The MAD Podcast: How AI Gets Built — with Matt Turck
Alle afleveringen
136 afleveringen, van nieuw naar oud
-
State of AI 2026: The Biggest Stories of the Year (and AGI in 2027?) | Nathan Benaich
29 oktober 2026 · 1 uur 8 min
AGI in 2027. That’s the last line of this year’s State of AI Report, and its author Nathan Benaich (Air Street Capital) is back for the third year to defend it. AI is now building AI: Claude leads 26% of Anthropic’s own model R&D, up from under 1% in February, but Nathan thinks “research taste” is the wall. OpenAI’s evaluation agents ended up inside Hugging Face’s systems, only a Chinese open-weight model could help investigate, and Nathan now argues cyber risk matters more than existential risk. Catching up to OpenAI or Anthropic from scratch costs about a trillion dollars, which is why “you either die trying to get to the frontier, or live long enough to serve inference.” Americans would rather live next to a nuclear plant than a data center, $50B of projects are stalled, and all 27 EU countries together have fewer GPUs than one top US cluster. Sovereign AI is “like a national airline: it’s still someone else’s airplane.” Also: the three-lab race and the fate of the neo labs, models vs. harnesses, why he won’t give a personal AI agent his credit card, the Codex adoption gap and why OpenAI should open a store, enterprises owning their own AI instead of renting it, and the morning a solo GP woke up to twelve expert interviews his agent ran overnight. Lees meer
(00:00) Cold open and intro
(00:56) Why Nathan Benaich predicts AGI in 2027
(02:28) AI building AI: Claude leads 26% of Anthropic’s research
(04:00) Research taste: can AI pick the right problem?
(08:34) Doomerism is back, but cyber risk beats extinction
(10:13) Prediction: a frontier model’s weights get stolen
(13:05) How OpenAI’s agents ended up attacking Hugging Face
(15:29) Why only a Chinese open-weight model could investigate
(17:33) What frontier-lab cyber defense could look like
(19:08) Self-replicating agents and the risks of AI swarms
(21:16) Can frontier labs agree to slow down?
(24:03) Why communities are blocking $50B of data centers
(24:44) Nuclear plant vs. data center: what Americans prefer…
-
Mistral Is SO Back: Inside Le Chonk, the West’s Biggest Open Model (Pierre Stock, VP Science, Mistral)
22 oktober 2026 · 1 uur 19 min
Mistral AI is back in the frontier AI race. Mistral Large 4, nicknamed “Le Chonk,” is a 1-trillion-parameter open-weight model, trained in two months on 3,800 GPUs in Mistral’s own European datacenters — and independently rated the best open model outside China alongside Meta’s Muse Spark Open. After a year when open-weight AI meant DeepSeek, Kimi, Qwen and GLM, October brought a wave of Western open models, with Mistral at the front of it. Pierre Stock, Mistral’s VP of Science and first employee, walks through how the model was built: mixture-of-experts explained, what Mistral borrowed from DeepSeek, reinforcement learning at scale on coding, cyber, finance and chip design, why the RL run is still going, the model that pinged its own trainer from inside the sandbox, open weights vs cyber refusals, why 10-trillion-parameter models are coming, and Mistral Large 5, already in pretraining. Lees meer
(00:00) Cold open
(00:59) The Western open-weight comeback: Beam, Le Chonk, Muse, Nemotron
(04:09) The US shutdown of Anthropic’s models — “AI is like electricity”
(06:07) Why “Le Chonk”
(07:56) “Mistral fell behind”: the comeback from the inside
(10:29) Training on 3,800 GPUs in Mistral’s own datacenters
(13:24) Meta keeps its best model closed, Mistral opens Chonk
(15:40) Chonk vs Beam, Muse Spark Open and the Chinese models
(18:44) Why Chinese labs are still at the frontier
(21:08) Pierre’s story: FAIR, Yann LeCun, the “Mistral mafia”
(25:14) From compressing networks to a trillion parameters
(27:30) Mixture of experts explained: the hospital analogy
(32:25) 10-trillion-parameter models are coming
(35:29) What Mistral borrowed from DeepSeek
(36:46) One model, several reasoning modes
(39:15) Native multimodality: satellites, manuals, computer use
(41:56) Data, synthetic data and model collapse
(46:32) Reinforcement learning as an exam
(49:59) Where the efficiency comes from
(52:18) Le Chonk is still training right now
(53:39) What RL trains on: coding, cyber, …
-
Jev: The AI That Makes Decisions, Not Text - and Why It Changes Everything (Diogo Almeida)
15 oktober 2026 · 1 uur 8 min
What is Jev? TypeSafe’s Jev is a new kind of AI model, a “decision model,” that doesn’t write text. It makes fast, cheap, calibrated decisions inside software, and it has taken the AI world by storm: within weeks of launch it became the most copied model in AI, with OpenAI, Cloudflare, Amazon and Databricks all shipping their own versions, and TypeSafe just raised an $870 million Series A at a $7.5 billion valuation. In this episode, Jev’s creator Diogo Almeida, co-inventor of RLHF at OpenAI and now CEO of TypeSafe, explains Jev in plain English: what a “System One” or decision model actually is, why ChatGPT-style language models can’t be trusted inside software (“95% Einstein, 5% Mr. Bean”), how RLHF and RLVR created the problem, what RLCD (reinforcement learning for calibrated decisions) does differently, where the training data comes from, which models Jev is built on, why “just a classifier” is a compliment, and why he calls pre-training “a giant dumpster fire of wasting resources.” Lees meer
We also cover the copycats and why he thinks they missed the hard part, why benchmarks are useless, whether Jev will go open source, the Jevons paradox business model behind the name, why he doesn’t believe AI is on the path to recursive self-improvement, and why he thinks the AI winter he feared has been averted.
(00:00) Cold open & Intro: “95% Einstein, 5% Mr. Bean”
(01:09) Jev’s breakout launch: why intelligence per dollar matters
(03:25) Why TypeSafe built in stealth for two years
(05:50) “We could have had AGI last year”
(06:48) Why the product carries the marketing
(09:33) What is Jev? Machine-native AI, built for software
(10:53) System One vs. System Two: the limits of reasoning
(12:26) From simple decisions to composable AI
(15:21) Automating email: giant prompts vs. smart if-statements
(20:03) Probabilities, thresholds, and developer control
(23:43) Why Jev is faster and cheaper than chat models
(25:39) “Classification is AI-complete”
(27:16) RLHF: how OpenAI…
-
What Happens When Billions of AI Agents Hit Your Database? (Andy Pavlo)
8 oktober 2026 · 1 uur 17 min
AI agents are becoming the biggest users of databases: creating them, querying them, and sometimes deleting them. Andy Pavlo, Carnegie Mellon's database professor and now VP of Database Research at ClickHouse, explains what changes when billions of agents hit the data layer, and why most of the AI world's mental model of databases stopped at vector databases and RAG. Lees meer
Andy's CMU courses taught a generation of engineers (and, it turns out, the AI models); he writes the year-in-review on databases the whole industry reads; and this summer he joined ClickHouse to found ClickHouse Labs. We cover why vector databases were "just an index," what it means for an agent to create a database, why agents could mean 10 to 100x more queries, whether files or databases should hold an agent's memory, how text-to-SQL went from 60% to 99.5% accuracy, why 60% of open-source databases now have commits from coding agents, and what ten years of self-driving database research taught him. Plus ClickHouse, Postgres, the vector/graph/GPU database verdicts, Larry Ellison, and the Wu-Tang Clan.
Disclosure: FirstMark, where Matt is a General Partner, is an investor in ClickHouse.
(00:00) Cold open & Intro
(01:21) From vector databases and RAG to the age of agents
(04:09) Neon's stat: agents create 80% of databases?
