didn't Write "The Vampyre" (Got the Credit but not the money)

In 1819, a vampire was born twice. First as a character in a story... and then again when a publisher stole that story and put someone else's name on it.

The story was "The Vampyre" – the first aristocratic vampire in English literature and the grandfather of Dracula.

The book, written by Dr. John Polidori from Byron’s initial idea, had Lord Byron's name on it, but Byron didn't write it.

His friend and physician Polidori did. Neither knew about the publication until it was already circulating.

One story, wrong name, massive influence.

It all began three years earlier during what's known as "the year without a summer."

In 1816, a volcano eruption in Indonesia had blanketed the world in ash, creating a cold, dark summer across Europe.

Think of it as the pandemic of 1816, except caused by a volcano they didn't know about.

Trapped indoors at Villa Diodati in Switzerland, Lord Byron, Percy Shelley, Mary Godwin (later Mary Shelley), and Byron's doctor John Polidori grew bored of reading German ghost stories to each other.

Remember, they had nothing else but books. So Byron proposed a contest – each person write their own horror story.

Mary Godwin went on to create "Frankenstein." Polidori took a fragment that Byron had written and abandoned, developing it into "The Vampyre."

They all enjoyed the stories and moved on with their lives. Mary Shelley later published her work to acclaim, then suddenly Polidori's tale got published without him knowing it...

In 1819, publisher Henry Colburn released Polidori's "The Vampyre" under Lord Byron's name.

Byron had fled England due to debts and scandal, making it the perfect time to profit from his celebrity.

The story became an immediate sensation – not because of its content alone, but because readers believed the famous poet had written it.

When Byron discovered this, he was furious, as was Polidori. But what could they do? It had already spread throughout Europe.

The story was out there, transforming into new works, inspiring new creators.

The pattern set in motion that day reaches all the way to our modern vampire tales – and parallels current AI debates.

Like AI outputs being credited to the company that created the model rather than the contributors who made it possible, "The Vampyre" became known by the most marketable name, not the actual creator.

Seventy-eight years after Polidori's story, Bram Stoker picks up the vampire concept and transforms it into something more powerful – "Dracula" – launching a genre that continues through "Twilight," "True Blood," and beyond.

And that's exactly where the AI conversation gets way complex.

From Gothic Horror to Modern AI: Ownership Matters, Fear Rules

At Anime Expo 2024, a Chinese digital artist named Yuumei is selling her vibrant, colorful work.

A buyer makes a purchase, looks closely at the art and says it was made with AI.

The artist denies it. Provides time-lapse videos of her process. The internet erupts.

But it wasn't AI at all. Yuumei had been tracing another artist's work, particularly an artist known as "Ask."

She called it "over referencing." The buyer gets a refund. The posts get deleted.

We're so focused on catching AI, we question all we see. The fear of AI is so pervasive that it's now a default suspicion when seeing impressive work.

We're not just worried about AI taking our work – we're worried about losing the ability to tell what's human and what isn't.

It's not about the tools anymore. It's about trust.

When an accomplished artist gets accused of using AI, it reveals our deeper anxiety.

The line between human and machine creativity is blurring. We can't tell the difference through our own eyes anymore.

Like it was in the early 19th century (what)?

In 1819, a publisher put a famous name on someone else's work to sell more copies. In 2024, we assume impressive work may be machine-generated.

Both involve the same fundamental question: who gets credit?

In the vampire story, publishing was new, open, with few rules.

That's exactly where we are with AI today.

No clear standards. Uncertain credit. Creative works flowing freely across boundaries we haven't fully defined yet.

And just like the literary scandal that led to Dracula, we're only at the beginning of this.

What matters isn't stopping the change – it's understanding what's driving our fears about it.

Because what scares us about AI, like vampires, isn't just what it does. It's what it represents about us.

The Insatiable Hunger for AI Data

Vampires need blood. AI needs data. Both feast on human creation to sustain themselves.

Both entities transform what they consume into something new – energy, power, capability.

Both raise questions about permission versus feeding.

Both trigger primal fears about something taking what's ours without permission.

So is training AI like feeding, or is it like learning? This question is at the heart of our copyright debates.

If AI is "feeding" like a vampire, then it's taking without giving back to society it drains.

If it's "learning," that's where copyright law arguably shouldn't apply, at least according to countries like Japan. The US isn't so sure yet.

We're in the early days of figuring out what copyright law means in every country. And is the knee-jerk reaction to say "it's feeding on what we have" just a way to stop it?

Or is it a normal human impulse in the face of things that resemble vampires – unknowable, powerful, and transformative?

This hunger for data ownership becomes especially important with AI because we can't unwind it.

We can't go backward. The data is in there.

So, ownership now means that AI companies claim it as transformed content they've learned from.

It’s not there as the content that went in as you remember. As we deal with ownership, we must deal with the transformative nature of what's happening here.

If Bram Stoker had to pay for the inspiration that led to "Dracula," who would have gotten the money?

The publisher who stole Polidori's work? Polidori himself? Byron for his original fragment?

That's the tangled creative “who’s the author” mess we're trying to sort out with AI.

People using ChatGPT for term papers aren't thinking about whether Shakespeare's estate got paid for all those sonnets in the training data.

They're just trying to finish their homework. But that's the thing about AI – we're all using it, but few understand how it works.

When does AI stop being a fancy copying machine and start being... intelligent?

And who gets to decide? Because right now, AI-created work is treated as guilty until proven human.

And that brings us to the copyright paradox itself.

The Copyright Paradox

You know, both Frankenstein and Dracula could get copyrights if they existed today - they used to be human.

