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Yeah $TIG is flying under the radar An open protocol for algorithm development listening to John explain how algorithms can optimise & help you extract maximum utility from any given chip DeepMind, OpenAI, Anthropic, they're all racing to own the algorithmic layer if they win, you pay a tax...

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Jack Ai-Leung

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38,463 просмотров • 2 месяцев назад •via X (Twitter)

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Shyam Sankar is trying to mobilize a country. The CTO of Palantir is ringing the alarm on the necessity to resurrect America’s industrial base, win the defense technology race against other countries, and empower workers across the nation without buying into the fears of AI doomerism. In this interview, we sat down to discuss his latest book, why he felt now was the right time to release it, and the message he hopes to communicate to the world. Thank you to Shyam Shyam Sankar for taking the time! Timestamps: 00:00 - Intro 01:02 - How Shyam got to Palantir 02:19 - Palantir's Journey over the years 07:40 - Why is now the time to mobilize 09:40 - Hardware vs Software 14:57 - The Youth and Communism 20:15 - US vs China 22:24 - AI Doomerism 26:21 - Palantir's impact on how Shyam thinks of AI 29:12 - CEOs on the AI doomer case vs optimistic case 34:50 - Rapid Fire
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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:
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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)
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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 • 2 месяцев 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
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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 📧

47,209 просмотров • 1 год назад

all of us use and love Jupiter, but how does one of the biggest DeFi platforms in Solana work under the hood? how do they about their product? what is managing infra at their scale like? how do they optimise performance? shek and Harkirat Singh dive deep into all of this and more with ⚔️ SIONG, cofounder of jupiter in episode #1 of the OnlyDevs pod. the cherry on top? we have two dev bounties using jupiter for you to apply and win $$ from. check out the pod and tweets below to learn more! timestamps: (00:00) intro of the video and bounty announcements (04:50) meet siong! (10:45) scaling user demand (12:40) building a token swap product (19:06) switching from JS to rust (23:08) optimizing algorithms & performance (28:23) open-sourced opportunities (30:02) monetization & decentralization (41:15) UX & adaptation (52:18) expanding solana's dev community
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all of us use and love Jupiter, but how does one of the biggest DeFi platforms in Solana work under the hood? how do they about their product? what is managing infra at their scale like? how do they optimise performance? shek and Harkirat Singh dive deep into all of this and more with ⚔️ SIONG, cofounder of jupiter in episode #1 of the OnlyDevs pod. the cherry on top? we have two dev bounties using jupiter for you to apply and win $$ from. check out the pod and tweets below to learn more! timestamps: (00:00) intro of the video and bounty announcements (04:50) meet siong! (10:45) scaling user demand (12:40) building a token swap product (19:06) switching from JS to rust (23:08) optimizing algorithms & performance (28:23) open-sourced opportunities (30:02) monetization & decentralization (41:15) UX & adaptation (52:18) expanding solana's dev community

Superteam India

16,744 просмотров • 1 год назад

Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!
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Here's my conversation with Lucas Atkins and the team at Arcee.ai on their path to training and releasing Trinity Large today. From going all in on open models built end to end in the US 6 months ago to having the model in hand is no easy feet. I loved this conversation on how to design a startup around open models and take a bold step to scale it up. I'm openly an Arcee fan, watching them take risk and pull it off. We discuss: - The state (and future) of open vs. closed models, - The business of selling open models for on-prem deployments, - The story of Arcee AI & going “all-in” on this training run, - The ATOM project, - Building frontier model training teams in 6 months, - and other great topics. I really loved this one, and think you well too. Chapters: 00:00:00 Intro: Arcee AI, Trinity Models & Trinity Large 00:08:26 Transitioning a Company to Pre-training 00:13:00 Technical Decisions: Muon and MoE 00:18:41 Scaling and MoE Training Pain 00:23:14 Post-training and RL Strategies 00:28:09 Team Structure and Data Scaling 00:31:31 The Trinity Manifesto: US Open Weights 00:42:31 Specialized Models and Distillation 00:47:12 Infrastructure and Hosting 400B 00:50:53 Open Source as a Business Moat 00:56:31 Predictions: Best Model in 2026 01:02:29 Lightning Round & Conclusions More great open model builder podcasts coming soon!