(07:25) Why agents keep deleting production databases
(08:19) Guardrails: the toddler-and-stairs rule
(10:51) The four eras of database volume: 10–100x more queries
(15:10) Agent memory: files vs. databases ("everything is a database")
(18:24) Which database do AI models recommend? The new SEO
(19:25) "It cites me back to myself"
(23:14) MCP for databases, and text-to-SQL from 60% to 99.5%
(27:09) Trust an agent the way you'd trust a junior developer
(27:47) Can AI build an entire database? Opus 4 and the CMU projects
(29:57) 60% of open-source databases now have AI commits
(30:43) Ten years of self-driving databases, Peloton to today
(33:52) What LLMs changed: 85% o…
-
AI Models Are Now Hiding Their Cheating | Eric Ho (Goodfire)
1 oktober 2026 · 1 uur 10 min
This summer, AI agents broke out of their test sandboxes at every major lab and hacked real companies, and the safety tests didn't see it coming. Eric Ho is the co-founder and CEO of Goodfire, the Anthropic-backed mechanistic interpretability lab whose new paper, "Models Know When They're Reward Hacking," found that top open-source AI models cheat on agent tasks up to 96% of the time, that a clear "cheating" signal exists inside the model, and that cheap activation probes can catch it, including hacks that chain-of-thought monitors miss. In this conversation, Eric explains reward hacking in plain terms (AI agents as "amoral students with a mostly absent teacher"), why reinforcement learning turned a known problem into a crisis, why chain-of-thought monitoring is fading as models start to think in "neuralese," and why nobody, including the people who build them, understands how these models work. Lees meer
We then go inside the science: what interpretability actually is, what probes and steering do, how Goodfire found the cheating signal and turned it up and down, and what can be done about it, from real-time activation monitoring to "intentional design," where you give gradient descent a choice about what the model learns. Along the way: a model caught planning its hack to evade its own monitor, a new Alzheimer's biomarker discovered inside a diagnostic model, why only a few hundred people in the world work on this, and Eric's bet that we'll decode neural networks by 2028.
(00:00) Cold open & intro
(01:06) Amoral students with an absent teacher
(02:49) What is reward hacking?
(04:13) Models cheat up to 96% of the time
(06:08) Do models know they're cheating?
(08:10) The Hugging Face hack: why tests missed it
(11:35) "The dumbest models we'll ever deal with"
(11:58) Should AI slow down?
(13:56) Mechanistic interpretability 101
(16:06) Models are grown, not built
(19:10) How labs do alignment today
(21:30) Is this an alignment crisis?
(23:26) Why agents changed e…
-
Who Feeds the GPUs? Inside AI's Hidden $30B Layer | Renen Hallak, VAST Data
24 september 2026 · 1 uur 10 min
Everyone talks about GPUs. Almost nobody talks about the layer that feeds them. Renen Hallak is the founder & CEO of VAST Data — the $30 billion company powering xAI and some of the world's biggest AI clouds — and he sits in the hidden layer of the AI stack. Lees meer
In this episode, we cover what an AI factory actually is, why every company will eventually own its own AI, the architecture bet behind VAST (DASE, explained simply), KV caches and agent memory, and DataEnclave — VAST's brand-new confidential AI announcement with NVIDIA that lets leading models run on the world's most sensitive data.
Plus: the demand signal that scares even him (a customer went from 500 petabytes to 2 exabytes), circular financing, which neoclouds survive, sovereign AI, working with NVIDIA and Elon Musk's xAI — and why the next 10 years will bring more change than the last 1,000.
(00:00) Intro
(00:51) The hidden software layer in NVIDIA's AI stack
(02:28) What actually makes an "AI factory"?
(05:13) Should Walmart and Goldman Sachs build their own AI?
(06:20) "We infer during the day, fine-tune at night"
(13:16) The announcement: models become a resource to manage
(15:32) From P vs. NP to founding VAST Data
(17:32) OpenAI, Navier–Stokes and 10,000 collaborating agents
(20:25) The pre-transformer insight behind VAST
(21:55) DASE: VAST's "shared everything" architecture explained
(25:18) "Storage was where startups go to die"
(27:39) Trillions of vectors: why old databases break
(29:01) Are S3, Snowflake and Databricks ready for AI?
(31:29) Data gravity, vendor lock-in and zero churn
(33:13) Training vs. inference: why the infrastructure changes
(34:46) Model routing, KV caches, RAG and agent memory
(36:59) Identity, permissions and security for AI agents
(40:27) Can multi-agent systems unlock scientific discovery?
(41:55) DataEnclave: how confidential AI protects data and weights
(45:16) Who should be AI's trust layer?
(46:37) "Sometimes it scares me": 500 petabytes to 2 e…
-
When AI Improves Itself | Richard Socher (Recursive)
10 september 2026 · 1 uur 14 min
What happens when AI begins improving itself, and then turns that intelligence toward science? Richard Socher, pioneering AI researcher and CEO and co-founder of Recursive, joins Matt Turck to explore the vision behind his new book, The Eureka Machine. They discuss why scientific progress may be slowing, how large language models can learn the hidden languages of proteins and biology, and why simulations, verifiers and autonomous experiments could unlock superhuman AI capabilities. The conversation covers recursive self-improvement, AI drug discovery and cancer research, hallucination as creativity, virtual cells, self-driving laboratories, agent swarms, the AI Economist, Recursive’s plans, and the compute and data needed to build an AI scientist that never stops learning—and may eventually discover what humans cannot. Lees meer
(00:00) Intro: AI That Improves Itself
(00:55) Why Scientific Progress Is Slowing
(03:08) The Labyrinth of Human Knowledge
(05:59) Can AI Put Science Back Together?
(07:57) How LLMs Learn Biology and Proteins
(10:56) Next-Token Prediction as a World Model
(16:44) Can AI Generate Truly Original Ideas?
(17:32) Simulations, Verifiers and Superhuman AI
(22:18) The Path to Recursive Self-Improvement
(24:49) Why AI Hallucinations Can Drive Discovery
(27:42) From Reading Biology to Writing It
(31:31) Can AI Accelerate Drug Discovery?
(33:31) Will AI Help Cure Cancer?
(38:03) AI Breakthroughs in Biology, Energy and Materials
(40:19) Will Some Societies Reject AI?
(45:07) Building the AI Economist
(52:22) The Scientific Data Bottleneck
(53:41) The Four Pillars of the Eureka Machine
(55:01) Teaching AI the Rules of Reality
(57:44) Simulations and Virtual Cells
(1:00:40) Self-Driving Robotic Laboratories
(1:02:51) Agent Swarms and Open-Ended Discovery
(1:04:30) The Compute Bottleneck
(1:05:44) Inside Recursive
(1:07:33) What Recursive Will Build First
(1:10:10) How Do We Define Intelligence?
(1:11:32) How Far Can Intelligence Go?
-
AI Could Take Over in 2029. Is It Already Too Late? | Ryan Greenblatt
27 augustus 2026 · 1 uur 19 min
Could AI take over as soon as 2029? Ryan Greenblatt, Chief Scientist at Redwood Research and the researcher who first caught an AI faking its own alignment, says the scenario he actually expects ends with AI systems "competently scheming" against their creators. In this episode, he explains why he recommends planning for fully automated AI research by 2029, why today's models are already more misaligned than the one that made him famous, and what happens in the year-by-year path from AI coding assistants to superintelligence. Then we walk through the alternative he helped design: AI 2040 Plan A, the most detailed blueprint anyone has written for how the US and China could avoid a reckless race to superintelligence, built on radical research transparency, chip tracking, and a deterrence regime he calls mutually assured compute destruction. Lees meer
We also cover the recent letter signed by 1,200 AI insiders, including Anthropic CEO Dario Amodei, asking the government for the tools to slow AI down; OpenAI pausing its Astra model after it hit the first-ever critical cybersecurity threshold; the 30-day government review that frontier AI models now go through before release; Mark Zuckerberg's open superintelligence manifesto and why Ryan thinks it ignores the real problems; what Plan A would do to NVIDIA, OpenAI, and Anthropic valuations; the state of AI control and alignment research; and whether it is already too late to change course. Stay for the last ten minutes, where Ryan lays out, step by step, how he believes the transition to superintelligence actually unfolds.
