正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

"Any feature we release, a competitor could release within two weeks." Matan Grinberg (Factory) on why the moat isn't software anymore. dsa (LiveKit) on building the framework for voice, video, and physical AI. George Sivulka (@HebbiaAI) on what it takes to win in vertical AI. They join @jason on... show more

This Week in AI

13,753 subscribers

102,755 次观看 • 1 个月前 •via X (Twitter)

新闻政治科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

.Factory is building something different for software teams. Here's my interview with Factory co-founder and CEO Matan Grinberg Matan Grinberg. He explains why Factory took a different approach to agentic coding and how he thinks about the future of software development. 0:00 Intro 0:36 Why Build Factory 6:57 The Illusion of Reasoning 10:12 Human vs Agent Workers 12:32 Should You Still Learn to Code? 17:25 5-10 Years in the Future 23:01 Future of Work 26:42 Where Factory is Headed 31:40 Factory Behind the Scenes 34:22 Factory with Memory 37:01 Should Companies Adopt Factory? 39:17 Saas is Dead? Disclosure: I'm a small investor in Factory.

.Factory is building something different for software teams. Here's my interview with Factory co-founder and CEO Matan Grinberg Matan Grinberg. He explains why Factory took a different approach to agentic coding and how he thinks about the future of software development. 0:00 Intro 0:36 Why Build Factory 6:57 The Illusion of Reasoning 10:12 Human vs Agent Workers 12:32 Should You Still Learn to Code? 17:25 5-10 Years in the Future 23:01 Future of Work 26:42 Where Factory is Headed 31:40 Factory Behind the Scenes 34:22 Factory with Memory 37:01 Should Companies Adopt Factory? 39:17 Saas is Dead? Disclosure: I'm a small investor in Factory.

Matthew Berman

17,500 次观看 • 11 个月前

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Amjad Masad (Amjad Masad), co-founder & CEO of Replit, has spent nearly a decade making programming accessible to everyone. With AI, that vision is closer than ever. In his AI Startup School talk, Amjad traces computing’s arc from mainframes to PCs to a future where AI agents generate software on demand—driving the value of traditional software toward zero and reshaping how companies are built. 00:22 - Evolution of Computing 01:27 - The Rise of Software Engineering 02:16 - Replit's Vision: Making Programming Accessible 02:42 - The Role of AI in Software Development 04:32 - Challenges and Infrastructure for AI Agents 05:03 - Building a Scalable and Secure Environment 06:08 - Future of Software Engineering Agents 09:25 - Autonomy Levels in AI Agents 15:56 - Predictions for the Software Market 18:31 - Impact on Business and Employment 19:58 - The Role of Generalist Employees 26:00 - Audience Q&A

Y Combinator

224,320 次观看 • 9 个月前

"I don't want to rent myself back from Sam Altman. That is my personal Black Mirror." Kanjun 🐙 (Imbue) on why open source agents are the only way out. Carina Hong (Axiom) on why nobody should trust a single line of unverified AI code. Jonathan Siddharth (Turing) on the data loop making the models smarter. They join @jason on This Week in AI Episode 8: 00:00 Intro 01:31 Open source agents vs. total lock-in 05:10 AI that aced the hardest math exam 09:52 The superintelligence accelerator 15:53 Anthropic's $30B run rate 22:27 OpenAI's identity crisis 28:27 Meta's token-burning leaderboard 44:08 Commodification of developers 58:27 Superintelligence in 36 months 01:08:41 Big Tech is failing at AI

"I don't want to rent myself back from Sam Altman. That is my personal Black Mirror." Kanjun 🐙 (Imbue) on why open source agents are the only way out. Carina Hong (Axiom) on why nobody should trust a single line of unverified AI code. Jonathan Siddharth (Turing) on the data loop making the models smarter. They join @jason on This Week in AI Episode 8: 00:00 Intro 01:31 Open source agents vs. total lock-in 05:10 AI that aced the hardest math exam 09:52 The superintelligence accelerator 15:53 Anthropic's $30B run rate 22:27 OpenAI's identity crisis 28:27 Meta's token-burning leaderboard 44:08 Commodification of developers 58:27 Superintelligence in 36 months 01:08:41 Big Tech is failing at AI

