The MAD Podcast with Matt Turck

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

If AI agents are the new digital knowledge workers, where exactly do they do their work? In this episode of the MAD Podcast, Ivan Burazin joins us to unpack the emerging infrastructure stack for AI agents and explain why every agent needs its own secure, stateful "computer." We explore the technical realities of sandboxes, dive into why legacy, stateless hyperscalers weren't built for these new workloads, and break down the mechanics of microVMs and custom schedulers alongside a contrarian prediction on an impending CPU shortage. Finally, Ivan delivers an absolute masterclass on product-led growth, community building, and go-to-market strategy for technical founders.

(00:40) Intro
(02:13) What is an AI agent sandbox?
(03:17) Security risks of running agents locally
(05:17) Stateful vs. stateless hyperscalers
(07:04) The history of cloud IDEs and the end of localhost
(09:45) Do all AI agents need a sandbox?
(12:26) Sandbox use cases: RL evals & background agents
(14:10) Unpacking the emerging AI Agent Stack
(16:20) The unsolved problem of agent memory and learning
(19:37) Where sandboxes fit in the agent harness
(21:35) OpenAI, Anthropic, and agent SDKs
(23:06) Ivan's founder journey: From CodeAnywhere to Daytona
(26:59) GTM strategies and building developer communities
(33:48) Why customer support is your best GTM strategy
(35:34) Leveraging Twitter during the AI super cycle
(40:50) The technical anatomy of a sandbox
(41:53) Why fast spin-up speeds maximize GPU efficiency
(46:09) Firecracker, QEMU, and isolation primitives
(49:58) Why sandbox snapshots and state forking matter
(51:40) Why Daytona built a custom scheduler from scratch
(55:24) The challenge of long-running stateful sandboxes
(58:10) The build your own sandbox trap
(1:01:03) Why AI agents might trigger a global CPU shortage
(1:02:46) The future of the AI Agent Stack

What actually happens before a frontier AI model gets released — and who decides whether it is safe enough? In this episode of The MAD Podcast, Matt Turck sits down with Zico Kolter — OpenAI board member, Head of the Machine Learning Department at Carnegie Mellon, and co-founder of Gray Swan — for a deep conversation on the real risks of frontier AI. They discuss how OpenAI’s safety oversight works before major model releases, why more powerful models do not automatically become safer, how jailbreaks and prompt injection expose real weaknesses in AI systems, why AI agents dramatically expand the attack surface, and where frontier AI is headed next. A clear, practical discussion on OpenAI, AI safety, AI security, AI agents, frontier models, red teaming, reinforcement learning, and the future of AI governance.

(00:00) Intro
(01:32) OpenAI board role and Safety & Security Committee
(03:53) How OpenAI reviews major model releases
(05:33) OpenAI’s preparedness framework explained
(09:46) Are frontier AI models getting safer?
(12:33) Why AI safety does not come from scale
(15:23) The four categories of AI risk
(19:38) Doomerism vs accelerationism in AI
(24:11) The six-month AI pause debate
(26:20) AI safety as a global effort
(28:04) How Zico Kolter got into machine learning
(31:05) OpenAI in the early days
(34:14) Why Carnegie Mellon became an AI powerhouse
(38:43) What Gray Swan does in AI security
(40:44) AI safety vs AI security
(43:15) The GCG jailbreak paper
(49:19) How AI labs responded to jailbreak research
(50:19) State-of-the-art AI defenses
(52:32) State-of-the-art AI attacks
(54:22) Why AI agents expand the attack surface
(58:39) Are AI agents ready for production?
(59:40) Mechanistic interpretability explained
(1:02:31) Will AI be safer in two years?
(1:03:46) Reinforcement learning and self-improving models
(1:08:09) Do post-transformer architectures matter?
(1:09:29) Best research directions in AI now
(1:11:00) Zico Kolter’s Intro to Modern AI course
(1:14:53) Why modern AI is simpler than people think

Felix Rieseberg leads engineering for Claude Cowork at Anthropic, one of the most important new agentic AI products in the market today. In this episode of The MAD Podcast, Matt Turck sits down with Felix to discuss Anthropic’s newly announced Claude Mythos Preview, why Felix sees it as a genuine step-function change, and what it means when frontier AI starts showing outsized cybersecurity capabilities.

