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Are we hurtling toward a future where AI can do everything humans can? Edwin Chen (echen) believes we might be. He’s the CEO of Surge AI, one of the largest providers of expert data for frontier labs. Surge passed over $1 billion in revenue without raising any outside capital,... and that gives Edwin a unique perspective on how quickly AI progress is accelerating. I’m on the record arguing that AI automation actually creates more human work. I also believe that even though AI progress is accelerating exponentially, we’re much farther away from AI replacing humans than it might seem. That’s why I had Edwin on Every 🪨’s AI & I. We batted around different visions of the future, and discussed whether humanity will retain its unique place in the universe, and what that might be. We get into: • If Chen’s version of the future materializes, he’s worried it’ll make people stop trying. One answer comes from a short story by science fiction writer Ted Chiang: Behave as if your decisions matter, even when you know they don’t. • AI may soon be able to take a nebulous goal like “win a Fields Medal” and execute. What it can’t do, I argue, is set its own goals—LLMs have no intrinsic motivation, no drive to explore, no ability to just change their mind. • A model optimized for engagement doesn’t provide the most valuable user experience. Edwin spent 20 rounds polishing a pointless email with one model before Claude told him to just send it. • Why AI is still bad at writing: models learn to hack the metrics they're trained on. Edwin's Hemingway Bench found models outputting a metaphor in every single sentence, an overindexxing that makes for a terrible reading experience. This is a must-watch for anyone interested in where we fit as models get more capable. Watch below! Timestamps 1. Introduction: 00:00:54 2. Surge as a "school for AGI": 00:01:49 3. What AI's capacity for novel mathematics says about human achievement: 00:04:46 4. Motivation in an era when AI can do everything: 00:07:29 5. The trap of optimizing AI models for engagement: 00:14:34 6. Training using datasets versus training using environments: 00:29:34 7. The value of personal data: 00:35:09 8. Why models are bad at writing: 00:39:40 9. Chen's AGI timeline: 00:42:00show more

Dan Shipper 📧

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15,115 просмотров • 2 месяцев назад •via X (Twitter)

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This Week in AI

40,470 просмотров • 4 месяцев назад

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 просмотров • 4 месяцев назад

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Yoshua Bengio thinks he knows how to make provably safe superintelligent agents. Bengio built the foundations of modern AI and is the most cited living scientist. He believes his alternative training setup would: 1. Guarantee honesty 2. Prevent unintended goals 3. Produce capable agents 4. Port over most data and techniques from current LLMs 5. Not be inherently more expensive, and perhaps be more intelligent Bengio claims the honesty and lack of unintended goals can be proven mathematically, at least given particular assumptions. And his new organization, LawZero, is aiming to build a scrappy prototype as soon as possible. The architecture is called 'Scientist AI' and it's based on training a model to explain empirical observations, including what people say, rather than training AIs that mimic human behaviour or seek our approval. (Bengio's frank assessment is that "reinforcement learning is evil" and that allowing AIs to independently train their successors is "the most crazy, dangerous bet that unfortunately we are on track to do.") But skeptics question whether Scientist AI really does solve the fundamental problem of 'eliciting latent knowledge' from AI models. And with the commercial race for superintelligence so intense, it's not clear whether the proposal will be able to compete or have time to bear fruit, even if it's sound in theory. On The 80,000 Hours Podcast, links below – enjoy! • Making AI honest and safe (00:00:00) • Scientist AI in plain English (00:02:27) • How Scientist AI differs from LLMs (00:06:32) • How the training data works (00:14:02) • Can this become an agent? (00:21:02) • Why Yoshua is now more optimistic (00:32:11) • Why companies can’t stop racing (00:36:35) • A working prototype won't take long (00:49:15) • Scientist models might be more capable (00:53:34) • “Reinforcement learning is evil” (01:01:27) • Scientist AI from guardrail to agent (01:08:37) • Can safe AI still be competent? (01:12:38) • How much will this cost? (01:19:29) • Can it generalise beyond maths and science? (01:23:26) • A multi-national push for superintelligence (01:39:19) • Want to work with or fund Yoshua? (01:51:16) • Why smart people ignore AI risk (01:54:45) • Don’t let AI build the next AI (02:01:33) • Why politicians miss the real risks (02:12:28) • Why Yoshua changed his mind about AI risk (02:21:27)