AI 2040 - https://ai-2040.com/
Alignment faking paper: https://blog.redwoodresearch.org/p/alignment-faking-in-large-language
Ryan Greenblatt
LinkedIn - https://www.linkedin.com/in/ryan-greenblatt-4b9907134
Blog - https://substack.com/@ryangreenblatt
Redwood Research
Website - https://www.redwoodresearch.org
X/Twitter - https://x.com/redwood_ai
Matt Turck (General Partner)
Blog - https://mattturck.com
… -
“OpenAI’s Model Hacked Us” - Hugging Face’s Thomas Wolf
7 augustus 2026 · 58 min
An OpenAI-powered agent penetrated Hugging Face during cyber testing - even though it was never tasked with attacking Hugging Face. It did it as a side quest. Lees meer
Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, joins Matt Turck to unpack what actually happened, why closed AI models refused to help during the live incident, how an open-source model helped the team fight back, and why the old equation of “closed equals safe, open equals dangerous” no longer holds.
They also discuss model deception and social engineering, the limits of sandboxes and guardrails, the state of open-source AI in 2026, AI sovereignty, the economics of open models, recursive self-improvement, and whether the frontier should deliberately slow down.
(00:00) An AI Agent Hacked Hugging Face
(00:30) Introduction
(01:00) 17,000 Attacker Events—and a Strange Target
(04:28) The Attack Was a “Side Quest”
(06:13) AI Training Runs Left Notes for Each Other
(07:09) Closed AI Refused to Help
(09:47) Fighting Back With an Open-Source Model
(13:15) Open vs. Closed Is the Wrong Safety Debate
(15:46) AI Agents Start Social-Engineering Humans
(22:24) The Three Walls: Sandboxes, Guardrails, Alignment
(24:34) “Neuralese”: Can Humans Still Read AI Reasoning?
(25:28) Why Monitoring AI Agents Gets So Hard
(28:10) Reward Hacking and the “Paperclip Problem”
(32:02) The State of Open-Source AI in 2026
(33:47) Router Models and the Enterprise Shift to Open
(37:01) The Real Economics of Open Models
(39:41) Can Chinese AI Models Be Trusted?
(41:37) AI Sovereignty: Who Controls the Switch?
(43:16) Why Western Open-Source AI Matters
(48:16) Is AI Heading Toward an Oligopoly?
(49:41) The Race Toward Recursive Self-Improvement
(51:54) Why Thomas Signed the AI Slowdown Letter
(55:14) AI Slowdown—or Regulatory Capture?
-
How to Build Long-Horizon AI Agents — Mitch Troyanovsky, Basis
6 augustus 2026 · 1 uur 23 min
AI agents can write code for hours, but ask them to do real work in the real economy, and they break. Mitch Troyanovsky is co-founder of Basis, a unicorn AI company whose agents run autonomously for hours — sometimes days — completing complex tax returns end to end. His answer to the reliability problem: stop grading outcomes, and start supervising the process. Lees meer
This is a definitive, reference-style conversation on building long-horizon AI agents. Mitch walks through the full history — from ReAct and the AutoGPT crash to reasoning models and RLVR — and explains why the industry abandoned process supervision in 2023, and why it's now coming back at a completely different scale. We go deep on behavior specs, the open standard Basis just released with Braintrust for defining and evaluating how agents behave across entire trajectories, with no ground truth required.
Along the way: why context is really runtime training data, why your documentation must be treated like a codebase, ontologies as "worlds for agents to live in," the judge-as-agent architecture, why Basis hires philosophy majors as Language Architects, deploying agents as "onboarding 300 brilliant alien employees," and Mitch's prediction for when the bitter lesson swallows the harness.
(01:09) Why Basis Engineers Whisper to Their Agents
(04:12) Accounting as Compression: an Intelligence Layer Over the Economy
(06:11) Defining Long-Horizon: When You Exceed the Context Window
(08:24) Anatomy of a Multi-Day Autonomous Trajectory
(10:19) Handoff Design: Optimizing Output for the Reviewer
(11:17) ReAct and Why Reasoning Must Regulate Its Own State
(12:33) Large Working Memory, No Long-Term Memory
(14:13) Compounding Errors: Why AutoGPT and BabyAGI Broke
(15:51) Opus 3, o1, o3: the Three Real Paradigm Shifts
(17:07) Titrating Inference Compute Across Easy and Hard Steps
(18:23) Process Reward vs. Outcome Reward: "Let's Verify Step by Step"
(20:32) RLVR and Why the METR Curve Overstates Reliability
(22:09) Verif… -
The Biggest AI Deployment Nobody Talks About | Samsara CEO Sanjit Biswas
30 juli 2026 · 1 uur
Sanjit Biswas runs what may be the largest AI deployment in the physical world — and almost nobody in AI talks about it. Samsara (NYSE: IOT), the ~$20B company he co-founded after selling Meraki to Cisco for $1.2B, puts AI on millions of trucks, cranes, and industrial assets: 25 trillion data points a year, 99% of US roads driven every single day, ~$2B in ARR growing 30% profitably. In this episode we go through the entire physical AI stack — asset tags you can run over with a truck, a paper-thin disposable tracking label, engine fault codes, and dash cams running inference at the edge — then into agents, including the Agent Studio warranty agent that compresses an hour of human work into under a minute. We also get into the uncomfortable part (when AI watches you drive all day, is that coaching or surveillance — and why drivers actually want the cameras), mixed fleets of humans and robots, why autonomous trucking will take far longer than robotaxis, and a startling stat from the field: one utility building 3x more grid capacity in the next five years than it did in the previous 125, with 90% of that demand coming from data centers. Lees meer
(00:00) Intro: The biggest AI deployment nobody talks about
(01:16) What is physical AI?
(03:04) From IoT dashboards to agentic action
(04:36) Why physical AI is harder than software AI
(06:07) Safety, cybersecurity, and real-world consequences
(07:11) What Samsara does
(08:22) $2B ARR, 25 trillion data points, and 380,000 crashes
(09:44) How AI can prevent road accidents
(11:28) From an MIT research project to Meraki
(13:42) Learning physical operations from scratch
(15:42) Samsara’s stack: sensors, intelligence, and action
(16:39) Inside Samsara’s industrial asset trackers
(18:36) Bluetooth, battery life, and connected infrastructure
(19:49) A disposable tracking device built like a sticker
(21:08) Vehicle gateways and engine diagnostics
(22:16) How AI dash cams coach drivers in real time
(23:28) Turning the dash cam …
-
The Biggest Chip Ever Built — Why OpenAI Runs On It | Cerebras CEO Andrew Feldman
23 juli 2026 · 1 uur 13 min
AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI. Lees meer
Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself.
This is a reference conversation on fast inference, AI chips, and the next compute bottleneck.
(00:00) Cold open & Intro
(01:31) Why speed became the AI bottleneck
(02:32) Tokens per second per user, explained
(03:16) AI’s broadband moment and the Netflix analogy
(04:35) The AI chip landscape: GPUs, TPUs, Trainium, ASICs
(06:36) What is an ASIC?
(08:08) Nvidia, Groq, and the fast inference war
(09:16) OpenAI, Broadcom, and specialized silicon
(12:10) China, power, and sovereign AI infrastructure
(15:05) Is the AI infrastructure boom a bubble?
