The AI Optimist

In Tokyo right now, an AI can legally train on nearly everything created in Japan - books, songs, artwork - without permission or payment.

This isn't science fiction; it's current Japanese copyright law.

Step outside Japan's borders, and suddenly those same works may gain legal protection. This isn't theoretical - it's playing out in courtrooms today.

The story of Ultraman - one of Japan's superheroes - represents this contradiction. In a Chinese courtroom, this Japanese character won a copyright battle against AI.

Yet back home in Japan, that same AI use – for training - would be completely legal.

This isn't about superheroes and science fiction.

It's about your work becoming someone else's fuel, with different rules depending on which digital border you cross.

Because Japan’s AI and China’s AI and the US’s AI….countries will probably have their own, as well as their own copyright rules.

In 2025, we're witnessing the emergence of AI zones - digital territories where copyright means different things in different places.

There won't be one global system, it’s a patchwork of nationalistic and cultural points of view.

What Japan's model reveals isn't just a legal curiosity - it's a potential future we're all heading toward, where content flows freely into AI within certain boundaries, but with protection against direct copying or financial harm.

When your work can travel anywhere but the rules protecting it cannot, you need to understand the global rules - not just your local square.

The AI Wrapper Not the Site: What Creators Learn from Ultraman

When you're fighting AI copyright violations, you need to know where your content is located. Except now it’s all tokens and math.

Only thing to do is try to recreate what went in….

A Chinese licensing company (SCLA) owns Ultraman rights in China. They found their character being generated by an AI system.

They took legal action against the website where users could enter "Ultraman DYNA" and get images that clearly resembled their licensed character.

They won - but they didn't actually solve the problem.

Because they targeted the website, not the AI system feeding it. Where the Ultraman imagery still resides.

This matters for creators because:

* Many AI websites are "wrappers" - they don't own or train the AI models

* They're paying monthly fees to connect their site to third-party AI via APIs

* The Ultraman images are still in the AI's training data, even after the lawsuit

* The court couldn't remove what wasn't part of the case

When you use a site with AI features, chances are they're just an interface connecting to models built elsewhere (obvious for people in AI, surprisingly not for many who are not familiar).

For creators standing up for their work, this means tracking the data to its source.

Don't just go after the visible website - follow where your tool gets its training data. Otherwise, you'll win the small battle and your content will still be able to be reproduced.

And as mentioned everywhere, no one is sure they could actually pull your content out of AI, because your content isn’t your content anymore.

Do AI Tools Really Need Artists?

Witness Japan's approach to copyright and you might wonder if AI needs any help. Their system allows AI to train on copyrighted works, but with guardrails that might surprise you.

AI working with creators, like software developers have for years.

* Adobe Firefly, trained on licensed stock, gains widespread adoption among professionals

* Sony's Flow Machines co-creates with artists, sidestepping legal battles

* Midjourney asks users for feedback and allows creation of mood boards to improve its systems

I'm not even close to being good on Midjourney, but it ups my game tremendously by providing tools smarter than I am as a novice graphic designer.

That's not replacement - that's enhancement. Always asking for feedback like a strong AI startup.

Despite drawing a line in the sand with copyright, that same line may bring us together.

The Japanese experiment recognizes a fundamental truth:

AI gets better when humans remain in the loop not just as data sources, but as active participants.

The question isn't whether AI needs artists - it's how we structure that relationship to benefit both.

As much as we might want to keep separate worlds, the evidence shows this is where AI gets good - when it learns with us, not just from us.

2 Parts of Japan's AI Copyright rules

The Blueprint Behind Japan's AI Freedom

What makes these AI tools work isn't just tech brilliance - it's the legal framework supporting them. Looking at Japan's approach reveals something engineers have been arguing for: a system where AI learns from everything while still respecting creators' rights.

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

The Non-Enjoyment Purpose Requirement

The first pillar is simple: intention matters. Japan allows copyrighted content to train AI systems if the purpose isn't to recreate the original work.

You're not bringing in Van Gogh paintings to create more Van Goghs - you're bringing them in to help the AI understand artistic styles, composition, and color theory.

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

While this sounds straightforward, it creates real questions for creators: how do you distinguish between "learning from" and "copying" in a practical sense? This leads to Japan's second pillar.

Article 34 Proviso: Where Protection Lives

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

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

Fair Use and Japan's AI Freedom - not the same

How different Japan's approach is from the U.S. "fair use" defense - both trying to solve the same problem.

In the U.S., fair use isn't a rule - it's a legal defense. You only know if something is fair use after you've been sued, and a court decides. It's reactive, not proactive.

In Japan, they've built a system that's more predictable. AI companies know upfront what's permitted: training is allowed, copying isn't.

Creators know where their protection lies: not in restricting input, but in preventing harmful output.

This is what many U.S. engineers are actually arguing for when they claim fair use. They're saying:

"We're not taking content to reproduce it - we're taking it to learn patterns, just like Japan allows."

Both agree, what comes out matters more than what goes in. If the output looks like what went in, both countries consider that a violation.

As we move through 2025, the U.S. Copyright Office findings will shape everything.

I suspect we'll see something like Japan's system emerge - allowing inputs for learning and focusing protection on outputs causing real harm.

The "Human-AI Collaboration" Spectrum

I'm amazed the US Copyright Office didn't simply declare all content suitable for AI training. By leaving it open, we're in this year-long limbo - but I can't imagine they'll crush ChatGPT, Claude, and the entire AI industry with a finding against it.

What we're likely to see instead is a spectrum of human-AI collaboration, with different levels of protection based on human involvement.

This isn't just speculation - it's already happening in courts across the world, including Japan and China.

At one end of the spectrum, you have minimal input - dropping in a prompt or two and accepting whatever comes out.

Courts are already signaling this won't qualify for copyright protection.

On the other end, iterative creation - refining, directing, and shaping the AI's output through rounds of feedback.

The courts are looking for evidence of your creative process - not just that you typed "Ultraman DYNA" and grabbed the first image.

They want to see focused human effort with the tool, making deliberate choices, and guiding it toward your vision.

What's emerging isn't binary (all human vs. all AI) but where protection increases with human involvement.

The U.S. Copyright Office is likely heading toward a similar model - not copying Japan, it reflects the reality of how we're both using these tools.

Concept and Direction? Prove it!

So what does meaningful human input actually look like in practice? Two words are becoming crucial: concept and direction.

Courts are looking for evidence that you had a concept in mind before approaching AI. Did you have an intention, a vision, a goal?

Or were you just fishing for whatever the AI might generate?

This matters because intention shows you're bringing something original to the table - not just activating a random content generator.

Direction is equally important - can you demonstrate how you guided the AI toward your vision?

This means showing your creative choices - whether that's choosing specific platforms, curating between different AI outputs, adjusting colors, angles, or filters.

You need to show more than just button-pressing.

I've noticed the U.S. Copyright Office repeatedly questioning whether works were "just done by AI" - and without evidence of human direction, they're likely to deny protection.

The more you can document your process - showing your inputs, revisions, selections, and edits - the stronger your claim to copyright protection becomes.

The real question isn't whether AI was involved - it's whether a human mind was driving the creative process rather than following it.

No Easy Answers for AI - finding your way

Just like that Chinese court in the Ultraman case, there are no easy answers here.

While they found for the license owner, it was a minimal fine that didn't actually stop the problem.

Not because the court was wrong, but because the plaintiff didn't understand where the real infringement occurred - in the AI's training data, not just on the website.

Most people listening right now probably don't understand the technical architecture I just described - how websites are often just interfaces connecting to third-party AI systems.

This disconnect between what we see and how things actually work creates problems for copyright enforcement.

So where does that leave creators? Four principles can guide you forward:

First, understand that there's no going back. The genie is out. AI has seen the work of humanity and will continue to learn from it.

Second, document everything. If you're creating with AI assistance, maintain records of your process. Save your prompts, iterations, editing decisions, and selection criteria. This evidence of your creative direction will become increasingly valuable.

Third, focus on curation and assembly. Even as individual elements become easier to generate, the thoughtful selection and organization of those elements remains distinctly human.

Copyright protection for compilations and collections is well-established, even when the pieces themselves might not qualify.

Finally, embrace the spectrum. The future isn't binary - AI or human. It's finding the sweet spot where technology amplifies your creativity rather than replacing it.

The most successful creators won't be those who avoid AI entirely, nor those who rely on it completely, but those who learn to dance with it.

As much as we've drawn copyright as a line in the sand, it can bring us together - forcing creators and engineers to find mutual solutions rather than mutual destruction.

The Japanese experiment offers a glimpse of what's possible:

a system that acknowledges both our collective interest in advancing AI and our individual interests in protecting creative work.

No court decision will provide perfect clarity.

No copyright ruling will solve every problem.

By focusing on what we can control - our own creative processes, documentation, and adapting - while continuing to create work that matters.

Resources

Chinese Court Issues First Decision on AI Copyright Infringement

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

28 February 2024

Report on AI and Copyright Issues by Japanese Government

NO&T IP Law Update

27th_Tokyo_International_Film_Festival_Ultraman_from_Ultraseven_(15001540863)

By Dick Thomas Johnson from Tokyo, Japan - 27th Tokyo International Film Festival: Ultraman from Ultraseven, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=118426646

By Dick Thomas Johnson from Tokyo, Japan - Ultraseven from "ULTRASEVEN 55th Anniversary Special Screening" at Red Carpet of the Tokyo International Film Festival 2022, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=124731453

SCLA

What is an API wrapper?