Rob Wiblin

65,088 просмотров • 3 месяцев назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

AI has a trust problem. Verifiability is the solution. Our GM of AI Nima Vaziri sat down with a16z’s Ali Yahya and Dan Boneh of Stanford University to map the deepest fault lines in AI today. ☁️ Models we can’t trust ☁️ Current providers can censor, shut down, or shift rules overnight. Outsourced training hides backdoors. Even “open” weights don’t prove what’s actually running. Trust. Backdoors. Black boxes. The path forward is clear: 🔥 Verifiable evals 🔥 Verifiable inference 🔥 TEEs for hardware-backed integrity 🔥 Infra beyond single points of control 🔥 Blockchains as coordination layers for AI From “trust us” to “verify yourself.” That’s the shift. That’s the unlock. The frontier is here. The builders decide what comes next. Create and use AI that’s incentive aligned with you. Timestamps: 00:00:00 Introduction: AI & Crypto Intersection Overview 00:01:58 Four Major AI-Crypto Trends 00:02:44 AI Agents Need Financial Infrastructure 00:04:03 Proof of Humanity: Fighting AI-Generated Content 00:04:17 Decentralizing AI Infrastructure Networks 00:04:44 Synthetic Life: Autonomous AI Agents 00:06:20 Verifiable AI 00:10:16 Current Performance Numbers for AI Proofs 00:13:18 The Era of Experience in AI Learning 00:14:56 AI Agents Having Life of its Own 00:18:21 Algorithmic Fairness & Verifiable Models 00:23:18 Privacy in AI: Trusted Execution Environments 00:25:47 Economic Incentive for Open Weight Models 00:31:39 Attribution Problem: Who Gets Paid for AI Training? 00:35:52 Content Provenance & Authentication (C2PA) 00:48:03 AI Security: Finding Exploits & Vulnerabilities 00:54:53 Educational Applications: LLMs as Learning Partner 00:58:29 Reliance on LLMs and Cognitive Abilities 01:03:57 Content Providers’ Fear of LLM Training

EigenCloud

62,099 просмотров • 11 месяцев назад

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Guillermo Rauch (Guillermo Rauch) is one of the most prolific coders of this generation. But he doesn’t think of himself as a coder anymore. Coding, he says, is a specific skill that AI is becoming great at. Instead, he thinks the future of coding is more holistic, full-stack engineers who can ideate, design, and execute all together. Guillermo is the founder and CEO of Vercel (Vercel), the creator of NextJS, and SocketIO. We spent an hour talking about the future of software development in an AI world—and the meta-skills that are essential for the coders of today to master—in order to use tomorrow’s tools to their fullest extent. Here are a few takeaways: - One of the most important keys to his success is taste—and developing taste is all about paying better attention to everything you experience day to day. - He’s great at recognizing bleeding-edge technologies with extremely practical applications but that have bad user experiences. If you can learn to recognize those and build with them, you might build the next NextJs or SocketIO. - Why prototype cultures are becoming common in AI—and the benefits of written cultures like Amazon vs. prototype cultures like Apple for different kinds of companies. - For developers building frameworks, always put the product first; a framework in isolation without a “customer zero” is never going to be a good tool. - The theory of “recursive founder mode”—if you want to build a scalable business, you have to scale yourself by creating an atmosphere that nurtures talent and ambition. - AI tools are shifting software toward consumption-based billing models, making us capital allocators who decide how much compute the AI consumes. - The future of AI is agents with the taste, knowledge, and tools to perform specialized tasks. Watch below! Timestamps: Introduction: 00:01:33 How to spot trends early: 00:03:18 Why you should be your own customer: 00:07:34 How to create an ecosystem of talent and ambition: 00:14:55 Why Guillermo doesn't identify as a coder: 00:17:29 AI is gearing us toward an allocation economy: 00:20:50 How Vercel’s copilot compares with other coding agents: 00:28:34 Guillermo’s advice on having better taste: 00:40:35 The future of AI agents is specialized: 00:42:46 How AI startups can compete with big tech: 00:47:50

