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While Silicon Valley obsesses over AGI, Mistral AI is betting that big enterprises and sovereign nations will want to own, not rent, their intelligence My conversation with co-founder & CTO Timothee Lacroix (Timothee Lacroix), for his first US podcast ever Was reminded during this conversation that Mistral is barely...

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Matt Turck

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64,519 görüntüleme • 6 ay önce •via X (Twitter)

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State of Enterprise AI 2026: Aaron Levie on Tokenmaxxing, The Rise of Headless, and AI-Proofing Your Job 00:00 Intro 01:18 Silicon Valley engineering vs. everyone else 05:35 Are enterprise CIOs actually bullish on AI? 08:51 Tokenmaxxing & why your AI bill is about to explode 11:34 The myth of falling token costs and AI spend escaping IT budgets 17:37 The $5B startup hiding in AI compute 18:14 The mosaic of models inside every enterprise 21:28 Why coding works and the rest of knowledge work doesn't 25:53 The Bob and Sally problem: access control breaks agents 30:31 Will enterprise AI really take 10 years to roll out 32:24 The capability overhang: why faster models slow diffusion 34:23 Data is the bottleneck (it always was) 39:02 The rise of internal forward-deployed engineers 41:23 Why the AI doomers are wrong about jobs 43:43 Headless software is inevitable 46:14 What replaces per-seat pricing 47:37 How Box itself is going headless 49:42 How the org chart actually evolves 1:00:33 Future-proofing yourself as an enterprise employee 1:06:40 Are we all just going to work for OpenAI and Anthropic? 1:07:11 Where startups can still win as the labs move up

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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

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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 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
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Every agent needs its own computer: my conversation with Ivan Burazin, CEO of Daytona, about sandboxes and the emerging agent stack. 00:00 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

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François Chollet (François Chollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the Lightcone Podcast, he traces that path from his early work on deep learning to the creation of the ARC Prize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

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BREAKING: AI has eaten the Internet. Data labeling is so over. & $30 trillion of human work is on the verge of automation. Inside The $2.2B AI Research Accelerator, Turing Founder & CEO, Jonathan Siddharth (), joins Sourcery to break down the severe power shift in AI training: from commodity data labeling → expert research Positioning Turing apart from AI data providers like Scale AI, Mercor, & Surge. (00:00) AI Ate The Internet (00:49) Training Superintelligence: the race to AGI (02:31) Viral tweet (03:24) What Turing actually does (04:43) The internet data is “used up” — where will new data come from? (05:34) Four pillars of superintelligence: multimodality, reasoning, tool use, coding (06:07) Automating $30T of global knowledge work (09:18) The $1B revenue opportunity (10:59) Why Turing is a research-first accelerator, not a data labeler (13:45) Jonathan’s Stanford AI Lab roots & founding DNA (17:57) How models are built: pre-training vs. post-training (20:14) RLHF, reinforcement learning, & “breaking the models” (25:19) GPT-5 and the myth of rapid takeoff (30:46) Safety debates and human-in-the-loop systems (34:53) Closing Enterprise Gap: finance, insurance, & pharma (39:23) Why proprietary enterprise data is the next moat in AI

