Pre-training
If post-training is a chef being taught to cook for a specific restaurant, pre-training is the process of building that chef’s pantry by hoarding every foodstuff on Earth.
Imagine a giant, automated warehouse. For months, it sends out robots to scrape every supermarket, farm, home kitchen, and garbage dump in the world. It collects everything: fresh vegetables, Michelin-star dishes, copyrighted recipes from locked safes, rotten meat, and poisonous berries. Everything is thrown into the warehouse, unsorted and unlabeled.
This chaotic warehouse is the pre-training dataset. The AI model is the engine built to find statistical patterns in this mess. It doesn’t “understand” what a vegetable is, or that a recipe is protected by copyright, or that poisonous berries are dangerous. It just learns that certain items often appear together. After months of processing, it can generate new combinations that look like plausible ingredients, but it might also spit out a verbatim copyrighted recipe or a dish laced with botulism.
This is the reality of pre-training. It is a brute-force statistical process that creates a powerful, knowledgeable, but completely amoral and unpredictable foundation.
The Two Inherent Dangers of Pre-training
- Indiscriminate Memorization (Copyright and Privacy)
The primary goal of pre-training is for the model to learn general patterns. But a critical side effect is that it also memorizes unique data points it sees repeatedly. If a copyrighted book, a private medical record, or a confidential corporate strategy document was in the scraped data, the model might memorize it.
This isn’t a bug; it’s a fundamental feature of how these models work. It cannot learn the “style” of an author without also memorizing their specific sentences. This creates a permanent, built-in copyright and privacy risk that cannot be easily removed.
- Bias and Toxicity Amplification
The internet is filled with biased, racist, and toxic content. Because pre-training is indiscriminate, the model learns these patterns as “knowledge.” It doesn’t learn that racism is wrong; it learns that certain words and stereotypes are statistically associated with each other.
When the model generates text, it reproduces these learned associations. The safety “guardrails” added during post-training are just a weak filter placed on top of this deeply flawed foundation. They are an attempt to stop the chef from serving the rotten food, but they do nothing to remove the rotten food from the pantry.
The Legal Bottom Line
The pre-training dataset is the scene of the crime. The choices made during data collection and curation determine the fundamental legal risks of the entire system. Any claims a company makes about its model’s safety or originality are secondary to the core question:
What did you put in the pantry?
Answering that question—through discovery, source code analysis, and model interrogation—is the key to understanding the true liability of any AI system.