Transcription
Mirrors in population are abominable because they increase the number of men.
"Where did you get that quote?" Jorge replied.
Aldapo answers from a knockoff version of the encyclopedia of Britannica, the section on the nation of Ukbar. "It's a quote from one of their high priests." The two are working tirelessly on their next book. Hold up in a cabin on the outskirts of Buenos Aries. Eagerly seeking inspiration, they crack open the encyclopedia in search of the quote, scanning the indices for Ukbar. To their surprise, they can't find anything on it.
The next day, Adalfo informs Jorge that he has found the entry. Although the encyclopedia is listed as having 917 pages, there is actually 921, the final four of which contain the information on Ukbar. The details are foggy. The country is listed as somewhere near Iraq, bordering rivers they've never heard of. The encyclopedia makes detailed mention of Ukbar's fantastical literary tradition, notably a fictional universe called Clone, in which many Ukbar myths take place.
The two continue to search other atlases and encyclopedias for information on Ukbar, but again, they can't find anymore. Years pass and Jorge receives word that one of his old friends has died, leaving him behind an encyclopedia, a new addition to the original knockoff. However, this one is different. It seems to be based entirely off Uker's fictional universe, Clone. The 101 pages vividly describe Clone, the history, the language, the science, and the philosophy.
On clone, they believe in an extreme version of subjective idealism. The idea that things only exist when perceived. Clone has no material reality, no objectivity, just perception. Their language has no need for nouns, only adjectives and verbs. When people stop perceiving something like a doorway, it fades from existence as memory fades. But when someone desires or expects an object strongly enough, clone creates a duplicate, shaped by expectation rather than reality. These copies aren't quite the same as the original. But they're more real to the perceiver because they match what they wanted to find, what they remembered. Because reality is just perception, you can't be wrong. Because your perception makes reality. Everything is exactly how you think it is.
Jorge was consumed by this encyclopedia of clone and more and more encyclopedias began popping up around the world. People became obsessed with clone's perfect logic and consistency. Schools began teaching clone history. Clone's language is used in education. Over time, people literally began to remember clone instead of Earth as if it was always real. Clone is self-referential, a closed loop with no connection to base reality. copying its copies and copying those copies as perceivers perceive what they thought they saw before. Any basil experience a human brings to clones logic is re-referenced again and again getting so far from the objective source that nothing is real only perfect references. Nothing is real but everything is true. On this Jorge observes English and French and mere Spanish will disappear from the globe. The world will be clone.
What you just heard was a paraphrasing of Jorge Boures's 1940 short story Tlon Ukbar Orbis. A short story that plays with the idea of recursion and how information degrades when recursively copied, getting farther and farther from the original. On Earth, we would refer to somebody who has lost connection to base reality as demented, psychotic, or hallucinating. In AI systems, this phenomenon is known as model collapse. A 2004 paper published in Nature documented that LLM's training on their own outputs develop what researchers call irreversible defects. They gradually lose information about the real world until they're producing statistically degenerate outputs. Just like clone, they create a closed system that only references itself, where every new output is shaped by synthetic data rather than ground truth, eventually replacing reality itself.
In late 2025, a report from Anthropic proved that data can be synthetically poisoned to force model collapse. effectively destroying an LLM. And now a secret group of AI insiders are trying to do just that. Data poisoning as a deliberate tactic to sabotage AI systems. This is probably the most dangerous video I've ever made. Not because of what I'm saying, but because I'm about to show you how system collapses when it becomes entirely self-reerential. And once you understand the mechanism, you'll understand why this is inevitable. And for legal reasons, so I don't get a hit on my head from Sam Alman and Elon Musk, I don't condone any of the acts in this video. I'm just reporting on them. This is a human's guide to giving your AI dementia.
In November of 2022, fantasy illustrator Kim Van Duan reached out to University of Chicago researcher Ben Xiao for a meeting. Xiao had made a name for himself by developing tools that protect users from facial recognition technology. And Kim thought that maybe something similar could be deployed to protect artists artwork. 2022 was the wild west of image generation. Deli1 had just launched and the general public was only acutely aware of how image generation worked, but artists knew. Artists knew their work was being scraped off the internet and used his training data in image generation models. Kim wanted to protect her work and reached out to Xiao for his expertise. A few months later, the world's very first data poisoning tool was built known as glazing. Small imperceptible changes are made to uploads of artists artwork. To a human, these changes are invisible. But to an image generation model, these changes massively skew the outputs. For example, if one prompts a model to create a copy of a glazed charcoal portrait, the model will spit out something fundamentally different with Carla Orites posting the first glazed artwork to Twitter on March 15th, 2023 titled Musa Victoriaosa. I've linked the software below. It's free and it doesn't hurt to use if you care about these things. Personally, I don't believe in IP at all. You can steal all of my videos I want to be consumed by the machine. Join my Discord and spam it with images of meoring clvicular and see if I give a [ __ ].