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DROPS E27: Ben from Talus Labs: Building the Future of Decentralized AI Agents In this episode Ben breaks down why AI isn’t a bubble, how Talus is redefining on-chain agents, and why optimism is something you earn and not something you just feel. We talk about: - Why AI is not a bubble and how mega-cap tech is funding the next AI wave through massive CapEx - How Talus enables decentralized AI agents that can act and transact trustlessly - The three big crypto × AI intersections: compute, data ownership, and intelligent on-chain workflows - Why AI needs verifiable, censorship-resistant execution layers and why blockchains provide that - Nuclear energy as a prerequisite for AGI-scale compute demand - Why SWE’s object model + parallelization is perfect for autonomous agent payments and priority fees And much more! Timestamps: 0:00 Introduction 1:31 Welcome To Drops 2:10 Getting Backers Like Polychain 3:16 Building While In College 7:51 VC’s vs Founders Differences Explained 10:01 Is There An AI Bubble 11:53 How Do We Quantify The AI Opportunity In Crypto 12:33 AI Is Difficult To Invest In, Why? 13:35 Risks To Look Out For To Ensure AI Investments Deliver 16:47 Energy Being A Big Risk For AI 20:50 How You Became Fascinated By Crypto & Blockchain 22:00 Why Does AI & Blockchain Intersection Make Sense Compute Side 24:39 Why Does AI & Blockchain Intersection Make Sense for Data Ownership 26:30 Complex Workflows With Intelligent Execution Meaning 28:19 Why Do We Need This On Blockchain 30:26 How Adding This Decentralized Layer Helps With The Black Box 32:01 Explain Talus To Your Mom 33:39 Talus Solved Blockchain Determinism Limitation Explained 35:43 Two Examples That Can Be Built On The Zapier Of Web 3 39:32 Why Get Investment From SUI 42:56 What’s Needed To Get To The Next Level 43:35 One Takeaway From Conversation 43:51 One Thing Pessimistic People Are Missing

MR SHIFT 🦁

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INTERVIEW: AI, Surveillance, & the Fight for Digital Sovereignty | Near's Illia Polosukhin What happens when powerful AI requires an ID, governments can restrict access by country, and your most private data flows through centralized systems? NEAR Protocol co-founder Illia (root.near) (🇺🇦, ⋈) joins David Hoffman to explain why AI export controls could fracture the internet, how nationalized AI could become a surveillance layer, and why decentralized, user-owned AI may be the only real alternative. They also unpack Ironclaw, confidential inference, private agents, and NEAR’s push to make AI and assets fully sovereign. [TIMESTAMPS] 0:00 Intro 3:47 Pandora’s Box Opens 6:03 AI Meets Government 7:16 Decentralizing the Stack 8:51 Why Ban Fable? 11:18 Internet Balkanization 13:57 KYC for AI? 18:43 Nationalized Models 21:07 Building Private AI 26:30 Agent Marketplace 30:26 User-Owned Alignment 35:28 Winning Against Giants 40:19 Regulation Fuels Crypto 42:24 Global AI Adoption 45:05 Compute Is the Bottleneck 49:00 Enterprise Use Cases 51:39 Confidential TVL Explained 55:25 Full Sovereign Mode

Bankless

59,588 görüntüleme • 2 ay önce

The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self
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The end of coding? *Epic* conversation with Guillermo Rauch of Vercel on vibe coding, MCP, agents and building products in the AI era. (00:00) - Intro: Guillermo's Story (02:08) - What is v0 and Why Did It Take Off So Fast? (04:10) - Building a hit product with < people (07:51) - Cursor, Replit, etc: v0 vs Other AI Coding Tools (10:35) - Everyone can cook - What is Vibe Coding? (17:05) - Is v0 Just Frontend? Moving Toward Full Stack 19:40 - How to Become a Great Vibe Coder (23:35) - A GUI for AI: The Future of Interfaces (29:46) - Developer Love vs Agent Love (33:41) - The Importance of Taste in the AI Era (39:10) - MCP: The Protocol for AI-to-AI Communication (43:11) - Security and the Risks of Agentic Web (45:25) - Are Enterprises Ready for the Agentic Future? (49:42) - Building a Data Flywheel (56:06) - The Vercel AI Cloud: From Pixels to Tokens (1:10:14) - Adapting to a Non-Developer Persona (1:13:47) - Retention, Margins, and the Business of AI (1:16:51) - The Secret Behind Vercel Last Year Growth (1:24:15) - The importance of Online Presence (1:30:49) - Everything, Everywhere, All at Once: Keeping your Sanity as a CEO (1:34:59) - Guillermo's Advice to Younger Self

