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Convention wisdom is that bioweapons are humanity's greatest weakness – 100x cheaper to make than to defend against. Andrew Snyder-Beattie thinks conventional wisdom is likely wrong. He has a plan cheap enough to do without government. Useful even in worst case scenarios like mirror bacteria. Effective enough to save... most people. In one of my all-time fav interviews he lays out a low-tech 4-step approach developed by his research team at Open Philanthropy, to fix a problem most have thought unsolvable. ASB is hiring for many roles in this project from logistics to biotech to manufacturing, and has $100s millions to deploy. Enjoy, links below! 2:10 How bad it could get 9:19 The worst-case scenario: mirror bacteria 18:14 Why low-tech 25:30 Prevention 31:21 The “4 pillars” plan 33:09 ASB is hiring now to make this happen 35:11 Everyone was wrong: biorisks are defence dominant 40:23 Pillar 1: Lungs 55:53 Pillar 2: Biohardening 1:15:19 Pillar 3: Detection 1:28:40 Pillar 4: The wrench hypothesis 1:40:12 The plan's biggest weaknesses 1:44:44 Would chaos make this impossible to pull off? 1:51:50 Would rogue AI make bioweapons? 1:57:57 We can feed the world even if all the plants die 2:07:03 Could a bioweapon make the Earth uninhabitable? 2:09:35 What ASB is hiring for 2:30:27 How to protect yourself and your family (On the 80,000 Hours Podcast, available anywhere you get podcasts.)show more

Rob Wiblin

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Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Today's interview is among the most chilling I've ever recorded: Tom Davidson on how AI-enabled coups could allow a tiny group to seize power His new paper, published today, lays out how AGI will naturally destabilise even mature democracies and encourage power grabs. The paper, plus links and transcript, below: 00:50 How AI enables tiny groups to seize power 12:31 Secret AI loyalties 24:32 Are human power grabs more plausible than 'rogue AI'? 37:31 Will AGI make it impossible to escape autocracy? 41:34 Threat 1: AI-enabled military coups 1:00:48 Threat 2: Autocratisation 1:13:31 Threat 3: Self-built hard power 1:21:23 Can you stage a coup with 10,000 drones? 1:29:22 Are people psychologically willing to do coups? 1:33:31 Will a balance of power between AIs prevent this? 1:49:28 How likely is this in the US? 1:56:58 Is a small group seizing power really so bad? 2:00:33 Countermeasure 1: Block internal misuse 2:10:27 Countermeasure 2: Cybersecurity 2:12:36 Countermeasure 3: Model spec transparency 2:21:55 Countermeasure 4: Sharing AI access broadly 2:26:45 Is it more dangerous to concentrate or share AGI? 2:32:38 In defence of open sourcing AI models 2:53:18 Preventing AI-enabled military coups in particular 3:03:00 How to help if you work at an AI company 3:07:09 The power ML researchers still have, for now 3:10:29 How to help if you're an elected leader Find it in any app by searching for 80,000 Hours Podcast.

Rob Wiblin

48,386 Aufrufe • vor 1 Jahr

The full conversation with Brent Beshore 0:00 – Intro 0:54 – Examining Your Life 04:50 – Fixing Relationships 15:10 – How Helping Can Hurt 27:19 – Control 35:42 – Impact of Drinking 45:35 – Running a Business With Love 55:40 – Win-Win Relationships 1:00:40 – Debt 1:14:34 – Incentives 1:24:14 – How to Hire and Fire CEOs 1:29:24 – What most people miss about hiring 1:39:25 – Brent’s Playbook after buying a company 1:46:26 – On Projections 1:50:58 – Revisiting Investments and Keeping Score 1:53:50 – To Be Hands-Off or Not 2:03:40 – Where People Go Wrong in Private Equity 2:09:13 – What is Success?

The full conversation with Brent Beshore 0:00 – Intro 0:54 – Examining Your Life 04:50 – Fixing Relationships 15:10 – How Helping Can Hurt 27:19 – Control 35:42 – Impact of Drinking 45:35 – Running a Business With Love 55:40 – Win-Win Relationships 1:00:40 – Debt 1:14:34 – Incentives 1:24:14 – How to Hire and Fire CEOs 1:29:24 – What most people miss about hiring 1:39:25 – Brent’s Playbook after buying a company 1:46:26 – On Projections 1:50:58 – Revisiting Investments and Keeping Score 1:53:50 – To Be Hands-Off or Not 2:03:40 – Where People Go Wrong in Private Equity 2:09:13 – What is Success?

