OpenAI’s Compute Chief: We Can’t Build Fast Enough | Sachin Katti

Is the AI industry actually overbuilding, or is the physical world moving too slowly to keep up? In this episode of the MAD Podcast, OpenAI's Head of Industrial Compute, Sachin Katti, takes us inside the "belly of the beast" of what may be the largest infrastructure project in human history. We explore the staggering physical reality of the AI boom—from $50 billion supercomputers and liquid-cooled data centers that "turn electrons into tokens," to overhauling the U.S. power grid and exploring nuclear energy. Sachin also pulls back the curtain on OpenAI's Stargate strategy, their move into custom silicon with Project Jalapeno, and the mind-bending reality that AI is now beginning to design the very chips that will power its own future.(00:00) — Cold open: “One of the largest things humanity has ever built”(00:30) — Welcome: Sachin Katti, Head of Industrial Compute at OpenAI(01:44) — Is this the biggest infrastructure buildout in history?(03:41) — Why OpenAI is building a new industrial muscle(04:54) — What an AI data center actually is(05:27) — “Factories turning electrons into tokens”(06:35) — Why AI data centers need liquid cooling everywhere(08:10) — The power problem: grids, generation, transmission, substations(10:43) — Behind-the-meter power and gas turbines(11:02) — Why nuclear “can’t come soon enough”(11:49) — Jalapeño: why OpenAI is designing its own AI chips(13:19) — Tokens per watt: the new metric that matters(13:38) — Why inference may now dominate AI compute(14:58) — Is OpenAI overbuilding compute?(16:47) — Why OpenAI thinks the bigger risk is not building fast enough(17:55) — Communities, jobs, water, and the local data-center debate(21:16) — How OpenAI chooses data-center sites(22:25) — What “industrial compute” means inside OpenAI(25:59) — Sachin’s path: Stanford, startups, Intel, OpenAI(28:05) — OpenAI’s compute portfolio: Microsoft, hyperscalers, neoclouds(29:37) — Stargate explained(31:21) — Abilene, Oracle, and the next wave of AI data centers(32:48) — How massive AI compute gets financed(34:05) — How OpenAI designed Jalapeño so quickly(35:59) — AI is starting to help design AI chips(36:20) — MRC: the networking problem behind 100,000 GPUs(38:47) — Bottlenecks: transformers, turbines, electricians, supply chains(40:29) — Guaranteed capacity: intelligence as a supply unit(42:08) — Will AI data centers move to space?

Is the internet ready for AI agents to take over our wallets and run their own businesses? In this episode of The MAD Podcast, Stripe's Emily Sands reveals how agentic commerce is rapidly shifting from a hypothetical concept to deployed financial infrastructure. From combating the rising existential threat of token theft to solving the bottleneck of "vibe deployment", Emily unpacks the shared payment tokens and real-time billing systems required to securely scale autonomous digital buyers and highlights a near future where agents operate as independent, end-to-end micro-firms.(00:00) — Cold open & Intro(01:24) — The rise of agentic e-commerce(02:11) — The spectrum of agent-led purchases(03:16) — How merchants adapt to AI-driven commerce(05:50) — Defining the levels of autonomy in AI shopping(07:08) — What is the Agent E-Commerce Protocol (AEP)?(08:49) — Shared payment tokens and secure AI transactions(09:58) — Who is adopting the Agent E-Commerce Protocol?(11:38) — Can agents negotiate and sell products?(13:32) — The macroeconomic impact of AI agents(14:46) — The boom of solopreneurs and AI-driven business creation(16:56) — Why building trust is the biggest roadblock for AI commerce(20:19) — How link wallets improve payment security(21:21) — Improving the user experience in AI shopping apps(23:16) — How the Link Wallet sets guardrails for AI agents(25:40) — One-time use virtual cards vs flexible AI wallets(28:03) — Unpacking the shared payment token primitive(29:59) — How stablecoins enable profitable AI microtransactions(35:03) — Managing liability: Who is at fault if an agent goes haywire?(36:38) — Why agent payments might be safer than human transactions(37:41) — What is Vibe Deployment?(40:13) — Why Stripe built Stripe Projects for agent deployment(41:22) — Why Stripe cares about orchestrating app deployments(42:50) — How tokens break the traditional SaaS billing model(44:34) — Why AI companies are moving to hybrid and usage-based billing(47:15) — Streaming payments and real-time token tracking(48:42) — The massive data challenge for AI company accountants(50:41) — Token theft: The fastest-growing fraud in the AI economy(52:04) — The cottage industry of free trial and multi-account abuse(54:16) — How fraudsters monetize stolen AI tokens on the dark web(01:00:06) — How Stripe Radar uses network density to fight AI fraud(01:01:15) — Tempo's role in the Agent E-Commerce Protocol(01:04:12) — The AI startup ecosystem is accelerating business creation(01:09:01) — The token cost shock: Are buyers getting carried away?(01:11:19) — 2026 Predictions: Agents running businesses end-to-end

NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models — and then give them away for free? Bryan Catanzaro is VP of Applied Deep Learning Research at NVIDIA and one of the people whose work quietly underpins modern AI: he helped create cuDNN (NVIDIA's first deep learning product), co-invented DLSS, and named and built Megatron, the framework behind how much of the industry trains large models. Today he leads Nemotron, NVIDIA's family of open models — and Nemotron 3 Ultra, released just weeks ago, is one of the strongest open-weights models to come out of the US.Matt Turck sits down with Bryan for a genuinely deep conversation: the real business logic behind a chip company building its own models, the state of open vs. closed AI, and whether the US is falling behind China in open models. Then they go inside Nemotron itself — four-bit (NVFP4) pretraining, hybrid Mamba-Transformer architecture, mixture-of-experts, multi-token prediction, and multi-teacher distillation — all explained in plain language. Plus a rare look at how a modern AI research org actually runs, what it was like working alongside Andrew Ng and Dario Amodei at Baidu, why Bryan doesn't believe in the singularity, and his contrarian case that open AI is safer than closed.A reference conversation for anyone trying to understand where AI is really headed.(00:00) — Cold open & Intro(01:33) — Is open source AI catching the frontier?(05:29) — Do closed labs blocking distillation slow open source down?(07:42) — Is the US falling behind China?(10:30) — Why companies actually choose open models(12:39) — A "crazy" 2008 bet: machine learning on GPUs(15:33) — Working with Andrew Ng and Dario Amodei at Baidu(17:41) — Coming back to NVIDIA: DLSS and the birth of Megatron(21:55) — The real reason NVIDIA builds its own models(24:28) — Is Moore's Law really dead?(33:37) — The Nemotron family: Nano, Super, Ultra(35:09) — Built for agents: why NVIDIA bets on speed(36:02) — How you train a 550B model in 4 bits(39:25) — Hybrid Mamba-Transformer, explained simply(42:31) — Mixture of experts — and why NVIDIA built NVL72 around it(47:26) — Why a 1-million-token context window matters(49:26) — Multi-token prediction: how the model predicts 5 tokens at once(52:47) — Multi-teacher distillation: teaching one model from many(58:01) — Where reinforcement learning goes next(01:00:16) — Inside NVIDIA's research org: "the mission is the boss"(01:04:03) — How NVIDIA decides who gets the GPUs(01:10:53) — Why NVIDIA still feels entrepreneurial after 33 years(01:12:58) — Why Bryan doesn't believe in the singularity(01:17:50) — The AI backlash(01:19:18) — The controversial case: open AI is safer than closed