This Week in AI

39,984 次观看 • 2 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

450,631 次观看 • 2 个月前

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇

New episode of This Week in AI is live @jason sits down with Tanay Kothari (Wispr Flow) and Richard Socher ( to break down the AI doom post that crashed markets, how to build an AI-first team, and why the agent era is already here. 00:00:00 - Welcome & Guest Introductions 00:09:32 - The Citrini doom post explained 00:14:28 - Why it's completely wrong 00:26:18 - Open source vs. closed models 00:38:05 - Building AI-first teams 00:43:42 - From 200 support agents to 4 00:50:34 - Producer Oliver's OpenClaw demo 01:16:29 - Apple on-shoring & the Taiwan chip risk 🎥 Watch the full episode here 👇

This Week in AI

11,116 次观看 • 3 个月前

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,366 次观看 • 3 个月前

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

.Ramp Labs (the AI unit of Ramp) has been cooking with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

Matt Turck

67,011 次观看 • 1 个月前

HK Consensus 2025 SPECIAL EP 8: Ram Kumar on AGI, Specialized Intelligence, and Agent Infrastructure Crypto needs AI that actually... understands crypto, not just slapping ChatGPT onto existing apps. In today's episode, Andy sits down with Ram of Openledger to explore: > The AI Hype Cycle > Speculation vs. Real AI Use Cases > The AI Infrastructure Stack > AI Agents as Smart Onchain Apps > What’s Next for AI in Crypto? Full episode links below. Timestamps: 00:00 Intro 00:41 Frax Ad 01:08 Global Conference Differences 02:01 What’s Next for Crypto AI? 06:12 AI Agent Infrastructure 10:29 Future of AI Agent Interactions 13:45 Mantle Ad 14:27 Redstone Ad 14:56 Building Specialized Agents 19:12 OpenLedger’s Value

HK Consensus 2025 SPECIAL EP 8: Ram Kumar on AGI, Specialized Intelligence, and Agent Infrastructure Crypto needs AI that actually... understands crypto, not just slapping ChatGPT onto existing apps. In today's episode, Andy sits down with Ram of Openledger to explore: > The AI Hype Cycle > Speculation vs. Real AI Use Cases > The AI Infrastructure Stack > AI Agents as Smart Onchain Apps > What’s Next for AI in Crypto? Full episode links below. Timestamps: 00:00 Intro 00:41 Frax Ad 01:08 Global Conference Differences 02:01 What’s Next for Crypto AI? 06:12 AI Agent Infrastructure 10:29 Future of AI Agent Interactions 13:45 Mantle Ad 14:27 Redstone Ad 14:56 Building Specialized Agents 19:12 OpenLedger’s Value

The Rollup

35,328 次观看 • 1 年前

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round

Most people experience AI through apps. Those apps run on infrastructure owned by a handful of companies. Ben Fielding and Harry Grieve of gensyn on why that needs to change, and what it's going to take to rebuild AI as crypto-native open infrastructure. 00:00 – Intro 00:29 – Misconceptions about AI infrastructure 01:20 – Why centralization in AI is a deeper problem than people realize 04:19 – Why AI needs crypto 05:51 – How AI models are trained 08:15 – The rise of autonomous AI agents with onchain identities 10:37 – Lightning round