Shane Parrish

107,921 Aufrufe • vor 2 Jahren

What if we had a thermostat for the climate? Make Sunsets luke iseman Andrew Song 0:00 Sunblock for the planet? 1:00 LOL, but for real though 1:09 Inspired by Neal Stephenson 2:10 A two man startup 2:40 Balloons and Sulfur Dioxide = a surprising solution 3:17 Legos! 3:29 Reflecting the sun, like volcanoes 4:20 So2 from maritime shipping has already proven to cool the surface 4:53 But it's the wrong way to do this 5:19 The right way to do it 5:50 But what about acid rain? 6:10 Putting it in perspective 6:55 How to DIY terraform 7:56 Taking measurements 8:40 Leave a comment if you get Nick's reference here 8:45 The computer 9:12 Launching a balloon 9:45 The lack of data 10:31 Is this really controversial? 11:20 Yes, this is actually legal 11:59 The morality of geoengineering 13:04 How to scale 14:18 Is this the solution we want? 15:10 Risks 15:35 The real question 16:29 Best and worst case scenarios 16:59 Termination Shock

What if we had a thermostat for the climate? Make Sunsets luke iseman Andrew Song 0:00 Sunblock for the planet? 1:00 LOL, but for real though 1:09 Inspired by Neal Stephenson 2:10 A two man startup 2:40 Balloons and Sulfur Dioxide = a surprising solution 3:17 Legos! 3:29 Reflecting the sun, like volcanoes 4:20 So2 from maritime shipping has already proven to cool the surface 4:53 But it's the wrong way to do this 5:19 The right way to do it 5:50 But what about acid rain? 6:10 Putting it in perspective 6:55 How to DIY terraform 7:56 Taking measurements 8:40 Leave a comment if you get Nick's reference here 8:45 The computer 9:12 Launching a balloon 9:45 The lack of data 10:31 Is this really controversial? 11:20 Yes, this is actually legal 11:59 The morality of geoengineering 13:04 How to scale 14:18 Is this the solution we want? 15:10 Risks 15:35 The real question 16:29 Best and worst case scenarios 16:59 Termination Shock

Freethink

14,773 Aufrufe • vor 1 Jahr

In 2025 AI coded full bacteriophage genomes that work better than anything found in nature. And crushed top virologists in a test of lab troubleshooting – beating human specialists in their area of expertise 45% to 22%. What could go wrong? But destroying the 'tacit knowledge' barrier to bioweapons development is just the beginning. AI can design crypto-ricin, likely as deadly as normal ricin, which evades the best gene synthesis screening available. PhDs with Claude nearly breached Anthropic's 'critical danger threshold' for bioweapons development. But most tests are still on undergrads: they're cheaper to hire. Russia, Iran, and North Korea all have active biological weapons programmes. And even the most dangerous biological AI tools are so open that limiting proliferation is near impossible in the near-term. The situation feel hopeless so I spoke to one of the world's top authorities on 'AI-Bio', Dr Richard Moulange, to find out why this situation isn't as bleak as it sounds. He proposes a range of shovel-ready projects, none of which fully solve the problem, but all of which would help move the needle: 1. Screen DNA synthesis orders. (Incredibly this is voluntary now.) 2. Give defenders a preview of models before they're fully public. 3. Def/acc: Stockpile broad-spectrum vaccines that cover entire virus families. 4. Prevent AI expertise in 'mirror bacteria', as their only use is mass murder. 5. Surveil waste-water to spot engineered pathogens before they spread. 6. Invest in intelligence so if a state releases a bioweapon, that can be proven, and the state punished. 7. Limit access and improve refusals to buy time. (FWIW to me the situation still seems very bad.) On the 80,000 Hours Podcast, wherever you get podcasts. Links below — enjoy! 01:11 AI can now design novel bacteriophage genomes 04:42 The end of the 'tacit knowledge' barrier 18:50 Are risks from bioterrorists overstated? 23:14 The 3 key disasters AI makes more likely 30:43 Which bad actors does AI help the most? 42:07 Experts are more scary than amateurs 47:32 Barriers to bioterrorists using AI 49:43 AI biorisks are sometimes dismissed (and that’s a huge mistake) 1:05:12 Advanced AI biology tools we already have or will soon 1:10:57 Rob argues that the situation is hopeless 1:19:38 Intervention #1: Limit access 1:34:28 Intervention #2: Get AIs to refuse to help 1:44:18 Intervention #3: Surveillance and attribution 1:58:28 Intervention #4: Universal vaccines and antivirals 2:12:01 Intervention #5: Screen all orders for DNA 2:21:57 AI companies talk about def/acc more than they fund it 2:28:44 Can you build a profitable business solving this problem? 2:33:08 This doesn't have to interfere with useful science (much) 2:35:16 What are the best low-tech interventions? 2:40:17 Richard's top request for AI companies 2:55:44 Grok shows governments lack legal levers 2:58:54 Best ways listeners can help fix AI-Bio