The conversation then goes deep on Claude Cowork: how it emerged from Claude Code, what the famous “10-day” story really means, why Anthropic believes AI needs access to the local computer, and how Cowork actually works under the hood. Felix explains why skills are just text files, why memory is often just text files too, and how Anthropic thinks about building trust in AI agents.

They also explore some of the biggest questions in AI product design and the future of software: why UX may matter as much as the model itself, why execution is becoming dramatically cheaper, what that means for product management and startups, and why Felix believes taste, alignment, and understanding humans may matter more than ever.

(00:00) Intro
(01:53) Claude Mythos Preview and the “step-function change”
(06:16) Why Anthropic is treating Mythos differently
(11:19) The real story behind Claude Cowork’s “10-day” build
(12:42) Why Anthropic realized Claude Code needed a non-technical version
(15:44) What Claude Cowork actually is
(17:03) Under the hood: virtual machines, tools, skills
(18:36) Where Cowork’s memory actually lives
(19:26) How Cowork connects to files, apps, and the internet
(20:45) Why Felix thinks the local computer is under-appreciated
(24:49) Trust: how do you get users comfortable with AI agents?
(28:45) What UX actually means for AI agents
(31:27) Anthropic Cowork's roadmap is only one month long
(34:12) Building 100 prototypes
(35:10) If execution is free, what becomes the bottleneck?
(37:25) Does it come down to taste?
(40:12) The hardest part of building Claude Cowork
(41:43) Advice for founders building AI agents
(44:21) SaaSpocalypse: what’s left for software startups?
(49:30) Where AI agents are going next
(51:20) Regulated industries and enterprise adoption
(54:15) Hot takes: what's underrated, overrated, and what Felix would build today

Are we truly on the verge of AI automating its own research and development? In this deep-dive episode of the MAD Podcast, Matt Turck sits down with Mostafa Dehghani, a pioneering AI researcher at Google DeepMind whose work on Universal Transformers and Vision Transformers (ViT) helped lay the groundwork for today's frontier models.

Moving past the hype, Mostafa breaks down the actual mechanics of "thinking in loops" and Recursive Self-Improvement (RSI). He explores the critical bottlenecks holding back true AGI—from evaluation limits and formal verification to the brutal math of long-horizon reliability.

Mostafa and Matt also discuss the shift from pre-training to post-training, how Gemini's Nano Banana 2 processes pixels and text simultaneously, and why the "frozen" nature of today's models means Continual Learning is the next massive frontier for enterprise AI and data pipelines.

(00:00) Intro
(01:17) What “loops” in AI actually mean
(05:04) Self-improvement as the next chapter of machine learning
(07:32) Are Karpathy’s autoresearch agents an early form of AI self-improvement?
(08:56) AI building AI: how close are we?
(10:02) The biggest bottlenecks: evals, automation, and long horizons
(12:36) Can formal verification unlock recursive self-improvement?
(14:06) What is model collapse?
(15:33) Generalization vs specialization in AI
(18:04) What is a specialized model today?
(20:57) Could top AI researchers themselves be automated?
(24:02) If AI builds AI, does data matter less than compute?
(26:22) Post-training vs pre-training: where will progress come from?
(28:14) Why pre-training is not dead
(29:45) What is continual learning?
(31:53) How real is continual learning today?
(33:43) Mostafa Dehghani’s background and path into AI
(36:13) The story behind Universal Transformers
(39:56) How Vision Transformers changed AI
(43:47) Gemini, multimodality, and Nano Banana
(47:46) Why multimodality helps build a world model
(52:44) Why image generation is getting faster and more efficient
(54:44) Hot takes
(54:53) What the AI field is getting wrong
(56:17) Why continual learning is underrated
(57:26) Does RAG go away over time?
(58:21) What people are too confident about in AI
(59:56) If he were starting from scratch today