a16z crypto

23,987 次观看 • 2 个月前

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Another mindblowing conversation with my good friend .Emad... Enjoy!! 00:00 - Introduction 00:22 - AI: The Biggest Shift in Human History 00:42 - AI’s Impact on Society and the Economy 01:03 - Conversation with Emad Mostaque Begins 01:50 - The Acceleration of AI and Economic Takeoff 03:07 - AI Intelligence: Beyond Human IQ 04:08 - The Rise of AI Chefs and Super Cooks 05:07 - Breaking AI Constraints: Compute and Energy 07:03 - The Future of AI: Ubiquitous Intelligence 08:04 - The Shift to Local AI Models 10:07 - Why Has Apple Lagged in AI? 11:21 - The AI Race: OpenAI, Grok, Gemini, and More 13:11 - China’s Open-Source AI Strategy 14:57 - AI Bias and Ethical Challenges 16:57 - AI’s Cross-Pollination and Memory 18:02 - Are AI Models Becoming Self-Aware? 19:16 - AI, Bitcoin, and Self-Sustaining Algorithms 21:26 - AI-Driven Economies and Autonomous Companies 23:41 - The Future of Labor: A World Without Jobs? 25:26 - AI-Powered Robots: The Next Workforce Revolution 27:28 - The End of Traditional Economic Models 30:27 - The Political Shift: Humanist vs. Transhumanist 33:04 - AI in Financial Markets: The End of Human Traders? 36:03 - The Evolution of Investing in an AI World 38:33 - AI’s Impact on Capital Formation and Business Disruption 40:01 - The Rise of Digital Twins and Post-Capital Society 42:45 - Building AI for Education, Healthcare, and Governance 46:42 - The Future of Money in an AI-Driven World 50:11 - Universal Basic AI: A New Economic Model 54:29 - The Deflationary Impact of AI and Crypto’s Role 57:02 - The AI Singularity: Five Years Until Everything Changes 58:56 - The Road Ahead: AI, Crypto, and the Future of Civilization 01:02:24 - Final Thoughts: The Most Exciting and Terrifying Time in History

Raoul Pal

326,827 次观看 • 1 年前

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

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

62,699 次观看 • 6 天前

LIVE from Davos: Satya Nadella joins the Besties! 🚨 Satya Nadella sits down with @jason and David Sacks from the main stage at USA House! -- Future of AI copilots and agents: how white collar work will change -- Scaling revenue and profits with flat headcount -- Views on diffusion and having the US AI stack win globally -- Foundation model concerns?: OpenAI's IP, will open-source win? -- SaaS adoption in the age of AI (0:00) Jason and Sacks welcome Microsoft CEO Satya Nadella (1:31) Future of AI copilots and agents, impact on white collar work (8:01) How Microsoft has scaled revenue and profits with flat headcount (10:50) The extreme competition in AI: Microsoft, xAI, Google, OpenAI, Anthropic (12:39) Views on diffusion, how the US tech stack can win globally (19:59) OpenAI deal, owning the IP, thoughts on open-source winning AI, Microsoft's AI stack, do they need a foundation model? (26:08) What enterprise software adoption looks like in the age of AI

LIVE from Davos: Satya Nadella joins the Besties! 🚨 Satya Nadella sits down with @jason and David Sacks from the main stage at USA House! -- Future of AI copilots and agents: how white collar work will change -- Scaling revenue and profits with flat headcount -- Views on diffusion and having the US AI stack win globally -- Foundation model concerns?: OpenAI's IP, will open-source win? -- SaaS adoption in the age of AI (0:00) Jason and Sacks welcome Microsoft CEO Satya Nadella (1:31) Future of AI copilots and agents, impact on white collar work (8:01) How Microsoft has scaled revenue and profits with flat headcount (10:50) The extreme competition in AI: Microsoft, xAI, Google, OpenAI, Anthropic (12:39) Views on diffusion, how the US tech stack can win globally (19:59) OpenAI deal, owning the IP, thoughts on open-source winning AI, Microsoft's AI stack, do they need a foundation model? (26:08) What enterprise software adoption looks like in the age of AI

The All-In Podcast

859,815 次观看 • 5 个月前

How has the day-to-day workflow of Mitchell Hashimoto (Mitchell Hashimoto) changed, thanks to AI tools? Timestamps: 00:00 Intro 07:19 HashiCorp origins 18:22 The 2010s startup scene in SF 23:11 Funding HashiCorp 25:23 The "Hashi stack" 38:28 The open-core pivot 48:08 Taking HashiCorp public 51:58 The almost-VMware acquisition 59:10 Mitchell’s take on AWS, GCP and Azure 01:06:02 AI’s impact on open source 01:07:00 Ghostty 01:19:13 How Mitchell uses AI 01:28:36 Open source + AI 01:31:46 The problem of Git and monorepos 01:39:57 Mitchell’s hiring practices 01:47:52 Mitchell’s AI adoption journey 01:50:41 Advice to future founders 01:55:03 Closing Also on YouTube, Spotify and Apple (see the next tweet) Brought to you by: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. Join me online at the Sonar Summit on March 3rd: • WorkOS – Everything you need to make your app enterprise ready.