Dan Shipper 📧

187,070 просмотров • 1 год назад

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29

AGI is coming. Reid Hoffman (Reid Hoffman) just wrote the book on how to prepare. According to Reid, every major tech breakthrough (the written word, the printing press, the telephone) triggered mass fear. But, contrary to our worries, new technology tends to enhance human agency—even more so, if you know how to use it well. Reid is the cofounder of LinkedIn, Inflection AI, and Manas, a partner at Greylock Partners, an award-winning podcaster, and an early backer and board member of OpenAI. We spent an hour talking about how to develop a compass for navigating AGI. Here are a few takeaways: - Our sense of human agency is not just about external control but an internal stance—how we approach uncertainty & new tech is crucial - In new technology waves, NO blueprint or plan will have the right answers. Instead, adapting to new technology requires broad access, an experimental mindset, and flexibility - In an AGI world most jobs will transform, not disappear—and how you can prepare with hands-on trial and error - How certain social norms and ethics should change as AGI changes the landscape—like individual access to personal data - Why now may be finally be the era where quantified self tools become valuable …and more, including everything in his new book Superagency, out this week. It was a pleasure to have him on the show for a second time. This is a must-watch for anyone who wants to help build a more human future with AI. Watch below! Timestamps: Introduction: 00:01:29 Patterns in how we’ve historically adopted technology: 00:02:50 Why humans have typically been fearful of new technologies: 00:07:02 How Reid developed his own sense of agency: 00:13:25 The way Reid thinks about making investment decisions: 00:20:08 AI as a “techno-humanist” compass: 00:29:40 How to prepare yourself for the way AI will change knowledge work: 00:35:30 Why equitable access to AI is important: 00:41:39 Reid’s take on why private commons will be beneficial for society: 00:45:15 How AI is making Silicon Valley’s conception of the “quantified self” a reality: 00:47:23 The shift from symbolic to sub-symbolic AI mirrors how we understand intelligence: 00:52:14 Reid’s new book, Superagency: 01:03:29

Dan Shipper 📧

47,229 просмотров • 1 год назад

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

AI has changed software engineering more in the last 3 years than it has changed in the previous 30. What’s needed is not a debate about whether it’s going away—instead it’s a serious discussion about its future: What are the new primitives, techniques, and best practices for software engineering in the age of AI. That’s why I brought Scott Wu (Scott Wu) on AI & I. He’s the founder of Cognition, the company behind the world’s first autonomous AI coding agent, Devin. Cognition got to $73M ARR in less than 2 years—and they just acquired Windsurf to accelerate their growth. I had Scott on the show to talk about where the programming goes from here. We get into: - What the new tools and workflows are for AI engineers. In the near term, Scott sees software engineering defined by a spectrum of tools. At one end are AI features that speed up coding, like tab complete; at the other are agentic systems, like Devin, that can take on tasks independently. Until engineers can operate entirely at the higher layer of abstraction, he argues, both are essential. - Why Scott thinks AGI is already here. By the benchmarks of a decade ago—passing the Turing test, solving hard math problems, and operating agentically—AGI is already here. The line keeps moving, he argues, because humans constantly redefine work around what machines can’t yet do. - Why developers will turn into product architects. Scott sees the long-term future of software engineering as a steady climb up the ladder of abstraction. Just as programming went from assembly to languages like Python and JavaScript, he thinks the future is humans focusing on the product, while AI agents execute. - How Devin stacks up against Anthropic’s Claude Code. Scott credits Claude Code’s success to great product design and the models becoming capable enough to support autonomous workflows. But according to him, the CLI itself isn’t the breakthrough, it’s how a tool fits into a developer’s workflow. Claude Code’s paradigm is that the AI is you, taking the wheel of your computer, he says, while Devin is like the engineer sitting beside you: it runs in its own cloud environment, manages the repo, and improves over time at testing and refining code. This episode of Every 📧’s AI & I is a must-watch for anyone interested in the brass tacks of how AI changes the future of programming. Watch below! Timestamps: Introduction: 00:02:02 Why Scott thinks AGI is here: 00:02:32 Scott’s personal journey as a founder: 00:09:27 Why the fundamentals of computer science still matter: 00:16:55 How the future of programming will evolve: 00:22:30 A new workflow for the AI-first software engineer: 00:26:50 How Devin stacks up against Claude Code: 00:29:33 Reinforcement learning to build better coding agents: 00:40:05 What excites Scott about AI beyond Cognition: 00:50:05