In 2025 AI coded full bacteriophage genomes that work better than anything found in nature. And crushed top virologists in a test of lab troubleshooting – beating human specialists in their area of expertise 45% to 22%. What could go wrong? But destroying the 'tacit knowledge' barrier to bioweapons development is just the beginning. AI can design crypto-ricin, likely as deadly as normal ricin, which evades the best gene synthesis screening available. PhDs with Claude nearly breached Anthropic's 'critical danger threshold' for bioweapons development. But most tests are still on undergrads: they're cheaper to hire. Russia, Iran, and North Korea all have active biological weapons programmes. And even the most dangerous biological AI tools are so open that limiting proliferation is near impossible in the near-term. The situation feel hopeless so I spoke to one of the world's top authorities on 'AI-Bio', Dr Richard Moulange, to find out why this situation isn't as bleak as it sounds. He proposes a range of shovel-ready projects, none of which fully solve the problem, but all of which would help move the needle: 1. Screen DNA synthesis orders. (Incredibly this is voluntary now.) 2. Give defenders a preview of models before they're fully public. 3. Def/acc: Stockpile broad-spectrum vaccines that cover entire virus families. 4. Prevent AI expertise in 'mirror bacteria', as their only use is mass murder. 5. Surveil waste-water to spot engineered pathogens before they spread. 6. Invest in intelligence so if a state releases a bioweapon, that can be proven, and the state punished. 7. Limit access and improve refusals to buy time. (FWIW to me the situation still seems very bad.) On the 80,000 Hours Podcast, wherever you get podcasts. Links below — enjoy! 01:11 AI can now design novel bacteriophage genomes 04:42 The end of the 'tacit knowledge' barrier 18:50 Are risks from bioterrorists overstated? 23:14 The 3 key disasters AI makes more likely 30:43 Which bad actors does AI help the most? 42:07 Experts are more scary than amateurs 47:32 Barriers to bioterrorists using AI 49:43 AI biorisks are sometimes dismissed (and that’s a huge mistake) 1:05:12 Advanced AI biology tools we already have or will soon 1:10:57 Rob argues that the situation is hopeless 1:19:38 Intervention #1: Limit access 1:34:28 Intervention #2: Get AIs to refuse to help 1:44:18 Intervention #3: Surveillance and attribution 1:58:28 Intervention #4: Universal vaccines and antivirals 2:12:01 Intervention #5: Screen all orders for DNA 2:21:57 AI companies talk about def/acc more than they fund it 2:28:44 Can you build a profitable business solving this problem? 2:33:08 This doesn't have to interfere with useful science (much) 2:35:16 What are the best low-tech interventions? 2:40:17 Richard's top request for AI companies 2:55:44 Grok shows governments lack legal levers 2:58:54 Best ways listeners can help fix AI-Bio

Rob Wiblin

10,608 Aufrufe • vor 4 Monaten

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Ryan Greenblatt is lead author of "Alignment faking in LLMs" and one of AI's most productive researchers. He puts a 25% probability on automating AI research by 2029. We discuss: • Concrete evidence for and against AGI coming soon • The 4 easiest ways for AI to take over • What evidence we have on how fast / long the intelligence explosion will go • Would misaligned AGI go rogue early or bide its time • Whether 'pause at human level' is naive or smart • Lots more. My head was often spinning during this interview, in a good way. Find it on the 80,000 Hours Podcast, links below. Enjoy! 1:29 How close are we to automating AI R&D? 5:15 Really, though: how capable are today's models? 13:01 Why AI companies get automated first 18:10 Most likely ways for AGI to take over 30:04 Would AGI go rogue early or bide its time? 34:53 "Pause at human level" 46:43 AI control vs AI alignment 52:38 Do we have to hope to catch AIs red-handed? 56:57 How would a slow AGI takeoff look? 1:05:04 Why might an intelligence explosion not happen for 8+ years? 1:17:05 Key challenges in forecasting AI progress 1:25:07 The bear case on AGI 1:30:59 The change to "compute at inference" 1:36:38 How much has pretraining petered out? 1:49:08 Could we get an intelligence explosion within a year? 1:53:08 Reasons AIs might struggle to replace humans 2:00:10 Things could go insanely fast when we automate AI R&D. Or not. 2:14:52 How fast would the intelligence explosion slow down? 2:27:53 Bottom line for mortals 2:34:00 Six orders of magnitude of progress... what does that even look like? 2:44:10 Neglected and important technical work people should be doing 2:48:16 What's the most promising work in governance? 2:51:37 Ryan's current research priorities

Rob Wiblin

34,618 Aufrufe • vor 1 Jahr

The next decade may be the most consequential longevity window of our lives. In a brand-new episode of the FoundMyFitness podcast, I sit down with immunologist and aging researcher Dr. Derya Unutmaz (Derya Unutmaz, MD) to explore a provocative possibility: AI may accelerate medicine fast enough to change what aging, cancer, and disease prevention look like within our lifetimes. Derya is not making these predictions from the sidelines. OpenAI has repeatedly featured his work as evidence that frontier models can contribute to real scientific discovery—including a case study in which GPT-5 helped his lab solve a three-year-old immunology mystery. We discuss how AI is already compressing months of biological analysis into hours, how digital twins could personalize treatment and shorten clinical trials, and whether disease could be predicted years before symptoms appear. We also get into: • Why he believes the next 10 years could add decades to human lifespan • Why cancer is so difficult to cure—and why he thinks that may change • When avoiding AI support could become medically irresponsible • What supercentenarians reveal about engineering greater resistance to aging • How cellular reprogramming might restore younger biological function • How to begin building a simple “mini digital twin” from your own health data You do not have to agree with every timeline to appreciate the stakes. This is one of the most ambitious conversations we have had about the future of biology and medicine. The episode is available now on YouTube, Spotify, and Apple Podcasts. Links in the comments below. Timestamps: 0:00 - Introduction 1:27 - Add 50 years to your life? 5:35 - Months of analysis in minutes 11:06 - Trials in weeks, not years 13:42 - How safe is safe enough? 17:57 - Have we hit AGI? 23:39 - The case for AI optimism 29:51 - Crossing the intuition threshold 36:32 - Is avoiding AI malpractice? 42:35 - Can AI catch cancer's next move? 46:09 - Which AI models to trust 51:45 - Claude vs. GPT in diagnosis 55:15 - Generalist vs. specialist AI 58:41 - Cancer's self-vs-self problem 1:02:35 - The end of cancer? 1:06:46 - A dog's custom cancer vaccine 1:08:47 - Can AI prevent overtreatment? 1:11:44 - Predicting disease years early 1:18:06 - Biology goes exponential 1:23:15 - Why prevention beats reversal 1:29:07 - Engineering aging resistance 1:34:23 - One gene, many outcomes 1:38:28 - Reverse-engineering the lucky 1:40:38 - From Dolly to Yamanaka factors 1:45:12 - Full-body rejuvenation 1:53:00 - What if AI thinks for a month? 1:55:42 - The biosecurity dilemma 2:00:28 - Limits of epigenetic clocks 2:06:51 - The cells that won't retire 2:09:38 - Can brain aging be reversed? 2:16:06 - The ultimate longevity prompt 2:18:06 - How to build a digital twin 2:27:42 - The personal health wiki 2:30:50 - When lab ranges mislead

