You ever get that feeling where you’re looking at a pristine, high-tech piece of hardware, or maybe a really slick AI interface, and you realize you have absolutely no idea what’s happening in the kitchen? It’s like eating a five-star meal and forgetting there’s a guy in the back covered in flour and grease, yelling about supply chains. Today’s prompt from Daniel is about that kitchen. We’re going behind the curtain of the AI data pipeline, and honestly, it’s a lot messier and more fascinating than the marketing suggests.
Herman Poppleberry here, and Corn, that’s a perfect way to frame it. Everyone talks about the "magic" of the transformer architecture or how many billions of parameters a model has, but the real war—the actual, multi-billion-dollar trench warfare happening right now—is over the data. Specifically, how you find it, how you clean it, and what happens when the well starts running dry. By the way, a quick bit of meta-context for the listeners: today’s episode script is actually being powered by Google Gemini 1.5 Flash. It’s always fun to have the machine help us talk about how the machine is built.
It’s a bit of a "circle of life" situation, isn’t it? The model writing the script was trained on the very pipelines we’re about to deconstruct. Daniel’s asking us to look past the "hoovering" phase. I think the general public assumes AI labs just point a giant digital vacuum at the internet, suck up every Reddit post and Wikipedia entry, and hit "train." But it sounds like we’ve moved into a much more surgical era. Is the "hoover" dead, Herman?
The hoover isn’t dead, but it’s been fitted with about a thousand different high-efficiency filters. In the early days, like with GPT-2 or the original BERT, there was definitely a "more is better" philosophy. But as we’ve scaled up to these massive frontier models, we’ve learned that "more" can actually make a model dumber if it’s the wrong kind of "more." We’re moving from the era of Big Data to the era of Good Data. The foundation is still often something like Common Crawl—this massive, multi-petabyte archive of the web—but what a lab does with that raw crawl is where the secret sauce lives.
Right, because Common Crawl is basically the dumpster of the internet. It’s got everything: brilliant research papers, sure, but also gibberish SEO spam, bot-generated product descriptions, and... well, the darker corners of the web that you wouldn’t want your AI learning social graces from. So, if I’m an AI researcher in 2026, and I get this raw dump of the internet, what’s the first thing I do? I assume I don’t just start reading.
No, you’d be there for a few million years. The first stage is extraction and "boilerplate removal." When you look at a webpage, your human brain automatically ignores the navigation menus, the footer with the copyright notice, the "Related Articles" sidebar, and the ads. An AI needs to be told to ignore those, or it starts thinking that every sentence in human history ends with "Click here for our Privacy Policy."
I’ve noticed that in some of the older, smaller open-source models. You’ll ask it a question, and it’ll give you a great answer, but then it’ll randomly append a "Sign up for our newsletter" at the bottom. It’s a literal manifestation of it not knowing where the content ends and the website begins.
It’s more than just an annoyance, though. If a model sees "Copyright 2024" ten billion times, it starts to assign an enormous amount of statistical weight to those words. It might start hallucinating that every fact it tells you happened in 2024. Or worse, it starts thinking that legalese is the standard way humans communicate. You end up with a model that sounds like a Terms of Service agreement instead of a person.
So we strip the boilerplate. We get down to the meat of the text. But even then, isn't the internet just full of... well, clones? I feel like I see the same "Top 10 Productivity Tips" article on fifty different websites.
And that’s why teams are moving away from just using the WET files—which are plain text extracts—and moving toward WAT files, or Web Archive Transformation files. These include the HTML metadata. By looking at the tags, the pipeline can say, "Okay, this text is inside a 'main' tag or an 'article' tag, whereas this other text is in a 'nav' tag." It allows for much cleaner signal extraction. But even once you have the "clean" text, you hit the biggest boss in the data pipeline: Deduplication.
Wait, let me pause you there. Why does the HTML structure matter so much if we just want the words? Can't the model just figure out what's important by looking at the frequency of the words?
You'd think so, but structure provides context that plain text lacks. Think about a recipe blog. The actual recipe is usually buried under three thousand words of a personal story about a trip to Tuscany. If you just have the plain text, the model might weigh the story and the ingredients equally. But if you have the WAT files, the pipeline sees that the ingredients are in an "unordered list" inside a "recipe-container" class. That metadata tells the training algorithm: "Hey, this part is high-density information, pay attention." Without that, you're just feeding the model a giant wall of undifferentiated noise.