Matt Turck

86,153 görüntüleme • 1 yıl önce

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
1:14:27

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 Turck

70,916 görüntüleme • 2 ay önce

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
1:13:56

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 Turck

101,212 görüntüleme • 3 ay önce

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion
41:50

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

91,088 görüntüleme • 1 ay önce

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:
1:11:41

My conversation with OpenAI co-founder Greg Brockman This is the most detailed first-person account of the 72 hours after Sam Altman was fired. We also go deep on what comes next: the global race to AGI, why ChatGPT stopped showing reasoning, how much of OpenAI's own code is now written by AI ("it's hard to know what percent is not"), and the untold story of how OpenAI actually started in 2015. 00:00:00 Introduction 00:00:49 Meeting Sam Altman and Starting OpenAI 00:02:40 Building the Founding Team 00:04:25 DeepMind's Lead Over OpenAI 00:04:54 Changing OpenAI to a For-Profit Model 00:06:05 Breakthrough Moments at OpenAI 00:08:22 What Dota 2 Meant for OpenAI 00:10:04 Reasoning Versus Prediction 00:11:59 Tensions Grow at OpenAI 00:15:44 Sam Altman's Firing 00:17:49 Greg Quits OpenAI 00:19:56 Sam Explores Deal with Microsoft's Satya 00:20:28 Petition for Altman's Return 00:23:43 Ilya Sutskever Leaves OpenAI 00:24:59 Lessons Learned after Sam Ousting 00:28:22 The Thing Ilya Said that Greg Can't Forget 00:32:22 Is AI Going Parabolic? 00:33:24 How Much of OpenAI's Code is Written by AI? 00:36:21 Do AI Chatbots Tell Us What We Want to Hear? 00:38:06 The Global AI Race to Reach AGI 00:38:40 What Happens if US Doesn't Reach AGI First? 00:39:49 Are Countries Stealing AI Advancements? 00:40:38 Why ChatGPT No Longer Shows Reasoning 00:41:47 The Finite Constraints of Compute 00:43:38 On Investing Early in Data Centers 00:46:31 The Future of Data Center Specialization 00:47:52 How to Decide Whose Queries to Serve 00:49:08 OpenAI on Consumer vs Enterprise Models 00:53:05 Data Centers in Space? 01:00:56 What Should AI Regulation Look Like? 01:04:33 The Future of AI-Powered Entrepreneurship 01:04:44 AI and Job Loss 01:07:15 The Skills Young People Should Invest In 01:11:30 What Does Success Look Like For You? Full episode on X below. Also find it on: • YouTube: • Spotify: • Apple:

Shane Parrish

450,952 görüntüleme • 4 ay önce

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,265 görüntüleme • 1 yıl önce

Bitcoin BREAKOUT Could Be Just the Start - AI May Reallocate $50 TRILLION | Bill Barhydt 00:00 Bitcoin Finally Breaks Out 01:12 Why Trump’s Policies Are Bullish for Bitcoin 05:22 The U.S. Debt Problem and the Battle With China 08:12 Why Trump Wants Lower Rates 11:59 Is Bitcoin’s Four-Year Cycle Still Alive? 15:55 Trump, Hyperliquid and the Future of Perps 17:38 Why Everything Is Becoming Tradable on Crypto 19:30 Bill Reveals What Abra Is Building Next 22:34 Where the Yield on Bitcoin Actually Comes From 26:44 The Financialization of Crypto Is Here 28:02 What Comes After Tokenization 32:57 The $50 Trillion Wealth Reallocation 33:26 Will AI Replace Financial Advisors? 35:30 Why 24/7 Markets Need AI 36:40 Bitcoin’s Role in an AI-Driven Financial System 39:09 Ethereum vs. Solana and the Future of Blockchains 42:00 What Happens When AI and Crypto Truly Merge? 46:06 Will Humans Eventually Merge With AI? 47:54 “AI Is Never Going to Be Dumber Than It Is Now” 49:02 Are Million-Dollar Bitcoin Targets Still Realistic? 49:38 Why Bitcoin Could Become the World’s Pristine Collateral
51:26