Nathan Lambert

26,872 просмотров • 5 месяцев назад

How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?
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How does math research change when the cost of trying your first dumb idea goes to zero? Daniel Litt joins Greg Burnham and Anson Ho to discuss what today’s models can and can’t do in math, and how far they are from doing high-quality research. 0:00:00 What's the hardest math problem AI can solve today? 00:16:08 How helpful are today’s AI models for math research? 00:23:36 Junk papers, LLM-generated proofs, and the refereeing crisis 00:27:21 AI enables searching through problems at scale 00:33:49 When will AI be good enough to publish in top math journals? 00:42:15 What are the returns to intelligence? 00:59:50 Will AI solve Millennium problems? 01:11:54 Is math full of low-hanging fruit? 01:18:47 How Daniel has adapted his professional life to AI progress 01:25:28 What do AI math benchmarks actually measure? 01:33:05 Designing the Open Problems benchmark 01:56:35 Do mathematicians believe heuristic arguments about conjectures? 02:01:24 What if FrontierMath: Open Problems gets solved? 02:06:53 Is AI on the cusp of accelerating math progress?

Epoch AI

178,703 просмотров • 5 месяцев назад

🚨New Episode: Please welcome Shiloe Bear to the Fort! As the founder of Creek Development, Shiloe has completed 63 commercial redevelopment projects and 5 land entitlement plays in the San Francisco Bay Area. Leveraging her deep construction expertise, she challenges project assumptions and incorporates impactful design to achieve remarkable outcomes for her clients and investors. We discuss: - Building Creek as an outsourced developer - Converting industrial buildings into biotech lab space - A deep dive into the Bay Area RE market - Why design is such an important aspect of development 00:00:00 - Intro 00:01:11 - Shiloe’s upbringing and journey to doing development in the Bay Area 00:08:25 - What are the projects you work on the most? 00:11:01 - Why is there such a demand for outsourced development? 00:11:47 - What makes your firm different? 00:16:50 - Minimum Viable Projects 00:21:47 - How early in a project do you need to be brought in? 00:25:14 - What are some obvious parts of a project where you can save money? 00:27:30 - How would you describe the Bay Area market right now? 00:33:35 - Office to Residential 00:37:46 - What’s the environment like for Oakland? 00:42:31 - How Shiloe is planning to pivot her business into doing their own developments 00:47:51 - How important are the relationships you’ve developed in The Bay over the past 10 years? 00:53:06 - The BioTech market and development strategy 01:00:55 - Why design matters 01:08:27 - How far out are labs looking for space? 01:09:46 - Lenders, Leases, and hold periods 01:12:36 - Why did this become your niche? 01:14:13 - Shiloe’s bird-watching skills & long-distance running
1:17:35

🚨New Episode: Please welcome Shiloe Bear to the Fort! As the founder of Creek Development, Shiloe has completed 63 commercial redevelopment projects and 5 land entitlement plays in the San Francisco Bay Area. Leveraging her deep construction expertise, she challenges project assumptions and incorporates impactful design to achieve remarkable outcomes for her clients and investors. We discuss: - Building Creek as an outsourced developer - Converting industrial buildings into biotech lab space - A deep dive into the Bay Area RE market - Why design is such an important aspect of development 00:00:00 - Intro 00:01:11 - Shiloe’s upbringing and journey to doing development in the Bay Area 00:08:25 - What are the projects you work on the most? 00:11:01 - Why is there such a demand for outsourced development? 00:11:47 - What makes your firm different? 00:16:50 - Minimum Viable Projects 00:21:47 - How early in a project do you need to be brought in? 00:25:14 - What are some obvious parts of a project where you can save money? 00:27:30 - How would you describe the Bay Area market right now? 00:33:35 - Office to Residential 00:37:46 - What’s the environment like for Oakland? 00:42:31 - How Shiloe is planning to pivot her business into doing their own developments 00:47:51 - How important are the relationships you’ve developed in The Bay over the past 10 years? 00:53:06 - The BioTech market and development strategy 01:00:55 - Why design matters 01:08:27 - How far out are labs looking for space? 01:09:46 - Lenders, Leases, and hold periods 01:12:36 - Why did this become your niche? 01:14:13 - Shiloe’s bird-watching skills & long-distance running

