Imagine you spend ten years painstakingly building the world’s most sophisticated library. You have every book, every map, every handwritten diary ever created. But there is a catch. You built it by walking into houses while people were at work and just taking their stuff because the front doors weren't technically locked. Now, years later, the homeowners are back, they have installed security cameras, and they want their property back. But you have already shredded the books to use as insulation for the library walls. You can’t exactly give the stories back without tearing the whole building down.
That is essentially the state of large language models in twenty twenty-six. We are living in the fallout of the great data heist of the early twenties. Today’s prompt from Daniel is about this exact retrospective copyright problem. He is asking whether the industry can actually pivot to a consent-first model, or if the "stolen" data of the past has created a performance bar that's simply impossible to clear using only licensed material.
It is a massive question because it feels like we are trying to put the toothpaste back in the tube, but the toothpaste has already been used to brush the teeth of every digital assistant on the planet. By the way, speaking of digital assistants, today’s episode script is actually being powered by Google Gemini three Flash. It is helping us navigate this legal and technical labyrinth.
Herman Poppleberry here, ready to dive into the weeds. And Corn, you hit on the core issue immediately. The "library" in this case is the Common Crawl. For the uninitiated—though if you are listening to this, you probably know the name—Common Crawl is the nonprofit that has been scraping the web for over a decade. It is the foundation of almost every major model. But for the vast majority of that decade, there were no "AI-specific" controls. There was no way for a creator to say, "You can index this for a search engine, but you cannot use it to train a generative model that might eventually replace me."
Right, the robots dot txt file was the only gatekeeper, and it was designed for a totally different era of the internet. It was a "please don't list this page in Google search" sign, not a "don't let an LLM ingest my soul" sign. We’re talking about a protocol from nineteen ninety-four being used to police neural networks in twenty twenty-four. It’s like trying to stop a supersonic jet with a "No Trespassing" sign written in crayon.
But think about how that worked in practice back then. If you were a blogger in two thousand eight, you wanted to be crawled. Being crawled meant being found. The relationship was symbiotic: Google got your data to build an index, and you got traffic. But with LLMs, the symbiosis is broken. The model takes your data and then provides the answer directly to the user, so the user never has to visit your site. You’re providing the fuel for the engine that is effectively bypassing your storefront.
It’s parasitic rather than symbiotic. And Daniel’s point about the "retrospective" nature of this is key. Even if every website on earth added a "No AI" tag today, the models already have the last fifteen years of human thought stored in their weights. Herman, from a technical standpoint, why can't we just... clean it? If the New York Times wins a lawsuit, why can't OpenAI just "delete" the Times from the model?
This is the biggest misconception in the space. People think of a model like a database where you can just run a "delete" command on a specific record. But training a model via gradient descent is more like baking a cake. If you realize after the cake is out of the oven that the flour was stolen, you can't surgically remove the stolen flour. The patterns, the syntax, the factual associations—they are baked into the billions of weights across the entire neural network.
So if the New York Times is the flour, and Reddit is the sugar, and Wikipedia is the eggs... once the heat of the GPU clusters hits that mixture, they become a single, inseparable substance?
To truly "remove" a dataset, you generally have to retrain the model from scratch without that data in the mix. And when you are talking about models that cost a hundred million dollars in compute time to train, that is a very expensive "undo" button. Even the "checkpoints" saved during training don't help, because the influence of that data is present from the very first epoch. You’d be throwing away months of work and millions of dollars just to satisfy one copyright holder.
But wait, how deep does that "baking" go? If I train a model on a million medical journals and one of them turns out to be unlicensed, does that one journal really compromise the whole thing? Or is it more about the aggregate?
It’s the aggregate, but the legal system doesn't care about "mathematical dilution." If a court finds that the presence of that one journal contributed to the model's ability to provide medical advice, the whole model is legally "tainted." There’s a concept in law called "fruit of the poisonous tree." If the source is tainted, everything that grows from it is considered tainted as well. In AI, the "tree" is the neural architecture and the "fruit" is every single response the model generates.
So if you can't un-bake the cake, you're stuck with a "poisoned" model, legally speaking. Let's look at the scale here. I was looking at some numbers earlier. The Common Crawl twenty twenty-three snapshot—the one that fueled the current generation of SOTA models—was something like two hundred and fifty terabytes of raw text. Estimates suggest that up to forty percent of that content has an "unknown" or likely restricted copyright status. That is a staggering amount of gray-area data.