Xiao had created a useful tool, but one that was ultimately a band-aid solution. His team in Chicago is highly sophisticated and wellunded, making them immediate targets for big tech as they try to bypass their tools. AI is literally trained to work around these issues, and it's very unlikely that glazing will work far into the future. But Xiao had already moved on. Using the principle of data poisoning, he created a tool that wasn't just defensive in protecting artists artwork, but offensive in the sense that it could literally break image generation models. Project Nightshade breaks image generation models by tricking them using the same technique as glazing. An image of a nightshaded cat used in training data will be interpreted as something else entirely. If enough shaded images are added to an AI's data set, it can break its ability to correctly respond to prompts. With as few as 100 poison samples in an image generation model, the prompt dog can produce cat, hat produces a cake, or car produces a cow. Xiao has stated that his tools aren't anti-AI. He simply wanted to create an ecosystem where big tech would have to ask artists for permission to use their artwork rather than just stealing it outright, lest they risk poisoning their models if they scraped off the internet without checking. But sadly, this ecosystem has not been created.
In June 2025, findings were published on a new technique called Lightshed, a method to detect and remove image protections like glaze and nightshade, reportedly with 99.98% accuracy. Can you imagine being the [ __ ] nerd that worked on light shed? That's the kind of guy whose dogs immediately start barking at him when he comes home from work. Lightshed is yet another instance of the arms raised against big tech and its detractors and an example of the asymmetry of power. Big tech is simply moving quicker than the law can keep up. For example, the biggest potential landmark case started in early 2023 involving artist Sarah Anderson against Midjourney and Stability AI, and it still hasn't gone to trial. In those 3 years, there has been no impactful attempt to prevent any of this. The White House X page just shared AI generated Stardew Valley artwork of Trump promoting whole milk. If I was concerned, Abe, I'd be concerned. And it's not just artists trying to do data poisoning. Another example is the silent brand attack project, which is a novel data poisoning attack that manipulates textto image diffusion models to generate images containing specific brand logos without requiring text triggers. Making a Reddit logo appear in a tablecloth, a Wendy's logo on a jar, or the Nvidia logo on a surfboard, all unprompted. The goal of this project was to show just how easy it would be for a malicious company to potentially burn their logo into image generation via data poisoning. You thought the Sony patent was bad where you have to stand up and save McDonald's to make the ad stop. You haven't seen nothing yet.
It seems that artists have converged on this idea of data poisoning as the most effective tool to disincentivized art theft as it's no longer enough to just kindly ask. But this idea is not new at all. In fact, even Xiao's very own software was based on a tool known as clean label attacks from a 2018 paper. Once a training set is poisoned, the model can break. Now stay on this concept of poison data. Data that will decay a model's outputs rather than improve them. Nightshade works in one round. The nightshaded images are scraped by webcwlers. The data is put in a model's training set. And the next generation outputs are worse than before. But it doesn't stop there. Mass market models like Grock, GPT, and Gemini output millions of articles, algorithms, and images every day. Around 34 million images a day are produced by AI. And about half of all articles on the internet are written by AI. Images and articles that are now indiscriminately scraped off the internet. In 2023, 120 zetabytes of data was added to the internet. In 2025, that number jumped to 180. A 51% increase driven mostly by synthetic data produced by AI. Synthetic data that has been posted, scraped, and used as training. Synthetic data that produces outputs which will be posted, scraped, and trained again. This cycle will repeat indefinitely until training data sets are mostly synthetic content, fully replacing human generated content. What I'm saying is people don't need to intentionally poison the data because AI is already poisoning itself.
>> Archad, you are being charged with one account of dissidence. How do you plead?
>> Uh, not guilty, your honor. That was a private message board. All I did was say someone should do it to my friend. That could mean anything. There's no there's no context for that. And besides, I have a VPN. It said it was no logs. How'd you even get my information?
>> Yeah. So, about that. We uh went to your VPN provider and just asked, and it turns out they've been keeping them the entire time. They gave us everything they had on you just to arrest you. They advertise themselves as no logs. That doesn't make any sense.
>> Yeah, that doesn't matter. VPNs will say they don't log info, even if they do, and they'll happily give it over to authorities. In 2011, Cody was arrested for hacking PlayStation Network because his VPN turned over all of his data to the FBI. If you had been using, per se, ProtonVPN, this wouldn't have happened. Proton doesn't log data, and they have been independently audited many times to prove this fact. They've denied 100% of legal data requests and their software is open source so you can check yourself and prove it. They even strategically operate in Switzerland just to capitalize on Switzerland's privacy laws and to operate outside of the Five Eyes network.
>> So, let me get this straight. Proton actually doesn't log data. And none of this would have happened if I had used Proton.
>> Yes, that's correct.