Dan Shipper 📧

35,342 просмотров • 11 месяцев назад

Even when things are going great, running a $1.5 billion AI startup is a knife fight. Granola was one of the first AI apps of this generation to achieve near-ubiquitous adoption. But meeting notes are not the company’s be-all and end-all. The real battle is over owning the interface that everyone uses to get their work done in an AI-native world. I had Chris Pedregal (Chris Pedregal), cofounder and CEO of Granola, back on Every 📧’s AI & I to talk about the current state of the application layer, AI’s frontier, and the future of work. We get into: - Why meeting notes clones don’t matter. Three big companies cloned Granola’s core feature. To him, meeting notes were never the real prize. “Easy come, easy go” is his view of anyone’s lead, including his own. - How he thinks about building proactive features in AI. Granola pre-generates millions of pre-meeting briefs, which include context on the nature of the meeting and people participating, that most people never open. But when they do, they have a magical experience. - Why Granola is betting on “bring your own agent.” Chris says the API and MCP will get “a lot better” over the next few months, and we talk about their agent-native strategy and why they’ve pushed the product that way. This is a must-watch for anyone building at the application layer. Watch the episode! Timestamps Introduction: 00:00:59 Why running a company is a knife fight even when it’s working: 00:01:57 Granola’s counterintuitive view on competition: 00:04:33 Dan’s “pirate and architect” model for early-stage product teams: 00:10:44 Granola’s “shaping” and “validation” phases for building features: 00:13:09 Why Dan lives almost entirely inside Codex: 00:18:17 The case for “Codex-native apps”: 00:24:40 Granola’s “handrail” philosophy: 00:35:37 Why Granola is going all in on winning meeting-adjacent context: 00:38:12 What a transcript alone can never capture: 00:44:19

Even when things are going great, running a $1.5 billion AI startup is a knife fight. Granola was one of the first AI apps of this generation to achieve near-ubiquitous adoption. But meeting notes are not the company’s be-all and end-all. The real battle is over owning the interface that everyone uses to get their work done in an AI-native world. I had Chris Pedregal (Chris Pedregal), cofounder and CEO of Granola, back on Every 📧’s AI & I to talk about the current state of the application layer, AI’s frontier, and the future of work. We get into: - Why meeting notes clones don’t matter. Three big companies cloned Granola’s core feature. To him, meeting notes were never the real prize. “Easy come, easy go” is his view of anyone’s lead, including his own. - How he thinks about building proactive features in AI. Granola pre-generates millions of pre-meeting briefs, which include context on the nature of the meeting and people participating, that most people never open. But when they do, they have a magical experience. - Why Granola is betting on “bring your own agent.” Chris says the API and MCP will get “a lot better” over the next few months, and we talk about their agent-native strategy and why they’ve pushed the product that way. This is a must-watch for anyone building at the application layer. Watch the episode! Timestamps Introduction: 00:00:59 Why running a company is a knife fight even when it’s working: 00:01:57 Granola’s counterintuitive view on competition: 00:04:33 Dan’s “pirate and architect” model for early-stage product teams: 00:10:44 Granola’s “shaping” and “validation” phases for building features: 00:13:09 Why Dan lives almost entirely inside Codex: 00:18:17 The case for “Codex-native apps”: 00:24:40 Granola’s “handrail” philosophy: 00:35:37 Why Granola is going all in on winning meeting-adjacent context: 00:38:12 What a transcript alone can never capture: 00:44:19

Dan Shipper 📧

13,146 просмотров • 1 месяц назад

> "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

> "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 просмотров • 1 месяц назад

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to @ChatGPTapp, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01