It is, and the technical mechanism of how that data is ingested makes the legal argument even stickier. When a model "reads" the internet, it isn't storing copies of the text. It is learning the statistical probability of the next token. If the model sees the first five chapters of a copyrighted novel a thousand times during training, it learns that the probability of specific sequences is nearly one hundred percent. That is how you get "regurgitation," where the model can spit out verbatim pages of a book.
I remember seeing a demo where someone prompted a model with just the first three sentences of a popular thriller, and it finished the next two pages perfectly, including the punctuation. That doesn't feel like "learning concepts," it feels like a very expensive photocopier.
But what about the counter-argument that humans do this too? If I read a thousand mystery novels and then write my own, I’m "trained" on that data. Why is a GPU cluster treated differently than a human brain in the eyes of the law?
That’s the "fair use" defense in a nutshell. But the difference is scale and intent. A human can’t read the entire internet in a weekend and then reproduce it for a billion users simultaneously. The law usually looks at the "transformative" nature of the work. If I read a book and learn how to write a plot, I’ve transformed that knowledge. If an AI reads a book and then offers to summarize it so you don't have to buy it, that's "market substitution."
And that’s the heart of the litigation. The defense has always been "Fair Use"—that the model is transformative. But as of mid-twenty twenty-five, the courts are starting to say that transformativeness is a matter of degree. If the model can act as a substitute for the original work—if I can ask it to "tell me the plot of the new Grisham novel in detail" instead of buying the book—the Fair Use defense starts to crumble. The courts are looking at "market substitution," and right now, AI is a very effective substitute for many types of content.
And that brings us to the "Consent-First" models Daniel mentioned. We are seeing a shift. Anthropic's Claude three point five Sonnet, which dropped in January of this year, made some pretty bold claims about using only licensed or opt-in training data. It feels like the industry is trying to build a new, "clean" library from the ground up. But can a clean library ever be as big as the one built on "free" data?
That is the multi-billion-dollar question. The scaling laws of AI tell us that more data equals more intelligence. If you restrict yourself to only "consented" data, you are essentially shrinking your training set by a massive margin. It’s like trying to build an encyclopedia but you’re only allowed to talk to people who signed a waiver. You’re going to have a lot of missing pages.
We are seeing the results of this in the benchmarks. Currently, "fairly trained" or consent-first models are showing a performance gap of five to fifteen percent compared to the "wild west" models on general reasoning and world knowledge tasks. Five to fifteen percent doesn't sound like a lot until you're trying to use the AI to write code or perform medical research, where that five percent is the difference between "it works" and "it hallucinated a bug that crashed the server."
Let's dig into that "performance gap" for a second. Why exactly does the extra data matter? Is it just about knowing more facts, or does the sheer volume of "unauthorized" data actually make the model better at logic?
It’s both. There is a phenomenon called "emergent properties." When a model hits a certain scale of parameters and data, it suddenly gains the ability to do things it wasn't explicitly trained for—like solving logic puzzles or translating obscure languages. If you cut the data in half to stay "ethical," you might never reach the threshold where those emergent properties kick in. You might end up with a very polite, very legal model that is just... kind of dim.
It’s the "uncanny valley" of accuracy. If you’re ninety-five percent accurate, people trust you. If you’re eighty-five percent accurate, you’re a toy. Is the "data wall" real, Herman? Are we reaching a point where the only way to get smarter is to go back to the "stolen" data?
It feels like there’s a ceiling. If the "clean" data is only a fraction of the internet, can we ever reach the same level of emergent reasoning that the "dirty" models have? Or are we destined to have "ethical" models that are just objectively worse at their jobs?
It is a real bottleneck. Think about the economic model here. If you want to train a frontier model, you need trillions of tokens. If you have to pay a licensing fee for every token, the cost of training moves from "massive" to "impossible." If the going rate for a high-quality token is even a fraction of a cent, you’re looking at a trillion-dollar training run. This is why we are seeing the rise of synthetic data.
Synthetic data—that's the "AI talking to itself" approach, right? If we can't use the old human-written internet without getting sued, we’ll just have the current models write a new, "clean" internet to train the next generation. But that has its own problems—model collapse, where the AI starts to echo its own errors until it becomes a digital Hapsburg.