Dr. Rhonda Patrick

236,243 Aufrufe • vor 1 Monat

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

AI models currently have a 50% chance of doing something that takes a human expert one hour. This doubles every 7 months. In 2 years? They could automate full workdays. In 4 years? A full month. I discuss the most important graph in AI today with Beth Barnes, the CEO of METR, which uncovered this rule of AI progress. Her bottom line: "It really doesn't seem like 2 years would be surprising for recursively self-improving AI." Beth also explains: where company safety testing fails, why there are no true closed-weight models, AI undermines leading powers, why she's come around on open weighting, and why models might be about to start playing dumb much more often. Enjoy! Available on the 80,000 Hours Podcast in all apps. Links below. 1:51 Can we see AI scheming in the chain of thought? 12:50 Alignment faking 17:33 We have to test models before they're even used inside AI companies 31:56 Each 7 months models can do tasks twice as long 51:31 METR's research finds AIs are solid at AI research already 58:18 AI may turn out to be strong at novel and creative research 1:07:55 Recursively self-improving AI might even be here in two years 1:14:29 Could evaluations backfire? 1:39:55 Do we need external auditors doing AI safety tests? 1:54:09 Why not work at AI companies 2:08:40 The new more dire situation has forced changes to METR's strategy 2:21:49 Overrated: Interpretability research 2:32:55 Overrated: Major AI companies' contributions to safety research 2:39:15 Could we ban using AI to enhance AI, or is that just naive? 2:45:31 Open-weighting models is often good 2:50:22 What we can learn about AGI from the nuclear arms race 3:10:43 AI is more like bioweapons because it undermines the leading power 3:42:09 What research METR plans to do next

Rob Wiblin

93,669 Aufrufe • vor 1 Jahr

ELECTION WATCH: Here's why the Liberals want a spring election, and why they're likely to make it happen. Timestamps: 0:00 Michelle thinks the Libs want a spring election, they have motive and opportunity 1:53 Motive #1: Carney wants a majority because he doesn't want to negotiate in a minority Parliament 7:40 Motive #2: This is the best polling the Liberals are going to get, it's all downhill from here 9:12 Motive #3: Carney’s approvals are sliding 12:00 Motive #4: The issues aren't in the Liberal’s favour beyond the spring 23:15 Opportunity #1: The fall Quebec election 30:03 Opportunity #2: The Canadian MSM coverage of US politics is going to change in the fall 35:12 Opportunity #3: The psychology of a spring election on the electorate 36:42 Opportunity #4: Liberals running government ads sign of a ramp-up for a spring election

ELECTION WATCH: Here's why the Liberals want a spring election, and why they're likely to make it happen. Timestamps: 0:00 Michelle thinks the Libs want a spring election, they have motive and opportunity 1:53 Motive #1: Carney wants a majority because he doesn't want to negotiate in a minority Parliament 7:40 Motive #2: This is the best polling the Liberals are going to get, it's all downhill from here 9:12 Motive #3: Carney’s approvals are sliding 12:00 Motive #4: The issues aren't in the Liberal’s favour beyond the spring 23:15 Opportunity #1: The fall Quebec election 30:03 Opportunity #2: The Canadian MSM coverage of US politics is going to change in the fall 35:12 Opportunity #3: The psychology of a spring election on the electorate 36:42 Opportunity #4: Liberals running government ads sign of a ramp-up for a spring election

