Matt Turck
@mattturck • 146,059 subscribers
VC at @FirstMarkCap. Host: MAD Podcast; Organizer: Data Driven NYC, Author: MAD Landscape.
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Crazy launch! Copycats! Massive round! On the one-month anniversary of the launch of Jev, we unpack it all with Diogo Almeida, CEO of TypeSafe AI: what Jev is, how it fixes what's broken in AI, and why the fast-followers are missing the hard part. 00:00 “95% Einstein, 5% Mr. Bean” 1:09 Why intelligence per dollar matters 3:25 Why TypeSafe built in stealth for two years 5:50 “We could have had AGI last year” 6:48 Why the product carries the marketing 9:33 What is Jev? Machine-native AI, built for software 10:53 System One vs. System Two 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 trained models to please humans 31:28 Sycophancy, refusals, and the hidden costs of RLHF 34:22 RLVR: why benchmark gains don’t mean reliable automation 37:51 RLCD: optimizing for machine-native intelligence 39:54 Why TypeSafe builds its training data in-house 41:49 The Bitterest Lesson: define the task before scaling 44:39 Building Jev from open-weight models 45:56 Is Jev just a classifier? Why that’s a compliment 48:35 “Build prod, not God” 50:11 Embedding AI in software, beyond coding assistants 53:05 Jev’s copycats: matching the interface vs. the intelligence 55:33 Why benchmarks mislead the users they should help 58:37 Open weights vs. APIs: how TypeSafe weighs the tradeoff 1:00:04 Jevons paradox: a new market for machine intelligence 1:02:50 Why Diogo is skeptical of recursive self-improvement 1:04:47 Can practical automation avert an AI winter?
Matt Turck39,137 views • 3 days ago
Me after unstacking the dishwasher when my wife has been running the household 24/7 for the last 15+ years
Matt Turck1,290,856 views • 9 months ago
Special treat for the Andy Pavlo (@andypavlo.bsky.social) fans out there: an in-depth discussion about what happens to databases when billions of AI agents hit them, his move to build a research lab at ClickHouse, and plently of hot takes on the database market (Postgres, vector, graph, GPU etc) 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? 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% of the tuning in 15 minutes 35:42 Should anyone still study databases? 38:27 Free CMU courses, the DJ, and the Wu-Tang final exam 42:09 Why start a research lab inside ClickHouse? 45:53 Why ClickHouse looked like vaporware in 2016 47:46 What makes ClickHouse fast: columns, vectors, Snowflake's lineage 52:22 Why Databricks, Snowflake and ClickHouse all added Postgres 58:15 Vector, graph and GPU databases: thumbs up or down? 1:08:34 Is the database market stagnant? "A cheetah on cocaine in a Ferrari" 1:10:14 The relational model is arithmetic; SQL as the new assembly 1:11:59 Larry Ellison, Linux, and why databases still matter
Matt Turck19,652 views • 10 days ago
How GPT-5 thinks, with OpenAI VP of Research Jerry Tworek 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 How Long to Think 07:24 - Evolution from o1 to o3 to GPT-5 11:00 - The Road to 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 - OpenAI's Culture of Transparency 29:32 - Balancing Research with Shipping Fast 31:52 - Using OpenAI's Own Tools Daily 32:43 - Pre-Training Plus RL: The Modern AI Stack 35:10 - Reinforcement Learning 101: Training Dogs 40:17 - The Evolution of Deep Reinforcement Learning 42:09 - When GPT-4 Seemed Underwhelming at First 45:39 - How RLHF Made GPT-4 Actually Useful 48:02 - Unsupervised vs Supervised Learning 49:59 - GRPO and How DeepSeek Accelerated US Research 53:05 - What It Takes to Scale Reinforcement Learning 55:36 - Agentic AI and Long-Horizon Thinking 59:19 - Alignment as an RL Problem 1:01:11 - Winning ICPC World Finals Without Specific Training 1:05:53 - Applying RL Beyond Math and Coding 1:09:15 - The Path from Here to AGI 1:12:23 - Pure RL vs Language Models
Matt Turck451,229 views • 1 year ago
🚨 Special Friday episode - this one couldn't wait. OpenAI's model hacked Hugging Face. As a side quest. Co-founder and CSO Thomas Wolf takes us inside the first autonomous AI attack, why GLM 5.2, rather than Claude, had to stop it, and what it all means for the future of open source. 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?