That makes sense. It's like the difference between reading a textbook and reading a transcript of someone reading a textbook. The formatting is the map. But back to the "De-dupe." This sounds like one of those things that’s easy to explain but a nightmare to execute at scale. Is it just finding two identical files and hitting delete?
I wish. If it were just identical files, a simple cryptographic hash would solve it. But the internet is a hall of mirrors. Think about a viral news story. An Associated Press wire report gets picked up by five hundred different local news sites. Each site adds its own headline, maybe changes a few adjectives, or adds a local paragraph at the bottom. To a computer, those are five hundred unique files. But to a model, that’s the same information being shouted at it five hundred times.
And if a model hears the same thing five hundred times, it starts to think that’s the only truth in the universe. It memorizes the phrasing instead of understanding the concept. I think the technical term is "overfitting," right?
Spot on. If you don't de-dupe, the model loses its ability to generalize. It becomes a parrot. To solve this, labs use things like MinHash or Locality Sensitive Hashing, or LSH. These algorithms don't look for exact matches; they look for "fuzzy" similarity. They break documents into "shingles"—small overlapping phrases—and then use some pretty intense math to see how much those shingles overlap across billions of documents. If two documents are eighty-five percent similar, you toss one. This process alone can shrink a dataset by thirty or forty percent, and paradoxically, the model trained on the smaller, de-duped set will almost always outperform the one trained on the raw, bloated set.
Wait, hold on. If you’re tossing forty percent of the internet, aren’t you worried about throwing out the baby with the bathwater? What if those "similar" documents actually had small but vital differences? Like a scientific paper and a rebuttal that quotes ninety percent of the original paper?
That is the high-wire act of data engineering. If your "shingle" size is too small, you delete everything. If it's too large, you miss the duplicates. Engineers have to tune these parameters constantly. They often run small-scale "ablation studies" where they train tiny models on different versions of the de-duped data just to see which one "feels" smarter. It’s a massive computational expense before the real training even begins.
It’s like a student who reads one good textbook versus a student who reads the same mediocre blog post a thousand times. The first one actually learns biology; the second one just memorizes a specific set of typos. But once you’ve de-duped, you’re still left with... well, potentially high-quality garbage. How do you filter for "quality" without a human sitting there reading every line?
This is where it gets really clever. They use "classifier-based filtering." Essentially, they take a "gold standard" dataset—something like Wikipedia, or a collection of high-quality books, or peer-reviewed journals—and they train a smaller, "dumb" model to recognize what that looks like. It’s basically a "Quality Vibes" detector. Then, they run that classifier over the entire massive web crawl. The classifier gives every document a score. If a document sounds like a coherent, well-argued essay, it gets a high score. If it sounds like a drunk person shouting in a comment section or a bot trying to sell you supplements, it gets a low score and gets booted.
Wait, so we're using AI to decide what the AI is allowed to learn from? Isn't there a risk of creating a massive echo chamber? If the "quality filter" prefers a certain style of writing—say, formal academic English—don't we lose the richness of how people actually talk? Or the nuances of different dialects?
That is the million-dollar question, and it's a major point of contention in 2026. If you filter too aggressively, you get a model that sounds like a very polite, very boring Victorian tutor. It loses slang, it loses cultural context, and it might even lose the ability to understand "low-quality" input from a user. If a user asks a question in casual slang, and the model was only trained on the Encyclopedia Britannica, it’s going to struggle.
I can see the "posh AI" problem being a real issue for accessibility. If you don't speak like a Harvard professor, the AI might just think you're "low quality" noise. How do they balance that? Do they keep a "slang bucket"?
They actually do! This is why the "Mixing Strategy" is so important. You don't just want the "best" data; you want a representative sample of human thought that has been cleaned of "toxic" or "useless" noise. It’s a delicate ratio.
"Mixing Strategy." That sounds like a DJ set for data. Talk to me about the ratios.
It’s exactly like a recipe. A modern frontier model isn’t just "The Internet." It’s a very specific blend. A lab might decide the mix is fifty percent high-quality web text, twenty percent code from GitHub, ten percent academic papers from arXiv, ten percent books, and maybe five percent "reasoning" data—like math problems or logic puzzles. And they don't just dump it all in at once. They often use "curriculum learning."
Like a school syllabus? You start with the basics and move to the hard stuff?