Michelle Rempel Garner

43,351 Aufrufe • vor 7 Monaten

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks

E114: Walrus 🦭/acc : Decentralizing Data Storage, the perfect complement to the SUI Network George Danezis is the Chief Scientist at MystenLabs.sui , co-creator of the Sui , and one the minds behind Walrus 🦭/acc Timestamps 0:00 Intro 2:00 Partnerships: Jupiter, Bitwise, SUI & Mantle 2:46 Sleeping Habits 4:05 Who is George? 4:23 What is a Chief Scientist? 5:16 Research at Microsoft vs. Now 6:59 What is “Good Research”? 8:49 Corporate Innovation vs. Reality 10:31 Why I Love Teaching Security 11:52 Self Custody with Trezor 12:44 Teaching vs. Real-World Application 16:28 What Makes an Idea Great? 17:51 The Idea That Never Worked Out 19:50 Being Late to Ideas, Not Early 20:56 The Essence of Peer-to-Peer Networks 23:50 Why We Need Peer-to-Peer Systems? 27:51 Why Decentralization Was Hard to Realize 31:09 Bitcoin's Game-Changing Impact 33:10 ETH Aha Moment 34:46 Facebook’s Conviction in Blockchain 37:39 Scaling DIEM/LIBRA to Sui 39:20 Biggest Issue at Facebook 40:20 Why Facebook Bet on Crypto 41:40 Why I Left Facebook 45:07 Balancing Passion and Pay 46:43 What is a Blockchain Good For? 51:42 Understanding Blockchain’s Limitations 53:21 CryptoKitties 54:36 Meme Coins: Culture vs. Scam 57:02 The Role of Norms in Crypto 1:00:32 What is the Sui Network? 1:02:32 Why Do We Need The Sui Network? 1:03:34 Layer 1 vs. Layer 2 1:05:07 Why Build a Layer One? 1:09:21 Top Layer One Competitors 1:10:45 Decentralization vs. Practicality 1:14:06 Minimum Decentralization 1:16:33 Data Storage & Its Biggest Problem 1:20:05 What is Walrus Protocol? 1:22:09 Why Walrus is Possible Now 1:25:35 Why Sui Network for Data Storage? 1:27:37 Vision of Walrus Protocol 1:28:59 Building a Neutral Tech Future 1:30:44 Using Existing Blockchains 1:33:03 Future of Decentralized Production 1:35:51 Prediction For The Next 12 Months 1:39:04 Concluding Remarks

MR SHIFT 🦁

111,082 Aufrufe • vor 1 Jahr

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

auto-research is starting to gain traction as a very viable paradigm for creating useful research discovery. now, that paradigm is still in its infancy and the infrastructure to hold all that trail of context as the agents blaze through experiments isn't well defined (to say the least). on that topic, I had the chance to chat with my boys francesco and giulio from paradigma about what underlying infra is needed to make this paradigm work. the paradigma's paradigm, which involves copious amount of DAGs, make this auto-research paradigm a paradigmatic case of essential infrastructure. here's the full video in full: - 0:00 - what is missing from auto-research? - 2:02 - giulio and francesco ai journey - 8:10 - research infra is the bottleneck? - 10:18 - paradigma vision of autonomous research - 13:17 - “important discovery per joules” - 17:15 - why is DAG the unit of research for auto-research? - 20:40 - is paradigma trying to replace the research publication? - 24:50 - how does knowledge is shared between experiments in the DAG? - 27:34 - what is even auto-research lol? - 33:53 - the value of the human mind in this auto-research future. - 37:00 - how do you reconcile hallucination in this auto-research paradigm? - 41:33 - the adoption of auto-research across varied fields? - 47:30 - ✨ introduction to the auto-research infrastructure. ✨ - 56:55 - where is the code? - 59:10 - full IDE next? - 1:03:20 - the place of the human in this DAG / code quality? manual node? token spent? - 1:16:02 - who’s the user for auto-research? - 1:18:13 - how to validate bad DAG? - 1:20:18 - ✨ auto-research agent results ✨ - 1:22:53 - ✨ how a big research DAG looks like? ✨ - 1:25:10 - how to get the canonical DAG for the final result? - 1:27:50 - the auto-research DAG being the new pre-print? - 1:30:05 - what’s next for paradigma and the auto-research infra? - 1:35:00 - what are they excited about research wise? enjoyyyyy my guys 🌹

Yacine Mahdid

12,091 Aufrufe • vor 2 Monaten

My conversation with Tracy Britt Cool. Warren Buffett called her his "fireman" because of her reputation at Berkshire Hathaway for turning around struggling businesses. In this conversation, you'll learn exactly how she did it, how she evaluates businesses, and the mistake that most people make when hiring. (0:00) Intro, recent reading, and family life (3:04) Alan Mulally's Turnaround at Ford (4:22) The Work Test (5:03) The Pampered Chef Turnaround (7:06) How Value Creation is Changing (8:38) Why Companies Fail to Adapt (10:09) Lessons from the Farm (16:57) Lessons from Warren Buffett (18:25) Ad Break (20:57) Buying Companies at Kanbrick (22:38) The 3 Components of Long-Term Thinking (25:11) Avoiding the Complexity Trap (26:23) How to Turnaround a Declining Business (28:03) Attracting Talent to a Declining Business (30:29) Matching Structure to Time Horizon (32:00) Growing Margins (33:25) The Process: What to Focus on (35:10) The 3 Buckets of Putting People First (37:00) How to Specifically Evaluate Talent (40:16) Identifying the People to Avoid at All Costs (42:23) Sourcing Deals (43:56) 5 Lenses to Evaluate a Business (45:14) How to Evaluate a Moat (49:29) How Quantitative Analysis Misleads (50:25) A Detailed Look at Return on Invested Capital (53:18) What Makes an Attractive Market (54:33) Finding High-Potential Businesses (57:00) The Post Close Playbook (1:02:03) Repeatable Business Systems (1:04:06) Why Copying What Works is Hard (1:06:01) Mistakes in the Past 5 Years (1:10:13) Debt and Leverage (1:12:20) 3 Ways to Think about AI (1:15:13) What Most People Get Wrong When Hiring (1:21:12) Businesses to Avoid (1:22:35) What Not to Do (1:24:31) Public vs. Private Company Boards (1:27:04) How Warren Buffett Taught Katharine Graham Business (1:29:28) Each Hire is a Million Dollar Decision (1:31:02) Evaluating Integrity (1:32:36) The One Word That Keeps People Honest (1:35:52) Principles & Lessons from Business History (1:36:59) Inflation (1:40:22) Public Company Heroes (1:41:41) Companies & Political Opinions (1:42:46) What is Success for you? Tracy Britt Cool is the co-founder of Kanbrick and the former CEO of Pampered Chef. At Berkshire Hathaway, she worked directly with Warren Buffett and was on the board of Kraft Heinz, Benjamin Moore, and Dairy Queen. (Includes paid partnerships. If you're interested in sponsoring, reach out.)

