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What are current economic models missing about AGI? How would we know if we were approaching explosive growth? Stanford economist Phil Trammell has been rigorously thinking about the intersection of economic theory and AI (incl. AGI) for over five years, long before the recent surge of interest in large...

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Epoch AI

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New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?
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New episode with former US Treasury Secretary and current OpenAI board member Larry Summers (Lawrence H. Summers). We discuss: - How he's been learning about AI. - The odds of the technology delivering a much faster economic growth regime (akin to the Industrial Revolution). - How AGI might change economic policymaking. Enjoy! Timestamps: (0:00:00) - Introduction. (0:00:46) - Larry's journey teaching himself about AI & deep learning since joining OpenAI’s board. (0:09:15) - How many hours per week has Larry been spending on OpenAI-related stuff? (0:10:16) - Which bottleneck to AI scaling does Larry think is the most underrated? (0:12:22) - Approximately what share of time do today's AI researchers spend on tasks that AI will be doing for them in five years? (0:15:01) - What explains the remarkable steadiness of US economic growth over the last 150 years? (0:19:42) - How likely is it that AI initiates a new growth regime with average growth that’s ~10x faster than today? (0:21:36) - What are the best economic arguments for believing AI won’t deliver a regime of ever-increasing growth rates? (0:25:33) - How much could AGI boost economic growth in developing countries merely by helping their policymakers make better decisions? (0:28:20) - How much better could monetary policy be if the Fed had AGI? (0:31:40) - How much would having AGI have helped US economic policymakers during the financial crisis & Great Recession? (0:36:07) - Is the CCP infiltrating and stealing the IP of major AI labs in the US and UK? (0:39:35) - At what point should AI be nationalised? (0:42:39) - How would Bill Clinton or Barack Obama be thinking about AI governance? (0:44:27) - If OpenAI restructures to a public benefit corporation, how does that change its incentives? (0:46:18) - What does Daron Acemoglu miss in his analysis of the economic impacts of AI?

Joseph Noel Walker

85,856 просмотров • 1 год назад

I hassled tylercowen about why he doesn't expect explosive economic growth from AGI. How could we possibly add 100 billion extra workers and only get 0.5% more growth? Also featuring Stalin's library, EU decels, and how Churchill was an underachiever. Hilarious and provocative throughout. My 4th interview with Tyler. He surprises me every time. Enjoy! Timestamps 0:00:00 - Economic Growth and AI 0:15:45 - Founder Mode and increasing variance 0:30:19 - Effective Altruism and Progress Studies 0:33:53 - What AI changes for Tyler 0:45:45 - The slow diffusion of innovation 0:50:41 - Stalin's library 0:53:07 - DC vs SF vs EU
1:00:33

I hassled tylercowen about why he doesn't expect explosive economic growth from AGI. How could we possibly add 100 billion extra workers and only get 0.5% more growth? Also featuring Stalin's library, EU decels, and how Churchill was an underachiever. Hilarious and provocative throughout. My 4th interview with Tyler. He surprises me every time. Enjoy! Timestamps 0:00:00 - Economic Growth and AI 0:15:45 - Founder Mode and increasing variance 0:30:19 - Effective Altruism and Progress Studies 0:33:53 - What AI changes for Tyler 0:45:45 - The slow diffusion of innovation 0:50:41 - Stalin's library 0:53:07 - DC vs SF vs EU

