About this episode

Jerry Tworek discusses his departure from OpenAI and the future of AI, emphasizing the limits of current models and the need for continual learning.

This episode features Jerry Tworek, a key architect behind OpenAI's breakthrough reasoning models (o1, o3) and Codex, discussing the current state and future of AI. Jerry explores the real limits and promise of scaling pre-training and reinforcement learning, arguing that while these paradigms deliver predictable improvements, they're fundamentally constrained by data availability and struggle with generalization beyond their training objectives. He reveals his updated belief that continual learning—the ability for models to update themselves based on failure and work through problems autonomously—is necessary for AGI, as current models hit walls and become "hopeless" when stuck. Jerry discusses the convergence of major labs toward similar approaches driven by economic forces, the tension between exploration and exploitation in research, and why he left OpenAI to pursue new research directions. He offers candid insights on the competitive dynamics between labs, the focus required to win in specific domains like coding, what makes great AI researchers, and his surprisingly near-term predictions for robotics (2-3 years) while warning about the societal implications of widespread…

People in this episode

Host: Jacob Effron

Guest: Jerry Tworek

Topics covered

• AI
• reinforcement learning
• pre-training
• continual learning
• automation
• OpenAI

Keywords

• OpenAI
• AGI
• reinforcement learning
• Codex
• automation
• AI research
• continual learning

Mentioned in this episode

Organizations: OpenAI

Products: Codex

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