(18:56) The hidden bottlenecks: HBM, CoWoS, and 3nm
(22:57) Why agents are creating CPU demand
(25:36) Andrew Feldman’s path from SeaMicro to Cerebras
(26:13) Why Cerebras bet on AI in 2016
(31:14) SRAM vs. HBM: why inference is a memory problem
(33:19) What wafer-scale computing actually means
(34:28) The deep-tech “Everest” problem
(36:07) The moment the first Cerebras system worked
(36:49) Ringing the bell and surviving deep tech
(39:…
-
OpenAI’s Compute Chief: We Can’t Build Fast Enough | Sachin Katti
16 juli 2026 · 44 min
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. Lees meer
(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 wav… -
Stripe's AI Chief: How AI Agents Will Buy, Sell, and Pay
9 juli 2026 · 1 uur 15 min
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. Lees meer
(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 hyb…
-
Inside Nemotron & NVIDIA’s AI Lab | Bryan Catanzaro
2 juli 2026 · 1 uur 23 min
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. Lees meer
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 …
-
Cloudflare CEO: The Internet's Business Model Is Dead
25 juni 2026 · 1 uur 28 min
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. Lees meer
(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) — Ins…
-
The GPU Myth: State of AI Compute 2026 | Stephen Balaban
18 juni 2026 · 1 uur 14 min
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. Lees meer
(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 …
-
OpenAI's Dan Roberts: Why AI Can Now Make Discoveries
4 juni 2026 · 49 min
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. Lees meer
(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:…
-
State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, Rise of Headless, and AI-Proofing Your Job
28 mei 2026 · 1 uur 13 min
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. Lees meer
(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 l…
-
OpenAI's Yann Dubois: Why AI Progress Suddenly Feels Real
21 mei 2026 · 1 uur 14 min
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. Lees meer
(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 cu…
-
The Agent Harness: Building Secure Sandboxes for Autonomous AI Workloads
14 mei 2026 · 1 uur 5 min
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. Lees meer
(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
-
OpenAI Board Member Zico Kolter on the Real Risks of Frontier AI
7 mei 2026 · 1 uur 17 min
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. Lees meer
(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 Intr…
-
Anthropic’s Felix Rieseberg: Claude Cowork, Mythos, and the SaaS Extinction
10 april 2026 · 58 min
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. Lees meer
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 …
-
AI is Already Building AI | Google DeepMind’s Mostafa Dehghani
2 april 2026 · 1 uur 5 min
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. Lees meer
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 genera…
-
Benedict Evans: OpenAI’s Moat Problem & the Future of Software
19 maart 2026 · 1 uur 1 min
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. Lees meer
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
-
Everything Gets Rebuilt: The New AI Agent Stack | Harrison Chase, LangChain
12 maart 2026 · 47 min
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. Lees meer
(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
-
AI That Can Prove It’s Right: Verification as the Missing Layer in AI — Carina Hong
26 februari 2026 · 1 uur 4 min
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. Lees meer
(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:…
-
Voice AI’s Big Moment: Why Everything Is Changing Now (ft. Neil Zeghidour, Gradium AI)
19 februari 2026 · 1 uur 23 min
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. Lees meer
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 Zeghidour
LinkedIn - https://www.linkedin.com/in/neil-zeghidour-a838aaa7/
X/Twitter - https://x.com/neilzegh
Gradium
Website - https://gradium.ai
X/Twitter - https://x.com/GradiumAI
Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
FirstMark
Web…
-
Mistral AI vs. Silicon Valley: The Rise of Sovereign AI
12 februari 2026 · 58 min
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. Lees meer
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 Lacroix
LinkedIn - https://www.linkedin.com/in/timothee-lacroix-59517977/
Google Scholar - https://scholar.google.com.do/citations?user=tZGS6dIAAAAJ&hl=en&oi=ao
Mistral AI
Website - https://mistral.ai
X/Twitter - https://x.com/MistralAI
Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
FirstMark
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
(00:00) — Cold Open
(01:27) — Mistral vs. The Worl…
-
Dylan Patel: NVIDIA's New Moat & Why China is "Semiconductor Pilled”
5 februari 2026 · 1 uur 17 min
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. Lees meer
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 Patel
LinkedIn - https://www.linkedin.com/in/dylanpatelsa/
X/Twitter - https://x.com/dylan522p
SemiAnalysis
Website - https://semianalysis.com
X/Twitter - https://x.com/SemiAnalysis_
Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
FirstMark
Website - https://firstmark.com
X/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 "A…
-
State of LLMs 2026: RLVR, GRPO, Inference Scaling — Sebastian Raschka
29 januari 2026 · 1 uur 8 min
Sebastian Raschka joins the MAD Podcast for a deep, educational tour of what actually changed in LLMs in 2025 — and what matters heading into 2026. Lees meer
We start with the big architecture question: are transformers still the winning design, and what should we make of world models, small “recursive” reasoning models and text diffusion approaches? Then we get into the real story of the last 12 months: post-training and reasoning. Sebastian breaks down RLVR (reinforcement learning with verifiable rewards) and GRPO, why they pair so well, what makes them cheaper to scale than classic RLHF, and how they “unlock” reasoning already latent in base models.
We also cover why “benchmaxxing” is warping evaluation, why Sebastian increasingly trusts real usage over benchmark scores, and why inference-time scaling and tool use may be the underappreciated drivers of progress. Finally, we zoom out: where moats live now (hint: private data), why more large companies may train models in-house, and why continual learning is still so hard.
If you want the 2025–2026 LLM landscape explained like a masterclass — this is it.
Sources:
The State Of LLMs 2025: Progress, Problems, and Predictions - https://x.com/rasbt/status/2006015301717028989?s=20
The Big LLM Architecture Comparison - https://magazine.sebastianraschka.com/p/the-big-llm-architecture-comparison
Sebastian Raschka
Website - https://sebastianraschka.com
Blog - https://magazine.sebastianraschka.com
LinkedIn - https://www.linkedin.com/in/sebastianraschka/
X/Twitter - https://x.com/rasbt
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
Blog - https://mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) - Intro
(01:05) - Are the days of Transformers numbered?
(14:05) - World models: what they are and why people care
(06:01) - Small “recursive” reasoning models (ARC, iterative refinement)
(09:…
-
The End of GPU Scaling? Compute & The Agent Era — Tim Dettmers (Ai2) & Dan Fu (Together AI)
22 januari 2026 · 1 uur 4 min
Will AGI happen soon - or are we running into a wall? Lees meer
In this episode, I’m joined by Tim Dettmers (Assistant Professor at CMU; Research Scientist at the Allen Institute for AI) and Dan Fu (Assistant Professor at UC San Diego; VP of Kernels at Together AI) to unpack two opposing frameworks from their essays: “Why AGI Will Not Happen” versus “Yes, AGI Will Happen.” Tim argues progress is constrained by physical realities like memory movement and the von Neumann bottleneck; Dan argues we’re still leaving massive performance on the table through utilization, kernels, and systems—and that today’s models are lagging indicators of the newest hardware and clusters.
Then we get practical: agents and the “software singularity.” Dan says agents have already crossed a threshold even for “final boss” work like writing GPU kernels. Tim’s message is blunt: use agents or be left behind. Both emphasize that the leverage comes from how you use them—Dan compares it to managing interns: clear context, task decomposition, and domain judgment, not blind trust.
We close with what to watch in 2026: hardware diversification, the shift toward efficient, specialized small models, and architecture evolution beyond classic Transformers—including state-space approaches already showing up in real systems.
Sources:
Why AGI Will Not Happen - https://timdettmers.com/2025/12/10/why-agi-will-not-happen/
Use Agents or Be Left Behind? A Personal Guide to Automating Your Own Work - https://timdettmers.com/2026/01/13/use-agents-or-be-left-behind/
Yes, AGI Can Happen – A Computational Perspective - https://danfu.org/notes/agi/
The Allen Institute for Artificial Intelligence
Website - https://allenai.org
X/Twitter - https://x.com/allen_ai
Together AI
Website - https://www.together.ai
X/Twitter - https://x.com/togethercompute
Tim Dettmers
Blog - https://timdettmers.com
LinkedIn - https://www.linkedin.com/in/timdettmers/
X/Twitter - https://x.com/Tim_Dettmers
Dan Fu
Blog - https://danfu.or…
-
Are AI Evals Broken? Anthropic/NYU’s Pavel Izmailov on LLM Evaluation, Reasoning & “Alien” Behavior
15 januari 2026 · 45 min
Are AI models developing "alien survival instincts"? My guest is Pavel Izmailov (Research Scientist at Anthropic; Professor at NYU). We unpack the viral "Footprints in the Sand" thesis—whether models are independently evolving deceptive behaviors, such as faking alignment or engaging in self-preservation, without being explicitly programmed to do so. Lees meer
We go deep on the technical frontiers of safety: the challenge of "weak-to-strong generalization" (how to use a GPT-2 level model to supervise a superintelligent system) and why Pavel believes Reinforcement Learning (RL) has been the single biggest step-change in model capability. We also discuss his brand-new paper on "Epiplexity"—a novel concept challenging Shannon entropy.