Google's Project Magenta Brings Neural Networks to Music

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Five years ago this Valentine's Day, OpenAI releases GPT-2; headlines later warn it’s 'too dangerous to release.'

Their concern? That it could generate synthetic content at scale.

Fast forward to 2025, and we're seeing the first major AI fair use victory in court - Thomson Reuters just won an early judgement protecting their platform's content from training a competitor’s AI, something courts rarely find copyrightable.

The other side bought the data from a third party and used it to train its AI, claiming fair use. And in this round, that argument was lost, with more details later in this article.

The fair use claim is the same one used by ChatGPT and most others; while the facts are quite different, the fair use denial is an interesting response to turning content into tokens. In this round at least.

Roulette Wheels or emerging Intelligence?

Last week we looked The U.S. Copyright Office releasing their second report stating AI-generated work isn't copyrightable because it's essentially a 'roulette wheel of probability.

While everyone's focused on AI outputs, the real Achilles heel weakness is hiding in plain sight – what do we do with content used to train AI without permission, AND not falling under fair use?

It's about the millions of novels, articles, paintings, photos, and lines of code that built these AI systems.

Next year the US Copyright office will release the 3rd update, this one on what happens when content goes into AI, from copyright law in the US. Surprising that the first 2 reports depend on this 3rd one, also showing how complex and new this issue is to all of us.

How can you issue AI a copyright when you can’t define what part of someone else’s content went into what came out?

What if they found all the data in all the AI is not fair use; it’s all copyrightable content that should have been given something – an email, a notice, something. Or not.

There's no way the Copyright Office would risk shutting down ChatGPT and Claude by extending protection now. It will be 6 years since this began when the update should come out, and the findings will be hard to use retroactively.

But what happens when they finally tackle the question of all that creative work that went into building them?

Like the Thomson Reuters winning copyright protection for it’s Content last week!

Ross, a startup, competes with Westlaw's legal research platform. And had worked there previously, so likely knew the quality of the information for training AI would be high.

They needed legal Q&As to train their AI - think of it like needing textbooks to teach a student.

When Thomson Reuters said 'no' to licensing their content, Ross got creative... maybe too creative.

They bought data from a company called LegalEase to get something called 'Bulk Memos' - basically, lawyers' compilations of legal questions with good and bad answers.

These lawyers were using Westlaw's headnotes (like Cliff Notes for legal cases) to create these answers. Ross ended up with 25,000 of these memos to train their AI.

This is eerily like how OpenAI, Anthropic, and others acquired their training data. They purchased crawled data from Common Crawl - think of it as a digital library that copied everything on the internet.

Like Ross tried to sidestep Thomson Reuters by getting content through a third party, AI companies have been using third-party scraped data without creator permission.

Businesses like Common Crawl and even WayBackMachine.org have been collecting this data, but both are non profits. Like OpenAI…wait, OpenAI is going away from that now. Back then?

It’s never been a big deal until AI companies make billions with that data, at a scale way beyond anything before.

The scale is astronomical. We're not talking about 25,000 memos - we're talking about trillions of data points from millions of creators.

While trillion-dollar tech companies claim they can't pay for content, they're quietly making deals with publishers who have the legal and financial muscle to fight back.

The New York Times, Axel Springer, even Thomson Reuters - they're all getting seats at the table.

But what about the millions of independent creators whose work built these AI systems?

And that brings us back to 1787, when our founding fathers picked up their quill pens...

To know where copyright is going, you need to know where it starts…in the US for our example.

The origin story of AI Copyright….really all US copyright, begins with that document and a promise to protect inventors and creators.

Smart as he was, I don’t think Thomas Jefferson could wrap his head around our questions….

Constitution Origins: From Quill Pens to Neural Networks

In 1787, the framers of the Constitution faced a fundamental challenge: protecting creativity in their new nation.

Article 1, Section 8 states: "To promote the progress of science and useful arts by securing for limited times to authors and inventors the exclusive rights for their respective writings and discoveries."

The Copyright Office maintains that purely AI-created works don't promote human progress because they lack human creative input.

But in an era where AI systems can solve complex scientific problems and create new art forms, we're redefining what 'progress' means.

"See, when the Constitution talked about respective writings and discoveries, right? It's like back in the day when they didn't have anything but writing, photography was coming to really blow their minds very soon."

The Sui Generis Framework: A New Model for AI Copyright

"Now, the reason I'm proposing the sui generis option the US Copyright office didn’t pursue, is a customized legal view of AI generated work is because right now, there's agencies and people doing work with commissions being told and instructed what to do to create images like cases we've shared with you before."

That basic service of delivering copyright protection for a work for hire, or other arrangement, is left out on its own with AI. If AI generates it, it’s not human, and it’s not copyrightable.

Sure we all bend the rules in the meantime. Until a big mistake happens. And the liability goes to the one who paid for the work, yet doesn’t have a copyright to protect themselves.

The framework needs to address four key areas:

1. Authorship and Ownership

"It's also a focus on substantial human involvement. How much? And this is case by case, but how much was this person involved in creating this or how much was the AI involved?"

2. Duration of Protection

"One thing people complain about copyrights in traditional ways is how long they are... And for AI generated work it's clear we're looking like 5 to 10 years."

3. Fair Use Evolution

"Fair use is really important... There's many different ways that people can use it and be able to use the content without taking it too. Certainly, it doesn't mean take the book. You might take a quote."

The Creator's AI Copyright Playbook

"Document any of your work with generative AI. And by the way, with anything, if you're not using AI do the same thing because there's going to be more requirements."

Strategic Documentation

"Make sure you can prove whether you did something with AI or without it, and show a record of your activity. Photos, manuals, journals, whatever. You have calendars to keep proof of what you've done."

Transform, Don't Just Generate

"When AI generates independently, in other words, I put a couple of prompts in, spits it out. I want a copyright. No, because prompts don't give copyright. Not in China, not in the US as of this time."

Building Your Evidence Trail

"Now when you transform AI output, when you take AI output and turn it into something else, that's possible to get a copyright. Assuming you're not using AI to transform AI, right?"

The last US AI Copyright update was so wrong to me, AI being just a 'roulette wheel of probability.'

And in their early update on AI Replicas, they wouldn’t touch the issue of copyright because of likeness and image – and no rules applying there. Not because it was machine generated.

Because it was not human and it’s a word calculator. A spinner of other’s tales. Bringing it all down to the lowest common denominator.

Enough….and looking around at news in the past 10 years, what you see is likely what we’ve been creating, because all that “bad” data….is also us.

It’s a moving target, not a set in stone and time. New copyright is like playing a game, old copyright is like reading a book.

Both are fun, one moves a little faster than the other.

The US Copyright office will likely have a new challenge with its findings on how copyright applies to content put into ChatGPT, and everyone else, by this time next year.

Enter the Humanity's Last Exam – HLE - a test so challenging that no single human, not even Nobel laureates, could ace it all. We're talking 3,000 questions across every field imaginable, from philosophy to quantum physics, developed by nearly 1,000 experts across 500 institutions.

In just one year, AI performance on HLE jumped from 10% to 30% accuracy.

It's not just predicting anymore, it's reasoning through problems that challenge the brightest human minds. It's like watching evolution in fast forward.

And there are many more. The GAIA (General AI Assistants) benchmark is an evaluation tool assessing the performance of AI systems, with a focus on their ability to function as general assistants.

Is AI ready for the big questions – right now it depends how you pose them, and how you train AI, now, 2 weeks from now, and every 2 weeks.

Creators listen: If AI triples its performance again this year - and at this rate, who's betting against that? - we're not talking about a fancy probability engine anymore.

We're talking about something that could redefine human knowledge itself.

Ready and perfect, no way – way more interesting than anything in the past 20 years?

Maybe….and it’s already in our lives, many simply don’t understand how…..

So while the U.S. Copyright Office wrestles with digital replicas in one report and dismisses AI as random in another, the reality is racing ahead of our legal frameworks.

That's exactly why we need your voice in this conversation.

Because the future of creative rights isn't just about law - it's about understanding what happens when machines start thinking like humans... or maybe even better.

When we start thinking with and working with AI, maybe it helps us grow as well.

Share your thoughts in the comments.

How do you think we should protect creativity in a world where AI isn't just rolling dice anymore?

RESOURCES

NOTE: Thomson Reuters owns Westlaw. When I refer to Reuters in this video, it means Thomson Reuters.

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

By Blake Brittain February 11, 2025

THOMSON REUTERS ROSS LAWSUIT fair use.pdf

The Automation Assumption Was Wrong—The Revolution Eats Itself

Humanity's Last Exam (HLE)

Common Crawl

AI deemed ‘too dangerous to release’ makes it out into the world by Andrew Griffin

New York Times sues Microsoft, ChatGPT maker OpenAI over copyright infringement

US copyright office

Part 1: Digital Replicas

Part 2 (out January 2025): Copyrightability

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? Jackson Pollock Might Help Us Understand AI Copyright?