Bitcoin BREAKOUT Could Be Just the Start - AI May Reallocate $50 TRILLION | Bill Barhydt 00:00 Bitcoin Finally Breaks Out 01:12 Why Trump’s Policies Are Bullish for Bitcoin 05:22 The U.S. Debt Problem and the Battle With China 08:12 Why Trump Wants Lower Rates 11:59 Is Bitcoin’s Four-Year Cycle Still Alive? 15:55 Trump, Hyperliquid and the Future of Perps 17:38 Why Everything Is Becoming Tradable on Crypto 19:30 Bill Reveals What Abra Is Building Next 22:34 Where the Yield on Bitcoin Actually Comes From 26:44 The Financialization of Crypto Is Here 28:02 What Comes After Tokenization 32:57 The $50 Trillion Wealth Reallocation 33:26 Will AI Replace Financial Advisors? 35:30 Why 24/7 Markets Need AI 36:40 Bitcoin’s Role in an AI-Driven Financial System 39:09 Ethereum vs. Solana and the Future of Blockchains 42:00 What Happens When AI and Crypto Truly Merge? 46:06 Will Humans Eventually Merge With AI? 47:54 “AI Is Never Going to Be Dumber Than It Is Now” 49:02 Are Million-Dollar Bitcoin Targets Still Realistic? 49:38 Why Bitcoin Could Become the World’s Pristine Collateral

The Wolf Of All Streets

39,026 görüntüleme • 6 gün önce

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI
57:20

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI

Matthew Berman

69,339 görüntüleme • 1 ay önce

I spoke with Dr. Roman Yampolskiy, the top AI safety expert in the world. He literally coined the term “AI safety” about 15 years ago. Roman explained if AGI is here, what the real threat of AI is to humanity, how many jobs could be lost, why he is bullish on bitcoin, how physical AI & robotics will likely play out, and whether the frontier model labs actually care about AI safety. This was a profound conversation with someone everyone should get to know. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:50 - Have we already hit AGI? 2:51 - Why we can't control superintelligence 5:14 - Could a global ban on superintelligence actually work? 8:45 - Are AI labs really taking safety seriously? 15:52 - Mass job loss & the economics of AGI 22:03 - The surprising link between AI & bitcoin 29:07 - A world of AI abundance, & why scarcity wins 31:45 - AI watermarks, & the death of trust 35:33 - Pros & Cons of physical AI world 38:40 - What would it take to trust humanoid robot? 39:50 - Brain computer interfaces: a good idea? 42:25 - Rise & risks of deapfakes 49:40 - Controlling AI: Governments vs private companies 54:07 - Open source vs closed, & the US vs China AI race 57:34- Is bitcoin the one crypto project built to last? 1:01:40 - Limitations of physical AI: bottlenecks & shortages 1:03:50 - Impact of healthcare with AI 1:06:09 - What would it take to actually declare AGI? 1:09:35 - How the public can help with AI safety
1:10:37

I spoke with Dr. Roman Yampolskiy, the top AI safety expert in the world. He literally coined the term “AI safety” about 15 years ago. Roman explained if AGI is here, what the real threat of AI is to humanity, how many jobs could be lost, why he is bullish on bitcoin, how physical AI & robotics will likely play out, and whether the frontier model labs actually care about AI safety. This was a profound conversation with someone everyone should get to know. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:50 - Have we already hit AGI? 2:51 - Why we can't control superintelligence 5:14 - Could a global ban on superintelligence actually work? 8:45 - Are AI labs really taking safety seriously? 15:52 - Mass job loss & the economics of AGI 22:03 - The surprising link between AI & bitcoin 29:07 - A world of AI abundance, & why scarcity wins 31:45 - AI watermarks, & the death of trust 35:33 - Pros & Cons of physical AI world 38:40 - What would it take to trust humanoid robot? 39:50 - Brain computer interfaces: a good idea? 42:25 - Rise & risks of deapfakes 49:40 - Controlling AI: Governments vs private companies 54:07 - Open source vs closed, & the US vs China AI race 57:34- Is bitcoin the one crypto project built to last? 1:01:40 - Limitations of physical AI: bottlenecks & shortages 1:03:50 - Impact of healthcare with AI 1:06:09 - What would it take to actually declare AGI? 1:09:35 - How the public can help with AI safety