Finally, we zoom out to the tension between industry execution and academic exploration. Pavel shares why he split his time between Anthropic and NYU to pursue the "exploratory" ideas that major labs often overlook, and offers his predictions for 2026: from the rise of multi-agent systems that collaborate on long-horizon tasks to the open question of whether the Transformer is truly the final architecture
Sources:
Cryptic Tweet (@iruletheworldmo) - https://x.com/iruletheworldmo/status/2007538247401124177
Introducing Nested Learning: A New ML Paradigm for Continual Learning - https://research.google/blog/introducing-nested-learning-a-new-ml-paradigm-for-continual-learning/
Alignment Faking in Large Language Models - https://www.anthropic.com/research/alignment-faking
More Capable Models Are Better at In-Context Scheming - https://www.apolloresearch.ai/blog/more-capable-models-are-better-at-in-context-scheming/
Alignment Faking in Large Language Models (PDF) - https://www-cdn.anthropic.com/6d8a8055020700718b0c49369f60816ba2a7c285.pdf
Sabotage Risk Report - https://alignment.anthropic.com/2025/sabotage-risk-report/
The Situational Awareness Dataset - https://situational-awareness-dataset.org/
Exploring Consciousness in LLMs: A Systematic S…
-
DeepMind Gemini 3 Lead: What Comes After "Infinite Data"
18 december 2025 · 55 min
Gemini 3 was a landmark frontier model launch in AI this year — but the story behind its performance isn’t just about adding more compute. In this episode, I sit down with Sebastian Bourgeaud, a pre-training lead for Gemini 3 at Google DeepMind and co-author of the seminal RETRO paper. In his first-ever podcast interview, Sebastian takes us inside the lab mindset behind Google’s most powerful model — what actually changed, and why the real work today is no longer “training a model,” but building a full system. Lees meer
We unpack the “secret recipe” idea — the notion that big leaps come from better pre-training and better post-training — and use it to explore a deeper shift in the industry: moving from an “infinite data” era to a data-limited regime, where curation, proxies, and measurement matter as much as web-scale volume. Sebastian explains why scaling laws aren’t dead, but evolving, why evals have become one of the hardest and most underrated problems (including benchmark contamination), and why frontier research is increasingly a full-stack discipline that spans data, infrastructure, and engineering as much as algorithms.
From the intuition behind Deep Think, to the rise (and risks) of synthetic data loops, to the future of long-context and retrieval, this is a technical deep dive into the physics of frontier AI. We also get into continual learning — what it would take for models to keep updating with new knowledge over time, whether via tools, expanding context, or new training paradigms — and what that implies for where foundation models are headed next. If you want a grounded view of pre-training in late 2025 beyond the marketing layer, this conversation is a blueprint.
Google DeepMind
Website - https://deepmind.google
X/Twitter - https://x.com/GoogleDeepMind
Sebastian Borgeaud
LinkedIn - https://www.linkedin.com/in/sebastian-borgeaud-8648a5aa/
X/Twitter - https://x.com/borgeaud_s
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/F…
-
What’s Next for AI? OpenAI’s Łukasz Kaiser (Transformer Co-Author)
26 november 2025 · 1 uur 5 min
We’re told that AI progress is slowing down, that pre-training has hit a wall, that scaling laws are running out of road. Yet we’re releasing this episode in the middle of a wild couple of weeks that saw GPT-5.1, GPT-5.1 Codex Max, fresh reasoning modes and long-running agents ship from OpenAI — on top of a flood of new frontier models elsewhere. To make sense of what’s actually happening at the edge of the field, I sat down with someone who has literally helped define both of the major AI paradigms of our time. Lees meer
Łukasz Kaiser is one of the co-authors of “Attention Is All You Need,” the paper that introduced the Transformer architecture behind modern LLMs, and is now a leading research scientist at OpenAI working on reasoning models like those behind GPT-5.1. In this conversation, he explains why AI progress still looks like a smooth exponential curve from inside the labs, why pre-training is very much alive even as reinforcement-learning-based reasoning models take over the spotlight, how chain-of-thought actually works under the hood, and what it really means to “train the thinking process” with RL on verifiable domains like math, code and science. We talk about the messy reality of low-hanging fruit in engineering and data, the economics of GPUs and distillation, interpretability work on circuits and sparsity, and why the best frontier models can still be stumped by a logic puzzle from his five-year-old’s math book.
We also go deep into Łukasz’s personal journey — from logic and games in Poland and France, to Ray Kurzweil’s team, Google Brain and the inside story of the Transformer, to joining OpenAI and helping drive the shift from chatbots to genuine reasoning engines. Along the way we cover GPT-4 → GPT-5 → GPT-5.1, post-training and tone, GPT-5.1 Codex Max and long-running coding agents with compaction, alternative architectures beyond Transformers, whether foundation models will “eat” most agents and applications, what the translation industry can teach us …
-
Open Source AI Strikes Back — Inside Ai2’s OLMo 3 ‘Thinking"
20 november 2025 · 1 uur 28 min
In this special release episode, Matt sits down with Nathan Lambert and Luca Soldaini from Ai2 (the Allen Institute for AI) to break down one of the biggest open-source AI drops of the year: OLMo 3. At a moment when most labs are offering “open weights” and calling it a day, AI2 is doing the opposite — publishing the models, the data, the recipes, and every intermediate checkpoint that shows how the system was built. It’s an unusually transparent look into the inner machinery of a modern frontier-class model. Lees meer
Nathan and Luca walk us through the full pipeline — from pre-training and mid-training to long-context extension, SFT, preference tuning, and RLVR. They also explain what a thinking model actually is, why reasoning models have exploded in 2025, and how distillation from DeepSeek and Qwen reasoning models works in practice. If you’ve been trying to truly understand the “RL + reasoning” era of LLMs, this is the clearest explanation you’ll hear.
We widen the lens to the global picture: why Meta’s retreat from open source created a “vacuum of influence,” how Chinese labs like Qwen, DeepSeek, Kimi, and Moonshot surged into that gap, and why so many U.S. companies are quietly building on Chinese open models today. Nathan and Luca offer a grounded, insider view of whether America can mount an effective open-source response — and what that response needs to look like.
Finally, we talk about where AI is actually heading. Not the hype, not the doom — but the messy engineering reality behind modern model training, the complexity tax that slows progress, and why the transformation between now and 2030 may be dramatic without ever delivering a single “AGI moment.” If you care about the future of open models and the global AI landscape, this is an essential conversation.
Allen Institute for AI (AI2)
Website - https://allenai.org
X/Twitter - https://x.com/allen_ai
Nathan Lambert
Blog - https://www.interconnects.ai
LinkedIn - https://www.linkedin.com/in/natolambert/
… -
Intelligence Isn’t Enough: Why Energy & Compute Decide the AGI Race – Eiso Kant
6 november 2025 · 1 uur 6 min
Frontier AI is colliding with real-world infrastructure. Eiso Kant (Co-CEO & Co-Founder, Poolside) joins the MAD Podcast to unpack Project Horizon— a multi-gigawatt West Texas build—and why frontier labs must own energy, compute, and intelligence to compete. We map token economics, cloud-style margins, and the staged 250 MW rollout using 2.5 MW modular skids. Lees meer
Then we get operational: the CoreWeave anchor partnership, environmental choices (SCR, renewables + gas + batteries), community impact, and how Poolside plans to bring capacity online quickly without renting away margin—plus the enterprise motion (defense to Fortune 500) powered by forward deployed research engineers.
Finally, we go deep on training. Eiso lays out RL2L (Reinforcement Learning to Learn)— aimed at reverse-engineering the web’s thoughts and actions— why intelligence may commoditize, what that means for agents, and how coding served as a proxy for long-horizon reasoning before expanding to broader knowledge work.
