OpenAI's o1 and o3 aren't just better language models—they actually think. You'll learn how reinforcement learning creates genuine reasoning capabilities, but also discover the dark side: "mode collapse" creates an artificial hivemind where models converge to eerily similar responses. The uncomfortable truth? Even the best RL refines existing knowledge rather than discovering new concepts, and there's a 1000x gap in data efficiency between AI and human brains. This episode cuts through the hype around reasoning models to show you what's real and what's still missing.

Topics Covered

- Large Reasoning Models (LRMs) vs. traditional LLMs

- Reinforcement learning mechanics (explained accessibly)

- The mode collapse problem (AI converging to similar responses)

- Data scaling wall and synthetic data challenges

- Why small models (32B parameters) are rising in importance

- The verification crisis in AI deployment