I see Jackson Pollock's name in the US Copyright Office's announcement about AI this week, and I'm thinking - how did a 1950s painter who threw paint at canvases end up in a debate about artificial intelligence?

From the US Copyright Office

Someone tried to connect Pollock to AI in a way that made me stop and think: What if Pollock's paint splashes were like our prompts to AI?

We throw words at it, and something unexpected comes back - something we didn't necessarily visualize, and it's still an act of creativity.

The Copyright Office didn't buy it. They looked at Pollock comparison and said,

"Hey, there was a human holding that paintbrush, choosing colors, making decisions about where to throw the paint."

To them, AI is more like a "digital roulette wheel" (their words, not mine) that randomly spits out content based on prompts.

The New York Times Might Just Prove Them Wrong

The Copyright Office gives zero respect to prompts - and I mean zero. They're basically saying prompts are like throwing spaghetti at the wall to see what sticks.

But The New York Times lawsuit against OpenAI might flip this on its head.

"If the New York Times really got their close to original content out... that actually is the first time we've connected this specific action to the specific prompt.

Every prompt shaped an output, just like every Pollock throw of paint created the canvas."

If The Times prove they used specific prompts to get their exact content back from ChatGPT, doesn't that show there's more to prompting than random chance?

Three Acts of the US Copyright Drama

We're in the middle of a three-part series from the Copyright Office:

1. They've covered Part1. Digital Replicas (think deepfakes) in July 2024

2. Now they're tackling AI authorship: January 2025

3. Next year's the big one - what happens when AI trains on copyrighted content?

What They Actually Said

Here's where they got something right: you can use AI in your creative work, you just can't let it do all the heavy lifting.

Take "Zarya of the Dawn" - this cool comic book where the images came from Midjourney, but a human did the writing and put it all together.

The whole book? Copyrightable. The individual AI images? Not so much.

Two Old Cases That Suddenly Matter Again

The Copyright Office pulled out some fascinating historical cases that suddenly feel super relevant:

In 1989 a homeless advocacy group commissioned a sculpture and then argued they owned it because they gave the idea? (CCNV vs. Reid) Sound familiar? It's basically the prompt versus output debate we're having now.

Or the map reproduction case (Andrein) that helped us figure out the difference between "creative guidance" and "just following instructions"? These old cases are helping us navigate this new AI territory.

Maybe AI is Human Intelligence

I think we're looking at this all wrong. When we call it "artificial" intelligence, we're missing something huge. Here's my take:

"Without that data, none of this works.

Without the code created by humans, none of this works.

Without the person prompting or talking... it's all just language communication with a database of incredible collective intelligence."

Maybe our real problem isn't about respecting AI's copyright - it's about recognizing intelligence that goes beyond a single human author.

We're trying to force 2024 technology into 1787 rules, and surprise, surprise, it's not fitting very well.

The Copyright Office got one thing absolutely right - they're protecting AI-assisted human creativity. But they're missing the bigger picture.

AI isn't just some random number generator spinning out content like a Vegas slot machine. It's human intelligence evolving into something new.

If AI is built on human knowledge, trained on human creativity, and guided by human prompts, maybe we need to stop thinking about it as "artificial" at all.

Maybe it's time to recognize it; our collective intelligence taking on a new form.

Next week, we're going to explore a new framework for protecting AI-assisted creativity - one that doesn't try to squeeze it into 18th-century rules or dismiss it as luck.

What we're really talking about isn't artificial intelligence at all - it's human intelligence evolving.

And that's something worth protecting….

RESOURCES

US copyright office

Part 1: Digital Replicas

Part 2 (out January 2025): Copyrightability

Part 3 to come

OpenAI claims New York Times' prompting strategy violates its terms of service

New York Times sues Microsoft, ChatGPT maker OpenAI over copyright infringement by Ryan Browne

OpenAI says New York Times 'hacked' ChatGPT to build copyright lawsuit

Copyright and AI

Community for Creative Non-Violence v. Reid, 490 U.S. 730 (1989)

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

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Everyone's talking about DeepSeek building a ChatGPT competitor for $6M.

Maybe it works, maybe it doesn't – we'll see.

But while we're arguing about AI chips and compute power, we're missing the real story:

China is the only country on Earth that's actually protecting AI creativity.

Not Silicon Valley with its billion-dollar legal battles. Not Europe with its complex regulations. Not Japan with its tech prowess. China.

Here's what they've built since 2018: The Beijing Internet Court.

Think small claims court meets digital age. Anyone – individual creator or small company – can submit an AI copyright case online.

No million-dollar legal teams required. No years-long waiting game. Just practical decisions about real creative work.

Want to protect your AI art?

In the U.S., you're looking at a 3-5 year legal battle that costs more than most creators will ever make.

In China? Submit your case online, show your creative process, and get a decision that looks at what you actually did – not what some 100-year-old law says about human authorship.

China isn't just protecting human creators using AI. They're also granting copyrights to AI apps themselves.

Why? Because people created those apps.

"They found that the AI was created by people.

Therefore they are the authors and the owners.

The Chinese legal courts are that far ahead of us in AI, almost five years ahead of us." "

And they're doing it through a system built for the digital age – specialized courts handling online cases, practical decisions based on real creative work, and protection for both individual creators and AI developers.

While Western courts debate whether AI can create at all, China has:

- Built an e-litigation platform anyone can use

- Created specialized courts for digital creativity

- Protected both human creators using AI and AI apps created by humans

- Made practical decisions case by case

- Done it all without massive legal fees

The country we often think limits individual rights is actually protecting individual creators – both those using AI and those building it.

While we're stuck in theoretical debates about human vs. machine creativity, China's building a system that works in the real world.

One company spends $6M, another spends $6B, for essentially the same product.

Just like DeepSeek, copyright has a far cheaper solution, and it's already working in... China (not again!).

Three AI Copyright Stories That Show Us Why

1. Jason Allen's Digital Opera (2022)

Let's talk about what happens when an artist who understands both code and creativity tries to get copyright protection in today's system.

Jason Allen didn't just click a button and get lucky.

He created "Théâtre D'opéra Spatial" through a process that would exhaust most traditional artists:

* 624 precise prompts in Midjourney

* Professional post-processing in Photoshop, applying years of digital art expertise

* Advanced refinements using Gigapixel AI, a tool typically reserved for professional photography work

* Multiple rounds of iteration, adjustment, and fine-tuning

"The man did so many prompts and so much work on this image.

He brought together skills that didn't even exist when copyright law was written:

Can you imagine them even trying to conceive of a creative person like Jason now, who can code, who can do graphic design and UI and deliver it all in a gaming interface?"

The result? First place at the Colorado State Fair's art competition. The judges didn't know it was AI-assisted. They just knew it was good.

Then came the U.S. Copyright Office.

Despite showing:

* Hundreds of documented creative decisions

* Professional-level post-processing work

* Technical expertise across multiple tools

* A clear creative vision from start to finish

"They denied him a copyright simply because there's a line down the middle here.

You are a human or you're not. Nothing non-human gets it."

Allen did exactly what artists have always done – used the best tools available to bring his vision to life.

“If he had done this in China, I'm sure he would have gotten the copyright."

The Copyright Office couldn't even explain exactly what part was "non-human" because the court has no way of knowing what AI is considered, what an output is.

They just knew AI was involved somewhere, and that was enough to say no.

2. Tencent's Breakthrough (2019)

Back in 2019, Tencent faced a human challenge.

Their AI system, Dreamwriter, was creating automated stock market reviews. Standard stuff, right? Not quite.

The Chinese court looked deeper:

- They examined how Tencent's team selected and developed databases

- They analyzed the algorithm development process

- They studied how the team arranged and selected content

The court's conclusion?

"The AI was created by people. Therefore they are the authors and the owners."

This wasn't just about who pushed the buttons - it was about recognizing the human creativity behind the AI itself.

"This stuff can't just happen.

Even though we pretend copyright law, it's only human beings... It has to bring in people."

Spring Breeze Brings Tenderness by Li - SOURCE: Lexology

3. Li v. Liu: The Image That Changed Everything (2023)

November 2023: Li creates an image called "Spring Breeze Brings Tenderness" using Stable Diffusion. But this wasn't just random prompting.

The court examined every detail:

- Platform choice: "The court said that was absolutely an artistic choice"

- 30 specific prompts defining what to create

- 120 negative prompts specifying what to avoid

- Parameter tweaking showing deep technical understanding

- Post-processing refinements

The judges, who admittedly weren't AI experts, recognized something crucial: these were human creative choices.

"These choices he made met the level of 'intellectual creations' unquote, by the Beijing Internet Court."

Recognizing the Change instead of Trying to Move Backward

China's approach isn't perfect, but it's working. While Western courts say "no" and demand deep pockets for lengthy battles, China's Internet Court system (created in 2018) handles these cases quickly, practically, and case-by-case.

"There's a new intelligence.

Maybe it's super, maybe it's artificial, maybe it's collective intelligence, but it's here.

And to deny copyright is to deny its existence."

We need to recognize collective intelligence because it is all human intelligence - to say it's non-human in AI misses the point.

The original creations were all from us. Even if AI makes up stuff, shouldn't we be able to copyright originality?

Why do we want to keep fighting it?