Chris Powers

64,474 просмотров • 2 лет назад

A Physics That Could Finally Unify Reality (Part 2/2) - James Ellias, DemystifySci #385 A quiet tremor sounds beneath the floorboards of physics, the still-living heartbeat of the forgotten search for the hidden substance that carries every wave and whisper of the universe. We walk through the fog of equations and theories of fundamental physics with James Ellias of James Ellias, and ask if there is a material truth lies beneath the symbols, or if we have to be satisfied with the short-sighted vision of mathematics alone. 00:00 Go! 00:04:37 Central Equations in Physics 00:08:20 The Importance of a Medium in Physics 00:10:00 Empowerment Through Understanding Physics 00:12:36 Rationality and Truth in Society 00:16:31 Existence vs Consciousness 00:20:30 Discussion on Existence and Consciousness 00:24:25 Role of Imagination in Existence 00:28:10 Properties and Entities in Physics 00:30:15 The Nature of Aether and Physical Mediums 00:36:08 Clarity and Understanding in Physics 00:40:26 Discussion on Force and Aether 00:44:57 Relationship of Entities and Actions 00:49:30 Theoretical Framework for Aether 00:58:04 Exploration of Aether Theories 01:00:40 Importance of Conciseness in Communication 01:02:00 Understanding Physics Before Proposing Hypotheses 01:05:15 Reevaluation of Flawed Theories 01:09:35 The Evolution of Key Physics Concepts 01:15:19 Context-Specific Nature of Constants 01:19:43 Historical Context in Physics 01:21:36 The Nature of Electrons 01:25:53 J.J. Thomson’s Evolving Perspective 01:29:24 Upcoming Work and Philosophical Frameworks 01:32:11 Collaborations
1:33:44

A Physics That Could Finally Unify Reality (Part 2/2) - James Ellias, DemystifySci #385 A quiet tremor sounds beneath the floorboards of physics, the still-living heartbeat of the forgotten search for the hidden substance that carries every wave and whisper of the universe. We walk through the fog of equations and theories of fundamental physics with James Ellias of James Ellias, and ask if there is a material truth lies beneath the symbols, or if we have to be satisfied with the short-sighted vision of mathematics alone. 00:00 Go! 00:04:37 Central Equations in Physics 00:08:20 The Importance of a Medium in Physics 00:10:00 Empowerment Through Understanding Physics 00:12:36 Rationality and Truth in Society 00:16:31 Existence vs Consciousness 00:20:30 Discussion on Existence and Consciousness 00:24:25 Role of Imagination in Existence 00:28:10 Properties and Entities in Physics 00:30:15 The Nature of Aether and Physical Mediums 00:36:08 Clarity and Understanding in Physics 00:40:26 Discussion on Force and Aether 00:44:57 Relationship of Entities and Actions 00:49:30 Theoretical Framework for Aether 00:58:04 Exploration of Aether Theories 01:00:40 Importance of Conciseness in Communication 01:02:00 Understanding Physics Before Proposing Hypotheses 01:05:15 Reevaluation of Flawed Theories 01:09:35 The Evolution of Key Physics Concepts 01:15:19 Context-Specific Nature of Constants 01:19:43 Historical Context in Physics 01:21:36 The Nature of Electrons 01:25:53 J.J. Thomson’s Evolving Perspective 01:29:24 Upcoming Work and Philosophical Frameworks 01:32:11 Collaborations

Anastasia

13,523 просмотров • 7 месяцев назад

$60 Billion SpaceX Cursor Deal? This week on The Brainstorm ⬇️ 0:00 — SpaceX buying Cursor for $60 B? We think this is the reason why 6:05 — SpaceX has reportedly committed ~$310B in 12 months acquiring its way toward the AI frontier 9:55 — "If you're not getting rate-limited by Claude, you might just be bad at using AI" 12:24 — The compute crunch that could hit in 2 years, and why we think few are pricing it in 15:10 — Tim Cook is out. Could Apple finally be about to start cooking? 18:05 — The AI gap at Apple that, if Microsoft is any guide, could prove costly 20:14 — Why AirPods, not the iPhone, may be Apple's most underrated AI weapon 33:49 — OpenAI is building hardware with Qualcomm. Why we don't think it'll really be a "phone" 40:12 — Why Google could go from paying Apple ~$20B/yr to Apple paying Google
43:20

