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My full interview with Tibo (Tibo) 0:45 Tibo's Lessons from Google DeepMind 4:22 Building OpenAI’s Relentless Culture 7:23 Astra & Next Gen Models 11:18 How Fast AI Changes Developer Workflows 14:27 ChatGPT & Codex Merging 20:25 OpenAI vs. Anthropic 23:37 Why OpenAI Keeps Resetting Limits 30:25 Recursive Self-Improvement 32:00... show more

Matthew Berman

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2,446,642 次观看 • 6 天前 •via X (Twitter)

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"Uncensored" Episode 4 🙌🏽 0:00 - 0:39 Intro 0:40 - 2:59 Introduction of Matt Steffanina 3:00 - 4:35 What made you post videos on social media? 5:25 - 7:55 Life of choreographers 10:25 - 11:45 Passion vs Business 11:46 - 13:20 "I'm Not happy making dance video anymore" 13:21 - 14:22 Identity crisis 14:36 - 19:43 Curiosity in Crypto 22:21 - 24:49 AI robot performance & getting into gaming + Avax Eco 25:22 - 27:18 Amazing projects in the space 27:19 - 32:18 About throwing events 32:20 - 35:39 Top memecoins and utility projects 35:40 - 37:34 Advice for upcoming creators

"Uncensored" Episode 4 🙌🏽 0:00 - 0:39 Intro 0:40 - 2:59 Introduction of Matt Steffanina 3:00 - 4:35 What made you post videos on social media? 5:25 - 7:55 Life of choreographers 10:25 - 11:45 Passion vs Business 11:46 - 13:20 "I'm Not happy making dance video anymore" 13:21 - 14:22 Identity crisis 14:36 - 19:43 Curiosity in Crypto 22:21 - 24:49 AI robot performance & getting into gaming + Avax Eco 25:22 - 27:18 Amazing projects in the space 27:19 - 32:18 About throwing events 32:20 - 35:39 Top memecoins and utility projects 35:40 - 37:34 Advice for upcoming creators

ProfitsOverWages 🧼🇩🇴

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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

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

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I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end

I spoke with Jordi Visser this week to break down: - AI mid-cycle slowdown - $90 trillion AI infrastructure buildout - memory and Micron's role in AI bottleneck - Fed under Warsh - what rate cuts could mean for markets - why bitcoin is positioned to be the best performing assets once the negatives of AI start to show up YouTube: Apple: Spotify: TIMESTAMPS: 0:00 - Intro 1:20 - Has bitcoin bottomed? 6:03 - Bitcoin vs the Mag 7: is the correlation breaking? 9:00 - Sam Altman, OpenAI & the government getting involved 11:11 - American open source vs Chinese AI models 14:28 - AI compute demand & the infrastructure buildout 20:37 - AI slowdown: how bad, how long? 22:44 - Where do markets go from here? 27:30 - AI agents in the workplace & security risk 34:50 - Memory, Micron & the AI bottleneck 39:53 - The next 10-20 years: robots, AI & society 44:18 - The Fed, rate cuts & inflation's impact on AI 49:55 - Why bitcoin wins in the end

Anthony Pompliano 🌪

64,370 次观看 • 1 个月前

My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI

My interview with Greg Brockman (Greg Brockman), Cofounder and President of OpenAI. 0:00 Intro 1:06 Scaling Sora 2:18 Are transformer models the future? 4:29 Building with AMD 5:50 Other kinds of compute 7:28 Bottlenecks 9:17 Deciding where to invest 11:38 Decoupling of the internet 16:29 Why ChatGPT is different 17:59 Proactive AI 19:39 Compressing Intelligence 20:48 ChatGPT’s Longest Thought Ever 21:13 Sora 2 29:47 Is my job in danger? 31:51 Building on top of OpenAI 34:11 Humans in the loop 35:36 Fully generated software 39:25 Agentic Commerce Protocol 40:41 What does 2026 look like? 42:43 AGI

Matthew Berman

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Deeply thoughtful conversation with Zico Kolter, board member at OpenAI and head of the machine learning department at Carnegie Mellon University, about AI safety, AI security, agents and frontier AI 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 Intro to Modern AI course 1:14:53 Why modern AI is simpler than people think

Matt Turck

33,195 次观看 • 3 个月前

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

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 Turck

66,936 次观看 • 2 个月前

EPISODE 142: Understanding the AI Wave Joe Lonsdale sits down with 8VC Partners Alex Kolicich & Jack Moshkovich Why is AI fundamentally disruptive? What does it mean for large software incumbents? Will LLMs become commodities? And are we entering a bubble, or just getting started? (00:00) Episode intro (01:40) Smart enterprise thesis (04:00) Did enterprise software dramatically boost productivity? (08:30) Understanding the AI wave (11:45) Will LLMs become commodities? (17:10) OpenAI vs Anthropic vs Meta vs Grok (20:30) Frontier models vs enterprise incumbents (25:27) What did Palantir get right? (32:30) AI case studies/new possibilities (43:00) The most surprising aspect of the AI wave (46:12) Are we entering a bubble? Or just getting started? (52:40) Biggest concern with AI (58:00) Biggest reason for optimism