Dwarkesh Patel

1,324,330 просмотров • 1 год назад

Will AI drive Europe towards a “high interest rates, no growth” future? How will automation impact entry-level workers and the structure of firms? What role does Europe play as the US and China continue to push AI progress? On this episode of Epoch After Hours, Luis Garicano 🇪🇺🇺🇦 joins Epoch AI researchers Anson Ho and Andrei Potlogea to discuss these questions. -- Timestamps -- 0:00:00 – Will AI trigger explosive growth? 0:06:26 – Short-run macroeconomic effects 0:11:29 – The decline of junior jobs 0:20:21 – The missing training ladder 0:39:31 – Europe’s AI regulation problem 0:52:46 – Who captures AI value? 01:08:17 – AI, interest rates & fiscal future
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Are AI benchmarks doomed? Greg Burnham and Tom Adamczewski join Anson Ho to push back on benchmark pessimism and dig into what the next generation of AI benchmarks could look like. (0:00:00) - Preview (0:00:36) - Intro: Are AI benchmarks doomed? (0:03:13) - The costs and benefits of benchmark development (0:11:48) - MirrorCode and scalable benchmarks (0:20:57) - AI speed-up in benchmark development (0:23:28) - The benchmark-reality gap (0:38:26) - Can an AGI benchmark exist? (0:43:18) - Beyond automated scoring (1:00:45) - How AI changes benchmark building in practice
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.Satya Nadella on: - why he doesn’t believe in AGI but does believe in 10% economic growth - Microsoft’s new topological qubit breakthrough and gaming world models - whether Office commoditizes LLMs or the other way around Links below. Enjoy! Timestamps 0:00:00 - Intro 0:05:48 - AI won't be winner-take-all 0:16:02 - World economy growing by 10% 0:22:23 - Decreasing price of intelligence 0:31:03 - Microsoft's Quantum breakthrough 0:43:35 - Microsoft's gaming world model 0:50:35 - Legal barriers to AI 0:56:30 - Getting AGI safety right 1:05:43 - 34 years at Microsoft 1:11:31 - Does Satya Nadella believe in AGI?
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.Satya Nadella on: - why he doesn’t believe in AGI but does believe in 10% economic growth - Microsoft’s new topological qubit breakthrough and gaming world models - whether Office commoditizes LLMs or the other way around Links below. Enjoy! Timestamps 0:00:00 - Intro 0:05:48 - AI won't be winner-take-all 0:16:02 - World economy growing by 10% 0:22:23 - Decreasing price of intelligence 0:31:03 - Microsoft's Quantum breakthrough 0:43:35 - Microsoft's gaming world model 0:50:35 - Legal barriers to AI 0:56:30 - Getting AGI safety right 1:05:43 - 34 years at Microsoft 1:11:31 - Does Satya Nadella believe in AGI?

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Even within our own research team, timelines for transformative AI differ substantially. In this episode, the two Epoch AI researchers with the longest and the shortest timelines for transformative AI candidly examine the roots of their disagreements. They discuss: How and why their timelines for specific milestones differ Current technical challenges for AI progress Why widespread automation beats geniuses in datacenters Pitfalls of conventional AI prediction approaches Critiquing "single AGI" or "utopia vs. doom" narratives How a world with AGI might look like And much, much more. (0:00:00) - Preview (0:01:08) - Contrasting AGI Timelines (0:08:30) - Updating Beliefs as Capabilities Advance (0:17:07) - Moravec’s Paradox and the Agency Challenge (0:32:40) - Missing Capabilities for AGI (0:47:43) - Beating benchmarks vs Being Useful (0:59:20) - AI Excelling in Some Tasks While Struggling with Others (1:07:33) - Economic Impact of AI vs the Internet (1:24:08) - Widespread Automation Beats Genius in Datacenters (1:51:37) - How Stories Shape Our Expectations of AI (2:03:24) - How AGI Will Impact Culture (2:10:46) - Beyond Utopia-or-Extinction (2:16:57) - AI's Impact on Wages and Labor (2:27:49) - Why Better Preservation of Information Accelerates Change (2:39:32) - Markets Shaping Cultural Priorities (2:55:51) - Challenges in Defining What We Want to Preserve (3:06:47) - Risk Attitudes in AI Decision-Making (3:12:50) - Historical Lessons for AI Coexistence (3:21:45) - A Warning Sign in Safety Discourse (3:30:20) - Revisiting Core Assumptions in AI Alignment (3:49:46) - Simple Models in Complex Domains

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159,795 просмотров • 1 год назад

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New Epoch podcast episode: How far can current AI trends continue? Jaime Sevilla and Yafah Edelman on where current AI trends carry us —and where they break. They disagree on mechanisms and outcomes, but agree on this: fast diffusion now, broad cognitive automation by ~2035, and extreme uncertainty after. (0:00:00) - Preview (0:00:41) - Intro: Does 5× compute scaling continue? (0:08:15) - Largest training run in 2030 & what does it imply? (0:12:44) - Impact on Software Engineering & other cognitive tasks (0:23:27) - Economic impacts near the end of the decade (0:31:34) - 2030 bifurcation: Slow down or take off? (0:35:49) - Physical vs cognitive automation (0:44:37) - Timelines and impact of full cognitive automation (1:02:37) - Returns to intelligence (1:08:51) - Three cruxes after 2035 (Robots, technology & intelligence) (1:16:28) - What happens in 2040? (1:23:16) - Recap: Three eras of forecasting (1:37:42) - Closing remarks