My conversation with Tracy Britt Cool. Warren Buffett called her his "fireman" because of her reputation at Berkshire Hathaway for turning around struggling businesses. In this conversation, you'll learn exactly how she did it, how she evaluates businesses, and the mistake that most people make when hiring. (0:00) Intro, recent reading, and family life (3:04) Alan Mulally's Turnaround at Ford (4:22) The Work Test (5:03) The Pampered Chef Turnaround (7:06) How Value Creation is Changing (8:38) Why Companies Fail to Adapt (10:09) Lessons from the Farm (16:57) Lessons from Warren Buffett (18:25) Ad Break (20:57) Buying Companies at Kanbrick (22:38) The 3 Components of Long-Term Thinking (25:11) Avoiding the Complexity Trap (26:23) How to Turnaround a Declining Business (28:03) Attracting Talent to a Declining Business (30:29) Matching Structure to Time Horizon (32:00) Growing Margins (33:25) The Process: What to Focus on (35:10) The 3 Buckets of Putting People First (37:00) How to Specifically Evaluate Talent (40:16) Identifying the People to Avoid at All Costs (42:23) Sourcing Deals (43:56) 5 Lenses to Evaluate a Business (45:14) How to Evaluate a Moat (49:29) How Quantitative Analysis Misleads (50:25) A Detailed Look at Return on Invested Capital (53:18) What Makes an Attractive Market (54:33) Finding High-Potential Businesses (57:00) The Post Close Playbook (1:02:03) Repeatable Business Systems (1:04:06) Why Copying What Works is Hard (1:06:01) Mistakes in the Past 5 Years (1:10:13) Debt and Leverage (1:12:20) 3 Ways to Think about AI (1:15:13) What Most People Get Wrong When Hiring (1:21:12) Businesses to Avoid (1:22:35) What Not to Do (1:24:31) Public vs. Private Company Boards (1:27:04) How Warren Buffett Taught Katharine Graham Business (1:29:28) Each Hire is a Million Dollar Decision (1:31:02) Evaluating Integrity (1:32:36) The One Word That Keeps People Honest (1:35:52) Principles & Lessons from Business History (1:36:59) Inflation (1:40:22) Public Company Heroes (1:41:41) Companies & Political Opinions (1:42:46) What is Success for you? Tracy Britt Cool is the co-founder of Kanbrick and the former CEO of Pampered Chef. At Berkshire Hathaway, she worked directly with Warren Buffett and was on the board of Kraft Heinz, Benjamin Moore, and Dairy Queen. (Includes paid partnerships. If you're interested in sponsoring, reach out.)

Shane Parrish

430,283 Aufrufe • vor 10 Monaten

World-renowned economist Richard Werner on where money comes from: banks just create it out of thin air, and keep a pile for themselves. (0:00) How Werner Predicted the Japanese Financial Crisis (14:16) How Banks Create Money From Nothing (24:09) You’re Being Lied to About the Bank’s Role in Economics (33:59) The Evils of the Federal Reserve (38:51) Why Are Banks Allowed to Create Money? (57:12) Was Leaving the Gold Standard a Mistake? (1:09:30) The Difference Between Banks and Central Banks (1:24:26) How Society and Culture Are Impacted by Banks (1:33:11) Did the US Purposely Destroy the Japanese Economy? (1:35:42) The Central Bank’s Attempt to Blacklist Werner (1:39:03) The CIA’s Threat to Werner (1:47:24) Why Werner’s Research on Credit Creation Scared the Central Banks (2:03:55) The Link Between Central Banks and Warfare (2:18:02) Where Is the US Economy Headed? (2:29:49) The World Bank’s Debt Trap to Exploit Developing Countries (2:35:34) The Dark Truth About Central Bank Digital Currency (2:40:19) Where Can People Learn More About This? Includes paid partnerships.