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American Optimist

417,594 次观看 • 6 个月前

Meta's CTO just told me that the next 18 months will be "absolute chaos." I sat down with Boz to talk about AI glasses as the next great computing platform, personal superintelligence –– and why 2028 is the year he keeps coming back to. (0:00) Intro to Boz (2:25) Meta-Branded Glasses: Design & Strategy (4:32) Privacy Lessons: From Google Glass to Meta AI (5:53) Competitors in the Smart Eyewear Space (7:08) The "Absolute Chaos" of the AI Era (8:07) Channel Giveaway & R&D Priorities (8:43) Personal Superintelligence for Everyone (11:51) Meta’s Neural Wristband (13:40) The Social Bottleneck & Building Trust (16:20) Privacy & Incognito Chat with Meta AI (18:13) The Ultimate Vision (19:48) Final Thoughts & Looking Ahead to 2028

Meta's CTO just told me that the next 18 months will be "absolute chaos." I sat down with Boz to talk about AI glasses as the next great computing platform, personal superintelligence –– and why 2028 is the year he keeps coming back to. (0:00) Intro to Boz (2:25) Meta-Branded Glasses: Design & Strategy (4:32) Privacy Lessons: From Google Glass to Meta AI (5:53) Competitors in the Smart Eyewear Space (7:08) The "Absolute Chaos" of the AI Era (8:07) Channel Giveaway & R&D Priorities (8:43) Personal Superintelligence for Everyone (11:51) Meta’s Neural Wristband (13:40) The Social Bottleneck & Building Trust (16:20) Privacy & Incognito Chat with Meta AI (18:13) The Ultimate Vision (19:48) Final Thoughts & Looking Ahead to 2028

Roberto Nickson

11,045 次观看 • 27 天前

My conversation with Jesse Zhang Decagon is building a customer service agent with AI, with plans for much more than that. It’s one of the more successful early AI applications, with tons of lessons for the rest of the market. I love how Jesse developed which problem to solve first, a set of lessons applicable to all entrepreneurs Enjoy Timestamps: 0:00 Intro 2:00 Competing in the AI Era 11:35 Finding Product-Market Fit 14:25 Anatomy of a Great Customer Interview 17:37 Everyone’s Wrong Until You Talk to Customers 19:25 AI Agents Replacing Human Labor 21:55 Why Customer Service 23:30 Coding vs. Customer Service 35:44 The Future of Voice and the Human UI 38:54 Why Voice-to-Voice Is So Hard 43:18 Learning from Customer Data 44:36 How CEOs Should Think About AI 47:06 The ROI of Coding and Productivity Tools 48:20 Every Brand Will Talk to You Through AI 56:00 Recruiting, Capital, and Demand 1:00:42 Investor Frenzy 1:22:17 The Long-Term Vision for Decagon 1:25:05 Kindest Thing

Patrick OShaughnessy

49,418 次观看 • 10 个月前

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:

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 次观看 • 4 个月前

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

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

Matt Turck

49,647 次观看 • 3 个月前

EXCLUSIVE: Microsoft just launched Copilot Vision in Edge—the first AI that can navigate the internet with you in real time. I sat down with Mustafa Suleyman (CEO of Microsoft AI) to discuss how it works, infinite memory, AI companions, agents, and more. Timestamps: 0:00 Intro 0:58 Microsoft Copilot Vision rundown 02:35 Initial reactions and use cases 04:57 How Copilot Vision works 06:02 Teaching AI to remember 08:13 How Microsoft AI is differentiating from OpenAI 09:27 The push for the true AI companion 11:28 Living with a co-intelligence in 10+ years 14:04 How Microsoft handles your data 16:11 When and how can you try Copilot Vision? 18:15 Agentic AI that controls your computer 20:25 What's next: support, gaming, and more 22:34 Preparing the next generation with Copilot 25:11 Mustafa’s advice for students and businesses

EXCLUSIVE: Microsoft just launched Copilot Vision in Edge—the first AI that can navigate the internet with you in real time. I sat down with Mustafa Suleyman (CEO of Microsoft AI) to discuss how it works, infinite memory, AI companions, agents, and more. Timestamps: 0:00 Intro 0:58 Microsoft Copilot Vision rundown 02:35 Initial reactions and use cases 04:57 How Copilot Vision works 06:02 Teaching AI to remember 08:13 How Microsoft AI is differentiating from OpenAI 09:27 The push for the true AI companion 11:28 Living with a co-intelligence in 10+ years 14:04 How Microsoft handles your data 16:11 When and how can you try Copilot Vision? 18:15 Agentic AI that controls your computer 20:25 What's next: support, gaming, and more 22:34 Preparing the next generation with Copilot 25:11 Mustafa’s advice for students and businesses

Rowan Cheung

268,106 次观看 • 1 年前

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.

