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How did a machine trained to predict the next token become capable of writing software, solving mathematics, analyzing images, using tools, and carrying out complex reasoning?

In Episode 2 of Zero to Singularity, we go inside the machinery of modern large language models and build an intuitive understanding of how today’s most advanced AI systems actually work.

We explore tokens and embeddings, transformers and self-attention, pretraining and post-training, gradient descent, reinforcement learning, context windows, memory, mixture-of-experts, multimodality, inference-time compute, and modern reasoning models.

We also tackle some of the biggest questions in artificial intelligence:

• What is an LLM actually doing when it generates an answer? • Why is self-attention so powerful? • Where is a model’s knowledge stored? • How does training turn billions of parameters into useful intelligence? • Why do AI systems hallucinate? • What is the difference between training and “thinking” at inference time? • Are reasoning models genuinely reasoning? • Do LLMs understand the world—or are they extraordinarily sophisticated pattern predictors? • How much do researchers really understand about what happens inside frontier models?

Along the way, we separate established mechanisms from company claims, scientific hypotheses, and speculation while examining research from the major laboratories shaping modern AI.

Zero to Singularity explores artificial intelligence from first principles to the technological frontier helping you understand not just what changed, but why it matters and how it actually works.

Research current through September 2026. This episode contains AI-generated audio.