And AI? It used to be human too, in the data and code that evolved it. Our thoughts are their ancestors, our dreams its guidance, and our egos its threat.

In the US copyright system, it's like a three-legged stool - take away any leg, and the whole thing falls. Let's see how AI handles each one.

First leg: fixed in tangible form. This is the easy part. AI passes this test without breaking a sweat - generated text saved to files, art appearing on screens. The output exists somewhere real.

Second leg: original work. AI struggles because it's trained on existing works. It can't prove originality. There's no clear chain of creation. Remember The Vampyre? At least we knew where that came from.

Third leg: created by a human author. In U.S. law, an "author" means a human author. Not a machine. Not an AI. A person.

This is why the U.S. Copyright Office won't register purely AI-generated work. It's not about quality. It's not about creativity.

It's about humanity. Even if an AI writes the next "Frankenstein," without human creative input, it goes straight to the public domain.

Think about The Vampyre. Wrong author name? Still copyrightable.

Borrowed heavily from Byron's fragment? Still copyrightable.

Why? Because Polidori was human, and he made creative choices. The publisher might have given false credit, but the human creativity was there.

With AI? We're still figuring out what counts as human creativity. Is a prompt enough?

How much editing do you need to do?

When does AI assistance become AI creation?

Dracula came out 78 years after The Vampyre.

Imagine if Bram Stoker had to track down that publisher who stole everyone's credit and pay them for "inspiration rights."

That's like paying a thief for stealing something that inspired you to make something better.

The copyright paradox creates what some call "the liar's dividend" - the incentive to falsely claim human authorship since AI work can't be copyrighted. And it still hard to prove a fake.

This creates a dynamic where honesty about using AI tools comes with a legal penalty.

And this is where our mirror problem begins.

The AI Mirror Problem

There's an old myth that vampires can't see a reflection in mirrors. Maybe that's AI's problem too - it creates, but it can't see itself creating. It can't understand its own process.

But neither can we, really. Lady Gaga wrote "Just Dance" in 10 minutes. Some artists labor for years on a single work.

When did time become the measure of creativity?

When did proving we're human become necessary to own what we make?

We've created AI in our image, gave it powers that both fascinate and terrify us, and now we're shocked when it reflects our nature back at us.

Classic human move, really. We've been creating external indicators of our internal powers and fears since we painted on cave walls.

What Joseph Campbell, the famous mythologist, would have called the shadow self, projected onto reality.

That's a lot of what's going on with AI.

The Yuumei case shows this mirror effect in action. Here's someone well-known, very successful.

You instantly respect her work when you look at it.

And they accuse her of using AI because AI becomes their fear. "I'm being cheated."

Everything we look at now, we wonder, is that real?

And that could be a good thing. For all the people pushing critical thinking with AI, it might be one of the best outcomes.

People question what they see.

But the more famous you become; the more likely people will accuse you of using AI.

And why is someone accusing AI like accusing them of doing something wrong?

In Campbell's framework, AI plays two mythological roles at once - it's both the hero and the threshold guardian.

It's the adventurer we send into unknown territory, and it's the intimidating figure at the boundary testing whether we're ready to go past the threshold.

What makes AI different isn't that it's some alien intelligence - it's that it's the hero and the threshold guardian in our story.

It challenges us to understand ourselves better by creating something that mimics us yet remains fundamentally other.

Maybe what we fear about AI isn't that it will drain us dry - vampires tend to keep their victims alive, after all.

What we fear is that it reflects something uncomfortably true about ourselves: our endless hunger for more, dreams of living forever, and consuming without consequences.

Maybe what we fear isn't the AI vampire at all. Maybe what terrifies us is staring into that AI mirror and seeing nothing staring back.

This technology holds up a mirror to humanity – one that shows our creativity isn't quite the magical, mysterious process we claim.

It reveals how we learn, how we create, how we transform ideas into something new. And sometimes reflections make us uncomfortable.

When we look at AI-generated work, we're not seeing a monster. We're seeing ourselves – our patterns, our inspirations, our collective creative consciousness. But with the creator's face erased.

I'm not saying ignore the real issues. When your creative work gets sucked into training data without your say-so, that stings.

When companies profit from models built on millions of unpaid contributions, that's unfair.

But painting AI as a blood-sucking monster misses the point. The path from Byron's fragment to Polidori's "The Vampyre" to Stoker's "Dracula" wasn't a feeding frenzy.

It was ideas evolving, transforming, improving – exactly what happens in AI, just faster and at scale.

This is human in the data and the people that created it.

It's human in the thoughts and stories and processes and code that was done by generations of people.

That's why the ownership question is so tricky.

We want clear lines between "mine" and "yours," but creativity has always been messier than that.

AI makes this messiness impossible to ignore.

Remember Yuumei? Her art was human-made but she got accused of being AI-generated.

The border between human and machine creation is blurring, not because machines are becoming more human, but because we're finally seeing how pattern-based human creativity really is.

And maybe we don't need to fight the vampire. Maybe we need to accept that our ideas live beyond us – transformed, reimagined, built upon.

That's not immortality stolen by a monster. That's the immortality we've always had.

As Joseph Campbell might say, AI isn't our enemy – it's our technological shadow self, reflecting both our strengths and deep flaws.

We've projected our consciousness onto code, just like ancient people projected it onto cave walls.

Neither vampires nor AI as a vampire is real.

It's a technology and intelligence and a way that we're looking at ourselves that sometimes we don't like, and other times is the key to understanding what we can become.

The mirror may be blank, but what do we do with that reflection?

That's entirely up to us.

Resources

Yuumei Art

The AI Vampire at the Door

Interview With The AI Vampire, Part 1

Villa Diodati

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com