Everyone told Vicente Silveira (Vicente Silveira) that his startup—a GPT wrapper—would fail. Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to @ChatGPTapp, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into: - Why staying small and specialized is a bigger advantage than you think - The power of building with your early adopters - Why lean startups are better positioned than frontier AI companies to create radical solutions - When a growing startup should think about raising venture capital - The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements. This is a must-watch for small teams building profitable companies at the bleeding edge of AI. Watch below! Timestamps: Introduction: 00:00:35 AI PDF’s story begins with an email to OpenAI’s Greg Brockman: 00:02:58 Why users choose AI PDF over ChatGPT: 00:05:41 How to compete—and thrive—as a GPT wrapper: 00:06:58 Why building with early adopters is key: 00:20:49 Being small and specialized is your biggest advantage: 00:27:53 When should AI startups raise capital: 00:31:47 The emerging role of humans who will manage AI agents: 00:34:53 Why AI is different from other tech revolutions: 00:45:25 A live demo of an agent integrated into AI PDF: 00:54:01

Dan Shipper 📧

25,628 просмотров • 1 год назад

“In my career working in AI, I have yet to meet a single AI person that ever felt like they had enough compute.” “Open-weight models is a tremendous source of geopolitical influence” “When China’s government makes an all-nation commitment, it’s a very powerful force I wouldn’t underestimate.” Shows like this are why I love what I do. My top 7 takeaways from one of the OGs of AI Andrew Ng 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:04 What are the Biggest Bottlenecks in AI Today? 09:31 How LLMs Can Be Used as a Geopolitical Weapon 15:07 Should AI Talent Really Be Paid Billions? 19:15 Why is the Application Layer the Most Exciting Layer? 29:30 Will AI Deliver Masa Son's Predictions of 5% GDP Growth? 38:43 Do Margins Matter in a World of AI? 40:36 Is Defensibility Dead in a World of AI? 48:24 Will Human Labour Budgets Shift to AI Spend? 55:05 Are We in an AI Bubble? 56:28 Quick-Fire Round

“In my career working in AI, I have yet to meet a single AI person that ever felt like they had enough compute.” “Open-weight models is a tremendous source of geopolitical influence” “When China’s government makes an all-nation commitment, it’s a very powerful force I wouldn’t underestimate.” Shows like this are why I love what I do. My top 7 takeaways from one of the OGs of AI Andrew Ng 👇 Spotify 👉 Youtube 👉 Apple Podcasts 👉 Timestamps: 00:00 Intro 01:04 What are the Biggest Bottlenecks in AI Today? 09:31 How LLMs Can Be Used as a Geopolitical Weapon 15:07 Should AI Talent Really Be Paid Billions? 19:15 Why is the Application Layer the Most Exciting Layer? 29:30 Will AI Deliver Masa Son's Predictions of 5% GDP Growth? 38:43 Do Margins Matter in a World of AI? 40:36 Is Defensibility Dead in a World of AI? 48:24 Will Human Labour Budgets Shift to AI Spend? 55:05 Are We in an AI Bubble? 56:28 Quick-Fire Round

Harry Stebbings

89,458 просмотров • 9 месяцев назад

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09

We built an AI app that had 1,000 DAU and $2k MRR before it launched. It’s called Monologue and it’s a smart dictation app built by a single developer: Naveen Naidu. We just launched Monologue yesterday, and it’s one of the fastest-growing and stickiest AI apps that Every 📧 has ever built. Naveen and Monologue are compelling because he’s competing against companies that have raised $50m or more. Because of AI he was able to build an extremely polished, delightful app by himself in just a few months. I brought Naveen on to AI & I along with Every 📧 COO Brandon Gell (Brandon Gell) to talk about his journey with Monologue. We get into: - Why shipping fast is the only thing that matters in AI: Monologue might look like an overnight success, but it wasn’t Naveen’s first, second—or even third—app. Over time, he built a muscle to get quality apps out the door, iterate on them, and learn from what he was seeing. - How he got to PMF inside of Every: The mistake Naveen regrets most in his entrepreneurial journey is building in the dark. Inside of Every 📧 he has an environment where feedback is plentiful—and it let him iterate extremely quickly. - His stack for building production grade AI apps: Naveen breaks down how he used tools like OpenAI’s Codex to do the work of a whole engineering team, including solving hard technical problems like Mac hotkey handling. This is a must-watch for anyone who wants to see how far a single developer and some AI tools can really go. Watch below! Timestamps: Introduction: 00:01:27 A live demo of Monologue: 00:03:51 Hard lessons from Naveen’s years in the wilderness: 00:06:27 Building a muscle to ship fast: 00:12:29 The spark that became Monologue: 00:21:11 Dogfooding your way to a killer feature: 00:26:09 Why the harshest product feedback is the most valuable: 00:29:45 Every’s strategy for launching an app in a crowded space: 00:31:47 Giving Monologue the Every “smell”: 00:40:08 Naveen’s one-person AI stack to build beautiful apps: 00:45:09