$60 Billion SpaceX Cursor Deal? This week on The Brainstorm ⬇️ 0:00 — SpaceX buying Cursor for $60 B? We think this is the reason why 6:05 — SpaceX has reportedly committed ~$310B in 12 months acquiring its way toward the AI frontier 9:55 — "If you're not getting rate-limited by Claude, you might just be bad at using AI" 12:24 — The compute crunch that could hit in 2 years, and why we think few are pricing it in 15:10 — Tim Cook is out. Could Apple finally be about to start cooking? 18:05 — The AI gap at Apple that, if Microsoft is any guide, could prove costly 20:14 — Why AirPods, not the iPhone, may be Apple's most underrated AI weapon 33:49 — OpenAI is building hardware with Qualcomm. Why we don't think it'll really be a "phone" 40:12 — Why Google could go from paying Apple ~$20B/yr to Apple paying Google

ARK Invest

64,826 просмотров • 2 месяцев назад

Sequoia’s Shaun Maguire wrote a private hardware manifesto arguing that over the next 25 years, most of the money will be made in hardware: "Every software revolution is preceded by a hardware revolution." "To have the iOS App Store that enabled Uber, DoorDash, and all of these great companies - you needed to have the iPhone." "This AI revolution - we're seeing what it can do from the software layer, but it's still limited by hardware." "The hardware we were doing for a long time was all following Moore's Law. It was all branching out of this decision in the mid-1950s to go all in on the silicon supply chain." "That has created magic, and there's still a couple orders of magnitude of juice to squeeze, but we’re hitting fundamental physics limits - Dennard scaling, things like that." "I think this tech tree is branching into humanoid robots, into silicon photonics, into orbital data centers - all of these new hardware areas where there's going to be 20+ years of progress." "There's going to be incredible businesses built on the back of this. And a lot of dumpster fires."
3:35

Sequoia’s Shaun Maguire wrote a private hardware manifesto arguing that over the next 25 years, most of the money will be made in hardware: "Every software revolution is preceded by a hardware revolution." "To have the iOS App Store that enabled Uber, DoorDash, and all of these great companies - you needed to have the iPhone." "This AI revolution - we're seeing what it can do from the software layer, but it's still limited by hardware." "The hardware we were doing for a long time was all following Moore's Law. It was all branching out of this decision in the mid-1950s to go all in on the silicon supply chain." "That has created magic, and there's still a couple orders of magnitude of juice to squeeze, but we’re hitting fundamental physics limits - Dennard scaling, things like that." "I think this tech tree is branching into humanoid robots, into silicon photonics, into orbital data centers - all of these new hardware areas where there's going to be 20+ years of progress." "There's going to be incredible businesses built on the back of this. And a lot of dumpster fires."

TBPN

218,127 просмотров • 3 месяцев назад

Matthew Smith has spent the last 18 months modeling every well, pipeline, storage facility, and power plant in the American natural gas system. His conclusion is that the US is heading toward a natural gas shortage with no precedent, beginning in 2028. By 2030, he believes we could exhaust our working natural gas storage entirely. The fuel everyone in AI is counting on, and that everyone assumes is abundant, is not there. And because gas sets the price of electricity in most of the country, he argues Americans will pay for the shortage in their power bills. Matthew has worked in energy markets for over 20 years and is the CIO of Chronometer Partners. This is his second time on the show, and he's one of my favorite people to talk to about energy. We discuss: - Why the bottleneck is moving from power to fuel - Why we can't just shut off exports - 2028 as the inflection point - Large-scale nuclear v. SMRs - Who wins, who loses, and what can still be done Enjoy! TIMESTAMPS 0:00 Intro 1:30 What Drives the Deficit 11:00 Why Supply Can’t Catch Up 20:35 The 2030 Gas Crisis 25:05 Winners and Losers 29:00 Nuclear and Solar 33:30 Consumers Pay the Bill 37:20 AI’s Next Shortage 45:25 Solutions and Global Stakes 51:15 The Coming Gas Knife Fight
59:44