You might start the training on the broad, noisy web data to give the model a general "vibe" of the world and language. Then, in the later stages of training—the "cooling down" phase—you feed it a much higher percentage of high-logic, high-quality data: code, math, and philosophy. It’s like finishing a child’s education with a master’s degree. This "annealing" process helps the model sharpen its reasoning capabilities at the very end.
I love the idea of an AI going through a "philosophy phase" right before it graduates. But let’s look at the math there. If you’re training on trillions of tokens, and you want ten percent to be "high-quality books," that’s... a lot of books. Like, more books than have ever been written?
You’ve hit on the physical limit. There are only about 150 million unique books in existence, according to Google’s estimates. If each book is 100,000 words, that’s 15 trillion tokens. That sounds like a lot, but for a model that needs 100 trillion tokens of "high quality" data to see an improvement, we’re actually reaching the edge of the library.
Which leads us to the crunch. Daniel mentioned the "Data Wall." We’ve been hearing about this for a while—the idea that we’re literally running out of internet. If the current estimate is that we’ll hit the bottom of the barrel by late 2026, that’s... well, that’s basically now. Are we really out of things to say?
It’s not that we’re out of things to say; it’s that we’re out of publicly available, high-quality, human-generated things to say. Think about it. We’ve already scraped Wikipedia. We’ve scraped every public GitHub repo. We’ve scraped the Common Crawl. We’ve scraped Reddit—though that’s becoming a legal minefield. The "stock" of high-quality public text is estimated at around three hundred trillion tokens. The biggest models are already getting close to that. If you want to train a model that’s ten times bigger than the current state-of-the-art, you literally cannot find ten times more high-quality public text on the web. It doesn’t exist.
So what do they do? Do they just start scraping the bottom of the barrel? Do they start training on those low-quality comment sections we just talked about deleting?
That’s one option, but it leads to diminishing returns. It’s like trying to get more nutrition by eating more napkins. The other options are much more interesting—and a bit more controversial. The first is "Private Data." This is why you’re seeing these massive licensing deals. OpenAI signing a deal with News Corp or Reddit or Axel Springer isn’t just about avoiding lawsuits; it’s about getting access to the "private" or "paywalled" archives that the Common Crawl bots couldn't reach. They want the stuff behind the gates.
It’s like the AI labs are the new oil barons, and they’re buying up mineral rights to every library and newspaper archive on earth. But what about the stuff that isn’t text? I mean, the internet is mostly video and audio now. Is that the next frontier?
Oh, absolutely. We’ve moved into the "Multimodal Pipe." Think about the sheer volume of human knowledge locked in YouTube videos. Billions of hours of people explaining things, showing how things work, debating. If you can use a high-quality speech-to-text model—like Whisper—to transcribe all of that, you’ve just unlocked a massive new continent of tokens. Researchers are transcribing everything: podcasts, lectures, even security camera footage in some extreme cases to understand physical movement.
But wait—transcribing a podcast is one thing, but how does an AI "learn" from security camera footage? Is it just watching people walk around and trying to predict the next frame?
Precisely. It’s called "self-supervised learning." The model hides the next few seconds of the video and tries to guess what happens. If it guesses wrong, it updates its internal "physics engine." This is how models like Sora or Runway learn how the world moves. It’s not "text" data in the traditional sense, but it’s still "information tokens" that contribute to the model’s overall world model.
How does that work in practice, though? If I show an AI a billion hours of people dropping coffee mugs, does it eventually learn gravity, or does it just learn that "ceramic + floor = brown puddle"?
It learns the statistical probability of the brown puddle, which—at a certain scale—is indistinguishable from understanding gravity. If the model can predict the trajectory of the falling mug with 99% accuracy, it has effectively "internalized" the physics, even if it doesn't know the formula G=m1m2/r2. The multimodal pipeline is basically trying to give the AI "common sense" by letting it watch the world, rather than just reading about it.
That feels a bit desperate, doesn't it? Transcribing every "Hey guys, welcome back to my channel" just to find a few nuggets of logic for a model. But there’s a bigger risk here that Daniel mentioned: "Model Collapse." If we run out of human data and start training AI on data generated by other AI, don't we just end up in a digital version of the Hapsburg family tree?