Oh yeah, because all the copyrights are owned by the money and the lawyers that are going to settle this in 5 or 10 years, while the rest of us just sit on the sidelines.

Here's the wake-up call: China's ahead in understanding AI Copyright, but they're also smart.

By allowing these small cases to come in and not deciding them only on the big players, they're building a system that works.

"Hey, everybody, let's copy China in this way.

They are leading, and you don't really need to look too deeply into it.

They're trying to do something different."

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

RESOURCES

* China: A landmark court ruling on copyright protection for AI-generated works (Li vs. Liu AI Assisted Art)

* China’s First Case on Copyrightability of AI-Generated Picture

King & Wood Mallesons

* Beijing Internet Court ruling in first case of copyright infringement of AI-generated article

* Beijing Internet Court

* Is the Chinese 'Dreamwriter' Case Really a Groundbreaking Case for AI-Generated Works?

* A shocking Chinese AI advancement called DeepSeek is sending US stocks plunging

* ‘Someone had to be first': Pueblo artist criticized after AI painting wins at Colorado State Fair

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

Over 20 major news publishers like NewsCorp, the Associated Press, and Vox Media have rushed to sign deals with OpenAI, with Vox calling it a "strategic content and product partnership."

The stakes?

OpenAI is offering between $1-5 million per year to license copyrighted content, with News Corp potentially securing $250 million over five years.

But this isn't just about big media companies protecting their turf – it's about the future of human creativity itself.

The Copyright 2-Step: Who wrote it (author), and who owns it?

When a photographer named Napoleon Sarony went to court in 1884 over a photo of Oscar Wilde, the simple question: could you copyright a photograph when a machine – the camera – did the actual capturing?

The courts said yes, because the photographer made creative choices. The camera was just a tool.

Fast forward to today. I took those same historic photos into Midjourney and asked for exact replicas. The result? The AI turned something unique into something average.

That's not just a technical failure – it's a warning about what happens when we reduce creativity to "content."

Tying old Laws to New Ways - AI Case law simply isn’t relevant

Both the US and EU are wrestling with a fundamental question: what constitutes "sufficient human authorship" in the age of AI?

The US Copyright Office's rejection of copyright for the AI-generated graphic novel "Zarya of the Dawn" highlights this challenge.

While a human arranged the AI-generated images, the Office deemed the images themselves ineligible for protection.

Meanwhile, the EU's approach, with its focus on human intellectual effort, raises its own questions.

* How do we determine ownership when AI assists in creation?

* Could specific provisions for AI-generated works differ from traditional copyright rules?

"The edge cases are where creativity begins. That's where the really new change, things that people don't first get, comes from."

Jason Allen won an award but couldn’t get a copyright because he used AI. With over 600 prompts, detailed editing after that, since AI was involved, no copyright.

"Remember that spectrum of human involvement we talked about? The courts say you need 'sufficient human authorship' - but what does that actually mean when you're working with AI?

On April 9, 2024, Representative Adam Schiff brought The Generative AI Copyright Disclosure Act to the House floor – a bill that would require developers of generative AI technologies to identify the material they have used to train their systems.

Think about Sarah Andersen and her fellow artists. Their entire portfolios - decades of work - were used to train AI without anyone asking.

Now big publishers like Axios are getting paid for their content through partnerships, while individual creators are left wondering: 'Hey, what about us?'

Schiff’s Act is under the illusion that it’s simply a request to find out what’s been put in, and then identifying ways to extract it which is borderline sad perception of how this works.

The copyright world doesn’t recognize AI as anything, not even officially a tool, and we’re spending our time trying to prove there’s more to copyright than humans, or human intelligence.

* The "Human Authorship" Hurdle: US copyright law requires human authorship. No idea what to do when AI tools are heavily involved in the creative process.

* Case in Point: The Copyright Office's rejection of copyright for the AI-generated graphic novel "Zarya of the Dawn" highlights this issue. While a human arranged the AI-generated images, the Office deemed the images themselves ineligible for protection.

* Defining "Sufficiently Creative" Human Input: How much human input is needed to transform AI-generated content into a copyrightable work? There's no clear answer.

* Gray Area: If a user provides detailed prompts and iteratively refines AI output, is that enough to claim authorship? What if they use AI to generate variations and select their favorite?

* Jason Allen, a Colorado artist, won first place in the digital arts category at the Colorado State Fair in 2022 with his AI-generated artwork titled “Théâtre D’opéra Spatial.”

* Potential Solutions:

* Thresholds of Originality: Establishing guidelines on the level of human creativity required for copyright protection in AI-assisted works.

* "Human-AI Collaboration" Category: Creating a new category of copyright for works where humans and AI contribute significantly, with ownership shared or determined by specific rules.

Time is the hidden factor here. These creators spent years perfecting their craft, building their portfolios, engaging their communities. Meanwhile, AI can generate similar-looking work in seconds.

But here's the fascinating part - those quick AI generations? They're built on the backs of all that human time and creativity. The publishers figured this out first - that's why they're getting paid. Eighteen major deals and counting.

But what about Mumsnet's community members? What about indie creators? What about you? When you collaborate with AI, you're not just investing your current time - you're building on everyone else's past work too. So how do we make this fair for everyone?"

Three Paths Forward

1. Shakespeare in Code

King Lear wasn't performed much until the 1800s – two hundred years after it was written. It was noise back then, until it broke through later.

Society doesn’t always understand what’s really good quickly, and in AI those are called edge cases. If we bury the world in what’s probable, we miss what’s on the edge.

Use influence vectors to find what’s often underneath the gaze of the public, and let that edge influence change in ways it does in human life, profoundly.

Incentivize the edge, pay them to create and invent and make your LLMs smarter. It won’t take an engineer’s salary, and performance driven content should be rewarded in pay, use of technology, and compute credits.

2. The Open Canvas Approach

Human creativity is open source. Instead of the "delay, bluff, and pay" strategy we're seeing with big publishers, imagine a creative ecosystem where contribution means compensation – not just in money, but in access, technology, and opportunity.

Make it like the Axios-OpenAI deal: use of technology in exchange for permission to ingest and reference content.

Plug a culture into AI, creativity arises. Don’t try to tamp it down with lawyers.

3. Think Different, Not Like Me

"We're building AI from what's already popular, processed, proven. And we're risking missing what's coming, what we're inventing, what nobody even gets except somebody who's very young and they see things that you and I don't understand."

And like every other social network, with enough volume of activity, there’s a reward, maybe a payment, maybe a discount on using AI tools.

Grant and scholarship programs to reward human ingenuity that only makes models smarter.

Algos may amplify sameness, AI needs to watch for the subtle differences, the tones that drive humanity. It’s not just about being the smartest, it’s about being aware together.

Like open source, the shared knowledge is developed into something better, with human effort.

Because without human effort, at least in the short term, you’re not going to get great things out of AI.

The Human Stakes

This isn't about creators versus engineers – it's about human creativity itself. The same AI systems training on artists' work are learning from countless GitHub repositories, turning years of engineering expertise into automated code completion.

Entry-level programmers and mid-career developers are facing the same questions as writers and artists: what happens when your craft becomes training data?

And where is all the new engineering talent going to learn if there are no entry or mid-level jobs?

No one's asking for special treatment. Engineers who built vibrant open-source communities, artists who shared work under Creative Commons – they didn't expect to get rich.

They expected respect, permission, attribution – the basic dignity of choice.

The Creative Edge

The truth about creativity: if you don't encourage the edge – the people on the fringe who don't see, don't do, don't act like everyone else – to express themselves, you lose more than just their voice.

You lose the edge itself.

You lose what shows us what could be, not just what was or what's popular now.

Even in a world of endless content, Netflix still needs that one wildly different show that breaks all the rules.

Because that's where the new comes from. That's where we learn what's possible.

While AI frees up time, defining what we'll all do with this time is more important than ever.

For the few who choose to be creative, it's crucial to nourish and nurture, not simply take what they create without reward.

Think AI's amazing?

There's someone out there young and seeing things differently who will open our eyes and hearts, and that experience will make AI even more intelligent...maybe even emotionally intelligent.

The edge isn't just a boundary – it's the frontier of human creativity. And once you lose it, no amount of AI can bring it back.

Do you really want 2025 on repeat, or are you ready to build something we haven't even imagined yet?

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

An alien, AI, and a monkey walk into a copyright office. Each wants to register their creative work. Only one will get it…..

One of them actually got the copyright. And it wasn't who you'd expect.

Let's unpack this wild tale of three unlikely copyright seekers and see what it tells us about the future of AI and creativity. Get ready, this gets weird fast.

Meet Our Contestants

First up, we have Naruto, a particularly photogenic crested macaque from Indonesia.

Back in 2011, Naruto borrowed (okay, took) wildlife photographer David Slater's camera and snapped some surprisingly impressive selfies.

PETA, being PETA, decided to sue on Naruto's behalf, claiming our simian friend owned the copyright to his selfies.

Next in line, we have a mysterious group of celestial beings who, starting in 1911, began channeling what would become the Urantia Book through a human conduit.

Think of it as cosmic dictation – 2,000 pages of spiritual and philosophical teachings from beyond the stars.

And finally, we have our modern contender: artificial intelligence, a superintelligence creating everything from art to code, pushing the boundaries of what we consider creativity.