How has the day-to-day workflow of Mitchell Hashimoto (Mitchell Hashimoto) changed, thanks to AI tools? Timestamps: 00:00 Intro 07:19 HashiCorp origins 18:22 The 2010s startup scene in SF 23:11 Funding HashiCorp 25:23 The "Hashi stack" 38:28 The open-core pivot 48:08 Taking HashiCorp public 51:58 The almost-VMware acquisition 59:10 Mitchell’s take on AWS, GCP and Azure 01:06:02 AI’s impact on open source 01:07:00 Ghostty 01:19:13 How Mitchell uses AI 01:28:36 Open source + AI 01:31:46 The problem of Git and monorepos 01:39:57 Mitchell’s hiring practices 01:47:52 Mitchell’s AI adoption journey 01:50:41 Advice to future founders 01:55:03 Closing Also on YouTube, Spotify and Apple (see the next tweet) Brought to you by: • Statsig — ⁠ The unified platform for flags, analytics, experiments, and more. • Sonar – The makers of SonarQube, the industry standard for automated code review. Join me online at the Sonar Summit on March 3rd: • WorkOS – Everything you need to make your app enterprise ready.

Gergely Orosz

59,544 次观看 • 3 个月前

My latest conversation with the always incredible Benedict Evans: OpenAI’s moat problem, the rise of ephemeral and improvised sofware, OpenClaw & agents. 00:00 Intro 01:06 OpenAI's Focus Shift 03:12 ChatGPT usage: a "mile wide, inch deep" 09:03 Why better models do not solve the real problem 13:58 Why AI product teams are strategy takers, not strategy setters 15:38 Do agents help create defensibility? 20:06 OpenClaw and the "Desktop Linux" moment for AI 25:52 Why "everyone will build their own software" is completely wrong 28:09 Improvised software vs. institutionalized software 29:23 Why there will be more software, not less 36:15 Are we heading toward value destruction before value creation? 38:03 Circular revenue, leverage, and AI bubble dynamics 38:53 Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity 45:23 Why AI job exposure charts can be misleading 52:15 How Fortune 500 Execs are actually deploying AI today 56:45 The White Space: What this means for founders and investors

My latest conversation with the always incredible Benedict Evans: OpenAI’s moat problem, the rise of ephemeral and improvised sofware, OpenClaw & agents. 00:00 Intro 01:06 OpenAI's Focus Shift 03:12 ChatGPT usage: a "mile wide, inch deep" 09:03 Why better models do not solve the real problem 13:58 Why AI product teams are strategy takers, not strategy setters 15:38 Do agents help create defensibility? 20:06 OpenClaw and the "Desktop Linux" moment for AI 25:52 Why "everyone will build their own software" is completely wrong 28:09 Improvised software vs. institutionalized software 29:23 Why there will be more software, not less 36:15 Are we heading toward value destruction before value creation? 38:03 Circular revenue, leverage, and AI bubble dynamics 38:53 Big Tech's Trillion-Dollar CapEx Crisis & Financial Gravity 45:23 Why AI job exposure charts can be misleading 52:15 How Fortune 500 Execs are actually deploying AI today 56:45 The White Space: What this means for founders and investors

Matt Turck

36,594 次观看 • 3 个月前

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!

My interview with Helen Toner on firing Altman, AGI geopolitics in China & Middle East, and military use of AI. Toner co-founded and now leads the Center for Security and Emerging Technology at Georgetown in DC, maybe the leading think tank on natsec and AI. We discuss: • Is China actually racing to build AGI? [00:27:56] • Why you shouldn't say 'alignment' [01:45:11] • Is Congress starting to take superintelligence seriously? [01:47:42] • Are data centres in the UAE “good for democracy”? [00:52:41] • Sure China could steal AI model weights, but does it matter [00:39:13] • Is it good if the world uses US not Chinese AI models? [00:21:58] • Why non-proliferation is a misguided model for AI [01:30:20] • Helen thinks AI progress isn't slowing down [01:50:10] • Will AI inevitably concentrate power? [01:07:49] • Helen’s defence of how the board handled firing Altman [00:01:32] • Will the military be willing to defer to AI in decision-making? [01:38:23] Available any place you get podcasts on the '80,000 Hours Podcast'. Links below — enjoy!