Epoch AI

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Metaplanet was the best performing stock in the world last year. I sat down with their CEO Simon Gerovich to discuss the strategy, what the best tools are for corporations to acquire bitcoin, the economic conditions in Japan, his growth expectations, and how they are educating Wall Street. Enjoy! YouTube: Apple Podcast: Spotify: TIMESTAMPS: 0:00 - Intro 0:22 - Metaplanet’s bitcoin journey 6:26 - Evaluating the tools to acquire more bitcoin 8:40 - Japan economic conditions 10:09 - Getting exposure to global markets 15:01 - Rate of growth expectations 18:54 - Interest and educating Wall Street 20:31 - How people can help
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Anthony Pompliano 🌪

188,440 просмотров • 1 год назад

.Dwarkesh Patel has a new book on AI out from Stripe Press and we arrive with a new Core Memory podcast with the man himself to talk about the book, his beard and his podcasting rise Timestamps: 0:00 – Intro: AI’s explosive evolution 1:30 – The “Scaling Era” & making the book 3:00 – Oral history as an art form 4:45 – Why scaling laws are mysterious 6:40 – Old soul in a young body 8:10 – Podcasting as a learning engine 11:00 – Debating, early influences & the beard 13:00 – AI needs a multidisciplinary lens 15:20 – Dwarkesh’s intellectual growth 18:00 – Startup dreams & origin story 21:00 – Debate legacy & reflection on public speaking 24:00 – What models still struggle with 26:00 – AI’s future: RL environments & bottlenecks 29:00 – How scale might hit a wall 32:20 – Can labs fast-follow superintelligence? 34:00 – The arms race for AGI dominance 38:00 – Will the first lab to AGI dominate forever? 40:00 – Automation of R&D: hype or real edge? 44:00 – Deep discussion on reasoning models 47:00 – Economic impact of true AI agents 50:00 – Finding overlooked genius guests 53:00 – Sarah Paine & accidental podcast hits 55:00 – The power of mentorship (Tyler Cowen) 57:00 – Outro: Growth, learning, and the hivemind
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.Dwarkesh Patel has a new book on AI out from Stripe Press and we arrive with a new Core Memory podcast with the man himself to talk about the book, his beard and his podcasting rise Timestamps: 0:00 – Intro: AI’s explosive evolution 1:30 – The “Scaling Era” & making the book 3:00 – Oral history as an art form 4:45 – Why scaling laws are mysterious 6:40 – Old soul in a young body 8:10 – Podcasting as a learning engine 11:00 – Debating, early influences & the beard 13:00 – AI needs a multidisciplinary lens 15:20 – Dwarkesh’s intellectual growth 18:00 – Startup dreams & origin story 21:00 – Debate legacy & reflection on public speaking 24:00 – What models still struggle with 26:00 – AI’s future: RL environments & bottlenecks 29:00 – How scale might hit a wall 32:20 – Can labs fast-follow superintelligence? 34:00 – The arms race for AGI dominance 38:00 – Will the first lab to AGI dominate forever? 40:00 – Automation of R&D: hype or real edge? 44:00 – Deep discussion on reasoning models 47:00 – Economic impact of true AI agents 50:00 – Finding overlooked genius guests 53:00 – Sarah Paine & accidental podcast hits 55:00 – The power of mentorship (Tyler Cowen) 57:00 – Outro: Growth, learning, and the hivemind

Ashlee Vance

34,839 просмотров • 1 год 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 • 6 месяцев назад

