technologyJun 10, 202537:44pending
OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents
About this episode
Hanson Wang and Alexander Embiricos from OpenAI's Codex team discuss their latest AI coding agent that works independently in its own environment for up to 30 minutes, generating full pull requests from simple task descriptions. They explain how they trained the model beyond competitive programming to match real-world software engineering needs, the shift from pairing with AI to delegating to autonomous agents, and their vision for a future where the majority of code is written by agents working on their own computers. The conversation covers the technical challenges of long-running inference, the importance of creating realistic training environments, and how developers are already using Codex to fix bugs and implement features at OpenAI.
Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital
Mentioned in this episode:
The Culture: Sci-Fi series by Iain Banks portraying an optimistic view of AI
The Bitter Lesson: Influential paper by Rich Sutton on the importance of scale as a strategic unlock for AI.
Get every episode summarized
Each time Training Data publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Training Data
Making Cities Awesome: Peregrine’s Nick Noone & Ben Rudolph
Training Data
Sep 1, 202652:27pending
Parallel’s Parag Agrawal: Building a New Web for AI Agents
Training Data
Aug 25, 202655:18pending
Rich Sutton and Khurram Javed: Why AI Models Stop Learning, and How to Start It...
Training Data
Aug 18, 202653:43pending
Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem
Training Data
Aug 4, 202647:22pending