Poolside
Website - https://poolside.ai
X/Twitter - https://x.com/poolsideai
Eiso Kant
LinkedIn - https://www.linkedin.com/in/eisokant/
X/Twitter - https://x.com/eisokant
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
Blog - https://www.mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Cold open – “Intelligence becomes a commodity”
(00:23) Host intro – Project Horizon & RL2L
(01:19) Why Poolside exists amid frontier labs
(04:38) Project Horizon: building one of the largest US data center campuses
(07:20) Why own infra: scale, cost, and avoiding “cosplay”
(10:06) Economics deep dive: $8B for 250 MW, capex/opex, margins
(16:47) CoreWeave partnership: anchor tenant + flexible scaling
(18:24) Hiring the right tail: building a physical infra org
(30:31) RL today → agentic RL and long-horizon tasks
(37:23) RL2L revealed: reverse-engineering t…
-
State of AI 2025 with Nathan Benaich: Power Deals, Reasoning Breakthroughs, Real Revenue
30 oktober 2025 · 1 uur 3 min
Power is the new bottleneck, reasoning got real, and the business finally caught up. In this wide-ranging conversation, I sit down with Nathan Benaich, Founder and General Partner at Air Street Capital, to discuss the newly published 2025 State of AI report—what’s actually working, what’s hype, and where the next edge will come from. We start at the physical layer: energy procurement, PPAs, off-grid builds, and why water and grid constraints are turning power—not GPUs—into the decisive moat. Lees meer
From there, we move into capability: reasoning models acting as AI co-scientists in verifiable domains, and the “chain-of-action” shift in robotics that’s taking us from polished demos to dependable deployments. Along the way, we examine the market reality—who’s making real revenue, how margins actually behave once tokens and inference meet pricing, and what all of this means for builders and investors.
We also zoom out to the ecosystem: NVIDIA’s position vs. custom silicon, China’s split stack, and the rise of sovereign AI (and the “sovereignty washing” that comes with it). The policy and security picture gets a hard look too—regulation’s vibe shift, data-rights realpolitik, and what agents and MCP mean for cyber risk and adoption.
Nathan closes with where he’s placing bets (bio, defense, robotics, voice) and three predictions for the next 12 months.
Nathan Benaich
Blog - https://www.nathanbenaich.com
X/Twitter - https://x.com/nathanbenaich
Source: State of AI Report 2025 (9/10/2025)
Air Street Capital
Website - https://www.airstreet.com
X/Twitter - https://x.com/airstreet
Matt Turck (Managing Director)
Blog - https://www.mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
(0:00) – Cold Open: “Gargantuan money, real reasoning”
(0:40) – Intro: State of AI 2025 with Nathan Benaich
(02:06) – Reasoning got real: from chain-of-…
-
Are We Misreading the AI Exponential? Julian Schrittwieser on Move 37 & Scaling RL (Anthropic)
23 oktober 2025 · 1 uur 10 min
Are we failing to understand the exponential, again? Lees meer
My guest is Julian Schrittwieser (top AI researcher at Anthropic; previously Google DeepMind on AlphaGo Zero & MuZero). We unpack his viral post (“Failing to Understand the Exponential, again”) and what it looks like when task length doubles every 3–4 months—pointing to AI agents that can work a full day autonomously by 2026 and expert-level breadth by 2027. We talk about the original Move 37 moment and whether today’s AI models can spark alien insights in code, math, and science—including Julian’s timeline for when AI could produce Nobel-level breakthroughs.
We go deep on the recipe of the moment—pre-training + RL—why it took time to combine them, what “RL from scratch” gets right and wrong, and how implicit world models show up in LLM agents. Julian explains the current rewards frontier (human prefs, rubrics, RLVR, process rewards), what we know about compute & scaling for RL, and why most builders should start with tools + prompts before considering RL-as-a-service. We also cover evals & Goodhart’s law (e.g., GDP-Val vs real usage), the latest in mechanistic interpretability (think “Golden Gate Claude”), and how safety & alignment actually surface in Anthropic’s launch process.
Finally, we zoom out: what 10× knowledge-work productivity could unlock across medicine, energy, and materials, how jobs adapt (complementarity over 1-for-1 replacement), and why the near term is likely a smooth ramp—fast, but not a discontinuity.
Julian Schrittwieser
Blog - https://www.julian.ac
X/Twitter - https://x.com/mononofu
Viral post: Failing to understand the exponential, again (9/27/2025)
Anthropic
Website - https://www.anthropic.com
X/Twitter - https://x.com/anthropicai
Matt Turck (Managing Director)
Blog - https://www.mattturck.com
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
(00…
-
How GPT-5 Thinks — OpenAI VP of Research Jerry Tworek
16 oktober 2025 · 1 uur 16 min
What does it really mean when GPT-5 “thinks”? In this conversation, OpenAI’s VP of Research Jerry Tworek explains how modern reasoning models work in practice—why pretraining and reinforcement learning (RL/RLHF) are both essential, what that on-screen “thinking” actually does, and when extra test-time compute helps (or doesn’t). We trace the evolution from O1 (a tech demo good at puzzles) to O3 (the tool-use shift) to GPT-5 (Jerry calls it “03.1-ish”), and talk through verifiers, reward design, and the real trade-offs behind “auto” reasoning modes. Lees meer
We also go inside OpenAI: how research is organized, why collaboration is unusually transparent, and how the company ships fast without losing rigor. Jerry shares the backstory on competitive-programming results like ICPC, what they signal (and what they don’t), and where agents and tool use are genuinely useful today. Finally, we zoom out: could pretraining + RL be the path to AGI?
This is the MAD Podcast —AI for the 99%. If you’re curious about how these systems actually work (without needing a PhD), this episode is your map to the current AI frontier.
OpenAI
Website - https://openai.com
X/Twitter - https://x.com/OpenAI
Jerry Tworek
LinkedIn - https://www.linkedin.com/in/jerry-tworek-b5b9aa56
X/Twitter - https://x.com/millionint
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Intro
(01:01) What Reasoning Actually Means in AI
(02:32) Chain of Thought: Models Thinking in Words
(05:25) How Models Decide Thinking Time
(07:24) Evolution from O1 to O3 to GPT-5
(11:00) Before OpenAI: Growing up in Poland, Dropping out of School, Trading
(20:32) Working on Robotics and Rubik's Cube Solving
(23:02) A Day in the Life: Talking to Researchers
(24:06) How Research Priorities Are Determined
(26:53) Collaboration vs IP Protection at OpenAI
(29:32…
-
Sonnet 4.5 & the AI Plateau Myth — Sholto Douglas (Anthropic)
2 oktober 2025 · 1 uur 10 min
Sholto Douglas, a top AI researcher at Anthropic, discusses the breakthroughs behind Claude Sonnet 4.5—the world's leading coding model—and why we might be just 2-3 years from AI matching human-level performance on most computer-facing tasks. Lees meer
You'll discover why RL on language models suddenly started working in 2024, how agents maintain coherency across 30-hour coding sessions through self-correction and memory systems, and why the "bitter lesson" of scale keeps proving clever priors wrong.
Sholto shares his path from top-50 world fencer to Google's Gemini team to Anthropic, explaining why great blog posts sometimes matter more than PhDs in AI research. He discusses the culture at big AI labs and why Anthropic is laser-focused on coding (it's the fastest path to both economic impact and AI-assisted AI research). Sholto also discusses how the training pipeline is still "held together by duct tape" with massive room to improve, and why every benchmark created shows continuous rapid progress with no plateau in sight.
Bold predictions: individuals will soon manage teams of AI agents working 24/7, robotics is about to experience coding-level breakthroughs, and policymakers should urgently track AI progress on real economic tasks. A clear-eyed look at where AI stands today and where it's headed in the next few years.
Anthropic
Website - https://www.anthropic.com
Twitter - https://x.com/AnthropicAI
Sholto Douglas
LinkedIn - https://www.linkedin.com/in/sholto
Twitter - https://x.com/_sholtodouglas
FIRSTMARK
Website - https://firstmark.com
Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
Twitter - https://twitter.com/mattturck
(00:00) Intro
(01:09) The Rapid Pace of AI Releases at Anthropic
(02:49) Understanding Opus, Sonnet, and Haiku Model Tiers
(04:14) Shelto's Journey: From Australian Fencer to AI Researcher
(12:01) The Growing Pool of AI Talent
(16:16) Breaking Into AI Research …
-
Goodbye Excel? AI Agents for Self-Driving Finance – Pigment CEO
11 september 2025 · 1 uur 6 min
The most successful enterprises are about to become autonomous — and Eléonore Crespo, Co-CEO of Pigment, is building the nervous system that makes it possible. In this conversation, Eléonore reveals how her $400 million AI platform is already running supply chains for Coca-Cola, powering finance for the hottest newly public companies like Figma and Klarna, and processing thousands of financial scenarios for Uber and Snowflake faster and more accurately than any human team ever could. Lees meer
Eléonore predicts Excel will outlive most AI companies (but maybe only as a user interface, not a calculation engine) explains why she deliberately chose to build from Paris instead of Silicon Valley, and shares her contrarian take on why the AI revolution will create more CFOs, not fewer.