Google DeepMind CEO Demis Hassabis says we’re in the ‘foothills of the singularity’ I sat down with him to talk about what that means, curing every disease, and human meaning post-AGI: 0:00 Intro 0:45 What Demis is most excited about at I/O 1:46 Have AGI timelines shifted? 3:30 What's still missing before AGI 6:50 AI curing every disease 9:19 What diseases get cured first? 10:50 What Demis works on after AGI 11:48 Human meaning after AGI 13:50 The human skills that get more valuable 15:19 What's underhyped in AI right now
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Google DeepMind CEO Demis Hassabis says we’re in the ‘foothills of the singularity’ I sat down with him to talk about what that means, curing every disease, and human meaning post-AGI: 0:00 Intro 0:45 What Demis is most excited about at I/O 1:46 Have AGI timelines shifted? 3:30 What's still missing before AGI 6:50 AI curing every disease 9:19 What diseases get cured first? 10:50 What Demis works on after AGI 11:48 Human meaning after AGI 13:50 The human skills that get more valuable 15:19 What's underhyped in AI right now

Rowan Cheung

119,059 просмотров • 2 месяцев назад

Altman says that if you asked for a definition of AGI five years ago based on software’s cognitive abilities, today’s models would already surpass it. He expects people to increasingly agree we've reached AGI—even as the goalposts move. What matters more now is economic usefulness: these models boost productivity and can already do meaningful work.
1:12

Altman says that if you asked for a definition of AGI five years ago based on software’s cognitive abilities, today’s models would already surpass it. He expects people to increasingly agree we've reached AGI—even as the goalposts move. What matters more now is economic usefulness: these models boost productivity and can already do meaningful work.

Wes Roth

19,575 просмотров • 1 год назад

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?
2:11:02

Episode w Dylan Patel and Asianometry out! A bonanza on how the semiconductor industry actually works. & what Xi could do if he becomes scaling pilled, how we can train models with 10,000x GPT-4's compute by end of decade, & much more. This one is very fun. Enjoy! Links below. 0:00:00 – Xi’s path to AGI 0:05:05 – Liang Mong Song 0:09:10 – How semiconductors get better 0:12:01 – China can centralize compute 0:19:35 – Export controls & sanctions 0:33:45 – Huawei’s intense culture 0:39:45 – Why the semiconductor industry is so stratified 0:41:50 – N2 should not exist 0:46:47 – Taiwan invasion hypothetical 0:50:15 – Mind-boggling complexity of semiconductors 1:00:06 – Chip architecture design 1:05:29 – Architectures lead to different AI models? China vs. US 1:11:06 – Being head of compute at an AI lab 1:17:17 – Scaling costs and power demand 1:37:59 – Are we financing an AI bubble?

Dwarkesh Patel

261,054 просмотров • 1 год назад

It was an honour to host the first ever public dialogue between David Deutsch (David Deutsch) and Steven Pinker (Steven Pinker). They are longstanding admirers of each other's work. I view Steve's defence of the Enlightenment as more empirical, whereas David's is built more on first principles. But beyond their shared rational optimism, what else do they agree – or disagree – about? Topics we discuss are well captured by the timestamps below. Links to YouTube, transcript, Apple Podcasts, & Spotify in tweet below. Enjoy! Timestamps: (0:00:00) - Introduction (0:03:09) - Is AGI even possible? (0:10:17) - Scientific method will constrain runaway superintelligence (0:11:56) - Does AGI need agency to be creative? (0:28:31) - Will AGIs be sentient? (0:37:06) - AGI & AI safety (0:47:44) - P(doom) & subjective probabilities (0:53:21) - Prediction markets (1:01:43) - Universal explainers & The Language Instinct (1:04:43) - Universal explainers & heritability of behavioural traits (1:27:59) - Differential technological development (1:36:09) - What explains the Great Stagnation? (1:46:13) - Should presumed physical limits to growth make us pessimistic?
1:52:05

It was an honour to host the first ever public dialogue between David Deutsch (David Deutsch) and Steven Pinker (Steven Pinker). They are longstanding admirers of each other's work. I view Steve's defence of the Enlightenment as more empirical, whereas David's is built more on first principles. But beyond their shared rational optimism, what else do they agree – or disagree – about? Topics we discuss are well captured by the timestamps below. Links to YouTube, transcript, Apple Podcasts, & Spotify in tweet below. Enjoy! Timestamps: (0:00:00) - Introduction (0:03:09) - Is AGI even possible? (0:10:17) - Scientific method will constrain runaway superintelligence (0:11:56) - Does AGI need agency to be creative? (0:28:31) - Will AGIs be sentient? (0:37:06) - AGI & AI safety (0:47:44) - P(doom) & subjective probabilities (0:53:21) - Prediction markets (1:01:43) - Universal explainers & The Language Instinct (1:04:43) - Universal explainers & heritability of behavioural traits (1:27:59) - Differential technological development (1:36:09) - What explains the Great Stagnation? (1:46:13) - Should presumed physical limits to growth make us pessimistic?