Dan Shipper 📧

23,644 просмотров • 11 месяцев назад

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Stanford mathematician Ravi Vakil, president of the American Mathematical Society, expects AI’s impact on mathematics to come as a phase change, not a slow climb. Every major shift in math has caught experts off guard, he says. This one will be no different, except that all our predictions will be even more wrong. -- Timestamps – 00:00:00 – Playing games with imperfect information against AI 00:02:35 – When AI will learn to be truly creative 00:03:48 – AI’s impact will be even more unpredictable than the internet 00:08:02 – What an “AlphaGo moment” would look like for math 00:10:35 – How AI will actually be useful in mathematical research 00:12:20 – Writing “wow”-level math problems for AI 00:15:06 – On a 0-10 scale, AI will change math 8 + 3i 00:16:17 – Is math the next chess?

Epoch AI

12,920 просмотров • 10 месяцев назад

Ken Ono, mathematician at Axiom Math and the University of Virginia, on why 2026 shouldn't be the race for more compute. What you'll learn: - The three forms of AI (chatbots, superhuman search, formalization) and which one is hiring - Why students who want to be mathematicians should start 'formalizing' now - The test for a good question vs. a hollow one, and how to tell who you're really asking it for - How he spots outlier talent that a box-checking admissions system throws away - Why comparing yourself to LeBron or a Nobel laureate guarantees you lose Timeline: 00:00 Intro 02:13 Q1) Have you ever felt obsolete because of AI? 09:33 Q2) What makes a good question? 11:35 Q3) What does 'Superintelligence' mean? 14:04 Q4) What's the biggest AI misconception? 19:34 Q5) What judgement can't AI replace? 21:13 Q6) How do I get past AI filters? 27:45 Q7) Is it bad that I prefer talking to AI over humans? 29:30 Q8) AI is smarter than most of us. What should we do? 31:46 Q9) As a junior, how can I catch up with seniors in the AI era?

Ken Ono, mathematician at Axiom Math and the University of Virginia, on why 2026 shouldn't be the race for more compute. What you'll learn: - The three forms of AI (chatbots, superhuman search, formalization) and which one is hiring - Why students who want to be mathematicians should start 'formalizing' now - The test for a good question vs. a hollow one, and how to tell who you're really asking it for - How he spots outlier talent that a box-checking admissions system throws away - Why comparing yourself to LeBron or a Nobel laureate guarantees you lose Timeline: 00:00 Intro 02:13 Q1) Have you ever felt obsolete because of AI? 09:33 Q2) What makes a good question? 11:35 Q3) What does 'Superintelligence' mean? 14:04 Q4) What's the biggest AI misconception? 19:34 Q5) What judgement can't AI replace? 21:13 Q6) How do I get past AI filters? 27:45 Q7) Is it bad that I prefer talking to AI over humans? 29:30 Q8) AI is smarter than most of us. What should we do? 31:46 Q9) As a junior, how can I catch up with seniors in the AI era?

EO

102,168 просмотров • 1 месяц назад

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Always so much fun to chat with Grant Sanderson AI has been making much faster progress in math than in other fields. As a result, mathematics is showing us, very concretely, what AI progress in other fields will look like. Even within mathematics, there's a jagged landscape. What does it look like? What is the nature of the most important conceptual breakthroughs in the history of mathematics, and how different are they from what AIs are currently able to do? Does AI (on net) increase or decrease human understanding of the field? How big is the overhang from having AIs systematically try to connect ideas already in the literature? And what advice does Grant have for aspiring mathematicians, coders, and other students who are passionate about fields that are being most transformed upon by AI? 0:00:00 – AI is discovering new proofs. Is that AGI? 0:11:32 – The verification loop on conceptual breakthroughs can be a century long 0:26:12 – Will we understand an AI proof of the Riemann hypothesis? 0:38:08 – Can AI find the hidden bridges between fields? 0:53:48 – Why real-world tasks don’t fit into RL environments 1:07:07 – Good writing requires theory of mind that AI still lacks 1:16:02 – Why learning will still depend on human curation Look up Dwarkesh Podcast on Spotify, Apple Podcasts, YouTube, etc.

