Hablemos con datos: ¿qué datos construyeron a los gigantes de la IA?

We’re living in an era where AI is reshaping how we work, communicate, and create. Whether it’s ChatGPT drafting emails, Claude dissecting legal documents, or Gemini summarizing research papers, these tools are becoming indispensable. But beneath their impressive capabilities lies a question that’s rarely discussed openly: What data are these models trained on? More pointedly: Is your data — your blog post, your code, your Reddit comment — powering these systems without your knowledge? As someone who has worked with data systems, built machine learning pipelines, and advised on ethical frameworks, I can tell you the short answer is yes. Most people — whether they’ve written a blog post, pushed code to GitHub, commented on Reddit, or published a book online — have had their content scooped up by AI training pipelines. The majority of the most advanced models today were trained on sprawling corpora of text and code harvested from the web, sometimes with permission, often without. These models didn’t arise from isolated labs — they were built from the traces of our digital lives. In this article, I’ll pull back the curtain on what data fuels popular AI models, explain how your contributions might be involved, and explore the legal and ethical debates shaping this space. Drawing from my work on data pipelines and real-world examples, I’ll keep it clear for everyone while diving just deep enough to satisfy the curious. My goal? To empower you with insight and spark a conversation about what’s at stake. How AI Models Are Trained — and What They’re Trained On Entrenar un modelo de lenguaje grande (LLM) es como enseñarle a una biblioteca súper inteligente a predecir y generar texto. Estos modelos consumen miles de millones, a veces billones, de palabras (texto, código y más) convirtiéndolas en patrones numéricos que capturan el lenguaje, la lógica y el contexto. Utilizando potentes GPU, las empresas perfeccionan estos modelos a lo largo de semanas, creando inteligencia artificial que puede chatear, codificar o analizar con asombrosa habilidad. ¿El ingrediente clave? Datos, y muchos. Entonces, ¿qué hay en estos conjuntos de datos? Normalmente incluyen: Common Crawl : un…