Joseph Noel Walker

111,747 просмотров • 2 лет назад

We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways
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We've got a podcast! In our first episode, Ege, Tamay and Jaime dig into: • What they expect AI to look like by 2030 • Why economists are underestimating the likelihood of explosive growth • The startling regularity in technological trends like Moore's Law • Moravec’s paradox, and how we might overcome it And more more! Timestamps: 00:00:00 Preview 00:00:37 What is Epoch AI? 00:02:32 Scaling Laws 00:08:43 Key Drivers 00:19:20 End of the Decade Predictions 00:21:18 Bottlenecks: Power 00:27:59 Bottlenecks: GPUs 00:32:07 Bottlenecks: Data 00:45:37 Bottlenecks: Latency 00:56:18 Bottlenecks: Failure Rates 01:03:55 AI Investment 01:07:11 Automation 01:12:10 Benchmarks & Moravec’s Paradox 01:19:45 Economic Impact 01:45:48 Open Questions & Takeaways

Epoch AI

152,591 просмотров • 1 год назад

I sat down with Jordi Visser to discuss why the Fed did not cut interest rates, how to think about inflation, Israel & Iran, how big bitcoin can get, AI, and Jordi's current market outlook. Enjoy! TIMESTAMPS: 0:00 - Intro 1:15 - How much will bitcoin be worth? 10:36 - Economic data and the Fed’s decision to not cut rates 19:33 - Legislation and the rise of stablecoins 31:24 - The power of AI and Perplexity 39:25 - Navigating Israel and Iran conflict 46:43 - Market outlook
51:51

I sat down with Jordi Visser to discuss why the Fed did not cut interest rates, how to think about inflation, Israel & Iran, how big bitcoin can get, AI, and Jordi's current market outlook. Enjoy! TIMESTAMPS: 0:00 - Intro 1:15 - How much will bitcoin be worth? 10:36 - Economic data and the Fed’s decision to not cut rates 19:33 - Legislation and the rise of stablecoins 31:24 - The power of AI and Perplexity 39:25 - Navigating Israel and Iran conflict 46:43 - Market outlook

Anthony Pompliano 🌪

44,316 просмотров • 1 год назад

New episode Much fun chatting with Laura 🌲 ⛰️! We discuss: - personal identity, and what, exactly, we want to preserve - what a “more humane transhumanism” might look like - scientific awe - seeing inside the cell - the antimemetic qualities of reversible cryopreservation - why cryopreservation might be the most leveraged problem in longevity - and much, much more Links below. Enjoy! Timestamps: (0:00:00) - Introduction. (0:01:17) - Personal identity: what, exactly, do we want to preserve? (0:05:20) - How has Laura's emotional relation to longevity evolved? (0:10:32) - Transhumanism: what do current visions lack? (0:17:34) - The sociopolitical implications of longevity. (0:29:29) - Awe in scientific discovery. (0:37:46) - Embodied thought-experiments, & seeing inside the cell. (0:42:05) - Visualising cells to think about cryopreservation. (0:46:08) - Are some fields / types of science more amenable to embodied thinking? (0:47:13) - Scientific mentorship. (0:55:26) - Longevity drug development. (0:57:46) - Laura's current takes on theories of ageing. (1:01:17) - Key regulatory bottlenecks to longevity drugs. (1:02:40) - Patterns among The Longevity Fund's most successful investments. (1:05:18) - Laura's questions about evolution and natural selection. (1:08:57) - Why work on cryopreservation?
1:16:46