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 次观看 • 1 年前

What does the former co-lead of interpretability at Deepmind and now co-founder of Goodfire think about how to keep AI from becoming (his words) "a generally evil guy"? I got to speak with Tom McGrath at South Park Commons the day after OpenAI announced the Hugging Face hack. Good timing! (00:30) Why AI Interpretability Matters Now (02:15) When AI Reward Hacking Becomes Real (05:07) The Core Problem of Interpretability (07:23) Intentional Design: Steering What Models Learn (12:43) Neural Geometry: The Shapes Inside AI Models (22:29) Turning Interpretability Research Into a Product (25:28) How Research Changes Inside a Startup (32:48) Science Fiction, AI Risk, and the Future

What does the former co-lead of interpretability at Deepmind and now co-founder of Goodfire think about how to keep AI from becoming (his words) "a generally evil guy"? I got to speak with Tom McGrath at South Park Commons the day after OpenAI announced the Hugging Face hack. Good timing! (00:30) Why AI Interpretability Matters Now (02:15) When AI Reward Hacking Becomes Real (05:07) The Core Problem of Interpretability (07:23) Intentional Design: Steering What Models Learn (12:43) Neural Geometry: The Shapes Inside AI Models (22:29) Turning Interpretability Research Into a Product (25:28) How Research Changes Inside a Startup (32:48) Science Fiction, AI Risk, and the Future

Jonathan Brebner

12,234 次观看 • 17 天前

How shaams 🐂 made $45,000,000 in crypto and advice to profitability (FULL INTERVIEW) 0:00 - Introduction 1:30 - How He Started 4:55 - Losing 6 Figures 10:20 - The Comeup 13:46 - Trading in College 14:34 - Making $500k+ on 1 Trade 15:54 - Dropping Out of College 18:45 - Trading in Miami 19:40 - Thesis Behind Trades 21:08 - How to Become Profitable 24:39 - Why Traders Lose 25:41 - Habits 28:14 - The Alpha Filter 30:29 - Risk Management 33:58 - Journaling 35:45 - Community 39:16 - Working with Fortune 44:22 - Taking Action 46:15 - Beginners Advice 51:27 - Building Connections 52:06 - Why Trade? 54:23 - What's Next? 56:52 - Final Steps (Fortune Inner Circle)

How shaams 🐂 made $45,000,000 in crypto and advice to profitability (FULL INTERVIEW) 0:00 - Introduction 1:30 - How He Started 4:55 - Losing 6 Figures 10:20 - The Comeup 13:46 - Trading in College 14:34 - Making $500k+ on 1 Trade 15:54 - Dropping Out of College 18:45 - Trading in Miami 19:40 - Thesis Behind Trades 21:08 - How to Become Profitable 24:39 - Why Traders Lose 25:41 - Habits 28:14 - The Alpha Filter 30:29 - Risk Management 33:58 - Journaling 35:45 - Community 39:16 - Working with Fortune 44:22 - Taking Action 46:15 - Beginners Advice 51:27 - Building Connections 52:06 - Why Trade? 54:23 - What's Next? 56:52 - Final Steps (Fortune Inner Circle)

James Wang

27,944 次观看 • 2 个月前

A LOT happened last week. ICYMI, koray kavukcuoglu (CTO of Google DeepMind and Chief AI Architect of Google) and Logan Kilpatrick discuss Gemini 3, the state of AI, and where we are on the path to AGI. Chapters: 0:00 - Intro 2:00 - Gemini 3 launch reception 4:16 - Continuous progress and innovation 6:47 - Key areas for Gemini improvement 11:45 - Product scaffolding for model improvement 13:56 - Chief AI architect role 17:04 - Engineering mindset and collaboration 18:37 - Future growth areas for Gemini 20:33 - From research to engineering mindset 23:22 - The rise of generative media 27:22 - Nano Banana Pro capabilities 29:31 - Towards unified model checkpoints 36:26 - Organizing for AI success 38:26 - Balancing exploration and scaling 41:40 - DeepMind's collaborative culture 45:21 - Innovating at Google 48:37 - Closing