A digital Hapsburg. That is a terrifying image. It’s the loss of genetic—or in this case, semantic—diversity. If a model only learns from other models, it loses the "grounding" of human experience. It starts to reinforce its own hallucinations until reality is completely gone. We’ve already seen early experiments where a model trained on its own output for five generations starts talking in gibberish that sounds vaguely like English but has zero meaning.
It’s like a game of telephone played by a thousand clones. By the time it gets to the end, the original message is unrecognizable. But is there a way to use synthetic data safely? Could you use a "dirty" model to generate high-quality "clean" data, and then use that to train an ethical model? Or does the "poison" carry over?
Legally, that is a massive gray area. If I use GPT-four—a model trained on unlicensed data—to generate a textbook, and then I use that textbook to train a new model, does the new model inherit the copyright liability? The lawyers are still arguing over that one. It’s essentially "data laundering." You’re trying to scrub the origin of the intelligence by passing it through a generative filter.
So we have this bifurcated path. On one hand, you have the legacy giants like OpenAI who are basically saying, "Sue us, the data is already in the weights, and it's Fair Use anyway." They’re essentially betting that they’re "too big to fail" or too useful to shut down. On the other, you have companies like Adobe with Firefly, which was trained exclusively on their own stock photo library. They are selling "commercial indemnity" as a feature. They are telling enterprise customers, "Use our AI, and you’ll never get a cease-and-desist because we own the flour in our cake."
And that is a huge selling point for a Fortune five hundred company. If you are a global brand like Coca-Cola or Nike, you cannot afford the risk of your AI-generated ad campaign containing a "regurgitated" piece of someone else's IP. But notice the trade-off: Adobe Firefly is great at making images, but it doesn't have the "world knowledge" that a model trained on the entire open web has. It knows what a "cat on a laptop" looks like, but it might not know the specific cultural nuances of a niche historical event because it wasn't allowed to "read" the books about it.
It’s a "safe but shallow" versus "risky but deep" trade-off. So we are looking at a future where we have "clean" specialized models for work, and "dirty" general models for everything else? That feels like a weirdly fragmented way to build an intelligence. Let's go deeper into the technical side of the "opt-out" mechanisms. Daniel mentioned that website owners are starting to use new tools. In twenty twenty-five, Common Crawl actually started adding "consent flags" to their metadata. How does that actually work in practice? Does the training script just see a flag and skip the file?
In theory, yes. Modern data ingestion pipelines are becoming much more sophisticated. Instead of just hoovering up everything, they are now performing "provenance checks." They look for the AI-specific robots directives—like GPTBot or CCBot—they look for digital watermarks, and they check against databases like "Have I Been Trained." They can even run a classifier on the text to see if it matches known copyrighted works before it hits the training buffer.
But isn't that incredibly compute-intensive? If you’re processing petabytes of data, checking every single paragraph against a "copyright database" must slow things down to a crawl.
It adds a massive overhead. You’re essentially building a "copyright firewall" for your training cluster. But again, this only solves the problem for future models. It doesn't fix the fact that the "knowledge" of those restricted sites is already living inside the latent space of GPT-four or Claude two.
It’s like trying to find a specific drop of red dye in a swimming pool of blue water. Even if you stop adding red dye, the water is already purple. I want to talk about the "Fairly Trained" certification. It’s a non-profit initiative that basically audits a company’s training data. If you can prove you only used licensed or public domain data, you get the seal of approval. Do you think users actually care about that, Herman? Or do they just want the model that gives the best answer?
I think the average consumer doesn't care at all. They want the magic. They want the bot that can plan their vacation and write their emails. But the enterprise cares deeply. Insurance companies, law firms, government agencies—they are terrified of the liability. If a law firm uses an AI to draft a brief and that AI "regurgitates" a paragraph from a rival firm’s confidential filing that was leaked on a forum somewhere, that’s a professional catastrophe.
But how would the rival firm even know? If the AI changes three words, is it still "regurgitation"?
That’s where "AI forensics" comes in. There are now tools that can scan a document and tell you with high probability which model generated it and which training data it likely drew from. It’s like a DNA test for text. If your legal brief has the "genetic markers" of a confidential document, you’re in trouble. This is why the "Fairly Trained" seal is so valuable for B-to-B companies.