Anthony Pompliano 🌪

110,601 görüntüleme • 5 gün önce

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem
22:52

Dust ( helps companies build AI teammates that work across their organization. Founded by former OpenAI researcher Stanislas Polu (Stanislas Polu) and his team, the company has spent the last three years building AI for work while the capabilities of frontier models have advanced at breakneck speed. At Startup School Paris, YC's Mathilde Collin sat down with Stanislas to talk about what it takes to build alongside frontier AI labs, why staying model agnostic is a competitive advantage, how AI is changing the way we work, and why the best startups are built around a problem founders can't stop thinking about. 01:09 — From Stripe to OpenAI to Founder 01:40 — Why Stan Left OpenAI 02:49 — What Dust Actually Does 03:46 — What Work Looks Like in 3 Years 04:53 — Where the Thesis Was Right (and Wrong) 07:05 — Building Alongside Frontier Labs 09:15 — Model Agnostic as a Moat 10:49 — How Frontier Labs Changed Fundraising 12:19 — Raising Small on Purpose 14:39 — What Mistral Got Right 15:11 — Building in France 16:37 — What Happens When Frontier Labs Enter Your Market? 18:27 — The Margin Problem

Y Combinator

54,342 görüntüleme • 1 ay önce

Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone Podcast, Pedro Franceschi explains why we're only months into a platform shift as significant as the invention of electricity, why the CEO needs to be the chief AI officer, and why founders should rethink what's possible when intelligence is available on demand. 01:13 – How Pedro Became AI-Pilled 04:08 – The Electricity Analogy 05:21 – Free the Claw 06:56 – Making AI Safe for Enterprise 10:57 – Why Most Companies Are Behind 13:09 – AI Teammates, Not Chatbots 14:22 – The Case for Tokenmaxxing 18:24 – The Company of One 20:54 – The One Thing AI Can't Replace 28:06 – Building Customer World Models 32:58 – Rebuilding Brex Around AI 39:02 – The CEO Must Be the Chief AI Officer 43:50 – Building Company AGI 51:43 – Why We're Still So Early
54:06

Brex co-founder and CEO Pedro Franceschi believes most people still underestimate how much AI will change the way companies are built. AI isn't just another tool, it's a new foundation for building products, teams, and companies. In this episode of Lightcone Podcast, Pedro Franceschi explains why we're only months into a platform shift as significant as the invention of electricity, why the CEO needs to be the chief AI officer, and why founders should rethink what's possible when intelligence is available on demand. 01:13 – How Pedro Became AI-Pilled 04:08 – The Electricity Analogy 05:21 – Free the Claw 06:56 – Making AI Safe for Enterprise 10:57 – Why Most Companies Are Behind 13:09 – AI Teammates, Not Chatbots 14:22 – The Case for Tokenmaxxing 18:24 – The Company of One 20:54 – The One Thing AI Can't Replace 28:06 – Building Customer World Models 32:58 – Rebuilding Brex Around AI 39:02 – The CEO Must Be the Chief AI Officer 43:50 – Building Company AGI 51:43 – Why We're Still So Early

Y Combinator

198,105 görüntüleme • 2 ay önce

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
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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 Turck

80,718 görüntüleme • 4 ay önce