The AI Optimist

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.

Like chasing a static object dancing in random neural networks.

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

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.

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.

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?

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.

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.

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.

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.

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.

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.

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 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.

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.

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

2. 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.

1. 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

   1. Prohibition on AI Training Use
      You 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].

   2. Licensing Requests
      We 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.

   3. Breach and Consequences
      Unauthorized 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.

   4. Monitoring and Enforcement
      We 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.

   5. Reservation of Rights
      All 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].

1. 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.

1. 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 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.

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

2. 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!

3. 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.

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

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

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

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

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

4. 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.

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."

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.

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.

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?

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.

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."

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

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

SongSecure

https://songsecure.com/

Credtent.org

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

AI Optimist Playlist

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.

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

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?

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.

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.

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.

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.

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

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.

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:

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

2. 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.

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.

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.

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

Pixsy - Fight Image Theft (founded 2014)

Created by Humans: Jan 2025

Patronus AI (2024)

CopyrightCatcher by Patronus

Open Music Initiative - using APIs

SongSecure

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

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.

"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 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.

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.

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...

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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."

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.

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.

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.

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.

That's entirely up to us.

Yuumei Art

The AI Vampire at the Door

Interview With The AI Vampire, Part 1

Villa Diodati

I’ve been talking with people about  what’s coming up and everyone wants this step-by-step easy way to go through business and life.

I understand we need direction we need focus; we’re all in love with this idea of setting goals and what happens is the unexpected.

Because when you base your ideas your business rolling it out simply on goals and something comes up a lot of times people will try to prove the goal right and what I mean by that is something comes up and you learn to adapt but instead of adapting some people say wait….

this is my goal I’ve got to prove it

This is just part of proving process and what I’ve really found in growth generation particularly which is what I work on with people.

I sit down in an unconventional setting and develop a custom solution for them that ties together the way that they communicate with people their messages and how they build their tribe.

Their audience how they really connect with people so while that’s all good you want to set those goals don’t get me wrong goals give you a direction and a focus what they also can do is get you so overly focused on what’s working that you don’t pay attention to what else might work because here’s what I’ve learned from doing this business for way too long….

the first idea you have rarely is the right one.

In fact if it is successful right away a lot of times it’s not a good sign because it can’t stay most businesses build growing slowly but surely and building up and what I want to share with you today is not that have those goals to be something as not having a direction but to not let the goals be the definition of your business.

Because when you go out and start adapting, you’ll see it’s not a straight line – it’s not step by step = I wish it was but it never ever is.

Look at any business, Google began by doing search but they didn’t really get big till they bought a company and created AdWords.

Pinterest began almost as a shopping cart and turned into one of the best visual tools. Rarely

is your first idea right; rarely is your goal necessarily aligned with the best way to do things and all I wanted to say in growth generation is it’s all about growth.

It’s all about adapting growing and seeing what you can do better and improving it and adapting your goals every 90 days.

Or whatever period allows you to test a goal before following the goal…

The post Adapt and make your goals prove themselves true. appeared first on Declan Dunn.

Facebook marketing continues to grow, morph, and dominate with Google. Find out how to get Facebook marketing going, even if you don’t spend a dime on ads. Facebook Audience Insights can open doors to targeting, customers, and what your first steps should be….

The first step is where confusion often arises because it is the top, not the bottom, of the funnel. Many assume you just buy ads or put out content driven by search, and the customer is immediately ready to buy (as they are in the bottom of the funnel).

In this first step alone, it takes an average company 1-6 months to begin defining the problem, before starting to evaluate solutions. If you throw an ad or content at them, with the intention to convert them to purchase, at this stage they are not ready to make that decision.

It’s better to create steady streams of content that build search traffic. Search traffic is an ongoing asset for your company, because it delivers new visitors year after year at a much cheaper cost compared to the price of AdWords. It also begins to create those 10-20 contacts between your customer and a purchase.

By simply creating content around the keywords and topics your audience finds interesting, you can introduce yourself to the audience.

Who Dev Exercise – who should you target?

This is a life science marketing example below

5 “new” skills to add – though not all new:

We have lots of data, but not lots of people who understand the long view this data demands.

It’s both.

Who specifically, by Job Title?

In larger companies, C-Level executives may be involved in the decision.

What Industry?  Education, Biotechnology, or Healthcare for example.

Where? Many companies are clustered around major cities in North American and Europe. Target them locally, so you know when to communicate and what style/tone will work for that market.

Moving beyond demographics into specific behaviors – repeat actions people take online and offline – leads to personalization. Start with the basics and find the 3 behaviors that turn those basics into customers.

The post Mini Facebook marketing case study and Growth Generation podcast #2 appeared first on Declan Dunn.

Growth Generation is about creating 3 growth marketing experiences; your personal one, your outsourcers and employees and even family and friends, and the customer experience in meeting, learning, and doing business with you.