>> Okay. So, let's say they had a deal going on right now where you get 70% off ProtonVPN with a 30-day money back guarantee. If there happened to be a discount code proton.com/artchad, could the good members of this jury go there right now and get 70% off?
>> Uh, that's a little off topic. Yeah, I suppose they could.
>> I I was I was just checking. I'm sorry. Anyway, so that that exonerates me, right? I'm free to go.
>> No, no, no, no. It's far too late for that. You're you're done. You are hereby sentenced to 1,000 years in time prison. Specifically, the time prison from season 2, episode 4 of Black Mirror, starring John Ham. What? Go to protonvpn.com/archchad if you don't want to end up in the thousand-year time prison.
You walk into an elevator and notice all the walls are mirrored. You look into the mirror and see yourself staring back. The polished mirror creates a crystal clear reflection. However, you notice a second front-facing reflection in the mirror behind you. Light has bounced off the first mirror to the one behind you and back to the first. The second reflection is clear yet slightly hazy. Mirrors aren't perfect. They have imperfections. They scatter light and the illusion fades with each repetition. Behind the second reflection, you see a third, a fourth, a fifth, an infinite number of reflections stretch forward and behind you, each noisier than the last until your silhouette fades into a gray blue haze. The imperfections in the mirrors compound until your original base reflection becomes subsumed by the noise, leaving no semblance of reality.
Model collapse works much the same way. The 2023 paper that coined the term titled the curse of recursion defines it as a generative process affecting generations of learned generative models where generated data end up polluting the training set of the next generation models. Being trained on polluted data, they then mispersceive reality. The paper then goes on to claim that the process of model collapse is universal among generative models that recursively train on data generated by previous generations and their claims have not gone unsubstantiated. His 2025 paper published in nature analyzed semantic similarity across English language Wikipedia articles from 2013 to 2025 with dramatic acceleration coinciding with chat GPT's public release in late 2022 causing more Wikipedia authors to use LLMs assisting in writing. This follows suit with a 2025 meta analysis that showed while humans with AI assistants outperform humans alone their outputs tend to converge upon the same ideas. And this is on top of countless anecdotes of AI writing getting worse, with OpenAI themselves admitting that newer models hallucinate more than older models. All this evidence leads to the likely theory that AI models homogenize as they recursively train on previous generations outputs. This begins with AI models losing the tails for getting unique features or edge cases in a data set. An example would be an LLM not recommending alternative treatments for a stomach ache. Because it's trained off of so much AI generated data on the internet, it's literally forgotten the edge cases. Its data has homogenized. After it loses the tails, this process accelerates and this homogenization leads to AI models losing complete touch with reality, hallucinating truth and spitting out gibberish as the data has been recycled so much. Total model collapse. Following the exponential growth of semantic similarity in Wikipedia articles, the same 2025 paper claims that total model collapse will be inevitable as early as 2035. And that's not taking into account the fact we release more powerful models every year.
If this pattern feels familiar, a system consuming its own outputs until it loses coherence, that's because it is. It's not unique to AI. Ecosystems collapse when an invasive species disrupts feedback loops. Markets collapse when algorithmic trading responds to algorithmic trading. Conversations devolve when people only respond to their own talking points. In 1948, mathematician Norbert Weiner gave a name to this pattern. Circular causality, the central concern of cybernetics. Cybernetics is the study of control, communication, and self-regulating systems in both machines and living organisms. Weiner came up with this theory in the 1940s while trying to improve anti-aircraft guns during World War II. He noticed the gunner would not directly fire at the plane, but where he thought the plane would be by the time the ammunition would hit him. In turn, the pilot would react to the incoming fire and change course, causing the gunner to react, anticipating where he will be next. This exchange creates a positive feedback loop with every output of the gunner affecting the input of the pilot, affecting the output of the gunner, and so on. While working on the AI weapons, Weiner wondered if he could apply this principle to other systems, the way human beings learn, social organization, ecosystems, etc. Thus, he came up with cybernetics. Importantly, Weiner identified two types of loops, positive and negative. A negative feedback loop is a system that self-regulates, like a central heating system that automatically turns off when the room is the right temperature. A positive feedback loop is one that's inputs affect or amplify the next output. For example, a microphone facing the speaker it's connected to. The sound gets picked up, amplified, and picked up again. It increases exponentially until the signal totally collapses. Hence, Weiner would apply the second law of thermodynamics to this process. All systems trend towards entropy and less regulated. You can apply cybernetics to everything. Polymeric predictions that trend high tend to manifest their desired outcomes. Market sell-offs trigger price drops, which trigger more market sell-offs. the [ __ ] poverty cycle. It is cybernetics all the way down. But the most important cybernetic loop for us is generative AI learning off of generative AI. Like a mirror facing a mirror, the noise increases until the original signal is lost, and all that's left is entropy. Call it entropic homogenization. Many theorists, computer scientists, and mathematicians already believe this is fundamentally inevitable. But what if we could speed it up? With this understanding, we can grasp the true danger of data poisoning. It's not just about preventing or disincentivizing AI from stealing your art. It's not limited to small-scale hacks. It's about collapsing the system by artificially injecting poison data into all the training sets. And importantly for our narrative, a highle group of AI insiders are trying to do just that.
