AI suddenly feels like it has crossed a threshold, and Yann Dubois, co-lead of the Post-training Frontiers team at OpenAI, joins Matt Turck to explain why. Yann’s team has led the post-training behind the company's reasoning models, including the recent GPT-5.5 release. In this conversation, we go inside the shift from raw model capability to useful, reliable systems: what changed with GPT-5.5, why reinforcement learning is moving beyond math and coding competitions into messy real-world work, how reasoning models like GPT-5.5 actually work, the difference between GPT-5.5 Thinking and GPT-5.5 Pro, why post-training has become one of the most important frontiers in AI, and why evals, model-as-judge, hallucinations, agentic workflows, GDPval, and continual learning are now central to the next phase of frontier models. Yann also shares why continual learning remains one of AI's biggest unsolved problems three years after ChatGPT, and where startups still have massive room to build as frontier models race ahead.

(00:00) - Cold open

(00:34) - Intro

(01:30) - Why recent AI progress feels like a step function

(04:13) - Model reliability & the rollercoaster of shipping 5.5

(07:33) - How OpenAI structures vertical and horizontal teams

(09:49) - Improving model efficiency and test-time compute

(12:32) - Yann Dubois' journey from Switzerland to OpenAI

(15:37) - Reasoning in 2026: Real-world utility vs verifiable rewards

(18:34) - GPT-5.5 Thinking vs Pro: Scaling test-time compute

(20:09) - How reasoning models become more efficient

(23:23) - Pre-training scaling and overcoming the data wall

(27:03) - Multimodal data, synthetic data, and embodied AI

(31:05) - Demystifying mid-training and post-training

(37:21) - Does RL create new capabilities in AI?

(38:53) - The challenges and frontier of scaling RL

(43:09) - Is building AI models a craft or a strict science?

(48:21) - How AI models generalize across different domains

(54:18) - How reinforcement learning cures AI hallucinations

(56:04) - Negative generalization and conflicting instructions

(58:05) - Can RL scale to law, medicine, and the broader economy?

(1:00:19) - The evaluation bottleneck and Model as a Judge

(1:04:21) - Continuous AI progress & continual learning

(1:08:49) - Will foundation models eat the agent harness?

(1:11:23) - Why startups should focus on the last mile of AI