The MAD Podcast with Matt Turck goes deep with the people building AI. Each week, Matt talks with top founders, researchers, and leaders about frontier models, agents, infrastructure, and the companies defining the AI economy. Guests include leaders from OpenAI, Anthropic, Google DeepMind, Hugging Face, Vercel, LangChain, Cloudflare, Snowflake, Datadog, Cerebras, and more.
Matt is a General Partner at FirstMark, a prolific investor in AI and the creator of the MAD Landscape, the industry’s reference map of the AI ecosystem.
New episodes weekly. Transcripts and more at mattturck.com/podcast.
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Mistral Is SO Back: Inside Le Chonk, the West’s Biggest Open Model (Pierre Stock, VP Science, Mistral)
Thursday, October 22, 2026 • Duration 01:19:03
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.
(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, finance, chips
(58:13) Partial rewards and looping penalties
(59:58) Did RL teach anything new? pass@k
(1:03:16) The sandbox story: the model that pinged its trainer
(1:07:44) Open weights vs cyber refusals
(1:12:05) Timestral: time series on a CPU
(1:14:18) AI doing AI research
(1:16:22) Coming up: Mistral Large 5
Jev: The AI That Makes Decisions, Not Text - and Why It Changes Everything (Diogo Almeida)
Thursday, October 15, 2026 • Duration 01:07:56
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.”
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
What Happens When Billions of AI Agents Hit Your Database? (Andy Pavlo)
Thursday, October 8, 2026 • Duration 01:16:46
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.
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%
AI Models Are Now Hiding Their Cheating | Eric Ho (Goodfire)
Thursday, October 1, 2026 • Duration 01:10:27
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.
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
Who Feeds the GPUs? Inside AI's Hidden $30B Layer | Renen Hallak, VAST Data
Thursday, September 24, 2026 • Duration 01:10:14
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.
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
(27:39) Trillions of vectors: why old databases break
(29:01) Are S3, Snowflake and Databricks ready for AI?
When AI Improves Itself | Richard Socher (Recursive)
Thursday, September 10, 2026 • Duration 01:14:15
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.
(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
AI Could Take Over in 2029. Is It Already Too Late? | Ryan Greenblatt
Thursday, August 27, 2026 • Duration 01:18:59
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.
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.
“OpenAI’s Model Hacked Us” - Hugging Face’s Thomas Wolf
Friday, August 7, 2026 • Duration 57:41
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.
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
How to Build Long-Horizon AI Agents — Mitch Troyanovsky, Basis
Thursday, August 6, 2026 • Duration 01:22:51
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.
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) Verifiable at Runtime: the Real Reason Coding Won(25:14) No Ground Truth, No Cheap Verification, No Data(26:55) Encoding Deterministic Checks From Human Review Process(29:18) Synthetic Data Limits: Generating Artifacts, Not Text(33:16) 100 Evals Pass — Does It Generalize to Production?(35:53) Primary Sources vs. Pre-Training Knowledge(36:37) Behavior Specs: Markdown, Judges, and True/False/N.A.(39:58) Specificity vs. Brittleness in Spec Authoring(42:18) Context as Runtime Training Data(44:21) Judge-as-Agent: Trajectory Maps and Sub-Agent Attribution(46:45) The Move 37 Objection: Reliability Over Optimality(50:02) The Magic Box Model: Building Without Weights Access(52:41) "Nothing Paradigm-Shifting Has Changed Since o3"(54:56) Open-Sourcing the Behavior Spec Standard With Braintrust(01:02:54) Ontology Design: Virtual Filesystems, Graphs, Embeddings(01:04:20) Canonical vs. Non-Canonical: Docs as Codebase(01:06:33) Language Architects and Writing for Runtime Interpretation(01:09:05) Deployed Intelligence: 300 Alien Employees With No Context(01:11:10) Closing the Loop: Signal → Context, Tools, Harness(01:12:50) Context Slop: the Mistake Most Agent Builders Make(01:14:29) Reward Function Design and Credit Assignment Over Trajectories(01:17:01) Will the Bitter Lesson Swallow the Harness?(01:18:46) Business Moats vs. Technical Moats(01:21:03) Paradigm Thinking Over Timeline ADHD
The Biggest AI Deployment Nobody Talks About | Samsara CEO Sanjit Biswas
Thursday, July 30, 2026 • Duration 01:00:30
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.
(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
(18:36) Bluetooth, battery life, and connected infrastructure
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