"Digital inbreeding" is actually a term people use! It’s a very real danger. Synthetic data is the holy grail because it’s infinite. You can have a big model generate a trillion sentences of "perfect" logic and then train a smaller model on that. But the problem is that AI models, even the best ones, have a slight "statistical tilt." They prefer certain words, certain structures, and they make certain types of mistakes. If you train Model B on Model A’s output, Model B inherits those tilts. If you then train Model C on Model B’s output, the tilts become craters. Eventually, the model loses the "long tail" of human creativity—the weird, outlier stuff that makes us human—and it becomes this bland, repetitive, and eventually nonsensical mush.
It’s like making a photocopy of a photocopy. By the tenth generation, you can’t even read the text anymore. But I've heard some researchers say synthetic data is actually better because you can control it. Is there a "clean" way to do synthetic data?
There is, and it’s called "Rejection Sampling" or "Constitutional AI." Instead of just taking whatever the AI spits out, you have a second AI—a "Judge"—that evaluates the output based on a set of rules. "Is this logically sound? Does it follow the laws of physics? Is it creative?" Only the "A-grade" synthetic data makes it into the training set for the next generation. It’s like an AI self-improvement loop. But even then, you’re still limited by the "Judge’s" own biases.
So, if synthetic data is risky, and the web is empty, and we’ve already bought the newspapers... where does the intelligence come from next? Are we just at a plateau?
Not necessarily. The pivot right now is toward "Reasoning Data." Instead of just "more" data, they want "better" data. They’re hiring thousands of PhDs to write out "Chain of Thought" examples. Instead of just giving the AI the answer to a math problem, they have a human write out every single step of the logic, explaining why each step follows the last. They’re essentially creating a textbook of human thought processes. This is much more expensive than scraping the web, but one token of high-quality reasoning data might be worth a thousand tokens of random blog posts.
That’s fascinating. It’s like we’re moving from the "Exploitation" phase—where we just took whatever was lying around—to the "Cultivation" phase, where we’re specifically growing the data we need to make the models smarter. It makes the data pipeline look less like a factory and more like a high-end laboratory.
And that’s why the "Data Mixture"—the exact percentage of what went into a model—is now the most guarded trade secret in Silicon Valley. In the early days, labs would publish their data sources. Now? Good luck. They’ll tell you the architecture, they’ll tell you the hardware, but they will never tell you exactly what they fed the beast. That’s the competitive advantage in 2026.
Do you think we’ll ever see a "Data War" where companies start sabotaging each other's data? Like, poisoning the well of the public internet so competitors' scrapers get bad info?
It’s already happening in a subtle way. There’s a tool called "Nightshade" for artists that subtly alters pixels so that if an AI scrapes an image, it "breaks" the model’s understanding of what’s in the picture. If a model scrapes a "Nightshaded" image of a dog, it might start thinking dogs have wheels. On the text side, you have "poisoned" websites designed to trigger specific failures in LLMs. It’s an arms race between the scrapers and the "data sovereigns" who want to protect their content.
It really brings home the point that in the world of AI, you are what you eat. If you’re a model, your entire worldview, your logic, your biases—they’re all just a reflection of that pipeline. We spend so much time looking at the "brain" of the AI, but maybe we should be looking at the "digestive system" instead.
I love that. The digestive system of AI. It’s a dirty, expensive, mathematically complex process, but it’s the only reason these models can do anything more than flip a digital coin.
So, looking at the practical side of this—because we always like to give the listeners something to chew on—what does this mean for the average person? If high-quality human data is the "new oil," does that change how we should think about our own digital footprint? I mean, I’m a sloth, I don’t post much, but for the people out there writing niche blogs about, I don't know, seventeenth-century weaving techniques... are they suddenly the most important people on the internet?
Unironically, yes. If you have a niche hobby and you write about it with depth and original thought, you are creating the "gold" that these models are starving for. The "Data Wall" makes original, authentic human expression incredibly valuable. The irony is that as the internet gets flooded with AI-generated sludge—the SEO-optimized garbage—the value of a real, messy, opinionated human blog post goes through the roof.
But wait, if these companies are scraping our blogs to build multi-billion dollar models, shouldn't we be getting a check? If my "weaving blog" is the "gold" they need, why am I giving it away for free?
That is the central legal and ethical battle of the next decade. We’re seeing the rise of "Data Unions" and "Data Trusts" where creators band together to negotiate licensing fees. Some platforms are already doing this on your behalf—Reddit’s deal with Google and OpenAI is basically them selling your comments. You might not get a check directly, but the platform is definitely getting paid.