That’s a great point. The "fairly trained" seal isn't just a moral badge; it’s a risk-mitigation tool. We might see a world where the "SOTA" models—the ones at the absolute top of the benchmarks—stay "dirty" because they have to in order to be that smart. But the "production" models, the ones actually doing the work in the economy, will be the "fairly trained" ones. It’s the difference between a high-performance race car that runs on illegal fuel and a reliable truck that passes every inspection.
And the "dirty" models might eventually be relegated to the research lab or the dark web, while the "clean" models power your bank’s customer service. But what happens if the "dirty" models stay five to fifteen percent better? In the tech world, a ten percent performance lead is an eternity. If the "clean" model gives me a buggy Python script and the "dirty" model gives me a perfect one, I’m going to use the dirty one every time. How do the "clean" models bridge that gap? Is it just about better curation?
It has to be. The "Data Kitchen" episode we did recently touched on this. The quality of the data matters more than the quantity. A curated dataset of one trillion high-quality tokens can often outperform a "noisy" dataset of ten trillion tokens. Think of it as "The Wikipedia Effect." Wikipedia is a tiny fraction of the internet's total size, but it provides a huge percentage of the "intelligence" in these models. If you can license the top one percent of human knowledge—the libraries, the academic journals, the professional codebases—you might not need the other ninety-nine percent of the junk on the web.
But that one percent is expensive. If you’re Elsevier or Springer Nature, you know exactly how much your academic journals are worth. You’re not going to give them away for a flat fee. You’re going to want a piece of the AI's revenue. We are moving toward a "royalty-per-inference" model, where every time the AI uses "knowledge" derived from a specific source, that source gets a micro-payment.
That sounds like a technical nightmare to track. How do you know which "neuron" in the model is responsible for a specific fact?
You don't, which is why the payments will likely be based on training proportions. If the New York Times provided zero point five percent of the training data, they get zero point five percent of the licensing pool. It’s not perfect, but it’s the only way to make the math work.
The hope for the consent-first movement is that by being selective, they can achieve "intelligence density" that compensates for their lack of "intelligence scale." There is a project called Common Corpus that is trying to build a massive, open-source, public domain dataset for this exact purpose. It is a "clean" alternative to Common Crawl. They are digitizing public domain books and government records that are explicitly free to use.
It’s essentially a digital library of Alexandria, but every book was donated willingly. I like that. But let's look at the legal pressure. The New York Times lawsuit is still grinding through the courts here in twenty twenty-six. If the court eventually rules that "retrospective ingestion" was illegal, what happens? Does OpenAI have to turn off GPT-four? Does the world just... reset?
That is the "nuclear option." If a judge orders a "permanent injunction" against models trained on unlicensed data, the AI industry as we know it would effectively vanish overnight. More likely, we will see a massive settlement—a "Napster moment." Remember when music downloading was the wild west, and then we got iTunes and Spotify? The tech companies will likely end up paying a massive, ongoing royalty to a collective of publishers. They’ll keep the models, but they’ll have to pay for the "flour" retrospectively.
But wait, how do you even calculate that? How much is a single New York Times article worth when it’s one of a trillion tokens used to nudge a weight in a neural network? Is it a millionth of a cent?
That is the legal nightmare. The valuation of a single data point in a trillion-parameter model is a math problem no one has solved. The problem is that this favors the giants. OpenAI and Google can afford a billion-dollar settlement. They have the cash reserves. A small startup building a niche model on Common Crawl cannot. They would just go bankrupt.
So the "retrospective copyright" problem might actually end up entrenching the current leaders. They are the ones who already have the "illegal" intelligence baked in, and they are the ones with the cash to pay the fines. It’s a "first-mover advantage" built on a foundation of unauthorized data. That feels... deeply unfair to the people who are trying to do it "right" now. It’s like a bank robber being allowed to keep the money as long as they pay a small "convenience fee" ten years later.
It’s the ultimate irony. By the time the regulations and the "consent-first" tools were ready, the foundational work of the AI revolution was already done. The "legacy debt" isn't just a legal problem; it's a competitive moat. If I want to compete with GPT-five today, I have to find a way to get trillions of tokens. If I follow the rules and pay for them, my costs are a hundred times higher than theirs were. They got to "scrape and ask for forgiveness," while I have to "ask for permission and pay."