World-renowned economist Richard Werner on where money comes from: banks just create it out of thin air, and keep a pile for themselves. (0:00) How Werner Predicted the Japanese Financial Crisis (14:16) How Banks Create Money From Nothing (24:09) You’re Being Lied to About the Bank’s Role in Economics (33:59) The Evils of the Federal Reserve (38:51) Why Are Banks Allowed to Create Money? (57:12) Was Leaving the Gold Standard a Mistake? (1:09:30) The Difference Between Banks and Central Banks (1:24:26) How Society and Culture Are Impacted by Banks (1:33:11) Did the US Purposely Destroy the Japanese Economy? (1:35:42) The Central Bank’s Attempt to Blacklist Werner (1:39:03) The CIA’s Threat to Werner (1:47:24) Why Werner’s Research on Credit Creation Scared the Central Banks (2:03:55) The Link Between Central Banks and Warfare (2:18:02) Where Is the US Economy Headed? (2:29:49) The World Bank’s Debt Trap to Exploit Developing Countries (2:35:34) The Dark Truth About Central Bank Digital Currency (2:40:19) Where Can People Learn More About This? Includes paid partnerships.

Tucker Carlson

5,378,901 Aufrufe • vor 1 Jahr

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.

Many are skeptical we’ll ever 'solve AI alignment.' If so — are we f**d? In our interview Buck Shlegeris argues that there are practical, non-galaxy-brained ways to reduce AI risk without knowing how to align AIs. This is 'AI control': finding ways to use advanced AI without knowing whether we can trust it. Enjoy — links and transcript below: 01:51 What's AI control and why is it hot? 10:44 Detecting human vs AI spies 18:10 How to catch AIs trying to escape 33:18 Cheapest AI control techniques 51:01 If we catch a model escaping... will we do anything? 53:39 Getting AI models to think they've already escaped 59:01 Will they be able to tell it's a setup? 1:16:13 The pitch to AI companies to do this 1:18:29 Will AIs get superhuman so fast that this is all useless? 1:30:39 Current alignment methods don't detect scheming 1:38:51 Is 'controlling' AIs kind of a dick move? 1:44:12 Could 10 safety-focused people in an AGI company do anything useful? 1:49:40 Benefits of working outside frontier AI companies 2:01:07 What other safety-related research looks best to Buck? 2:02:02 If an AI escapes, is it likely to be able to beat humanity from there? 2:09:22 Will misaligned models have to go rogue ASAP, before they're ready? Find it in any app searching for 80,000 Hours Podcast.

Rob Wiblin

53,742 Aufrufe • vor 1 Jahr

Pamela Anderson Is PROOF That All Women Will HIT The Wall! ⏲️TIME STAMPS⏲️ 0:00 : Stream loading… 13:40 : INTRO 16:00 : The different kinds of S!mps 19:20 : RP is the truth 22:10 : 304’s are rewarded 25:25 : First story: Pamela Anderson | The wall is undefeated 🥛 30:00 : A woman’s sexual market value is perishable 32:50 : You have time on your side fellas 36:40 : These girls are in the movie Smile 😀 42:10 : Reading chats 💬 43:30 : Next Story: Trey Songz ordered to pay 11 million… 51:20 : Women who like celebrities are an L | Women have strange fantasies 52:30 : Next Story: Jets QB Tyrod Taylor | Frank is too nice 🐕 55:00 : She pissed on the side of the street 🤢 56:30 : Back to the story 59:40 : 🥷’s shot the club up 1:01:40 : Her dating history 🤯 1:10:50 : Chats | Superchat all your W’s 1:19:55 : Schedule for this week 1:21:30 : We create, lose weight, buy real estate 1:25:45 : OUTRO | Zoom call is loading… —SWITCH TO CASTLE CLUB 🏰— 1:31:25 : Caller #1 Should I be removed from her credit? 1:33:50 : #2 How much money do I need to buy a house in Canada? 🇨🇦 / You can be aware, but don’t cancel yourself 1:37:50 : #3 Advice for a new business owner (ATM) 1:42:30 : #4 Should I surrender my car? What’s a buy here pay here? 1:50:50 : #5 car advice for a business owner 1:59:10 : Why you need good credit 2:01:00 : #6 Don’t take those personal loans/ Focus on one skill first 2:07:00 : #7 How can I get better at networking? 2:09:00 : #8 High yield savings account advice 2:12:45 : #9 DM’s on demand/ Work on fixing your accent 2:15:30 : #10 I’m in HVAC, but I want to get into law enforcement/ Get rid of your tattoos 2:21:00 : #11 Fitness advice for getting abs 2:27:30 : #12 What content is safe for TikTok and YouTube? 2:33:10 : OUTRO

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Pamela Anderson Is PROOF That All Women Will HIT The Wall! ⏲️TIME STAMPS⏲️ 0:00 : Stream loading… 13:40 : INTRO 16:00 : The different kinds of S!mps 19:20 : RP is the truth 22:10 : 304’s are rewarded 25:25 : First story: Pamela Anderson | The wall is undefeated 🥛 30:00 : A woman’s sexual market value is perishable 32:50 : You have time on your side fellas 36:40 : These girls are in the movie Smile 😀 42:10 : Reading chats 💬 43:30 : Next Story: Trey Songz ordered to pay 11 million… 51:20 : Women who like celebrities are an L | Women have strange fantasies 52:30 : Next Story: Jets QB Tyrod Taylor | Frank is too nice 🐕 55:00 : She pissed on the side of the street 🤢 56:30 : Back to the story 59:40 : 🥷’s shot the club up 1:01:40 : Her dating history 🤯 1:10:50 : Chats | Superchat all your W’s 1:19:55 : Schedule for this week 1:21:30 : We create, lose weight, buy real estate 1:25:45 : OUTRO | Zoom call is loading… —SWITCH TO CASTLE CLUB 🏰— 1:31:25 : Caller #1 Should I be removed from her credit? 1:33:50 : #2 How much money do I need to buy a house in Canada? 🇨🇦 / You can be aware, but don’t cancel yourself 1:37:50 : #3 Advice for a new business owner (ATM) 1:42:30 : #4 Should I surrender my car? What’s a buy here pay here? 1:50:50 : #5 car advice for a business owner 1:59:10 : Why you need good credit 2:01:00 : #6 Don’t take those personal loans/ Focus on one skill first 2:07:00 : #7 How can I get better at networking? 2:09:00 : #8 High yield savings account advice 2:12:45 : #9 DM’s on demand/ Work on fixing your accent 2:15:30 : #10 I’m in HVAC, but I want to get into law enforcement/ Get rid of your tattoos 2:21:00 : #11 Fitness advice for getting abs 2:27:30 : #12 What content is safe for TikTok and YouTube? 2:33:10 : OUTRO