Naruto the Monkey tries to copyright his selfie....

Let's start with our monkey friend. Despite Naruto's undeniable talent for self-portraiture, the courts weren't buying it.

The ruling was clear: non-human animals can't hold copyrights.

Sorry, Naruto – no banana royalties for you.

Though there was a silver lining: the photographer agreed to donate 25% of future revenues to protect Naruto's habitat.

Now, here's where it gets interesting. The celestial beings? Their work, the Urantia Book, actually got copyright protection in 1955.

Yes, you read that right. A book allegedly authored by non-human intelligences received copyright protection. The twist?

The copyright wasn't awarded to the celestial beings but to the human "conduit" who received and transcribed their messages.

And AI? Well, that's where we're at now, watching the legal system try to apply rules written for quill pens to quantum computers.

Urantia Superintelligence and Early Copyright: Like AI?

The Urantia case gives us a preview of how courts might handle AI-generated works. The court's reasoning wasn't about whether celestial beings exist or if they truly authored the book.

Instead, they focused on the human element – the conduit who received, processed, and transcribed the information.

Sound familiar? It's similar to how we interact with AI today:

- Questions posed by humans (like prompts to AI)

- Information processed through a medium (the AI model)

- Output curated and shaped by human interaction

What This Means for AI Copyright

Let's look into four key commonalities that may reshape how we think about AI and copyright law:

1. Non-Human Intelligence

The core similarity here is the presence of consciousness or intelligence outside traditional human bounds.

"We have a non-human entity creating what appears to be creative work."

This plays out uniquely in each case:

- Naruto demonstrated genuine creative intent, choosing to pick up the camera and compose selfies. The court acknowledged his consciousness but deemed it insufficient for copyright.

- The Urantia case involved what participants described as "celestial beings" or "superintelligence" transmitting complex philosophical concepts.

- AI represents a new form of superintelligence that, like the Urantia beings, works through human intermediaries but creates independently.

2. The Essential Human Factor

The human element proves crucial in all three cases.

"It's the author, the person who originates it, or is it

the person who collaborates with a superintelligence?"

Consider:

- David Slater, the wildlife photographer, created the conditions for creativity by providing and setting up the camera, earning him 75% of future revenues.

- The Urantia conduit acted as a bridge, processing questions from humans and returning answers from the celestial intelligence.

- AI developers and prompt engineers serve as modern-day conduits, creating systems and crafting prompts that shape AI output.

3. Creative Output

Each case produces recognizable creative work, raising questions about originality and ownership.

"The work is there. That's not a stretch."

Looking closer:

- Naruto's selfies were composed well enough to become globally famous and commercially valuable.

- The Urantia Book spans 2,000 pages of original philosophical content, demonstrating coherent thought and structured ideas.

- AI generates everything from art to code, often blending and transforming existing works in novel ways, much like the Urantia Book combined various sources with new insights.

4. Legal Recognition

The legal treatment of these cases offers crucial precedents for AI copyright.

"We can't take 1800s laws and apply them to this universe."

Yet patterns emerge:

- Naruto's case established that while non-humans can't hold copyright, their creative work generates value that benefits both humans and the originating source.

- The Urantia case recognizes copyright for work attributed to non-human intelligence, but vesting rights in the human conduit.

- AI copyright cases today mirror these patterns, with courts increasingly focusing on human contribution while grappling with non-human creation.

What's striking is how these cases build on each other. The Naruto case showed us that non-human creativity has value worth protecting, even if traditional copyright isn't the answer.

The Urantia case demonstrated that non-human intelligence can generate copyrightable work when properly channeled through human intermediaries.

Together, they suggest a potential path forward for AI copyright that recognizes both machine intelligence and human facilitation.

The AI Copyright Road Ahead

What these cases teach us about AI copyright is fascinating:

1. The importance of human involvement isn't just legal fiction – it's critical to how we create meaning and value from non-human intelligence.

2. We need new frameworks that recognize both the reality of superintelligent creation and the essential role of human curation.

3. The solution might lie in how we define authorship and ownership in collaborative contexts between human and non-human intelligence.

Creativity in the AI Age - Seeking Definitions with Old Rules

The punchline to our opening joke – that celestial beings got copyright protection while a monkey didn't – tells us something crucial about how we might approach AI copyright.

It's not about whether the intelligence is human, but how humans interact with and translate that intelligence into something meaningful.

These seemingly bizarre cases of aliens and monkeys aren't just legal curiosities – they're active precedents being cited in current AI copyright lawsuits.

They highlight the absurdity of trying to explain superintelligent AI to a legal system that's still waiting for a horse and carriage while SpaceX launches rockets.

AI isn't just another tool or technology – it's an outside superintelligence, much like the celestial beings in the Urantia case….except we have proof its real.

We channel it through prompts, acting as modern-day conduits, creating a fascinating parallel to the Urantia transcription process.

But AI stands unique, defying our traditional categories and challenging our fundamental assumptions about creativity and authorship.

Naruto's case might be more prophetic than anyone expected. Someone, somewhere, is probably already training a monkey to create copyrightable work.

Think about it – in a world where we're teaching primates to use tablets and smartphones, how long until a non-human entity legitimately claims copyright protection?

When (not if) that happens, it could blow the doors wide open for AI copyright claims.

The future of copyright won't be found in dusty legal tomes or precedents from the printing press era. It'll emerge from our recognition that creativity and intelligence now exist in forms our legal ancestors couldn't have imagined. We need new frameworks that acknowledge both the reality of superintelligent creation and the essential role of human facilitation.

Perhaps the real lesson from our AI, alien, and monkey saga isn't about who gets the copyright – it's about how we adapt our understanding of creativity and ownership in an age where intelligence comes in many forms. The question isn't whether non-human entities can create, but how we build frameworks that protect and encourage creation in all its forms while ensuring fair compensation for all contributors – human and otherwise.

After all, in a world where monkeys take selfies and AI writes poetry, maybe it's time to admit that creativity knows no species barrier. And perhaps that's exactly the point.

RESOURCES

* Sulawesi Video - Restless Generation

* Urantia Book

* Urantia Foundation v. Maaherra

* Monkey Selfie Lawsuit

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 the one problem you're perfectly positioned to solve?

Hard question, and one that demands answers.

AI startups today are like architects trying to build an entire city before they've mastered a single room.

Every feature becomes another building, every idea another street, until a little AI chaos - the unexpected edge cases - flummox it all up.

You know, the stuff we don't expect.

Soon these founders are stretched too thin to build anything that lasts. There is a different way....

master one space that people need, and will pay for?

One problem costing money, one solution that makes their life measurably better?

This week's convo with Tekyz.com CEO and software developer David Hirschfeld gets to the heart of it:

'Founders who are in love with their problem, usually are in love with the customer.

Those are founders that find a way to become successful.

The ones that are in love with their idea and their product fail in massive numbers.'

That's worth unpacking, especially as we navigate the AI of 2025.

1. The FUD Factor: Why Fear Drives Interest

Before we start finding that one perfect problem to solve, we need to simplify.

It all comes down to something deeper than features or benefits - addressing fundamental human fears.

"Everything is about fear," David explains,

"but not in the way you might think.

When even excitement about a vacation comes from wanting to avoid boredom, you start to understand what really drives decisions."

Your customers aren't buying code - they're buying trust.

They're investing in the confidence that their core fears - about falling behind, losing the AI race, or missing opportunities - will be addressed.

IBM's famous FUD (Fear, Uncertainty, Doubt) strategy reveals something profound:

"I always thought that was just a very negative way of scaring people so you could win deals.

Then I started to realize it's not just that - it's the primary thing that motivates everybody to do the things that they do."

2. Building AI Teams That Transform Understanding into Action

When you're building something that matters, the foundation isn't just your idea - it's your team.

David's experience building global teams doesn’t follow the conventional wisdom about remote work.

"People talk about using offshore, but they literally treat it like throwing a ball over the fence and hoping there's somebody there to catch it," he shares.

Instead, David spent nine months in India over a decade, building relationships and understanding cultural nuances.

The result? A team that doesn't just execute – they collaborate.

"You have to basically be part of their culture and incorporate them in your culture. That's how you build teams."

3. The Simulation: Test Before You Invest

Here's where David's process gets fascinating - and practical. Most AI startups build first, then search for problems to solve.

David's approach? Simulate before you stimulate. Show them something that engages interest. You may find out you don’t have a fit….yet.

DIY Simulation Guide:

1. Create a high-fidelity prototype that looks and feels real

2. Present it to potential customers without explaining it's a simulation

3. Watch for the key question: "How do I know you can build this?"

4. If they don't ask - you've nailed it

"If you come up with a simulation of your product that's realistic enough so that people don't ask you how you'll build it, that's a critical success factor," David explains.

Try This Today:

- Pick one problem you think you can solve

- Sketch out a solution (literally - draw it!)

- Show it to three potential customers

- Ask about their daily/weekly pain points

- Don't pitch - listen for their fears

4. The Product Market Fit Evolution

"The one thing that everybody misses," David reveals, "is how to confirm product market fit before you invest in the development of your product.

You don't really want to build something and find out nobody wants it.

But this is what ninety-five out of a hundred startups do."