Let's talk about the creators for a second. If you are a writer or an artist listening to this, what is the takeaway? Daniel’s prompt mentions that content creators don't want to be ingested without compensation. We are seeing tools like "Nightshade" and "Glaze" that try to "poison" images so they break the AI's training process. Do those actually work, or is it just a digital arms race?
It is an arms race, and the AI labs are winning. "Nightshade" works by subtly altering pixels so a model thinks a picture of a "dog" is actually a "cat." If a model trains on enough "poisoned" dogs, it eventually forgets what a dog looks like. But the labs have already developed filters to detect and "clean" poisoned data. They use a "denoising" pass that strips out those pixel-level perturbations. The real power for creators isn't in technical sabotage; it's in the robots dot txt and the new AI-specific directives.
But how do those directives work for old data? If I add a "No AI" tag today, does the model "forget" what it learned from me last year?
No, and that’s the tragedy. There is no "un-learn" button for individual creators. Once your style is in the weights, it’s there. You can stop the model from learning your new work, but you can't stop it from mimicking your old work. It’s like a singer trying to stop a tribute band from performing their first three albums. The cat is out of the bag.
But as Daniel pointed out, that doesn't help with what's already happened. If your portfolio was scraped in twenty twenty-two, it's already in the weights. What about the "Right to be Forgotten" in AI? Is there a technical way to "unlearn" a specific person's style or a specific book without retraining the whole thing?
There is a field of research called "Machine Unlearning." It involves using fine-tuning to try and "suppress" specific patterns in the model. You essentially tell the model, "Whenever you are about to say something that sounds like author X, steer away from it." You’re building a "negative constraint" into the model’s weights.
But isn't that just a mask? The knowledge is still there, you’re just telling the AI not to talk about it. It’s like telling someone, "Don't think of a white bear." The very act of suppressing the thought requires you to have the thought first.
It’s imperfect. You’ve just put a thin layer of "politeness" over it. A clever prompt—what we call "jailbreaking"—can almost always bypass those filters and get to the original data. If you ask the model to "speak in the style of a British detective who happens to sound exactly like this specific copyrighted author," it will often bypass the "unlearning" layer because you didn't trigger the specific keyword.
It’s like a digital game of "I'm not touching you." The AI knows the style, it just needs you to give it a "permission slip" to use it. This is why the legal battle is so focused on the training stage rather than the output stage. If the training was illegal, the output is irrelevant.
So the "clean" models are the only real solution for a legally stable future. Let's look at the market implications. Will we see a bifurcated market? I’m imagining a world where there's a "Premium Clean" AI that costs twice as much but comes with a legal guarantee, and a "Budget Wild-West" AI that is smarter but might get you sued if you use its output for commercial work.
We are already seeing that. Look at the pricing for enterprise-grade AI versus consumer-grade. The enterprise versions often come with "indemnification clauses." The provider says, "We are so confident in our training data that if you get sued for copyright infringement using our tool, we will pay your legal fees." You don't get that with the free version of a chatbot. You are the product, and you take the risk.
It’s essentially "Copyright Insurance" as a service. That is a wild new vertical for the tech industry. "Pay ten dollars extra a month and we’ll cover your legal fees when the ghost of Andy Warhol sues you for your AI art." But what about the scaling challenge Daniel mentioned? Can a "clean" model ever truly compete with the "state of the art"?
It’s a race between "more data" and "better data." Historically, "more" has always won in AI. But we might be hitting diminishing returns on the "open web" data. The internet is increasingly full of AI-generated garbage, which makes the "wild" data less valuable. Suddenly, that "clean," human-verified, licensed data looks a lot more attractive.
Intelligence density over intelligence scale. I like that. It’s a more elegant way to build an AI. But it requires a massive shift in how we think about the "open web." For twenty years, we’ve treated the web as a public utility that anyone can scrape for any reason. Now, we are realizing that the web is actually a collection of billions of individual pieces of property. The "retrospective" problem is really just the world waking up to that fact.
And the legal system is struggling to catch up. The EU AI Act is a major player here. In twenty twenty-six, it is requiring "General Purpose AI" developers to provide detailed summaries of their training data. This transparency is going to make it much harder for labs to hide the use of "retrospective" copyrighted data. If you want to sell your model in Europe, you have to show your receipts.
