Why AI Progress Suddenly Feels Real - my conversation with @yanndubs, who co-leads the Post-Training Frontiers team at @OpenAI
00:00 - Intro
01:30 - Why recent AI progress feels like a step function
04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5
07:33 - How OpenAI structures vertical and horizontal teams
09:49 - Improving model efficiency and test-time compute
12:32 - Yann's 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