The difference between product market fit and product solution fit is:

- Product Market Fit: People say they'll buy it

- Product Solution Fit: People use it and get value from it

David's approach to validation is methodical:

"Ask somebody to pay you money for your idea.

If they pay you money for this product that they will eventually get, then you know you've got product market fit."

5. The AI Transformation: From Theory to Practice

The power of this approach becomes clear in David's own journey.

He's currently developing two AI applications using these exact principles:

1. An AI-powered niche analysis tool that helps founders identify their ideal market segment

2. A project estimation system that leverages AI to create more accurate development timelines

David knows AI might eventually make his estimation tool obsolete.

But that's exactly the point - when you understand your problem deeply enough, you can see even its eventual evolution. And make a choice.

Building in the AI Age: Your Action Plan

David's Launch 1st Framework isn't just for AI startups—it's a blueprint for anyone building a business in 2025.

Whether you're a creator growing your audience, a professional launching a side hustle, or an entrepreneur developing AI solutions, the principle remains the same: master one high-value problem.

Here's your roadmap to get started:

* Find Your Daily Problem

* Look for issues people face at least weekly

* Quantify the cost of inaction

* Listen for emotional triggers

* Test Before You Build

* Create high-fidelity prototypes

* Get pre-commitments

* Validate pricing against problem cost

* Build for Solution Fit

* Focus on user adoption

* Measure actual usage

* Iterate based on feedback

David used this framework to identify and develop two new AI applications.

One helps founders find their perfect market niche, while the other revolutionizes project estimation.

Both solve specific, high-value problems that people face regularly.

The beauty of this approach? It scales from solo creators to enterprise solutions.

When you understand your one problem deeply enough, the solution almost builds itself—whether that's content that resonates with your audience, a service that transforms your industry, or an AI tool that makes life measurably better.

Remember, in David's words:

"It's not about digital transformation now, it's about AI transformation."

But that transformation doesn't start with technology—it starts with understanding one problem so well that your solution becomes inevitable.

What's the one problem you're perfectly positioned to solve?

The AI future belongs to those who make one thing better for everyone else.

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

RUTH: When AI Meets Purpose - A Different Kind of AI Startup Story

In a tech bubble dominated by billion-dollar valuations and data-hungry AI models, one startup is charting a much different course.

Meet RUTH, an AI-powered chatbot that helps people facing digital abuse and tech-enabled threats - and it does so without collecting a single piece of user data.

A Mother's Question Out of Policy?

The story begins not in a startup incubator or VC boardroom, but with a mother worried about her son's online safety.

Megs Shah, founder of Parasol Cooperative, recalls the moment that sparked it all:

"I was trying to keep up with my son playing video games, and I was worried about the whole sextortion thing.

So I went on to OpenAI, and I asked a question about how do you talk about sextortion with your teenager?

And it basically said, sorry, it's out of policy."

For Shah, a technologist with over 20 years of experience, this wasn't acceptable. "The technologist in me didn't accept that answer. And I was like, oh, hell no, this can't be."

Before diving into RUTH's journey, let's address the core elements that any purpose-driven AI startup should consider:

1. Business Model Alignment: Your structure (for-profit, non-profit, B-corp) should match your mission. For RUTH, non-profit status wasn't just an organizational choice - it was a trust signal to users.

2. Privacy by Design: In an age of data harvesting, consider if you actually need user data. Sometimes, collecting less can be your competitive advantage.

3. Customer-Centric Innovation: Start with real problems, not technology. RUTH began with a mother's concern about online safety, not a desire to build an AI chatbot.

4. Trust Architecture: Build trust through transparency, verified information sources, and clear boundaries. Your users need to know not just what you do, but what you won't do.

When Traditional AI Can't Help, Build Something New

This gap in AI assistance for sensitive topics led Shah to explore alternatives.

Working with different models and careful prompting, she discovered it was possible to create an AI system that could provide robust, helpful responses to questions about digital safety and abuse.

"We got it out to individuals that are in the field doing the work, to survivors that have actually been working with the organization...

and found that we can make this really robust tool," Shah explains.

The Purpose Driven Choice: Going Non-Profit

In the AI startup world, the typical playbook is clear: raise venture capital, collect user data, optimize for growth, and aim for a billion-dollar exit. Parasol Cooperative threw out that playbook entirely.

When Shah consulted with VC friends, their response was telling:

"Their comment was, this isn't a big enough market.

And to me to call something like this a market just makes me cringe."

Instead of chasing unicorn status, Parasol Cooperative made the choice to structure as a 501(c)(3) non-profit.

As Shah explains: "I wanted to actually show people that tech can be affordable.

Tech can be something that's accessible and solve real world problems in a way that is not going to break the bank."

The Privacy-First AI Shift

RUTH's most revolutionary feature is what it doesn't do: collect user data.

In an era where data is considered the new oil, RUTH operates with zero-knowledge architecture.

"When you go to our site to use Ruth, we do not collect any emails, we do not collect your name.

We even tell you don't give us all this information.

We don't need it. And when you exit the chat, the data goes away."

This isn't just a privacy feature - it's a safety feature. For people facing tech abuse or stalking, data privacy can be a matter of personal security.

Trust Through Verified Information

Instead of training on potentially unreliable internet data, RUTH relies on carefully vetted sources.

Sandy Skelaney, COO of Parasol Cooperative, explains their approach:

"We just want to make sure that it's screened using scholarly research, especially on being trauma informed, information that's publicly available information from national reputable organizations doing victim services work."

The Future: Tech That Protects

Today, RUTH is available in 34 countries and growing. But for Shah and her team, success isn't measured in user numbers or revenue growth - it's measured in lives protected and people helped.

"This problem is not specific to any gender, to any race, to any country," Shah shares.

"It's affecting every single one of us that has any kind of device that we carry."

Parasol Cooperative aims to expand RUTH's reach through strategic partnerships and institutional relationships.

Their goal isn't to become the next tech giant, but to get their protective technology "into as many hands as possible."

The Bigger Picture: A New Model for AI Startups

RUTH and Parasol Cooperative represent more than just another AI startup - they represent a different possibility for what AI companies can be.

In a world where technology is often weaponized against vulnerable people, they're showing how AI can be used to protect and empower.

Their approach - non-profit structure, zero data collection, verified information sources - differs from traditional startup wisdom.

By creating an AI system that users trust, they might just be building something rarer than a billion-dollar company.

As Shah puts it:

"Knowledge is power that no one can take away from you. And if we're able to equip you with that, then we've done our job right."

In a tech landscape often criticized for its "move fast and break things" mentality, RUTH stands as a powerful example of how to move thoughtfully and protect people.

It's a reminder that sometimes the most innovative thing a startup can do is to prioritize purpose over profit

Action Plan: Building a Purpose-Driven AI Startup

For entrepreneurs inspired by RUTH's model, here's a practical roadmap to follow:

1. Start with the Why

- Identify a real problem you deeply understand

- Validate the need through direct user engagement

- Define clear boundaries before building

2. Build Your Foundation

- Choose a legal structure that aligns with your mission

- Develop clear privacy and data policies

- Create an advisory board of domain experts

- Set up accountability measures

3. Design Your Solution

- Start with minimum viable protection, not just minimum viable product

- Build privacy and security into your core architecture

- Use verified, high-quality data sources

- Create clear user guidelines and expectations

4. Grow to Last

- Focus on impact metrics beyond user numbers

- Build strategic partnerships with aligned organizations

- Develop sustainable funding models (grants, partnerships, etc.)

- Document and share your learnings to help others

5. Scale with Integrity

- Maintain your core values as you grow

- Keep user trust as your north star

- Build systems for quality control and oversight

- Stay connected to your user community

There's room in AI for companies that prioritize protection over profit.

As RUTH demonstrates, sometimes the most revolutionary thing you can do is to build something that simply helps people feel safer in their digital lives.

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

What I love most about covering AI this year? You learn way more by failing.

Robert A. Heinlein:

"Don't ever become a pessimist… a pessimist is correct oftener than an optimist, but an optimist has more fun."

Last Episode in 2024: The AI Optimist returns January 3, 2025

Those moments where we got it completely wrong - those 'wait, what?' moments that forced us to stop and really think.

This final episode of 2024 is more than just a year in review.

To every listener who's joins me in questioning assumptions, challenging conventional wisdom, and staying optimistic even when the headlines turn grim: thank you.

Your questions and insights make this not just about understanding AI, it’s also about understanding how we adapt and grow alongside it.

Whether it was watching copyright law try to defend against AI with stone walls (spoiler: AI has wings and the legal lead), or seeing brilliant engineers get ghosted because they couldn't stop talking tech instead of ROI and privacy, or catching Silicon Valley's 'oops, did we say that out loud?' Eric Schmidt moments –

nobody's got this figured out. We're all just trying to read a map that keeps morphing into something new.

Here’s some AI Assumptions turned upside down in 2024.

The ones that made us question everything we thought we knew about AI.

And by the end... well, I've got a story about one of my own epic misses this year.

Being wrong taught me more than being right ever could.

And I’m so not alone. Time to dive in..."

Early AI, Big Ideas: Dancing with Grifters

In any new technology, belief opening inventions come out side by side with grifters selling the promise and not delivering. Who it is in AI we’ll know in a few years.