Rob Wiblin

20,369 次观看 • 7 个月前

NEW EP: How Blockchain Fixes AI's Biggest Problem with Anand Iyer The race to control AI isn’t complete without crypto having a say. In today's episode, Andy sits down with anand iyer to explore: > Why $300B Went to Centralized AI vs $1B to Decentralized > How One Developer Could Code the Next Billion Dollar AI Company > Why Teenagers Are Using ChatGPT as Their Therapist > The Case for Staking Against AGI > Why AI Tokens Doubled While Everything Else Crashed > How Crypto's Capital Coordination Could Reshape AI Ownership Full episode links below. Timestamps: 00:00 Intro 00:58 Magic Ad 01:26 Starknet Ad 01:52 Anand’s Investing Thesis 06:29 Data Center Arms Race 13:31 AI Power Consumption 21:26 Training and Enhancing AI Models 26:33 SummerFi Ad 27:15 Mantle Ad 27:56 AI Crypto Company Challenges 35:01 Centralized AI Sentiment Change 39:05 Onchain IPO Era 45:26 New Models for AI Founders 48:43 Anand Reacts to Tweets

NEW EP: How Blockchain Fixes AI's Biggest Problem with Anand Iyer The race to control AI isn’t complete without crypto having a say. In today's episode, Andy sits down with anand iyer to explore: > Why $300B Went to Centralized AI vs $1B to Decentralized > How One Developer Could Code the Next Billion Dollar AI Company > Why Teenagers Are Using ChatGPT as Their Therapist > The Case for Staking Against AGI > Why AI Tokens Doubled While Everything Else Crashed > How Crypto's Capital Coordination Could Reshape AI Ownership Full episode links below. Timestamps: 00:00 Intro 00:58 Magic Ad 01:26 Starknet Ad 01:52 Anand’s Investing Thesis 06:29 Data Center Arms Race 13:31 AI Power Consumption 21:26 Training and Enhancing AI Models 26:33 SummerFi Ad 27:15 Mantle Ad 27:56 AI Crypto Company Challenges 35:01 Centralized AI Sentiment Change 39:05 Onchain IPO Era 45:26 New Models for AI Founders 48:43 Anand Reacts to Tweets

The Rollup

12,829 次观看 • 11 个月前

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us

My full interview with Roman Chernin, Co-founder & Chief Business Officer of Nebius Nebius just signed a $17 billion deal with Microsoft and a $3 billion deal with Meta. These are two of the biggest tech companies on Earth - and they're coming to Nebius for AI infrastructure. But the company does so much more than that: - Full-stack AI cloud (data centers → software → managed services) - Nebius Token Factory Nebius Token Factory for managed inference & post-training - Partners include Shopify, Higgsfield, Jetbrains My 5 takeaways with Roman Chernin below - thanks for the conversation! Full interview also available on YT ⬇️ Timestamps: 00:00 - Introduction 01:47 - Overview of Nebius $NBIS 03:23 - Roman's background and role at Nebius 04:10 - The $17B Microsoft & $3B Meta deals 05:29 - Why hyperscalers trust Nebius over building in-house 07:06 - Nebius's full-stack approach: Data centers to managed services 08:39 - Customer segmentation: Hyperscalers & Frontier AI Labs vs AI Startups vs Enterprises 14:41 - Token Factory explained: Managed inference & post-training 17:41 - When to switch from closed-source to open-source models 20:59 - Real results: Process achieves 26% cost reduction 25:05 - Why developers love building on Nebius 28:40 - AI agents and the future of infrastructure interaction 32:22 - NVIDIA's Groq acquisition: What it means for inference 35:38 - Why AI adoption will surprise us

Elliot Garreffa

29,119 次观看 • 4 个月前

How OpenAI made Codex The full episode with Alexander Embiricos is now live: Timestamps 0:00:00 Intro: Meet the Codex Team 0:00:21 Origin Story of Codex 0:15:03 How Users Are Adopting Codex: Surprising Patterns 0:36:38 Is AI Eating Software Engineering? 1:00:05 Building a Startup in the Age of AI Agents 1:07:58 Why Some Students Excel with AI Tools 1:10:56 Hiring at OpenAI: What Stands Out