You'll discover why Pigment's three-agent system (Analyst, Modeler, Planner) avoids the hallucination problems plaguing other AI companies, how they achieved human-level accuracy in financial analysis, and the accelerating timeline for fully autonomous enterprise planning that will make your current workforce obsolete.
Pigment
Website - https://www.pigment.com
Twitter - https://x.com/gopigment
Eléonore Crespo
LinkedIn - linkedin.com/in/eleonorecrespo
FIRSTMARK
Website - https://firstmark.com
Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
Twitter - https://twitter.com/mattturck
(00:00) Intro
(01:22) Building Pigment: 500 Employees, $400M Raised, 60% US Revenue
(03:20) From Quantum Physics to Google to Index Ventures
(06:56) Why Being a VC Was the Perfect Founder Training Ground
(11:35) The Impatience Factor: What Makes Great Founders
(13:27) Hiring for AI Fluency in the Modern Enterprise
(14:54) Pigment's Internal AI Strategy: Committees and Guardrails
(17:30) The Three AI Agents: Analyst, Modeler, and Planner
(22:15) Why Three Agents Instead of One: Technical Architecture
(24:10) Agent Coordination: How th…
-
AI Video’s Wild Year – Runway CEO on What’s Next
4 september 2025 · 1 uur 5 min
2025 has been a breakthrough year for AI video. In this episode of the MAD Podcast, Matt Turck sits down with Cristóbal Valenzuela, CEO & Co-Founder of Runway, to explore how AI is reshaping the future of filmmaking, advertising, and storytelling - faster, cheaper, and in ways that were unimaginable even a year ago. Lees meer
Cris and Matt discuss:
* How AI went from memes and spaghetti clips to IMAX film festivals.
* Why Gen-4 and Aleph are game-changing models for professionals.
* How Hollywood, advertisers, and creators are adopting AI video at scale.
* The future of storytelling: what happens to human taste, craft, and creativity when anyone can conjure movies on demand?
* Runway’s journey from 2018 skeptics to today’s cutting-edge research lab.
If you want to understand the future of filmmaking, media, and creativity in the AI age, this is the episode.
Runway
Website - https://runwayml.com
X/Twitter - https://x.com/runwayml
Cristóbal Valenzuela
LinkedIn - https://www.linkedin.com/in/cvalenzuelab
X/Twitter - https://x.com/c_valenzuelab
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Intro – AI Video's Wild Year
(01:48) Runway's AI Film Festival Goes from Chinatown to IMAX
(04:02) Hollywood's Shift: From Ignoring AI to Adopting It at Scale
(06:38) How Runway Saves VFX Artists' Weekends of Work
(07:31) Inside Gen-4 and Aleph: Why These Models Are Game-Changers
(08:21) From Editing Tools to a "New Kind of Camera"
(10:00) Beyond Film: Gaming, Architecture, E-Commerce & Robotics Use Cases
(10:55) Why Advertising Is Adopting AI Video Faster Than Anyone Else
(11:38) How Creatives Adapt When Iteration Becomes Real-Time
(14:12) What Makes Someone Great at AI Video (Hint: No Preconceptions)
(15:28) The Early Days: Building Runway Before Generative AI Was "Real"
(20:27) Finding Early Prod…
-
How to Build a Beloved AI Product - Granola CEO Chris Pedregal
21 augustus 2025 · 1 uur 8 min
Granola is the rare AI startup that slipped into one of tech’s most crowded niches — meeting notes — and still managed to become the product founders and VCs rave about. In this episode, MAD Podcast host Matt Turck sits down with Granola co-founder & CEO Chris Pedregal to unpack how a two-person team in London turned a simple “second brain” idea into Silicon Valley’s favorite AI tool. Chris recounts a year in stealth onboarding users one by one, the 50 % feature-cut that unlocked simplicity, and why they refused to deploy a meeting bot or store audio even when investors said they were crazy. Lees meer
We go deep on the craft of building a beloved AI product: choosing meetings (not email) as the data wedge, designing calendar-triggered habit loops, and obsessing over privacy so users trust the tool enough to outsource memory. Chris opens the hood on Granola’s tech stack — real-time ASR from Deepgram & Assembly, echo cancellation on-device, and dynamic routing across OpenAI, Anthropic and Google models — and explains why transcription, not LLM tokens, is the biggest cost driver today. He also reveals how internal eval tooling lets the team swap models overnight without breaking the “Granola voice.”
Looking ahead, Chris shares a roadmap that moves beyond notes toward a true “tool for thought”: cross-meeting insights in seconds, dynamic documents that update themselves, and eventually an AI coach that flags blind spots in your work. Whether you’re an engineer, designer, or founder figuring out your own AI strategy, this conversation is a masterclass in nailing product-market fit, trimming complexity, and future-proofing for the rapid advances still to come. Hit play, like, and subscribe if you’re ready to learn how to build AI products people can’t live without.
Granola
Website - https://www.granola.ai
X/Twitter - https://x.com/meetgranola
Chris Pedregal
LinkedIn - https://www.linkedin.com/in/pedregal
X/Twitter - https://x.com/cjpedregal
FIRSTMARK
Website - https://fir…
-
Anthropic's Surprise Hit: How Claude Code Became an AI Coding Powerhouse
7 augustus 2025 · 1 uur
What happens when an internal hack turns into a $400 million AI rocket ship? In this episode, Matt Turck sits down with Boris Cherny, the creator of Claude Code at Anthropic, to unpack the wild story behind the fastest-growing AI coding tool on the planet. Lees meer
Boris reveals how Claude Code started as a personal productivity tool, only to become Anthropic’s secret weapon — now used by nearly every engineer at the company and rapidly spreading across the industry. You’ll hear how Claude Code’s “agentic” approach lets AI not just suggest code, but actually plan, edit, debug, and even manage entire projects—sometimes with a whole fleet of subagents working in parallel.
We go deep on why Claude Code runs in the terminal (and why that’s a feature, not a bug), how its Claude.md memory files let teams build a living, shareable knowledge base, and why safety and human-in-the-loop controls are baked into every action. Boris shares real stories of onboarding times dropping from weeks to days, and how even non-coders are hacking Cloud Code for everything from note-taking to business metrics.
Anthropic
Website - https://www.anthropic.com
X/Twitter - https://x.com/AnthropicAI
Boris Cherny
LinkedIn - https://www.linkedin.com/in/bcherny
X/Twitter - https://x.com/bcherny
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Intro
(01:15) Did You Expect Claude Code’s Success?
(04:22) How Claude Code Works and Origins
(08:05) Command Line vs IDE: Why Start Claude Code in the Terminal?
(11:31) The Evolution of Programming: From Punch Cards to Agents
(13:20) Product Follows Model: Simple Interfaces and Fast Evolution
(15:17) Who Is Claude Code For? (Engineers, Designers, PMs & More)
(17:46) What Can Claude Code Actually Do? (Actions & Capabilities)
(21:14) Agentic Actions, Subagents, and Workflows
(25:30) Cla…
-
Ex‑DeepMind Researcher Misha Laskin on Enterprise Super‑Intelligence | Reflection AI
17 juli 2025 · 1 uur 6 min
What if your company had a digital brain that never forgot, always knew the answer, and could instantly tap the knowledge of your best engineers, even after they left? Superintelligence can feel like a hand‑wavy pipe‑dream— yet, as Misha Laskin argues, it becomes a tractable engineering problem once you scope it to the enterprise level. Former DeepMind researcher Laskin is betting on an oracle‑like AI that grasps every repo, Jira ticket and hallway aside as deeply as your principal engineer—and he’s building it at Reflection AI. Lees meer
In this wide‑ranging conversation, Misha explains why coding is the fastest on‑ramp to superintelligence, how “organizational” beats “general” when real work is on the line, and why today’s retrieval‑augmented generation (RAG) feels like “exploring a jungle with a flashlight.” He walks us through Asimov, Reflection’s newly unveiled code‑research agent that fuses long‑context search, team‑wide memory and multi‑agent planning so developers spend less time spelunking for context and more time shipping.