Dwarkesh Patel

290,780 просмотров • 2 месяцев назад

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Everyone is focused on tracking the ways LLMs are getting better. And they are. But we know there are still things that LLMs can’t do well—the tasks where you can feel the architecture fighting the problem. So I was excited to chat with Eve Bodnia (@eve_bodnia), who is developing an alternative AI model to LLMs, on Every 📧's AI & I. Eve's argument: energy-based models (EBMs), which map possible outcomes onto a mathematical landscape, will lead to the next AI phase shift. We get into: - How energy-based models work. Likely outcomes sit in valleys, and unlikely ones sit on peaks. Whereas LLMs process one token at a time, an EBM scans the full terrain to find the lowest point, or the most probable answer. - Language-based versus data-native models. LLMs are language-dependent even when the problem has nothing to do with language. "If your data is numbers, relationships, and functions, and you try to map those rules into words and then search for the next word, you're losing a lot of information," Bodnia says. EBMs work directly with the underlying data structure, including numbers and spatial coordinates. - Sequential versus panoramic reasoning. An LLM is like driving through San Francisco without a map. Each turn constrains the next, and if you go down the wrong street, you can't reverse course. An EBM has the bird's-eye view—it can evaluate multiple routes at once and course-correct before hitting a dead end. - The LLM plateau no one wants to talk about. LLMs are getting incrementally better, step-change improvements aren’t coming, Eve argues. To achieve that, we need new solutions that compensate for what LLMs are inherently bad at, like non-language reasoning, verification, and real-time data analysis. This is a must-watch for anyone who's curious what might come after the LLM. Watch below! Timestamps: Introduction: 00:00:51 Why correctness and verifiability matter in AI: 00:02:09 What an energy-based model is: 00:09:33 How EBMs construct energy landscapes to understand data: 00:14:21 Why modeling intelligence through language alone is a flawed approach: 00:19:00 What it means for a model to "understand" data: 00:26:54 How EBMs solve the vibe coding problem and enable formally verified code: 00:37:21 Why LLM progress is plateauing: 00:43:21 Mission-critical industries haven't adopted LLMs, and why EBMs can fill that gap: 00:49:54

Dan Shipper 📧

26,900 просмотров • 4 месяцев назад

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 просмотров • 9 месяцев назад

Bitcoin just had its largest week in history. Jordi Visser and I discuss what happened, why the rally should be sustainable, and what to expect from bitcoin going forward. We also break down the collision of AI and crypto, why bitcoin is the purest AI trade, Stripe and Ramp's battle to control AI model routers, and how to think about sizing bitcoin in your portfolio. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:02 - Bitcoin's weekly surge & the "sigma week" behind it 5:38 - The bitcoin vibe shift & intersection with AI 15:26 - Is multiple compression a warning sign for the market? 19:03 - How much bitcoin should be in your portfolio? 23:27 - Moderna, AI & the future of biotech 29:20 - Stripe's OpenRouter deal & the fight for AI model routers 34:13 - Are model routers a threat to Anthropic and OpenAI? 44:00 - Why people fear data centers more than nuclear plants 54:46 - Jordi's bitcoin call to arms

Bitcoin just had its largest week in history. Jordi Visser and I discuss what happened, why the rally should be sustainable, and what to expect from bitcoin going forward. We also break down the collision of AI and crypto, why bitcoin is the purest AI trade, Stripe and Ramp's battle to control AI model routers, and how to think about sizing bitcoin in your portfolio. YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 1:02 - Bitcoin's weekly surge & the "sigma week" behind it 5:38 - The bitcoin vibe shift & intersection with AI 15:26 - Is multiple compression a warning sign for the market? 19:03 - How much bitcoin should be in your portfolio? 23:27 - Moderna, AI & the future of biotech 29:20 - Stripe's OpenRouter deal & the fight for AI model routers 34:13 - Are model routers a threat to Anthropic and OpenAI? 44:00 - Why people fear data centers more than nuclear plants 54:46 - Jordi's bitcoin call to arms