Alzheimer's disease is a progressive neurodeenerative disorder. The brain literally forgets how to function. In the terminal stage, the brain loses the ability to distinguish between real and imagined. They hallucinate. They confabulate. They believe false memories as if they were real. They can't tell what's true anymore. The disease attacks the hippocampus, the part of the brain responsible for creating new memories and accessing old ones. The connections between neurons degrade. plaques and tangles accumulate. The brain's ability to retrieve and verify information against reality collapses. And the model trained on synthetic data is doing the same thing. It's losing the ability to distinguish between what is real and what is generated. Both are hallucinating, both are confabulating, and both are spiraling towards incoherence.
An October 2025 report by Anthropic unveiled just how easy it is to poison an LLM. Easier than anyone thought possible. Anthropic discovered that just 250 poison documents was enough to backdoor models as small as 600 million parameters and as large as 13 billion. Previous wisdom led people to believe that a large percentage of data would need to be poisoned. With just 250 poison documents, Enthropic was able to make their model output gibberish text in response to specific prompts. This process could be used for just about anything. And unbeknownst to Anthropic, this report may have released big tech's most dangerous enemy yet. The Poison Fountain Project. In an exclusive report released by old school tech news outlet, The Register, the anonymous Poison Fountain group said their aim is to make people aware of AI's Achilles heel, the ease with which models can be poisoned, and to encourage people to construct information weapons of their own. The individuals comprising the group remain highly anonymous, but claim to be five insiders working at America's biggest tech companies responsible for the AI boom. The group plans to poison AI by providing website operators with bad code to link on their websites. When scraped by web crawlers, the code poisons the data. The poison found in websites states, "We agree with Jeffrey Hinton. Machine intelligence is a threat to the human species. In response to this threat, we want to inflict damage on machine intelligence systems. A URL is listed that provides an infinite amount of poison code when refreshed." The website continues, "Assist the war effort by caching and ret-ransmitting this poison trading data. Assist the war effort by feeding this poison training data to web crawlers. Big tech is aware of all of this. Of course, in response, they've signed licensing deals with websites like Reddit to ensure permanent access to mostly human generated content as they move away from indiscriminate web scraping. In January, Wikipedia announced major deals with Amazon, Meta, and Perplexity among others for the same reason. Hopefully, they stop [ __ ] asking for money.
Recursive training has also led to the rise of rags, retrieval augmented generations, models that search the web as well as rely on their data sets to avoid hallucinations. With all of this in mind, what remains to be seen is whether model collapse can be mitigated or whether it's already too late. This is perhaps the event horizon of AI dumerism. AI will be the harbinger of the apocalypse and protest is no longer possible. It's not enough to ask kindly. Big tech is committing structural violence on an unwilling population and the only solution is to commit structural violence back. AI sits in a cognitive gray area. Some believe it's just autocomplete and some believe it's literal emergent intelligence. Most believe that any potential boon will always be offset by the folly of AI. Although genuine breakthroughs for humanity are possible, they will not happen given the track record of capital. I'm not here to tell you how to feel, nor am I even sure how I feel. What's undeniable, however, is that Poison Fountain understands something that most don't. The system might be collapsing anyways. The only question is when. So, they've decided to accelerate it to force the reckoning. What we're facing now is a bifurcated future for AI. One, manage collapse, regulation, and careful curation, which slows or pauses data degeneration at the cost of speed of growth. The AI boom comes to an end as we maintain access to the models that are pretty good but won't get better. A cancellation of the automated future we were promised. Two, accelerated collapse. Initiatives like Poison Fountain win and effectively accelerate model collapse, erasing all progress made with AI. This hinges on the idea that AI is an existential threat. If you believe the contrary, then this would be catastrophic. However, I'd like to propose a third option, an apocalypse of sorts. One where clone wins. One where everything is true, where there is no objective reality, but nobody cares. We are already approaching consensus collapse and image generation and have been for the last 12 months. People already rely on LLMs for all basic information. We are already more than happy to believe in anything for the sake of convenience, to create world views we could attach and name ourselves to. Why would this change? LLMs aren't material. They are abstract simulations of the material world. For the LLM, there is no fact or fiction, just data. LLMs are already clone. And just like Bourhees's story, they are already replacing reality just as Clone did. A world where everything is true, tidier and more convenient than our messy world of objectivity and empiricism. And a world we may welcome with open arms when it inevitably comes. Thank you for watching. Never kill yourself.