How OpenAI made Codex The full episode with Alexander Embiricos is now live: Timestamps 0:00:00 Intro: Meet the Codex Team 0:00:21 Origin Story of Codex 0:15:03 How Users Are Adopting Codex: Surprising Patterns 0:36:38 Is AI Eating Software Engineering? 1:00:05 Building a Startup in the Age of AI Agents 1:07:58 Why Some Students Excel with AI Tools 1:10:56 Hiring at OpenAI: What Stands Out

Anjney Midha

31,764 次观看 • 9 个月前

"LM Arena is a cancer on AI. Labs have entire teams dedicated to hacking it." Edwin Chen (echen), CEO of Surge AI, on why the industry's favorite benchmark is broken and how Surge hit $1.2 billion in revenue without ever raising. Aravind Srinivas (Aravind Srinivas), CEO of Perplexity, on Apple's AI advantage, Claude Code economics, the endgame of coding, and Perplexity Computer. They join @jason on This Week in AI Episode 10: 00:00 Intro to Aravind Srinivas and Edwin Chen 05:25 Edwin on Surge: School for AGI 10:47 What Apple's next CEO should do 21:20 "The iPhone is not getting disrupted by AI" 23:55 Bootstrapping Surge past $1B without raising 30:58 Claude Code as a loss leader 33:30 Are we in the endgame for coding? 41:34 30% headcount growth, 5x revenue 50:29 "People don't buy models, they buy products" 58:00 "LM Arena is a cancer on AI" 1:05:41 Model Council and orchestrating frontier models Full episode on YT, Spotify, and Apple Podcasts below: Perplexity Surge AI

"LM Arena is a cancer on AI. Labs have entire teams dedicated to hacking it." Edwin Chen (echen), CEO of Surge AI, on why the industry's favorite benchmark is broken and how Surge hit $1.2 billion in revenue without ever raising. Aravind Srinivas (Aravind Srinivas), CEO of Perplexity, on Apple's AI advantage, Claude Code economics, the endgame of coding, and Perplexity Computer. They join @jason on This Week in AI Episode 10: 00:00 Intro to Aravind Srinivas and Edwin Chen 05:25 Edwin on Surge: School for AGI 10:47 What Apple's next CEO should do 21:20 "The iPhone is not getting disrupted by AI" 23:55 Bootstrapping Surge past $1B without raising 30:58 Claude Code as a loss leader 33:30 Are we in the endgame for coding? 41:34 30% headcount growth, 5x revenue 50:29 "People don't buy models, they buy products" 58:00 "LM Arena is a cancer on AI" 1:05:41 Model Council and orchestrating frontier models Full episode on YT, Spotify, and Apple Podcasts below: Perplexity Surge AI

This Week in AI

40,129 次观看 • 2 个月前

Andrew Ng (Andrew Ng) on how startups can build faster with AI. At AI Startup School in San Francisco. 00:31 - The Importance of Speed in Startups 01:13 - Opportunities in the AI Stack 02:06 - The Rise of Agent AI 04:52 - Concrete Ideas for Faster Execution 08:56 - Rapid Prototyping and Engineering 17:06 - The Role of Product Management 21:23 - The Value of Understanding AI 22:33 - Technical Decisions in AI Development 23:26 - Leveraging Gen AI Tools for Startups 24:05 - Building with AI Building Blocks 25:26 - The Importance of Speed in Startups 26:41 - Addressing AI Hype and Misconceptions 37:35 - AI in Education: Current Trends and Future Directions 39:33 - Balancing AI Innovation with Ethical Considerations 41:27 - Protecting Open Source and the Future of AI

Andrew Ng (Andrew Ng) on how startups can build faster with AI. At AI Startup School in San Francisco. 00:31 - The Importance of Speed in Startups 01:13 - Opportunities in the AI Stack 02:06 - The Rise of Agent AI 04:52 - Concrete Ideas for Faster Execution 08:56 - Rapid Prototyping and Engineering 17:06 - The Role of Product Management 21:23 - The Value of Understanding AI 22:33 - Technical Decisions in AI Development 23:26 - Leveraging Gen AI Tools for Startups 24:05 - Building with AI Building Blocks 25:26 - The Importance of Speed in Startups 26:41 - Addressing AI Hype and Misconceptions 37:35 - AI in Education: Current Trends and Future Directions 39:33 - Balancing AI Innovation with Ethical Considerations 41:27 - Protecting Open Source and the Future of AI

Y Combinator

851,955 次观看 • 11 个月前