Cloudflare CEO and co-founder Matthew Prince joins Matt Turck for a wide-ranging and fascinating conversation about what happens when the Internet is no longer mostly used by humans, but by bots, AI agents and machines. Matthew explains why Cloudflare now sees automated traffic overtaking human traffic online, why agents could create a massive explosion in Internet traffic, and why the old web business model built around clicks, ads, and pageviews may be breaking. We also go deep on what Cloudflare actually does, how it built one of the world’s most important Internet networks, why products like Workers, AI Gateway, edge inference, Durable Objects, sandboxes, and agent security matter, and how Cloudflare is reorganizing itself for the AI era. Along the way, Matthew shares wild Cloudflare origin stories involving hacker kids, human rights groups, cricket in Pakistan, root servers, Eurovision, JPMorgan, and the strange paths that led Cloudflare from scrappy startup to critical Internet infrastructure.(00:00) — Cold open(00:34) — Intro(01:27) — The moment bots passed humans online(04:05) — "Agent," "bot," "crawler" — what they really mean(05:28) — Why your AI agent visits 5,000 sites to do one thing(06:27) — The internet's business model is breaking(06:52) — What happens to "brands" when machines do the buying(08:11) — What Cloudflare actually does, explained simply(10:29) — Hackers, human rights groups & an accidental product-market fit(13:37) — Building a global network (and the Telecom Pakistan cricket story)(21:10) — One hacker, from Turkish escort sites to Eurovision to JP Morgan(30:54) — Fundraising, VCs & an unlikely founding team(37:06) — How Cloudflare became an AI infrastructure company(40:24) — Cloudflare Workers and why the edge wins for inference(44:30) — AI Gateway: auditing, guardrails & runaway costs(47:05) — Why agents need a new kind of compute(52:13) — A "Log4j every week": security in the agentic era(56:03) — Inside Cloudflare: 241 billion tokens and "Cloudflare OS"(01:05:02) — Builders, sellers — and "measurers"(01:06:30) — The decision Matthew thinks every company will face(01:11:09) — What to do if AI is coming for your job(01:13:56) — Content Independence Day & the new economics of the web(01:18:27) — Pay-per-crawl, micropayments & out-scaling Visa(01:20:20) — A better internet: Spotify, local news & "holes in the cheese"

Many people said GPU compute would become a commodity. The opposite happened — and a new category of "neoclouds" is now racing to build the physical backbone of the AI boom. Stephen Balaban, co-founder and CTO of Lambda, explains why the conventional wisdom was exactly wrong, why we're still massively underbuilding compute, and what it actually takes to stand up a gigawatt-scale AI factory: land, power, cooling, networking, and a financing stack most people have never heard of. We go deep on the physics of how energy becomes tokens, NVIDIA's real moat, why a 2023 GPU can lease for more today than the day it shipped, and Stephen's provocative vision of "neural software." Plus the wild Lambda origin story — from a facial recognition startup to a camera in a baseball cap to a near-billion-dollar cloud business. This is the state of AI compute in 2026, from inside one of the companies building it.(00:00) — Cold open(01:21) — Why GPU compute was never a commodity(02:45) — The H100 price index and what it gets wrong(04:02) — The real moat: technology or financing?(05:57) — Winner-take-all, or room for many neoclouds?(06:48) — Are we overbuilding or underbuilding AI compute?(09:26) — What if AI gets 10x more compute-efficient?(10:44) — The real bottleneck: land, power, and shell(11:38) — The backlash against data centers — and the misinformation(15:00) — Opening the hood: from photons to tokens(17:11) — Extracting more value from the same chip(19:26) — Frontier inference and distributed training, explained(23:26) — What actually drives compute cost(25:21) — Lambda's chip stack and the NVIDIA relationship(26:17) — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat(28:59) — Networking, storage, and the one-click cluster(34:46) — Renting vs. owning, and full vertical integration(36:24) — How global is Lambda? Does location still matter?(38:44) — The financing stack: off-take agreements, SPVs, and credit(41:16) — Why a 2023 GPU leases for more today(42:36) — A futures market for compute?(43:54) — Origin story: facial recognition, Perceptio, and Apple(47:03) — The Lambda hat and Dream Scope(48:59) — The $60K bet that became a cloud business(52:00) — Holding the team together through the hard times(54:30) — Bringing on a new CEO; Stephen as CTO(57:33) — Matching xAI on high-velocity deployment(59:29) — "AI won't write software — it will become the software"(01:01:30) — Neural software vs. vibe coding(01:04:25) — Do agents change the compute layer?(01:06:14) — Self-assembling software inside Lambda(01:08:18) — Gigawatt-scale AI factories(01:08:57) — One person, one GPU(01:12:04) — Hot takes: overrated and underrated in AI

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, CEO of Daytona, 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