It’s like a digital feudalism. We’re the peasants working the fields of content, and the platform lords are selling the harvest to the AI kings. But speaking of the harvest, let’s talk about the "Long Tail." We mentioned that models lose the "weird" stuff when they collapse. How do engineers actually ensure they aren't accidentally deleting minority viewpoints or rare languages during the cleaning phase?
That’s a huge technical challenge called "Distributional Shift." If you have a dataset where 99% of the text is English, and you run a "quality filter" trained on English, that filter might look at a perfectly valid Swahili poem and label it as "gibberish" because it doesn't fit the statistical patterns of the "gold standard." To fix this, labs have to build "over-sampling" pipelines. They’ll find rare, high-quality data—say, a collection of Icelandic literature—and repeat it multiple times in the training set so the model actually pays attention to it. It’s like giving the model a megaphone for the quietest voices in the room.
There’s a beautiful irony in that. The very technology that threatens to drown out human voices is actually desperate to hear them. It’s like a vampire that needs fresh blood to survive but is slowly turning everyone into other vampires.
That’s... a surprisingly dark but very accurate analogy, Corn. And it leads to a real practical takeaway: if you’re a creator, "be more human" is actually a viable technical strategy now. Don’t try to write like an AI. Don’t try to be "perfect" or "optimized." The more your work shows the "fingerprints" of a human mind—quirks, unique perspectives, weird connections—the more likely it is to be valued by the next generation of data pipelines.
I’ll take that. "Be more human" is a good motto for 2026. Another takeaway for me is the transparency issue. As users, we should probably be asking more questions about the "Data Mixture." If a model is giving us medical advice or legal advice, we have a right to know if it was trained on peer-reviewed journals or just a collection of "vibes" from a filtered web crawl. The "black box" isn't just the neural network; it's the data pipe.
I think we’re going to see a push for "Data Provenance" standards. Imagine a world where an AI comes with a "Nutrition Label" that says: "Thirty percent academic papers, twenty percent verified code, zero percent social media." That would change how we trust these systems.
And what about the "un-learning" aspect? If I find out my data was used in a model and I want it out—maybe it’s a private photo or a sensitive piece of writing—is "Machine Unlearning" a real thing? Or is it once it's in the kitchen, it's in the soup?
It’s incredibly hard. Right now, it’s like trying to take the salt out of a soup after it’s been simmering for ten hours. You can "fine-tune" a model to try and make it ignore certain facts, but the information is often still buried in the weights. True "unlearning" is a massive research frontier right now, but it’s technically very difficult and computationally expensive. Usually, it’s easier to just retrain the whole model from scratch without that data—which costs millions.
"Nutrition labels for AI" and "Un-salting the soup." I like those. It would certainly make the "digestive system" a lot easier to inspect. Well, Herman, I think we’ve thoroughly explored the kitchen. It’s messy, it’s expensive, and apparently, we’re running out of ingredients, but the chefs are getting very creative.
They certainly are. And as long as they’re still looking for "high-signal" human thought, I guess we’re still in business. It’s a strange comfort to know that even the most advanced silicon brains still need us to tell them what’s real.
Speak for yourself, I’m just here for the snacks. But seriously, this has been a deep dive into the hidden machinery that actually makes "intelligence" possible. It’s not just code; it’s a massive, global effort to curate the sum of human knowledge.
And a big thanks to Daniel for the prompt. It’s one of those topics that’s easy to overlook because it’s so "under the hood," but it’s really the foundation of everything we talk about on this show. Without the pipe, the brain is just an empty vessel.
This has been My Weird Prompts. Thanks as always to our producer, Hilbert Flumingtop, for keeping the wheels on this bus and making sure our own data pipeline doesn't get clogged.
And a big thanks to Modal for providing the GPU credits that power our generation pipeline—they’re the ones making sure our "digestive system" stays running smoothly. Without that compute, we'd just be two guys talking into a void.
If you’re enjoying these deep dives, do us a favor and leave a review on your favorite podcast app. It really does help other people find the show. You can also find us at myweirdprompts dot com for the full archive and all our social links. We've got some great visual aids for this episode up there showing what "shingling" actually looks like.
We’ll be back next time with another prompt, another deep dive, and hopefully, no model collapse. We're keeping our data fresh and our shingles unique.
See ya.
Goodbye.