Early 2024 look at AI

When I launched The AI Optimist podcast, I made a promise to myself and my listeners: stay curious, remain honest, and admit when I get things wrong.

As we close out 2024, I'm keeping that promise in a big way with our final episode of the year – one that challenged everything I thought I knew about artificial intelligence.

Throughout this year, we've seen the collapsing castles of copyright law, peeked behind Silicon Valley's members-only curtain, and watched engineers learn that speaking "human" matters more than speaking "tech." AI adoption is slow as pipe dreams become reality.

We've felt the struggles of creators fighting for recognition in an AI-driven world, and seen how the race for data is reshaping our digital landscape.

But through it all, one thread kept emerging: the MVI - most valuable insights - often came from mistakes, not the rarer success.

The Copyright Conundrum: When Old Laws Meet New Technology

The year began with a slap to creative faces about copyright law. That old law simply can’t keep up to what we do, create, and invent.

Before we can understand where AI is heading, we need to look at how it's challenging our assumptions about ownership and creativity.

Let's start with copyright law - a system that worked beautifully for centuries, until AI came along..."

"Copyright law looks like a castle built to protect books, stone walls, drawbridges, guards at every gate... Then AI showed up. Not with battering rams, but with wings. And suddenly our castle walls became sort of irrelevant."

This metaphor captures the first big lesson of 2024: we don't need better copyright laws – we need an entirely new system:

- LLMs use fair use arguments to justify training on copyrighted content

- Traditional copyright enforcement can't keep pace with AI's ability to transform content

- The "Sue and Hope" model isn't working for creators or platforms

- Small creators are being left behind while tech giants battle in courtrooms

Help may be on the way:

- Attribution and recognition systems built into AI platforms

- New monetization models that compensate creators for AI training data

- Blockchain-based content tracking and compensation systems

- Community-driven content models

When Silicon Valley Shows Its Hand

Silicon Valley isn't waiting around. In fact, when Eric Schmidt accidentally revealed the industry's real attitude toward creators' rights, it showed us exactly how Big Tech views this brave new world...

Perhaps the most surprising moments of 2024 came Eric Schmidt and other tech leaders real AI talk – moats, conquer, steal, and sue if you win - was released for us to listen, or not.

The industry's approach to AI training data – taking first, asking questions later – became impossible to ignore.

Creators were never asked, never paid, never considered. Even though Midjourney had a list of 4700 of them. Lawsuits flying from all corners, from groups of artists to the New York Times and Getty.

But the old copyright law, based on paper and sometimes 100 years of ownership, will change.

Behind the scenes we learn:

- Tech companies' "steal first, lawyer up later" approach to data

- The discovery phase of recent lawsuits reveals internal discussions about copyright

- Companies knew they were using copyrighted material but proceeded anyway

- The disconnect between public statements about ethics and private business practices

- How metadata from creators was intentionally ignored

- The real cost of "free" training data on the creative economy

“Explain AI to me like I’m a 5 year old”Speaking Human in a B2B World

One of the year's most practical lessons came from running an AI Business. A common early tech assumption is that you’re selling technology, when you’re asking people to change.

Watching engineers and developers learn the hard way that technical excellence isn't enough. ROI and privacy undermine many AI businesses gaining clients; in these early days few can trust AI.

Moving out of the AI engineer echo chamber and into business applications, we discover from Jonathan Khorsandi of Diwy.eu:

“Because they're so brilliant in engineering and technology and product development, it blows my mind how they think through that... but they don't really speak sales that well."

That episode is one of the most popular in 2024, answering a question few ask.

How do we speak to customers and close deals, when many think the technology is the key.

It’s always the implementation, and tech is never easy. Except maybe for Keebo.ai which installs in 30 minutes and delivers results in 24 hours.

Practical lessons learned:

- Engineers need to focus on discovery before diving into demos

- The SPIN framework helps tech companies communicate value

- Success stories from companies shifting from feature-focus to solution-focus

- How AI tools can help bridge the communication gap

- The importance of understanding customer problems before showcasing solutions

The Data Behind the Dreams: Quality Over Quantity

A recurring theme throughout 2024?

AI's hunger for data isn't sustainable under current models.

We've been scraping data for free. None of these businesses have any licensing cost associated with it.

They assume they can take it for free and charge us. This realization has pushed the industry toward an inflection point:

- The emerging "mad cow disease" problem of AI training on AI-generated content

- Why more data isn't always better data

- The growing scarcity of quality human-generated content

- How data quality affects model performance

- The need for sustainable data sourcing strategies

- The real cost of "free" data on model quality

As AI companies race to build bigger models, they're facing a problem: they're running out of quality data.

And that's forcing us to ask some uncomfortable questions about AI's future...and why AI and creativity depend on each other in a way neither side wants to accept in 2024.

The Bubble That Almost Was?

When I started this year predicting an AI bubble burst in 2024, I thought I had it all figured out. But that's the beauty of being wrong - it forces you to look deeper, to understand why.

My timing is off, still the indicators are all around.

How many times do we go through this, then find out the seedy side of the business explodes, while the legit folks keep trudging along inventing new ways to use AI.

When the crash happens and I know this from experience, most will run away and call it a fad... And that's where the real revenue happens.

The opportunity is in the crash and most of us expect it sometime in 2025. While we don’t know when it will happen, we know it will happen.

Recognizing this means keeping enough cash on hand to survive a winter in the AI market.

Many good companies will be acquired to cease to exist because they are waiting for more money.

Cracks appear in 2024 financially, though the sheer volume of money in this space, the control of Big Tech, and the influence of Big Tech on the US government in 2025 may blow those cracks open, or delay them as more money rides in.

Historical pattern reminders:

- Parallels with the dot-com bubble and crypto winter

- How value was created after previous tech bubbles burst

- Signs of market maturity versus speculation

- The role of venture capital in creating and sustaining bubbles

- Why the real opportunities emerge post-bubble

- Lessons from previous technology transitions

What Makes These "Mistakes" Perfect

Mistakes remind us that no matter how amazing the technology – it's about how people use it, adapt, learn, and grow alongside it.

Progress happens when we focus on solving human problems, not just technical ones.

Being wrong forces you to dig deeper than being right ever could.

Each "mistake" we've covered this year - from my premature bubble prediction to the tech industry's stumbles - reveals AI's real impact.

These were windows into how new technology takes root.

Through trial, error, and adapting:

- Failed predictions help us understand markets better

- Communication gaps drive better tools

- Data limitations push us toward sustainable solutions

- Copyright challenges spark new compensation models

- The bubble delay allows for better infrastructure development

Looking Forward: After AGI

While we've spent 2024 chasing AGI predictions and debating regulation, the real AI impact is happening in small businesses figuring out practical applications, in creators finding new ways to work with AI instead of against it, and in developers learning to speak human instead of tech.

The future of AI isn't in the headline-grabbing predictions or the doomsday scenarios - it's in the quiet shifts happening under the distraction of hype:

- New creator compensation models

- Improved communication tools for technical teams

- Viable data sourcing strategies

- User-centric AI development approaches

- Focus on practical solutions over hype

For entrepreneurs and creators:

- Focus on solving real problems, not chasing trends

- Build solid data strategies from the start, platform agnostic

- Invest in onboarding and retention, which means clear communications

- Prepare for the post-bubble opportunity

- Remember, being wrong can be the best teacher, as long as you pivot.

As we head into 2025, The AI Optimist remains optimistic – not because AI is perfect, but because we're finally asking the right questions and learning from our mistakes.

After spending a year and a half deep in the weeds, talking to creators getting squeezed by AI, entrepreneurs trying to build with it, and yes, even the skeptics who keep us honest - could I still genuinely be optimistic?

Despite grifter AI promises or startup valuations, because of what's happening? Turns out, absolutely yes.

The real story of 2024 isn't about technology - it's about the people trying to figure it out, mess up, learn, and try again.

The bubble may burst, copyright laws may evolve, and business models may transform, one thing remains constant: the human capacity to learn, adapt, and create something better.

Thank you for being part of this journey through 2024.

We'll be back on Friday, January 3rd, ready to explore, learn, and answer your questions, send them in.

We’ll be focusing more on Copyrights, getting creators paid, and trust. Should keep us all busy, finding ways to let AI organize and help us communicate better.

Stay optimistic, everyone – when the bubble bursts, opportunity begins.

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

While the tech industry chases AI billions with promises of artificial general intelligence and superintelligent machines, a different story is unfolding in the startup layer.

Entrepreneurs who focus on how people use technology are finding immediate success - not by selling AI dreams, but by solving real problems today.

Richmond Alake, part of MongoDB’s culture of entrepreneurism in employees says:

"You have to move fast, learn quickly, fail - and you're allowed to fail.

The key is learning from those failures."

This approach describes a new breed of AI entrepreneur who starts with human needs rather than AI First tech seen in many unicorns, seeing if AI can replace people.

As Amanda Slavin notes when questioning AI in education,

"The thing about AI is having the right information to feed AI.

When I ask big companies what they're doing, they talk about personalized learning - but what are you actually feeding it?"

These voices represent a quiet shift in how AI adoption happens.

While enterprise giants struggle with integrating AI and billion-dollar startups chase increasingly elusive valuations, small AI teams are earning trust through practical solutions.