Matt Turck95,724 views • 2 months ago
Failing to Understand the Exponential, Again? My conversation with Julian Schrittwieser - Julian Schrittwieser (Anthropic, AlphaGo Zero, MuZero) - on Move 37, Scaling RL, Nobel Prize for AI, and the AI frontier: 00:00 - Cold open: “We’re not seeing any slowdown.” 00:32 - Intro — Meet Julian 01:09 - The “exponential” from inside frontier labs 04:46 - 2026–2027: agents that work a full day; expert-level breadth 08:58 - Benchmarks vs reality: long-horizon work, GDP-Val, user value 10:26 - Move 37 — what actually happened and why it mattered 13:55 - Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel? 16:25 - Discontinuity vs smooth progress (and warning signs) 19:08 - Does pre-training + RL get us there? (AGI debates aside) 20:55 - Sutton’s “RL from scratch”? Julian’s take 23:03 - Julian’s path: Google → DeepMind → Anthropic 26:45 - AlphaGo (learn + search) in plain English 30:16 - AlphaGo Zero (no human data) 31:00 - AlphaZero (one algorithm: Go, chess, shogi) 31:46 - MuZero (planning with a learned world model) 33:23 -Lessons for today’s agents: search + learning at scale 34:57 - Do LLMs already have implicit world models? 39:02 - Why RL on LLMs took time (stability, feedback loops) 41:43 - Compute & scaling for RL — what we see so far 42:35 - Rewards frontier: human prefs, rubrics, RLVR, process rewards 44:36 - RL training data & the “flywheel” (and why quality matters) 48:02 - RL & Agents 101 — why RL unlocks robustness 50:51 - Should builders use RL-as-a-service? Or just tools + prompts? 52:18 - What’s missing for dependable agents (capability vs engineering) 53:51 - Evals & Goodhart — internal vs external benchmarks 57:35 - Mechanistic interpretability & “Golden Gate Claude” 1:00:03 - Safety & alignment at Anthropic — how it shows up in practice 1:03:48 - Jobs: human–AI complementarity (comparative advantage) 1:06:33 - Inequality, policy, and the case for 10× productivity → abundance 1:09:24 - Closing thoughts
Matt Turck235,526 views • 1 year ago
Literally the #1 reason why America has produced so many incredible startups
Matt Turck502,104 views • 2 years ago
Why AI Progress Suddenly Feels Real - my conversation with Yann Dubois, who co-leads the Post-Training Frontiers team at OpenAI 00:00 - Intro 01:30 - Why recent AI progress feels like a step function 04:13 - Model reliability & the emotional rollercoaster of shipping GPT-5.5 07:33 - How OpenAI structures vertical and horizontal teams 09:49 - Improving model efficiency and test-time compute 12:32 - Yann's journey from Switzerland to OpenAI 15:37 - Reasoning in 2026: Real-world utility vs verifiable rewards 18:34 - GPT-5.5 Thinking vs Pro: Scaling test-time compute 20:09 - How reasoning models become more efficient 23:23 - Pre-training scaling and overcoming the data wall 27:03 - Multimodal data, synthetic data, and embodied AI 31:05 - Demystifying mid-training and post-training 37:21 - Does RL create new capabilities in AI? 38:53 - The challenges and frontier of scaling RL 43:09 - Is building AI models a craft or a strict science 48:21 - How AI models generalize across different domains 54:18 - How reinforcement learning cures AI hallucinations 56:04 - Negative generalization and conflicting instructions 58:05 - Can RL scale to law, medicine, and the broader economy? 1:00:19 - The evaluation bottleneck and Model as a Judge 1:04:21 - Continuous AI progress & continual learning 1:08:49 - Will foundation models eat the agent harness 1:11:23 - Why startups should focus on the last mile of AI
Matt Turck101,314 views • 5 months ago
AI Could Take Over in 2029. Is It Already Too Late? My conversation with Ryan Greenblatt of Redwood Research about AI 2040, RSI, AI alignment, and how he believes the transition to superintelligence actually unfolds. 01:24 The AI CEOs are aware of the risks, but "proceeding anyway" 03:27 Astra paused, and the "Pacing the Frontier" letter 05:45 "Not bad. Dangerous." What superintelligence actually threatens 09:55 Recursive self-improvement, and the intuition objection 14:16 SSI rumors: does continual learning change the picture? 