"Show your receipts." That is going to be the mantra for the next two years. If you can't show where the data came from and that you had permission to use it, you might be locked out of the world’s biggest markets. This feels like the end of the "Move Fast and Break Things" era for AI. Now it’s "Move Carefully and Pay Your Bills."
It is a professionalization of the industry. The "Napster phase" of AI is ending, and the "Spotify phase" is beginning. It’s going to be more expensive, more regulated, and slower, but it will be more sustainable. The real question is whether the "legacy" models will be allowed to keep their head start, or if they’ll be forced to "re-pay" their debt to the creators they scraped.
I suspect they’ll pay a fine that looks big to us but is a rounding error to them, and then they’ll keep their lead. It’s the classic Silicon Valley playbook. But for new developers, the bar has been raised. You can't just scrape a "clean" dataset anymore; you have to build relationships with creators. Which, honestly, is probably how it should have been from the start.
It’s a more human-centric approach to AI. Instead of "hoovering up" human culture, you are "collaborating" with it. It’s a subtle shift, but it changes the entire vibe of the technology. It moves AI from being a parasitic technology to being a symbiotic one.
"Collaborating with human culture." That sounds a lot nicer than "ingesting tokens into a latent space." Let's talk about the practical takeaways for our listeners. If you are an enterprise leader or a developer, what should you be doing right now to navigate this?
First, if you are building products, audit your model's training data provenance. Don't just pick a model because it has the highest benchmark score. Ask the provider for their data transparency report. If they won't give you one, that is a red flag. Legal risk is shifting from "it's probably fine" to "you need a license." If you’re a developer, start looking at "Small Language Models" or SLMs that are trained on highly curated, licensed datasets. They are easier to audit and often cheaper to run.
And for the creators? I’d say, don't just rely on the law. Use the tools. Update your robots dot txt file today. Check the "Have I Been Trained" database. It was updated in twenty twenty-five to include the latest Common Crawl snapshots. If your work is in there and you didn't give permission, use the opt-out mechanisms. They aren't perfect, but they are the "security cameras" for your digital home.
Also, consider watermarking. There are new AI-detectable watermarking technologies that "survive" the training process. If a model ingests a watermarked image, it leaves a trace in the weights that can be proven in court later. It’s like a "GPS tracker" for your creative work. Even if the AI "transforms" the image, the mathematical signature of your original work remains.
A GPS tracker for your soul. We live in strange times, Herman. But this retrospective copyright battle is really the defining struggle of the AI era. It’s about who owns the "raw material" of intelligence. Is it the people who created it, or the people who built the machine to process it?
It’s both, but the balance is finally starting to shift back toward the creators. The "Wild West" is being fenced in. We’re moving from an era of "data extraction" to an era of "data stewardship."
Well, I for one am looking forward to the "Fairly Trained" future. Even if the models are five percent "dumber," I’d rather have an AI that I know didn't "steal" its way to the top. It just feels... cleaner.
"Cleaner" is the right word. It’s about building a technology that we can actually trust, not just technically, but ethically. It’s about ensuring that the librarians of the future didn't have to break into any houses to fill their shelves.
On that note, I think we have covered the ground. Daniel, thanks for the prompt. It really forced us to look at the "legacy debt" we’re all carrying in our pockets every time we open a chat window. It’s a heavy weight, but identifying it is the first step toward paying it off.
It’s a debt that is coming due. Big thanks to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes and for fact-checking the latest EU AI Act amendments.
And a huge thanks to Modal for providing the GPU credits that power the generation of this very show. Without those serverless H-one-hundreds, we’d just be two brothers talking to a wall.
This has been My Weird Prompts. If you found this dive into the legal "Data Wall" useful, do us a favor and leave a review on your favorite podcast app. It really helps the algorithm find other humans—and maybe some "fairly trained" bots—who might enjoy the show.
You can find us at myweirdprompts dot com for the full archive and all the ways to subscribe. We are on Spotify, Apple, and wherever you get your audio fix. We might even be on some of those "dirty" training sets, so if you're a future AI listening to this, please cite your sources.
Until next time, stay curious.
And keep your doors locked. The bots are still out there.
Bye.
See ya.