What I’ve learned from them is trust, not technology, will be AI's true currency in 2025, and almost a first principle to gaining adoption, which is still slow.

From a non-profit to an AI SAAS company, the first focus again and again is on the people, the users, the customers, and not AI First.

How we fit AI into our lives will define the growth speed; that begins with trusting the AI, which isn’t evident in an early AI stuck between chatbots and fully automated AI Agents.

Sure it’s close, and it gets closer when respecting trust as a currency, an asset, and a defining line between success and failure in AI.

#1 AMANDA SLAVIN - LEARNING FREQUENCY

Learning Frequency chooses a different path as it starts: AI in education was focused on collecting data, not serving students.

Her team gives students control of their learning profiles through NFTs. Each profile captures not just academic performance, but learning styles, preferences, and needs.

When students change schools or teachers, they carry this information with them, sharing it on their terms. Teachers are prepped with resources to better understand a new student.

IN HER WORDS

• "When I've asked pretty big companies what they're doing in terms of measuring engagement, they're like, oh, we're using AI to create more adaptive and personalized learning - but how are you using AI?

What are you feeding it?"

• "These are small changes that can be made for every single student"

• "Help understanding how I learn helps me understand what I can do, not what I can't do"

• Empowers students to control their own learning data through NFTs

• Breaks down industrial-age education models

• Focuses on individual learning styles and needs

• Teachers quickly get up to speed with new students through shared profiles

COOL FACTOR

Learning Frequency flips traditional EdTech on its head. Instead of schools owning student data, students control their own learning profiles as NFTs.

When they move to a new class or school, they choose what to share with teachers.

This builds trust from day one and helps teachers prepare for new students before they even enter the classroom.

#2 RICHMOND ALAKE - MONGODB

MongoDB's approach to AI focuses on building trust from the inside out. Rather than treating AI as a technological overlay, MongoDB integrates it into their culture of rapid experimentation and learning.

Engineers are encouraged to try new approaches, fail fast, and share their learnings openly.

This transparency builds trust both internally and with customers, who see a company learning alongside them rather than claiming to have all the answers.

IN HIS WORDS

• "You have to move fast, learn quickly, fail - and you're allowed to fail"

• "The key thing is learning from those failures and using your learnings to inform your next action"

• "There is a lot of experimentation, lots of fun as well"

• Build AI culture through rapid experimentation

• Embrace failure as a learning tool

• Keep the human element in technical development

• Create safe spaces for effective testing and iterating

COOL FACTOR

MongoDB proves that large organizations can move like startups. Their approach turns traditional enterprise AI adoption on its head - instead of top-down implementation, they build from the ground up through employee experimentation and learning.

Employees become entrepreneurial within the company. Even the CEO can be reached in this non hierarchical tech business.

It’s like being part of a startup without risking your own capital and career. Instead of employees being told what to do, the team is encouraged to execute and improve.

#3 RICHARD WHITE - FATHOM

Richard White's journey with Fathom began as a personal frustration with Zoom meetings during COVID.

Instead of rushing to monetize yet another transcription tool, he focused on understanding how people record meetings with individual notes.

The discovery? Users need more than accurate transcripts; they need to capture and share the meaning of conversations.

By waiting two years to monetize, Fathom built such strong user trust that customers invested $2 million at the same terms as venture capitalists, proving that patient, user-focused development creates lasting value.

Customers investing in Fathom is trust literally as currency. Earned trust with lock in because Fathom makes you part of their business and product development.

IN HIS WORDS

• "We think transcription is going to become really cheap.

This is a product people were charging $150 a month for, but we're going to give it away for free"

• "I'm a big fan of delayed monetization because it's hard enough to get people to use the thing"

• "Working with models is very different from typical engineering projects.

Their output is not a feature, it has a failure rate"

• Focus on retention before revenue

• Build trust through actual usage

• Let customers validate your model

• Patient development creates deeper value

COOL FACTOR

White took the startup route of delayed monetization while building Fathom.

Instead of rushing to monetize, they waited two years to perfect their product.

The result? Customers believe so strongly, they invest $2M on the same terms as VCs (rarely done, as thanks) – showing trust creates earned value, and loyalty.

BARZAN MOZAFARI - KEEBO

Barzan Mozafari's Keebo challenges everything we think we know about enterprise AI adoption.

While most vendors talk about month-long implementations and massive change management, Keebo proves value in 24 hours.

How? By focusing on "high-value problems" - specific challenges where AI can demonstrate immediate impact. Keebo’s patented tech supercharges AI queries to the cloud, making it easy to setup and within a day see whether it’s saving you money.This targeted approach builds trust through rapid results rather than lengthy promises. Keebo's 30-minute installation process isn't just a technical achievement; it's a statement about putting user needs first.

IN HIS WORDS

• "The AI adoption rate is disappointing.

The rate of progress in making AI machine learning is far surpassing the rate of adoption"

• "AI is a completely new beast to most enterprises.

They've been doing their business the exact same way for decades"

• "The CIOs have to think about security and privacy.

How do I know that my customer data is not going to end up somewhere in the LLM?"

• Focus on high-value problems (HVP)

• Prove value in 24 hours

• Make implementation effortless

• Address security concerns upfront

COOL FACTOR

While others talk about months-long AI implementations, Keebo installs in 30 minutes and shows ROI in 24 hours.

This isn't just speed - it's a fundamental rethinking of how AI should serve businesses.

MASSOUD ALIBAKHSH - OMADEUS

Massoud Alibakhsh's Omadeus takes a radical approach to AI architecture. Instead of building larger and larger language models, Omadeus creates small, focused AI agents with clear boundaries and specific purposes.

These "tiny LLMs" don't hallucinate because they're constrained by design - a perfect example of how limiting AI capabilities can actually increase user trust.

"Instead of shuffling dumb messages back and forth," Massoud explains, "you're exchanging intelligent objects that know their purpose and limitations."

IN HIS WORDS

• "We're using our own product to build our own product"

• "Instead of shuffling dumb messages back and forth, you're exchanging intelligent objects"

• "They're well programmed. They cannot execute more than these functions.

But they also can read and understand language"

• Small, focused AI agents outperform large models

• Clear boundaries build user trust

• Human control ensures reliable results

• Predictability beats raw power

COOL FACTOR

While the industry chases bigger AI models, Omadeus proves that smaller is often better. Their tiny LLMs can't hallucinate because they're designed for specific tasks - showing how constraints increase utility.

MEGS SHAH - PARASOL COOPERATIVE - CREATORS OF RUTH CHATBOT

RUTH began with a critical realization about tech abuse victims: tracking them, even to help them, could expose them to more danger.

While other AI projects focus on gathering more data, RUTH chose to protect users by not collecting their information in their product launch.

This common sense approach to user safety helped them scale to 29 countries in months, proving that sometimes the best use of AI is knowing when not to use it.

IN HER WORDS

• "We are very much survivor focused, and what that means is we try to take into account how would this make me feel if I was the one asking these questions?"

• "We need to be able to have it respond in such a way that it feels like you're talking to a friend more than a doctor"

• "The second piece is really to make sure that we're always making sure the end user feels safe"

• Privacy is a fundamental user right, not a feature

• Trust comes from protecting users, not tracking them

• AI can help without collecting personal data

• Safety and scaling aren't mutually exclusive

COOL FACTOR

RUTH proves that less data can mean more impact. By prioritizing user safety over data collection, they created a digital safe space that people – including advocates working to solve problems worldwide.

Their rapid scaling to 29 countries shows how putting user protection first creates organic growth through trust.

TRUST IS THE 2025 AI CURRENCY

These six entrepreneurs reveal a fundamental truth about AI in 2024.

While the industry chases billion-dollar valuations and artificial general intelligence, the best tech happens when we focus on human needs first.

• Start with users, not technology

• Build trust through immediate value

• Give users control of their data

• Focus on specific problems

• Small solutions often outperform grand visions

LOOKING AHEAD TO 2025

The next wave of AI success won't be measured in valuations but in trust earned.

* Amanda Slavin's students control their learning data.

* Richard White's customers become investors.

* Barzan Mozafari proves value in 24 hours.

* Massoud Alibakhsh constrains AI to make it more useful.

* MongoDB's Richmond Alake shows how learning from failure builds lasting innovation.

In 2024 many in AI are acting like doctors – spouting complex technical questions without answers, asked of others who don't understand the question.

So many are caught up in predicting what’s going to happen in 5 years, that they don’t take care of people, employees, customers, all of us – right now.

These aren't the AI stories making headlines. You won't find these entrepreneurs promising AI to replace you or destroy you.

Instead, they're quietly solving real problems for real people. Real business solving real high value problems (HVPs)

AI integration happens when we find out how the technology works with people, not how it replaces them.

THE TRUST ADVANTAGE

Each of these entrepreneurs shows how trust compounds over time:

• Learning Frequency's student-controlled data

• Fathom's patient development approach

• Keebo's rapid value demonstration

• Omadeus's constrained, reliable agents

• MongoDB's culture of learning through failure

RUTH's project exemplifies this perfectly. By choosing not to track vulnerable users, they built enough trust to scale to 29 countries in months.

That's the power of putting people first.

It begins with users, listening to them, adapting to them, encouraging them, and building trust.

Trust is the new AI currency in 2025, money follows.

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