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OpenAI’s Yann Dubois: Why AI Progress Suddenly Feels Real
The MAD Podcast with Matt Turck · 2026-05-21
Speaker 1 | 00:00 - 00:26
You need to reach this level of reliability to really make any of these AI tools very useful, and I think we just crossed that probably December last year, at least at OpenAI. Now we can trust these models to do a lot of the work that we are doing. The last few months have been pretty wild. We moved from like competitions to usefulness to users and that’s what we are feeling right now. I think most of the time the Bionic is the last mile.
Speaker 1 | 00:26 - 00:34
There will always be a lot of space left for this last mile in different verticals. And I would highly e…
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Aaron Levie (@levie)
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This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real.
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Matt Turck (@mattturck)
- This fantastic conversation with @yanndubs is also available on Spotify, Apple Podcasts and here on YouTube (like and subscribe!): [3 ❤️]
- 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 [18 ❤️ 4 🔄]
Zara Zhang (@zarazhangrui)
- GitHub:
- Introducing the Claude Code Lark/Feishu Bridge 🌉 (open-source)
Talk to Claude Code in Lark/Feishu like a colleague
- Use Claude Code on your phone via Lark chat
- Manage multiple CC sessions as group chats in Lark (one chat = one session), say goodbye to messy terminal tabs
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- Claude Code can write Lark Docs for you; you can even @ mention it in the comment and it will reply
- Forward any Lark messages to Claude and it can just get the task done
- Claude can send you interactive cards with buttons and UI
Open-source; try it now: [28 ❤️ 2 🔄]
Nikunj Kothari (@nikunj)
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- ex founders are THE driving force helping scale some of the most iconic companies (@tryramp, @mercor_ai, @figma, @AnthropicAI, @cognition and others).
if you are an ex founder wanting to hang out with a stellar peer group, sign up for our next event! [53 ❤️ 2 🔄]
Dan Shipper (@danshipper)
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1/ If an engineer is trying to pick between a pre Series-B company and a BigCo/BigLab –> stop talking to them immediately. They are clearly not ready for a startup.
2/ If someone isn’t willing to take a 70% cash paycut (relative to BigCo/BigLab) –> stop talking to them immediately. They will be unhappy/stressed.
3/ You learn a lot about a candidate during the negotiation/closing process. Do not be afraid to walk away if you get new information.
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Claude (@claudeai)
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由 Follow Builders 自动生成 · 2026-05-22