Are we witnessing the first real signs of AI becoming a scientist? In this episode of The MAD Podcast, Matt Turck sits down with Dan Roberts, lead of the Foundations of Reinforcement Learning team at OpenAI, to explore one of the biggest shifts happening in AI: the rise of reasoning models, test-time compute, and reinforcement learning as engines of scientific discovery. Dan brings a rare perspective - from theoretical physics, black holes, quantum information, and deep learning theory - to explain how models are learning to “think,” why language may be such a powerful foundation for intelligence, what recent AI math breakthroughs really mean, and whether we are beginning to see AI systems that can contribute to science itself.

(00:00) Intro: AI's wild week in mathematics

(01:21) What OpenAI's Foundations of RL team does

(03:08) Dan's journey: from black holes and quantum gravity to frontier AI

(07:04) Are AI systems becoming useful for real science?

(08:21) The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic

(08:52) Why the OpenAI result was an act of exploration

(10:25) OpenAI vs. DeepMind: informal reasoning vs. formal proof

(12:13) RL 101: learning by doing, not just watching

(15:10) Why reinforcement learning works

(15:58) How RL breaks: sparse feedback and long-horizon tasks

(17:03) RLHF: how human feedback shaped early language models

(18:48) Move 37, self-play, and the search for novel strategies

(22:16) Explore vs. exploit in scientific discovery

(24:49) Why RL may now be "the cake," not the cherry on top

(25:46) Why RL started working with large language models

(27:29) Is RL "sucking supervision through a straw"?

(28:47) Why language may be the grounding layer for intelligence

(31:46) A contrarian take on the Bitter Lesson

(32:41) What test-time compute actually is

(34:50) How RL gives models the ability to think

(35:40) Verifiable rewards, math, coding, and the messy real world

(38:00) What physics can teach us about AI

(42:08) Is there a thermodynamics of AI?

(43:08) From Erdős problems to Einstein-level AI

(45:16) Is AI already doing original science?

(45:51) How far are we from AI automating AI research?

(47:41) Why Dan is excited about the future of science

Aaron Levie, co-founder and CEO of Box, returns to the MAD Podcast with the clearest read in tech on what AI is actually doing inside the world's largest enterprises right now - not the hype version, the real one. After hundreds of Fortune 500 CIO conversations this year, Aaron explains why we're still in "day one" of the agent era, why one badly written agent run can now cost $1,000 in compute, and why progress at the AI labs is paradoxically slowing enterprise deployment. We get into the token cost shock now reshaping IT budgets, why coding agents have reached escape velocity while the rest of knowledge work hasn't, the rise of headless software and what replaces per-seat pricing, the emergence of the forward-deployed engineer as the hottest job in tech, why Aaron thinks the AI doomers are wrong about jobs, and where startups can still win as the labs move up the stack.

(00:00) Intro

(01:18) Silicon Valley engineering vs. everyone else

(05:35) Are enterprise CIOs actually bullish on AI?

(08:51) Tokenmaxxing & why your AI bill is about to explode

(11:34) The myth of falling token costs and AI spend escaping IT budgets

(17:37) The $5B startup hiding in AI compute

(18:14) The mosaic of models inside every enterprise

(21:28) Why coding works and the rest of knowledge work doesn't

(25:53) The Bob and Sally problem: access control breaks agents

(30:31) Will enterprise AI really take 10 years to roll out?

(32:24) The capability overhang: why faster models slow diffusion

(34:23) Data is the bottleneck (it always was)

(39:02) The rise of internal forward-deployed engineers

(41:23) Why the AI doomers are wrong about jobs

(43:43) Headless software is inevitable

(46:14) What replaces per-seat pricing

(47:37) How Box itself is going headless

(49:42) How the org chart actually evolves

(1:00:33) Future-proofing yourself as an enterprise employee

(1:06:40) Are we all just going to work for OpenAI and Anthropic?

(1:07:11) Where startups can still win as the labs move up

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

Is OpenAI trapped without a defensible moat? World-renowned independent tech analyst Benedict Evans returns to the MAD Podcast and argues that foundation models have zero network effects, making them closer to commodity infrastructure than the next iOS. We unpack OpenAI’s "mile wide, inch deep" usage problem, why simply having a "better model" does not solve the core UX challenge, and whether the hyperscalers' massive CapEx spending is a sustainable strategy or a fast track to financial gravity.

We also explore the reality behind the recent "SaaSpocalypse", the structural shift from traditional enterprise systems to "improvised" and "ephemeral" software, and where the actual white space lies for founders and investors navigating the artificial intelligence hype cycle.

