What does it really mean when GPT-5 “thinks”? In this conversation, OpenAI’s VP of Research Jerry Tworek explains how modern reasoning models work in practice—why pretraining and reinforcement learning (RL/RLHF) are both essential, what that on-screen “thinking” actually does, and when extra test-time compute helps (or doesn’t). We trace the evolution from O1 (a tech demo good at puzzles) to O3 (the tool-use shift) to GPT-5 (Jerry calls it “03.1-ish”), and talk through verifiers, reward design, and the real trade-offs behind “auto” reasoning modes.
We also go inside OpenAI: how research is organized, why collaboration is unusually transparent, and how the company ships fast without losing rigor. Jerry shares the backstory on competitive-programming results like ICPC, what they signal (and what they don’t), and where agents and tool use are genuinely useful today. Finally, we zoom out: could pretraining + RL be the path to AGI?
This is the MAD Podcast —AI for the 99%. If you’re curious about how these systems actually work (without needing a PhD), this episode is your map to the current AI frontier.
00:00 - Intro
01:01 - What Reasoning Actually Means in AI
02:32 - Chain of Thought: Models Thinking in Words
05:25 - How Models Decide Thinking Time
07:24 - Evolution from O1 to O3 to GPT-5
11:00 - Before OpenAI: Growing up in Poland, Dropping out of School, Trading
20:32 - Working on Robotics and Rubik's Cube Solving
23:02 - A Day in the Life: Talking to Researchers
24:06 - How Research Priorities Are Determined
26:53 - Collaboration vs IP Protection at OpenAI
29:32 - Shipping Fast While Doing Deep Research
31:52 - Using OpenAI's Own Tools Daily
32:43 - Pre-Training Plus RL: The Modern AI Stack
35:10 - Reinforcement Learning 101: Training Dogs
40:17 - The Evolution of Deep Reinforcement Learning
42:09 - When GPT-4 Seemed Underwhelming at First
45:39 - How RLHF Made GPT-4 Actually Useful
48:02 - Unsupervised vs Supervised Learning
49:59 - GRPO and How DeepSeek Accelerated US Research
53:05 - What It Takes to Scale Reinforcement Learning
55:36 - Agentic AI and Long-Horizon Thinking
59:19 - Alignment as an RL Problem
1:01:11 - Winning ICPC World Finals Without Specific Training
1:05:53 - Applying RL Beyond Math and Coding
1:09:15 - The Path from Here to AGI
1:12:23 - Pure RL vs Language Models
