Module 1: The Generative Turn (Discriminative vs. Generative)

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Previously titled “Module 1 - The Generative Turn (Discriminative vs. Generative)” — renamed by the publisher on Aug 19, 2026

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Recorded13 Dec 2025

Publisherfeed.podbean.com ↗

Handlerec:14586384

Show notes

Welcome to Episode One of The Generative Shift. This episode introduces the core change behind modern AI, the move from discriminative models that draw decision boundaries to generative models that learn the full structure of data. Instead of predicting labels using conditional probability, generative systems model the joint distribution itself, which allows them to create rather than classify. This shift reshapes the math, the architecture, and the compute requirements, moving from compression focused networks to expansion driven systems that grow structure from noise. It is harder and more expensive, but it is the foundation of everything that follows. In the next episode, we will explore where this expansion lives by stepping into latent space and understanding how models represent meaning itself.

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