The MAD Podcast with Matt Turck

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