Why Every AI Agent Needs Its Own Computer | Ivan Burazin (Daytona)

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"

Voice used to be AI’s forgotten modality — awkward, slow, and fragile. Now it’s everywhere. In this reference episode on all things Voice AI, Matt Turck sits down with Neil Zeghidour, a top AI researcher and CEO of Gradium AI (ex-DeepMind/Google, Meta, Kyutai), to cover voice agents, speech-to-speech models, full-duplex conversation, on-device voice, and voice cloning.We unpack what actually changed under the hood — why voice is finally starting to feel natural, and why it may become the default interface for a new generation of AI assistants and devices.Neil breaks down today’s dominant “cascaded” voice stack — speech recognition into a text model, then text-to-speech back out — and why it’s popular: it’s modular and easy to customize. But he argues it has two key downsides: chaining models adds latency, and forcing everything through text strips out paralinguistic signals like tone, stress, and emotion. The next wave, he suggests, is combining cascade-like flexibility with the more natural feel of speech-to-speech and full-duplex conversation.We go deep on full-duplex interaction (ending awkward turn-taking), the hardest unsolved problems (noisy real-world environments and multi-speaker chaos), and the realities of deploying voice at scale — including why models must be compact and when on-device voice is the right approach.Finally, we tackle voice cloning: where it’s genuinely useful, what it means for deepfakes and privacy, and why watermarking isn’t a silver bullet.If you care about voice agents, real-time AI, and the next generation of human-computer interaction, this is the episode to bookmark.Neil ZeghidourLinkedIn - https://www.linkedin.com/in/neil-zeghidour-a838aaa7/X/Twitter - https://x.com/neilzeghGradiumWebsite - https://gradium.aiX/Twitter - https://x.com/GradiumAIMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFirstMarkWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) Intro(01:21) Voice AI’s big moment — and why we’re still early(03:34) Why voice lagged behind text/image/video(06:06) The convergence era: transformers for every modality(07:40) Beyond Her: always-on assistants, wake words, voice-first devices(11:01) Voice vs text: where voice fits (even for coding)(12:56) Neil’s origin story: from finance to machine learning(18:35) Neural codecs (SoundStream): compression as the unlock(22:30) Kyutai: open research, small elite teams, moving fast(31:32) Why big labs haven’t “won” voice AI4(34:01) On-device voice: where it works, why compact models matter(46:37) The last mile: real-world robustness, pronunciation, uptime(41:35) Benchmarking voice: why metrics fail, how they actually test(47:03) Cascades vs speech-to-speech: trade-offs + what’s next(54:05) Hardest frontier: noisy rooms, factories, multi-speaker chaos(1:00:50) New languages + dialects: what transfers, what doesn’t(1:02:54 Hardware & compute: why voice isn’t a 10,000-GPU game(1:07:27) What data do you need to train voice models?(1:09:02) Deepfakes + privacy: why watermarking isn’t a solution(1:12:30) Voice + vision: multimodality, screen awareness, video+audio(1:14:43) Voice cloning vs voice design: where the market goes(1:16:32) Paris/Europe AI: talent density, underdog energy, what’s next