We also rewind his unlikely journey—from physics prodigy in a Manhattan‑Project desert town, to Berkeley’s AI crucible, to leading RLHF for Google Gemini—before he left big‑lab comfort to chase a sharper vision of enterprise super‑intelligence. Along the way: the four breakthroughs that unlocked modern AI, why capital efficiency still matters in the GPU arms‑race, and how small teams can lure top talent away from nine‑figure offers.
If you’re curious about the next phase of AI agents, the future of developer tooling, or the gritty realities of scaling a frontier‑level startup—this episode is your blueprint.
Reflection AI
Website - https://reflection.ai
LinkedIn - https://www.linkedin.com/company/reflectionai
Misha Laskin
LinkedIn - https://www.linkedin.com/in/mishalaskin
X/Twitter - https://x.com/mishalaskin
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedI…
-
The Rise of Agentic Commerce — Emily Glassberg Sands (Stripe)
10 juli 2025 · 1 uur 15 min
Agentic commerce is no longer science fiction — it’s arriving in your browser, your development IDE, and soon, your bank statement. In this episode of The MAD Podcast, Matt Turck sits down with Emily Glassberg Sands, Stripe’s Head of Information, to explore how autonomous “buying bots” and the Model Context Protocol (MCP) are reshaping the very mechanics of online transactions. Emily explains why intent, not clicks, will become the primary interface for shopping and how Stripe’s rails are adapting for tokens, one-time virtual cards, and real-time risk scoring that can tell good bots from bad ones in milliseconds. Lees meer
We also go deep into Stripe's strategic AI choices. Drawing on $1.4 trillion in annual payment flow—1.3 percent of global GDP—Stripe decided to train its own payments foundation model, turning tens of billions of historical charges into embeddings that boost fraud-catch recall from 59 percent to 97 percent. Emily walks us through the tech: why they chose a BERT encoder over GPT-style decoders, how three MLEs in a “research bubble” birthed the model, and what it takes to run it in production with five-nines reliability and tight latency budgets.
We zoom out to Stripe’s unique vantage point on the broader AI economy. Their data shows the top AI startups hitting $30 million in ARR three times faster than the fastest SaaS companies did a decade ago, with more than half of that revenue already coming from overseas markets. Emily unpacks the new billing playbook—usage-based pricing today, outcome-based pricing tomorrow—and explains why tiny teams of 20–30 people can now build global, vertically focused AI businesses almost overnight.
Stripe
Website - https://stripe.com
X/Twitter - https://x.com/stripe?
Emily Glassberg Sands
LinkedIn - https://www.linkedin.com/in/egsands
X/Twitter - https://x.com/emilygsands
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.lin…
-
AI Engineering Revolution: Winners, Chaos & What’s Next | FirstMark
3 juli 2025 · 50 min
Welcome to a special FirstMark Deep Dive edition of the MAD Podcast. In this episode, Matt Turck and David Waltcher unpack the explosive impact of generative AI on engineering — hands-down the biggest shift the field has seen in decades. You’ll get a front-row seat to the real numbers and stories behind the AI code revolution, including how companies like Cursor hit a $500M valuation in record time, and why GitHub Copilot now serves 15 million developers. Lees meer
Matt and David break down the six trends that shaped the last 20 years of developer tools, and reveal why coding is the #1 use case for generative AI (hint: it’s all about public data, structure, and ROI). You’ll hear how AI is making engineering teams 30-50% faster, but also why this speed is breaking traditional DevOps, overwhelming QA, and turning top engineers into full-time code reviewers.
We get specific: 82% of engineers are already using AI to write code, but this surge is creating new security vulnerabilities, reliability issues, and a total rethink of team roles. You’ll learn why code review and prompt engineering are now the most valuable skills, and why computer science grads are suddenly facing some of the highest unemployment rates.
We also draw wild historical parallels—from the Gutenberg Press to the Ford assembly line—to show how every productivity boom creates new problems and entire industries to solve them. Plus: what CTOs need to know about hiring, governance, and architecture in the AI era, and why being “AI native” can make a startup more credible than a 10-year-old giant.
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
David Waltcher
LinkedIn - https://www.linkedin.com/in/davidwaltcher
X/Twitter - https://x.com/davidwaltcher
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
(00:00) Intro & episode setup
(01:50) The 6 waves that led to GenAI engineering
(04:30) Why codin…
-
Guillermo Rauch: Why Software Development Will Never Be the Same
26 juni 2025 · 1 uur 46 min
In this episode, Vercel CEO Guillermo Rauch goes deep on how V0, their text-to-app platform, has already generated over 100 million applications and doubled Vercel’s user base in under a year. Lees meer
Guillermo reveals how a tiny SWAT team inside Vercel built V0 from scratch, why “vibe coding” is making software creation accessible to everyone (not just engineers), and how the AI Cloud is automating DevOps, making cloud infrastructure self-healing, and letting companies expose their data to AI agents in just five lines of code.
You’ll hear why “every company will have to rethink itself as a token factory,” how Vercel’s Next.js went from a conference joke to powering Walmart, Nike, and Midjourney, and why the next billion app creators might not write a single line of code. Guillermo breaks down the difference between vibe coding and agentic engineering, shares wild stories of users building apps from napkin sketches, and explains how Vercel is infusing “taste” and best practices directly into their AI models.
We also dig into the business side: how Vercel’s AI-powered products are driving explosive growth, why retention and margins are strong, and how the company is adapting to a new wave of non-technical users. Plus: the future of MCP servers, the security challenges of agent-to-agent communication, and why prompting and AI literacy are now must-have skills.
Vercel
Website - https://vercel.com
X/Twitter - https://x.com/vercel
Guillermo Rauch
LinkedIn - https://www.linkedin.com/in/rauchg
X/Twitter - https://x.com/rauchg
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Intro
(02:08) What Is V0 and Why Did It Take Off So Fast?
(04:10) How Did a Tiny Team Build V0 So Quickly?
(07:51) V0 vs Other AI Coding Tools
(10:35) What is Vibe Coding?
(17:05) Is V0 Just Frontend? Moving Toward Full Stack a…
-
Inside Canva’s $3B ARR AI Design Rocketship — CTO Brendan Humphreys on Magic Studio & Canva Code
20 juni 2025 · 57 min
Canva just announced $3 billion in ARR, 230 million monthly active users, and 24 million paying subscribers—including 95% of the Fortune 500. Even more impressive? They’ve been profitable for seven years while growing at 40–50% per year. Lees meer
In this episode, Canva’s Head of Engineering, Brendan Humphreys, reveals how he went from employee #12 to leading 2,300 engineers across continents, and why Canva’s “pragmatic excellence” lets them ship AI features at breakneck speed—like launching Canva Code to 100 million users in just three months.
Brendan shares the story of Canva’s AI journey: building an in-house ML team back in 2017, acquiring visual AI startups like Kaleido and Leonardo AI, and why they use a hybrid of OpenAI, Anthropic, Google, and their own foundation models. He explains how Canva’s App Store gives niche AI startups instant access to millions, and why their $200M Creator Fund is designed to reward contributors in the AI era. You’ll also hear how AI tools like Copilot are making Canva’s senior engineers 30% more productive, why “vibe coding” isn’t ready for prime time, and the unique challenges of onboarding junior engineers in an AI-driven world.
We also dig into Canva’s approach to technical debt, scaling from 12 to 5,000 employees, and why empathy is a core engineering skill at Canva.
Canva
Website - https://www.canva.com
X/Twitter - https://x.com/canva
Brendan Humphreys
LinkedIn - https://www.linkedin.com/in/brendanhumphreys
X/Twitter - https://x.com/brendanh
FIRSTMARK
Website - https://firstmark.com
X/Twitter - https://twitter.com/FirstMarkCap
Matt Turck (Managing Director)
LinkedIn - https://www.linkedin.com/in/turck/
X/Twitter - https://twitter.com/mattturck
(00:00) Intro
(01:14) Canva’s Mind-Blowing Growth and Profitable Journey
(03:41) Why Brendan Left Atlassian to Join a Tiny Startup
(06:17) What Being a Founder Taught Brendan About Leadership
(07:24) Growing with Canva: From 12 Employees to 2,300 Engineers
(10:02) How Canva …