Matthew Smith has spent the last 18 months modeling every well, pipeline, storage facility, and power plant in the American natural gas system. His conclusion is that the US is heading toward a natural gas shortage with no precedent, beginning in 2028. By 2030, he believes we could exhaust our working natural gas storage entirely. The fuel everyone in AI is counting on, and that everyone assumes is abundant, is not there. And because gas sets the price of electricity in most of the country, he argues Americans will pay for the shortage in their power bills. Matthew has worked in energy markets for over 20 years and is the CIO of Chronometer Partners. This is his second time on the show, and he's one of my favorite people to talk to about energy. We discuss: - Why the bottleneck is moving from power to fuel - Why we can't just shut off exports - 2028 as the inflection point - Large-scale nuclear v. SMRs - Who wins, who loses, and what can still be done Enjoy! TIMESTAMPS 0:00 Intro 1:30 What Drives the Deficit 11:00 Why Supply Can’t Catch Up 20:35 The 2030 Gas Crisis 25:05 Winners and Losers 29:00 Nuclear and Solar 33:30 Consumers Pay the Bill 37:20 AI’s Next Shortage 45:25 Solutions and Global Stakes 51:15 The Coming Gas Knife Fight

Patrick OShaughnessy

949,157 просмотров • 20 часов назад

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
39:32

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

107,448 просмотров • 4 месяцев назад

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 просмотров • 2 месяцев назад

Here's my interview with , one of the top ai agent traders We discuss everything from what he's buying on this dip, how he finds tokens, his favorite groups and more 0:00 Is the party over for AI agents? 2:55 Is Virtuals Protocol your main playground? 4:27 What's an example of a mispriced token? 5:39 What's Virtuals biggest competitor? 5:57 If you had to restart, would it be on Virtuals? 9:23 What ai agents are you buying? 16:05 How are you finding ai agents? 19:45 Who would you recommend following on X? 21:25 How are you evaluating tokens? 23:50 Do you think any of this tech will be used in web2? 26:20 Do you speak with the dev before buying? 27:05 Where do you post alpha? 29:00 Walking through a recent play 38:00 What is your price target on Anon? 40:20 What platform are you LPing on? 44:10 Do you use any tools? 46:50 Are there any upcoming tokens you're looking at?
50:30

Here's my interview with , one of the top ai agent traders We discuss everything from what he's buying on this dip, how he finds tokens, his favorite groups and more 0:00 Is the party over for AI agents? 2:55 Is Virtuals Protocol your main playground? 4:27 What's an example of a mispriced token? 5:39 What's Virtuals biggest competitor? 5:57 If you had to restart, would it be on Virtuals? 9:23 What ai agents are you buying? 16:05 How are you finding ai agents? 19:45 Who would you recommend following on X? 21:25 How are you evaluating tokens? 23:50 Do you think any of this tech will be used in web2? 26:20 Do you speak with the dev before buying? 27:05 Where do you post alpha? 29:00 Walking through a recent play 38:00 What is your price target on Anon? 40:20 What platform are you LPing on? 44:10 Do you use any tools? 46:50 Are there any upcoming tokens you're looking at?

Gorilla

32,519 просмотров • 1 год назад

How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell
49:13

How is an open ecosystem powering the next generation of AI for developers? Recording live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. Together they unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models. This relentless pace of hardware and software innovation is reshaping the AI landscape. Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications. 00:00 Live from AMD's Advancing AI 2025 Event 00:30 Introduction to Anush Elangovan 01:38 The MI350 GPU Series Unveiled 04:57 CDNA4 Architecture Explained 07:00 The Future of AI Infrastructure 08:32 AMD's Developer Cloud and ROCm 7 11:50 Cultural Shift at AMD 14:48 Open Source and Community Contributions 18:35 Software Longevity and Ecosystem Strategy 22:19 AI Agents and Performance Gains 27:36 AI's Role in Solving Power Challenges 28:11 Thanking Anush 28:42 Introduction to Sharon Zhou 29:45 Sharon's Focus at AMD 30:39 Engaging Developers with AMD's AI Tools 31:24 Listening to the AI Community 33:56 Open Source and AI Development 45:04 Future of AI and Self-Improving Models 48:04 Final Thoughts and Farewell

Galileo

37,186 просмотров • 1 год назад

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold
43:06

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold

Michaël van de Poppe

122,039 просмотров • 1 месяц назад