4 Content Strategy Lessons, 50 million visitors and 20 years later

I’ve been helping cure expertitis, an affliction where the person learning follows and copies someone else’s model, instead of using that to invent and execute their own model. The result is confusion, wasted time, and wilting business.

Growth Generation plants a seed in you, because you are the answer, you taking actions that you have the capability to execute. Learning by doing is the best way to learn, so why are so many stuck in the 20th century model of follow the guru?

Returning to my roots as a mentor and educator, for the past few years I’ve been developing experiential learning systems, and applying them in small and large businesses.

What is Growth Generation?

If you want to seriously grow your marketing you will need a tested, well thought out plan. This podcast serves as the framework for your plan.

When you listen to this podcast, each week you’ll have a guide showing you:

Skipping any part of this 3-step marketing plan will seriously impact your ability to create customers.

Maybe you’ll follow your boss’s whim, throw money at AdWords with conversion to customer rates that don’t match your 30-day results, because the audience doesn’t know you and isn’t ready to buy right now!

Growth marketing is not rocket science, but it is a simple science:

The Plan begins and continues with content marketing; if you don’t do it, research who is doing it. Leverage content research to deeply understand your competition and learn how to differentiate yourself. But if you don’t create content, you won’t be found on search or social media or anywhere people look to before purchasing.

For the past 5 years, Growth marketing has revealed the Average Customer Journey, from first contact to becoming a customer, takes 90 Days to over a year to close. That means that your target audience is not waiting to buy, or even requesting quotes and demos because they are interested now.

They are figuring things out, like you. You need to gain their trust and guide them because only 1 out of 3 people in your funnel are even ready to purchase, or to move the purchase inside their organization to get approvals.

Here’s the way to do it, the scientific way, that begins with people.

The post Growth marketing: What is the Growth Generation Podcast about? appeared first on Declan Dunn.

You want to share your story, so you begin writing it down, separate from your audience. You try to deliver something to make them notice.

What you’re doing is pitching darts at a target that doesn’t exist, because the dynamic nature of content marketing and social media means the old ways don’t work.

Broadcasting tons of content is one old way that is dying, being replaced by a focus on listening, responding, and following up.

In this first podcast, I share my Origin Story, how I learned to build a community teaching 50 million teachers and students with no funding, and no original content.

It’s called Culture Sculpture, letting the audience share their story, and a weekly podcast.

What’s your story?

The post Culture Sculpture Podcast – The Origin Story appeared first on Declan Dunn.

You’ve heard about building communities by creating your own content, yet look around and except for a few celebrities and Interwebs celebrities, that model doesn’t often work because the audience is watching instead of participating.

In this podcast (edited from my Origin Story), I’m sharing the 4 lessons learned in building an organic community built entirely by content donated by the audience.

The true genius, the real experts, are your audience. And it’s not that hard to get them to share, and then get their friends to share more.

Take a listen and let me know your questions.

The post 4 Content Strategy Lessons, 50 million visitors and 20 years later appeared first on Declan Dunn.

Interviews are one of the best ways to generate content and connect you to people in this world increasingly driven by social media. What I found in my mentoring over the past 5 years is that most people – myself included – really didn’t know how to conduct an interview, how to make it flow, and most importantly how to listen.

I’m sharing this video excerpt – focusing on  How to Create an Interview That Generates Results & Joint Ventures PDF document that accompanies this…feel free to download this and use it to set up your next interviews.

While this video whitepaper shares my viewpoint, the most important insight for me is to develop your listening skills and to ask good questions, and make the person you are interviewing feel comfortable and in a place where they can share their insights, their talents, and most importantly, helps them really let you know how it works for them.

Watch the video, you can also download the audio only version here.

I’m sure you have questions about interviewing other people, via Skype, video, etc….I’ve found that with longer interviews unless you can include some visuals, it’s best to share it as audio.  What’s your biggest challenge with interviews? Can I help shed some answers to your questions?

If you’d like to know how to use email effectively, in conjunction with this video, check out this post

The post How to Create an Interview That Generates Results & Joint Ventures appeared first on Declan Dunn.

“I think being mainstream is probably a detriment.  You don’t have a particular audience to go for so you do what the movie studios do, you have to spray everybody, which costs you a fortune.” Stephen Simon, founder, Spiritual Cinema Circle (first audio interview excerpt below is about 8 minutes).

Then I learned that within months of opening, they had 3,000 subscribers at about $20 a month, and I believe this rose to near 30,000 when they were acquired by Gaiam in 2006.

This was one of my first projects after leaving the hard core world of lead generation, CPA deals, and affiliate marketing.  This project helped me understand the power of the niche and of small teams, and in an amazing way, the ability of dreams to become real.  Just listen to this interview and you’ll understand what I mean.

Think of this when you try to go mainstream, or bash people with your old school tactics in social media.  There is a change, and it is profitable.

Believe it…Stephen Simon did, and does, and it’s all driven by Friends.

The post New Media Niche Discovery #2 – Spiritual Cinema Circle Audio Interview (You Don’t Have to Spray to Succeed) appeared first on Declan Dunn.