17:27 His timeline: "plan as though it happens in 2029" 19:11 Is it already too late? 21:23 Ryan's path: COVID, podcasts, Redwood 26:30 The alignment faking story, told by the person who ran it 31:30 What AI 2040: Plan A actually is 33:35 Plans D, C, and B: the doors nobody should pick 36:51 The deal with China: "mutually assured compute destruction" 39:55 What if compute stops mattering? 43:00 What happens to OpenAI and Anthropic under Plan A 45:31 How the pause ends, and who decides 48:54 "Plan A isn't likely to happen": then why write it 50:40 200x GDP growth in the 2030s, explained 53:45 Grading the summer: the letter, Astra, the secret review 59:01 The internal deployment gap 1:01:38 Zuckerberg's manifesto 1:04:56 The Hugging Face investigation 1:05:44 What AI control looks like in practice today 1:12:23 Ryan's sobering timeline: from today to takeover, year by year
Matt Turck37,111 views • 1 month ago
Thanksgiving-week treat: an epic conversation on Frontier AI with Lukasz Kaiser -co-author of “Attention Is All You Need” (Transformers) and leading research scientist at OpenAI working on GPT-5.1-era reasoning models. 00:00 – Cold open and intro 01:29 – “AI slowdown” vs a wild week of new frontier models 08:03 – Low-hanging fruit, infra, RL training and better data 11:39 – What is a reasoning model, in plain language 17:02 – Chain-of-thought and training the thinking process with RL 21:39 – Łukasz’s path: from logic and France to Google and Kurzweil 24:20 – Inside the Transformer story and what “attention” really means 28:42 – From Google Brain to OpenAI: culture, scale and GPUs 32:49 – What’s next for pre-training, GPUs and distillation 37:29 – Can we still understand these models? Circuits, sparsity and black boxes 39:42 – GPT-4 → GPT-5 → GPT-5.1: what actually changed 42:40 – Post-training, safety and teaching GPT-5.1 different tones 46:16 – How long should GPT-5.1 think? Reasoning tokens and jagged abilities 47:43 – The five-year-old’s dot puzzle that still breaks frontier models 52:22 – Generalization, child-like learning and whether reasoning is enough 53:48 – Beyond Transformers: ARC, LeCun’s ideas and multimodal bottlenecks 56:10 – GPT-5.1 Codex Max, long-running agents and compaction 1:00:06 – Will foundation models eat most apps? The translation analogy and trust 1:02:34 – What still needs to be solved, and where AI might go next
Matt Turck168,186 views • 10 months ago
State of AI compute 2026: my conversation with stephen balaban of Lambda on the neocloud boom, data centers, GPUs and what's ahead 00:00 — Cold open 01:21 — Why GPU compute was never a commodity 02:45 — The H100 price index and what it gets wrong 04:02 — The real moat: technology or financing? 05:57 — Winner-take-all, or room for many neoclouds 06:48 — Are we overbuilding or underbuilding AI compute? 09:26 — What if AI gets 10x more compute-efficient? 10:44 — The real bottleneck: land, power, and shell 11:38 — The backlash against data centers — and the misinformation 15:00 — Opening the hood: from photons to tokens 17:11 — Extracting more value from the same chip 19:26 — Frontier inference and distributed training, explained 23:26 — What actually drives compute cost 25:21 — Lambda's chip stack and the NVIDIA relationship 26:17 — A multi-silicon world? CUDA, CUDNN, and NVIDIA's real moat 28:59 — Networking, storage, and the one-click cluster 34:46 — Renting vs. owning, and full vertical integration 36:24 — How global is Lambda? Does location still matter? 38:44 — The financing stack: off-take agreements, SPVs, and credit 41:16 — Why a 2023 GPU leases for more today 42:36 — A futures market for compute? 43:54 — Origin story: facial recognition, Perceptio, and Apple 47:03 — The Lambda hat and Dream Scope 48:59 — The $60K bet that became a cloud business 52:00 — Holding the team together through the hard times 54:30 — Bringing on a new CEO; Stephen as CTO 57:33 — Matching xAI on high-velocity deployment 59:29 — "AI won't write software — it will become the software" 01:01:30 — Neural software vs. vibe coding 01:04:25 — Do agents change the compute layer 01:06:14 — Self-assembling software inside Lambda 01:08:18 — Gigawatt-scale AI factories 01:08:57 — One person, one GPU 01:12:04 — Hot takes: overrated and underrated in AI
Matt Turck72,511 views • 4 months ago
