Large Language Models

The class of system at the center of Kyle’s AI-and-knowledge thread — the kind of model (GPT and successors) that generates text by predicting a “reasonable continuation” one word at a time. What Is ChatGPT Doing and Why Does It Work is the mechanical account: GPT’s roughly 175 billion parameters are “just” enough to model next-word probabilities “well enough” to produce essay-length text, and those parameters are tuned by Gradient Descent over many training epochs. The piece’s larger surprise is that the success of LLMs may reveal that “meaningful human language has more structure and simplicity than we ever knew.”

Across Kyle’s essays, LLMs are treated less as a technical curiosity than as a force reshaping our relationship to knowledge. Books 2.0 frames investing in and watching “things like large language models” as the reason to better understand the medium of how people take in information. The Cogsec Chronicles turns to the risk side: as AI systems ingest the majority of our data, “the output of those same systems will start to shape people’s framing for reality” — a language-and-power concern (Wittgenstein’s “the limits of your language are the limits of your world”) that names LLMs as a “critical limitation of our language.”

Context: Large language models are neural networks trained on very large text corpora to predict the next token in a sequence; modern instances (the GPT family, Claude, Gemini, and others) scale to billions or trillions of parameters and exhibit broad language, reasoning, and code abilities that emerge from this next-token training objective.

Where this appears

• What Is ChatGPT Doing and Why Does It Work — Wolfram’s mechanical explanation of GPT’s 175B parameters and next-word prediction
• Gradient Descent — the training procedure that tunes an LLM’s parameters
• Books 2.0 — LLMs as the prompt to rethink the medium of how people consume knowledge
• The Cogsec Chronicles — LLM output as a force that shapes people’s framing of reality (cognitive-security risk)