New episode Much fun chatting with Laura 🌲 ⛰️! We discuss: - personal identity, and what, exactly, we want to preserve - what a “more humane transhumanism” might look like - scientific awe - seeing inside the cell - the antimemetic qualities of reversible cryopreservation - why cryopreservation might be the most leveraged problem in longevity - and much, much more Links below. Enjoy! Timestamps: (0:00:00) - Introduction. (0:01:17) - Personal identity: what, exactly, do we want to preserve? (0:05:20) - How has Laura's emotional relation to longevity evolved? (0:10:32) - Transhumanism: what do current visions lack? (0:17:34) - The sociopolitical implications of longevity. (0:29:29) - Awe in scientific discovery. (0:37:46) - Embodied thought-experiments, & seeing inside the cell. (0:42:05) - Visualising cells to think about cryopreservation. (0:46:08) - Are some fields / types of science more amenable to embodied thinking? (0:47:13) - Scientific mentorship. (0:55:26) - Longevity drug development. (0:57:46) - Laura's current takes on theories of ageing. (1:01:17) - Key regulatory bottlenecks to longevity drugs. (1:02:40) - Patterns among The Longevity Fund's most successful investments. (1:05:18) - Laura's questions about evolution and natural selection. (1:08:57) - Why work on cryopreservation?

Joseph Noel Walker

17,580 просмотров • 1 год назад

I sat down with Jordi Visser to discuss 2025 performance, outlook for 2026, the rapid impact of artificial intelligence on productivity / GDP / economic growth, bitcoin's rise or fall, and how individuals can use AI tools to create more value in their work and lives. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:55 - 2025 market recap and biggest lessons 13:53 - 2026 outlook: can GDP growth accelerate? 20:36 - 2026 asset outlook: stocks, commodities, crypto 27:51 - How to using AI to make better decisions and investing ideas 32:42 - Silvia tax/parenting use cases and better portfolio construction with AI 39:47 - Karpathy’s tweet: AI is changing programming 48:23 - Building apps and tools with AI and importance of curiosity + persistence 1:00:02 - 2026 prediction: task-specific AI agents 1:04:12 - Health, longevity, and Jordi’s new Substack
1:07:33

I sat down with Jordi Visser to discuss 2025 performance, outlook for 2026, the rapid impact of artificial intelligence on productivity / GDP / economic growth, bitcoin's rise or fall, and how individuals can use AI tools to create more value in their work and lives. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:55 - 2025 market recap and biggest lessons 13:53 - 2026 outlook: can GDP growth accelerate? 20:36 - 2026 asset outlook: stocks, commodities, crypto 27:51 - How to using AI to make better decisions and investing ideas 32:42 - Silvia tax/parenting use cases and better portfolio construction with AI 39:47 - Karpathy’s tweet: AI is changing programming 48:23 - Building apps and tools with AI and importance of curiosity + persistence 1:00:02 - 2026 prediction: task-specific AI agents 1:04:12 - Health, longevity, and Jordi’s new Substack

Anthony Pompliano 🌪

40,283 просмотров • 7 месяцев назад

The Fed didn't cut last week and the tariff deadline of April 2nd is fast approaching — lots of uncertainty in markets until then. I sat down with Jordi Visser to discuss the Fed’s interest rate decision, stock market outlook, Tesla, AI, Nvidia, and how investors are approaching April 2nd. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:52 - Should the Fed have cut rates? 3:30 - Evaluating the stock market right now 7:23 - Evaluating Tesla right now 10:32 - Economic data uncertainty 13:48 - How investors are approaching April 2 21:10 - Scott Bessent on All-In podcast 26:50 - AI & Nvidia 34:21 - The future of AI
39:59

The Fed didn't cut last week and the tariff deadline of April 2nd is fast approaching — lots of uncertainty in markets until then. I sat down with Jordi Visser to discuss the Fed’s interest rate decision, stock market outlook, Tesla, AI, Nvidia, and how investors are approaching April 2nd. Enjoy! YouTube: Spotify: Apple: TIMESTAMPS: 0:00 - Intro 0:52 - Should the Fed have cut rates? 3:30 - Evaluating the stock market right now 7:23 - Evaluating Tesla right now 10:32 - Economic data uncertainty 13:48 - How investors are approaching April 2 21:10 - Scott Bessent on All-In podcast 26:50 - AI & Nvidia 34:21 - The future of AI

Anthony Pompliano 🌪

89,198 просмотров • 1 год назад