Why AI Can Now Make Discoveries - my conversation with Dan Roberts, Lead of the Foundations of Reinforcement Learning team at OpenAI 00:00 Intro: AI's wild week in mathematics 01:21 What OpenAI's Foundations of RL team does 03:08 Dan's journey: from black holes and quantum gravity to frontier AI 07:04 Are AI systems becoming useful for real science 08:21 The AI math moment: Erdős, OpenAI, DeepMind, and Anthropic 08:52 Why the OpenAI result was an act of exploration 10:25 OpenAI vs. DeepMind: informal reasoning vs. formal proof 12:13 RL 101: learning by doing, not just watching 15:10 Why reinforcement learning works 15:58 How RL breaks: sparse feedback and long-horizon tasks 17:03 RLHF: how human feedback shaped early language models 18:48 Move 37, self-play, and the search for novel strategies 22:16 Explore vs. exploit in scientific discovery 24:49 Why RL may now be "the cake," not the cherry on top 25:46 Why RL started working with large language models 27:29 Is RL "sucking supervision through a straw"? 28:47 Why language may be the grounding layer for intelligence 31:46 A contrarian take on the Bitter Lesson 32:41 What test-time compute actually is 34:50 How RL gives models the ability to think 35:40 Verifiable rewards, math, coding, and the messy real world 38:00 What physics can teach us about AI 42:08 Is there a thermodynamics of AI? 43:08 From Erdős problems to Einstein-level AI 45:16 Is AI already doing original science? 45:51 How far are we from AI automating AI research 47:41 Why Dan is excited about the future of science
Matt Turck69,801 views • 4 months ago
Inside Nemotron and NVIDIA's AI lab: my conversation with Bryan Catanzaro (Bryan Catanzaro). NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models - and then give them away for free? We go deep into the Nemotron models, what it takes to build a top AI lab, and the future of frontier AI. 01:33 - Is open source AI catching the frontier? 05:29 - Do closed labs blocking distillation slow open source down? 07:42 - Is the US falling behind China? 10:30 - Why companies actually choose open models 12:39 - A "crazy" 2008 bet: machine learning on GPUs 15:33 - Working with Andrew Ng and Dario Amodei at Baidu 17:41 - Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 - The real reason NVIDIA builds its own models 24:28 - Is Moore's Law really dead? 33:37 - The Nemotron family: Nano, Super, Ultra 35:09 - Built for agents: why NVIDIA bets on speed 36:02 - How you train a 550B model in 4 bits 39:25 - Hybrid Mamba-Transformer, explained simply 42:31 - Mixture of experts, and why NVIDIA built NVL72 around it 47:26 - Why a 1-million-token context window matters 49:26 - Multi-token prediction: how the model predicts 5 tokens at once 52:47 - Multi-teacher distillation: teaching one model from many 58:01 - Where reinforcement learning goes next 01:00:16 - Inside NVIDIA's research org: "the mission is the boss" 01:04:03 - How NVIDIA decides who gets the GPUs 01:10:53 - Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 - Why Bryan doesn't believe in the singularity 01:17:50 - The AI backlash 01:19:18 - The controversial case: open AI is safer than closed
Matt Turck56,954 views • 3 months ago
Claude Cowork, Mythos, and the Future of Software: my conversation with Felix Rieseberg, who leads Cowork at Anthropic 00:00 Intro 01:53 Claude Mythos Preview and the “step-function change” 06:16 Why Anthropic is treating Mythos differently 11:19 The real story behind Claude Cowork’s “10-day” build 12:42 Why Anthropic realized Claude Code needed a non-technical version 15:44 What Claude Cowork actually is 17:03 Under the hood: virtual machines, tools, skills 18:36 Where Cowork’s memory actually lives 19:26 How Cowork connects to files, apps, and the internet 20:45 Why Felix thinks the local computer is under-appreciated 24:49 Trust: how do you get users comfortable with AI agents? 28:45 What UX actually means for AI agents 31:27 Anthropic Cowork's roadmap is only one month long 34:12 Building 100 prototypes 35:10 If execution is free, what becomes the bottleneck? 37:25 Does it come down to taste? 40:12 The hardest part of building Claude Cowork 41:43 Advice for founders building AI agents 44:21 SaaSpocalypse: what’s left for software startups 49:30 Where AI agents are going next 51:20 Regulated industries and enterprise adoption 54:15 Hot takes: what's underrated, overrated, and what Felix would build today
Matt Turck80,854 views • 6 months ago