While Silicon Valley obsesses over AGI, Timothée Lacroix and the team at Mistral AI are quietly building the industrial and sovereign infrastructure of the future. In his first-ever appearance on a US podcast, the Mistral AI Co-Founder & CTO reveals how the company has evolved from an open-source research lab into a full-stack sovereign AI power—backed by ASML, running on their own massive supercomputing clusters, and deployed in nation-state defense clouds to break the dependency on US hyperscalers.Timothée offers a refreshing, engineer-first perspective on why the current AI hype cycle is misleading. He explains why "Sovereign AI" is not just a geopolitical buzzword but a necessity for any enterprise that wants to own its intelligence rather than rent it. He also provides a contrarian reality check on the industry's obsession with autonomous agents, arguing that "trust" matters more than autonomy and explaining why he prefers building robust "workflows" over unpredictable agents.We also dive deep into the technical reality of competing with the US giants. Timothée breaks down the architecture of the newly released Mistral 3, the "dense vs. MoE" debate, and the launch of Mistral Compute—their own infrastructure designed to handle the physics of modern AI scaling. This is a conversation about the plumbing, the 18,000-GPU clusters, and the hard engineering required to turn AI from a magic trick into a global industrial asset.Timothée LacroixLinkedIn - https://www.linkedin.com/in/timothee-lacroix-59517977/Google Scholar - https://scholar.google.com.do/citations?user=tZGS6dIAAAAJ&hl=en&oi=aoMistral AIWebsite - https://mistral.aiX/Twitter - https://x.com/MistralAIMatt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFirstMarkWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) — Cold Open(01:27) — Mistral vs. The World: From Research Lab to Sovereign Power(03:48) — Inside Mistral Compute: Building an 18,000 GPU Cluster(08:42) — The Trillion-Dollar Question: Competing Without a Big Tech Parent(10:37) — The Reality of Enterprise AI: Escaping "POC Purgatory"(15:06) — Why Mistral Hires Forward Deployed Engineers (FDEs)(16:57) — The Contrarian Take: Why "Agents" are just "Workflows"(19:35) — Trust > Autonomy: The Truth About Agent Reliability(21:26) — The Missing Stack: Governance and Versioning for AI(26:24) — When Will AI Actually Work? (The 2026 Timeline)(30:33) — Beyond Chat: The "Banger" Sovereign Use Cases(35:46) — Mistral 3 Architecture: Mixture of Experts vs. Dense(43:12) — Synthetic Data & The Post-Training Bottleneck(45:12) — Reasoning Models: Why "Thinking" is Just Tool Use(46:22) — Launching DevStral 2 and the Vibe CLI(50:49) — Engineering Lessons: How to Build Frontier AI Efficiently(56:08) — Timothée’s View on AGI & The Future of Intelligence

Dylan Patel (SemiAnalysis) joins Matt Turck for a deep dive into the AI chip wars — why NVIDIA is shifting from a “one chip can do it all” worldview to a portfolio strategy, how inference is getting specialized, and what that means for CUDA, AMD, and the next wave of specialized silicon startups.Then we take the fun tangents: why China is effectively “semiconductor pilled,” how provinces push domestic chips, what Huawei means as a long-term threat vector, and why so much “AI is killing the grid / AI is drinking all the water” discourse misses the point.We also tackle the big macro question: capex bubble or inevitable buildout? Dylan’s view is that the entire answer hinges on one variable—continued model progress—and we unpack the second-order effects across data centers, power, and the circular-looking financings (CoreWeave/Oracle/backstops).Dylan PatelLinkedIn - https://www.linkedin.com/in/dylanpatelsa/X/Twitter - https://x.com/dylan522pSemiAnalysisWebsite - https://semianalysis.comX/Twitter - https://x.com/SemiAnalysis_Matt Turck (Managing Director)Blog - https://mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFirstMarkWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(00:00) - Intro(01:16) - Nvidia acquires Groq: A pivot to specialization(07:09) - Why AI models might need "wide" compute, not just fast(10:06) - Is the CUDA moat dead? (Open source vs. Nvidia)(17:49) - The startup landscape: Etched, Cerebras, and 1% odds(22:51) - Geopolitics: China's "semiconductor-pilled" culture(35:46) - Huawei's vertical integration is terrifying(39:28) - The $100B AI revenue reality check(41:12) - US Onshoring: Why total self-sufficiency is a fantasy(44:55) - Can the US actually build fabs? (The delay problem)(48:33) - The CapEx Bubble: Is $500B spending irrational?(54:53) - Energy Crisis: Why gas turbines will power AI, not nuclear(57:06) - The "AI uses all the water" myth (Hamburger comparison)(1:03:40) - Circular Debt? Debunking the Nvidia-CoreWeave risk(1:07:24) - Claude Code & the software singularity(1:10:23) - The death of the Junior Analyst role(1:11:14) - Model predictions: Opus 4.5 and the RL gap(1:14:37) - San Francisco Lore: Roommates (Dwarkesh Patel & Sholto Douglas)