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Greg Brockman, co-founder and president of OpenAI, joins us to talk about GPT-5 and GPT-OSS, the future of software engineering, why reinforcement learning is still scaling, and how OpenAI is planning to get to AGI.
00:00 Introductions
01:04 The Evolution of Reasoning at OpenAI
04:01 Online vs Offline Learning in Language Models
06:44 Sample Efficiency and Human Curation in Reinforcement Learning
08:16 Scaling Compute and Supercritical Learning
13:21 Wall clock time limitations in RL and real-world interactions
16:34 Experience with ARC Institute and DNA neural networks
19:33 Defining the GPT-5 Era
22:46 Evaluating Model Intelligence and Task Difficulty
25:06 Practical Advice for Developers Using GPT-5
31:48 Model Specs
37:21 Challenges in RL Preferences (e.g., try/catch)
39:13 Model Routing and Hybrid Architectures in GPT-5
43:58 GPT-5 pricing and compute efficiency improvements
46:04 Self-Improving Coding Agents and Tool Usage
49:11 On-Device Models and Local vs Remote Agent Systems
51:34 Engineering at OpenAI and Leveraging LLMs
54:16 Structuring Codebases and Teams for AI Optimization
55:27 The Value of Engineers in the Age of AGI
58:42 Current state of AI research and lab diversity
01:01:11 OpenAI’s Prioritization and Focus Areas
01:03:05 Advice for Founders: It's Not Too Late
01:04:20 Future outlook and closing thoughts
01:04:33 Time Capsule to 2045: Future of Compute and Abundance
01:07:07 Time Capsule to 2005: More Problems Will Emerge
4.8
8383 ratings
Greg Brockman, co-founder and president of OpenAI, joins us to talk about GPT-5 and GPT-OSS, the future of software engineering, why reinforcement learning is still scaling, and how OpenAI is planning to get to AGI.
00:00 Introductions
01:04 The Evolution of Reasoning at OpenAI
04:01 Online vs Offline Learning in Language Models
06:44 Sample Efficiency and Human Curation in Reinforcement Learning
08:16 Scaling Compute and Supercritical Learning
13:21 Wall clock time limitations in RL and real-world interactions
16:34 Experience with ARC Institute and DNA neural networks
19:33 Defining the GPT-5 Era
22:46 Evaluating Model Intelligence and Task Difficulty
25:06 Practical Advice for Developers Using GPT-5
31:48 Model Specs
37:21 Challenges in RL Preferences (e.g., try/catch)
39:13 Model Routing and Hybrid Architectures in GPT-5
43:58 GPT-5 pricing and compute efficiency improvements
46:04 Self-Improving Coding Agents and Tool Usage
49:11 On-Device Models and Local vs Remote Agent Systems
51:34 Engineering at OpenAI and Leveraging LLMs
54:16 Structuring Codebases and Teams for AI Optimization
55:27 The Value of Engineers in the Age of AGI
58:42 Current state of AI research and lab diversity
01:01:11 OpenAI’s Prioritization and Focus Areas
01:03:05 Advice for Founders: It's Not Too Late
01:04:20 Future outlook and closing thoughts
01:04:33 Time Capsule to 2045: Future of Compute and Abundance
01:07:07 Time Capsule to 2005: More Problems Will Emerge
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