A LOT happened last week. ICYMI, koray kavukcuoglu (CTO of Google DeepMind and Chief AI Architect of Google) and Logan Kilpatrick discuss Gemini 3, the state of AI, and where we are on the path to AGI. Chapters: 0:00 - Intro 2:00 - Gemini 3 launch reception 4:16 - Continuous progress and innovation 6:47 - Key areas for Gemini improvement 11:45 - Product scaffolding for model improvement 13:56 - Chief AI architect role 17:04 - Engineering mindset and collaboration 18:37 - Future growth areas for Gemini 20:33 - From research to engineering mindset 23:22 - The rise of generative media 27:22 - Nano Banana Pro capabilities 29:31 - Towards unified model checkpoints 36:26 - Organizing for AI success 38:26 - Balancing exploration and scaling 41:40 - DeepMind's collaborative culture 45:21 - Innovating at Google 48:37 - Closing

Google AI

181,340 次观看 • 9 个月前

NEW EP: Why AGI Will Happen This Decade with Emad Mostaque The AI revolution is here, but *actually* who controls the intelligence we'll all rely on? In today's episode, & Wayne Sim sit down with Emad of Intelligent Internet to cover: > Who Controls AI? > The Ghibli Effect by ChatGPT-4o > Why Most AI Models Are Inefficient > Universal Basic Intelligence > AI Atlantis: Economic Disruption & Rebirth > Credibly Neutral AI Infrastructure Full episode links below. Timestamps: 00:00 Intro 00:56 Mantle Ad 01:37 Ghibli AI Use 04:50 Replacing Humans with AI? 05:21 Artificial Remote Intelligence 10:14 Emad’s Opinion on Better AI Models 12:25 Concentration of Power with AI? 16:34 Training AI Models 20:46 Compute Demand Collapse? 27:00 Abundance of Open Source AI 30:04 Shift Towards Specialized Models 34:05 RedStone Ad 34:18 Frax Ad 34:44 Approaching Biases in AI 45:16 End of Original Thoughts? 50:30 Intelligent Internet & Reinforced Learning 59:07 Coordinating Incentives

NEW EP: Why AGI Will Happen This Decade with Emad Mostaque The AI revolution is here, but actually who controls the intelligence we'll all rely on? In today's episode, & Wayne Sim sit down with Emad of Intelligent Internet to cover: > Who Controls AI? > The Ghibli Effect by ChatGPT-4o > Why Most AI Models Are Inefficient > Universal Basic Intelligence > AI Atlantis: Economic Disruption & Rebirth > Credibly Neutral AI Infrastructure Full episode links below. Timestamps: 00:00 Intro 00:56 Mantle Ad 01:37 Ghibli AI Use 04:50 Replacing Humans with AI? 05:21 Artificial Remote Intelligence 10:14 Emad’s Opinion on Better AI Models 12:25 Concentration of Power with AI? 16:34 Training AI Models 20:46 Compute Demand Collapse? 27:00 Abundance of Open Source AI 30:04 Shift Towards Specialized Models 34:05 RedStone Ad 34:18 Frax Ad 34:44 Approaching Biases in AI 45:16 End of Original Thoughts? 50:30 Intelligent Internet & Reinforced Learning 59:07 Coordinating Incentives

The Rollup

32,804 次观看 • 1 年前

Interview with quantum maxer, e/acc founder, thermodynamic pioneer, CEO & Cofounder of Extropic Gill Verdon/ for Episode 41 of S³ 0:00 What’s behind you? 1:29 What is Extropic? 4:08 Young Gill 7:12 Why Extropic? 11:25 Secret quantum lab experience 13:05 How does Extropic work? 16:20 Hardest technical challenge 17:50 Quantum & silicon strapping 20:56 Why decentralize AI 24:00 Doomerism 25:48 E/acc vitality 28:13 Regulatory capture 30:32 Founder learnings 31:36 How do you think? 33:23 What does deep tech get wrong? 35:53 How do we inspire people? 37:22 Current life philosophy

Interview with quantum maxer, e/acc founder, thermodynamic pioneer, CEO & Cofounder of Extropic Gill Verdon/ for Episode 41 of S³ 0:00 What’s behind you? 1:29 What is Extropic? 4:08 Young Gill 7:12 Why Extropic? 11:25 Secret quantum lab experience 13:05 How does Extropic work? 16:20 Hardest technical challenge 17:50 Quantum & silicon strapping 20:56 Why decentralize AI 24:00 Doomerism 25:48 E/acc vitality 28:13 Regulatory capture 30:32 Founder learnings 31:36 How do you think? 33:23 What does deep tech get wrong? 35:53 How do we inspire people? 37:22 Current life philosophy

Jason Carman

654,905 次观看 • 2 年前