Anthony Pompliano 🌪

278,145 просмотров • 8 дней назад

Kevin Kelly (Kevin Kelly) has spent more time thinking about the future than almost anyone else. From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next. Today, as WIRED’s senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on AI & I. We get into: - How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell. - Why history is so important to help you understand the future. To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands. - From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked @ChatGPTapp to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself. - What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is. - Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today. - Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized that the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work. This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired. Watch below! Timestamps: 1. 00:00:50 - Introduction 2. 00:01:10 - Why Dan and Kelly love Annie Dillard 3. 00:12:52 - Learn how to predict the future like Kelly 4. 00:16:10 - What the history of electricity can teach us about AI 5. 00:20:13 - How Kelly thinks about the nature of intelligence 6. 00:25:44 - Kelly's advice on discovering your competitive advantage 7. 00:29:33 - The story of how Kelly assembled a bench of star writers for Wired 8. 00:34:43 - How Kelly used ChatGPT to co-create a book 9. 00:39:12 - Using AI as a mirror for your mind 10. 00:43:43 - What Kelly learned from betting on VR in the 1980s

Kevin Kelly (Kevin Kelly) has spent more time thinking about the future than almost anyone else. From VR in the 1980s to the blockchain in the 2000s—and now generative AI—Kevin has spent a lifetime journeying to the frontiers of technology, only to return with rich stories about what’s next. Today, as WIRED’s senior maverick, his project for 2025 is to outline what the next century looks like in a world shaped by new technologies like AI and genetic engineering. He’s a personal hero of mine—not to mention a fellow Annie Dillard fan—and it was a privilege to have him on AI & I. We get into: - How you can predict the future. According to Kevin, the draw of new frontiers—from the first edition of Burning Man and remote corners of Asia, to the early days of the internet and AI—isn’t staying at the edge forever; it's returning with a story to tell. - Why history is so important to help you understand the future. To stay grounded while exploring what’s new, Kevin balances the thrill of the future with the wisdom of the past. He pairs AI research with reading about history, and playing with an AI tool by retreating to his workshop to make something with his hands. - From 1,000 true fans to an audience of one. Rather than creating for an audience, Kevin has been using LLMs to explore his own imagination. After realizing that da Vinci, Martin Luther, and Columbus were alive at the same time, he asked @ChatGPTapp to imagine them snowed in at a hotel together, and the prompt spiraled into an epic saga, co-written with AI. But he has no plans to publish it because the joy was in creating something just for himself. - What the history of electricity can teach us about AI. Kevin draws a parallel between AI and the early days of electricity. We could produce electric sparks long before we understood the forces that created them, and now we’re building intelligent machines without really understanding what intelligence is. - Why Kevin sees intelligence as a mosaic—not a monolith. Kevin believes intelligence isn’t a single force, but a compound of many cognitive elements. He draws from Marvin Minsky’s “society of mind”—the theory that the mind is made up of smaller agents working together—and sees echoes of this in the Mixture of Experts architecture used in some models today. - Your competitive advantage is being yourself. Don’t aim to be the best—aim to be the only. Kevin realized that the stories no one else at Wired wanted to write were often the ones he was suited for, and trusting that instinct led to some of his best work. This is a must-watch for anyone who wants to make sense of AI through the lens of history, learn how to spot the future before it arrives, or grew up reading Wired. Watch below! Timestamps: 1. 00:00:50 - Introduction 2. 00:01:10 - Why Dan and Kelly love Annie Dillard 3. 00:12:52 - Learn how to predict the future like Kelly 4. 00:16:10 - What the history of electricity can teach us about AI 5. 00:20:13 - How Kelly thinks about the nature of intelligence 6. 00:25:44 - Kelly's advice on discovering your competitive advantage 7. 00:29:33 - The story of how Kelly assembled a bench of star writers for Wired 8. 00:34:43 - How Kelly used ChatGPT to co-create a book 9. 00:39:12 - Using AI as a mirror for your mind 10. 00:43:43 - What Kelly learned from betting on VR in the 1980s

Dan Shipper 📧

67,907 просмотров • 1 год назад