(00:00) Intro

(01:06) OpenAI's Focus Shift

(03:12) ChatGPT usage: a "mile wide, inch deep"

(09:03) Why better models do not solve the real problem

(13:58) Why AI product teams are strategy takers, not strategy setters

(15:38) Do agents help create defensibility?

(20:06) OpenClaw and the "Desktop Linux" moment for AI

(25:52) Why "everyone will build their own software" is completely wrong

(28:09) Improvised software vs. institutionalized software

(29:23) The Jevons Paradox: Why there will be more software, not less

(36:15) Are we heading toward value destruction before value creation?

(38:03) Circular revenue, leverage, and AI bubble dynamics

(38:53) Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity

(45:23) Why AI job exposure charts can be misleading

(52:15) How Fortune 500 Execs are actually deploying AI today

(56:45) The White Space: What this means for founders and investors

Harrison Chase, co-founder and CEO of LangChain, joins the MAD Podcast to explain why everything in AI is getting rebuilt. As agents evolve from simple prompt-based systems into software that can plan, use tools, write code, manage files, and remember things over time, the real frontier is shifting from the model itself to the stack around the model. In this conversation, we go deep on harnesses, subagents, filesystems, sandboxes, observability, memory, and the new infrastructure required to make AI agents actually work in the real world.

(00:00) Intro - meet Harrison Chase

(01:32) What changed in agents over the last year

(03:57) Why coding agents are ahead

(06:26) Do models commoditize the framework layer?

(08:27) Harnesses, in plain English

(10:11) Why system prompts matter so much

(13:11) The upside — and downside — of subagents

(15:31) Why a useful agent needs a filesystem

(18:13) The core primitives of modern agents

(19:12) Skills: the new primitive

(20:19) What context compaction actually means

(23:02) How memory works in agents

(25:16) One mega-agent or many specialized agents?

(27:46) Has MCP won?

(29:38) Why agents need sandboxes

(32:35) How sandboxes help with security

(33:32) How Harrison Chase started LangChain

(37:24) LangChain vs LangGraph vs Deep Agents

(40:17) Why observability matters more for agents

(41:48) Evals, no-code, and continuous improvement

(44:41) What LangChain is building next

(45:29) Where the real moat in AI lives

What if AI didn’t just sound right — but could prove it? In this episode of the MAD Podcast, Matt Turck sits down with Carina Hong, a 24-year-old former math olympiad competitor and Rhodes Scholar, and the founder/CEO of Axiom Math, to unpack how AxiomProver earned a perfect 12/12 on the Putnam 2025 and why formal verification (via Lean) may be the missing layer for reliable reasoning. Carina argues we’re entering a “math renaissance” where verified reasoning systems can tackle problems that currently take researchers months — and potentially push beyond math into verified code, hardware, and high-stakes software. They go inside the “generation + verification” loop, what it means to build AI that can be trusted, and what this approach could unlock on the road to superintelligent reasoning.

(00:00) Intro

(01:25) Why the World Needs an AI Mathematician

(02:57) Scoring 12/12 on the World's Hardest Math Test (Putnam)

(04:05) The First AI to Solve Open Research Conjectures

(06:59) Does AI Solve Math in "Alien" Ways? (The Move 37 Effect)

(08:59) "Lean": The Programming Language of Proofs Explained

(10:51) How Axiom's Approach Differs from DeepMind & OpenAI

(16:06) Formal vs. Informal Reasoning (And Auto-Formalization)

(17:37) The AI "Reward Hacking" Problem

(20:18) Building an AI That is 100% Correct, 100% of the Time

(23:23) Beyond Math: Verified Code & Hardware Verification

(25:12) The Brutal Reality of Competitive Math Olympiads

(29:30) From Neuroscience to Stanford Law to Dropout Founder

(33:57) How Axiom Actually Works Under the Hood (The Architecture)

(37:51) The Secret to Generating Perfect Synthetic Data

(40:14) Tokens, Proof Length, and Inference Cost

(42:58) The "Everest" of Mathematics: Scaling Reasoning Trees

(46:32) Can an AI Win a Fields Medal?

(47:25) "Math Renaissance": What Changes if This Works

(55:47) How Mathematicians React to AI (And Why Proof Certificates Matter)

(57:30) Becoming a CEO: Dropping Ego and Building Culture

(1:00:42) Recruiting World-Class Talent & Building the Axiom "Tribe"