Myron Gaines

51,313 Aufrufe • vor 1 Jahr

Even 'aligned AGI' naturally kills democracy and leads to oligarchy, or worse. That's the take of Anthropic's past alignment evals team lead, Prof David Duvenaud. Once humans aren't needed to do jobs or serve in the military, to governments we look like "meddlesome parasites". With voters unable to contribute but engaged in incessant activism to extract resources from others – resources the country needs to avoid domination by rivals – the attraction of mass disenfranchisement could be overwhelming. In 2025 David co-authored "Gradual Disempowerment", which aimed to lay out this and many other political, economic, and cultural forces that could sideline ordinary people (and maybe all people) in the presence of machines that can cheaply do everything humans will do. Most controversially, David and colleagues believe that competitive forces will compel disempowerment, even if all those AIs are aligned and loyal to their users. I wasn't sure how much I believed this vision of how the future might play out, so I interviewed him for The 80,000 Hours Podcast to probe how well it holds up. He and I covered: 01:30 The case that alignment isn’t enough 14:15 How smart AI advice still leads to terrible outcomes 19:05 How gradual disempowerment occurs 22:10 Economics: Humans become "meddlesome parasites" 29:37 Humans are a "criminally decadent" waste of energy 40:48 Is humans losing control actually bad, ethically? 57:47 Politics: Governments stop needing people 1:10:47 Can human culture survive in an AI-dominated world? 1:27:20 Will the future be determined by competitive or coordinative forces? 1:35:00 Can we find a single good post-AGI equilibria for humans? 1:45:17 Do we know anything useful to do about this? 1:56:42 How important is this problem compared to other AGI issues? 2:05:42 Improving global coordination may be our best bet 2:08:14 The 'Gradual Disempowerment Index' 2:11:22 The government will fight to write AI constitutions 2:17:48 “The intelligence curse” and Workshop Labs 2:23:48 Mapping out disempowerment in a world of aligned AGIs 2:30:10 What do David’s CompSci colleagues make of all this? Links below — enjoy!

Even 'aligned AGI' naturally kills democracy and leads to oligarchy, or worse. That's the take of Anthropic's past alignment evals team lead, Prof David Duvenaud. Once humans aren't needed to do jobs or serve in the military, to governments we look like "meddlesome parasites". With voters unable to contribute but engaged in incessant activism to extract resources from others – resources the country needs to avoid domination by rivals – the attraction of mass disenfranchisement could be overwhelming. In 2025 David co-authored "Gradual Disempowerment", which aimed to lay out this and many other political, economic, and cultural forces that could sideline ordinary people (and maybe all people) in the presence of machines that can cheaply do everything humans will do. Most controversially, David and colleagues believe that competitive forces will compel disempowerment, even if all those AIs are aligned and loyal to their users. I wasn't sure how much I believed this vision of how the future might play out, so I interviewed him for The 80,000 Hours Podcast to probe how well it holds up. He and I covered: 01:30 The case that alignment isn’t enough 14:15 How smart AI advice still leads to terrible outcomes 19:05 How gradual disempowerment occurs 22:10 Economics: Humans become "meddlesome parasites" 29:37 Humans are a "criminally decadent" waste of energy 40:48 Is humans losing control actually bad, ethically? 57:47 Politics: Governments stop needing people 1:10:47 Can human culture survive in an AI-dominated world? 1:27:20 Will the future be determined by competitive or coordinative forces? 1:35:00 Can we find a single good post-AGI equilibria for humans? 1:45:17 Do we know anything useful to do about this? 1:56:42 How important is this problem compared to other AGI issues? 2:05:42 Improving global coordination may be our best bet 2:08:14 The 'Gradual Disempowerment Index' 2:11:22 The government will fight to write AI constitutions 2:17:48 “The intelligence curse” and Workshop Labs 2:23:48 Mapping out disempowerment in a world of aligned AGIs 2:30:10 What do David’s CompSci colleagues make of all this? Links below — enjoy!

Rob Wiblin

58,298 Aufrufe • vor 6 Monaten

Episode #92: How AI Changes Governments & Business Models Mike Vichich is the Co-founder and CEO of Pursuit, which helps companies drive more revenue from the public sector. We talk about how AI is changing Helmer’s 7 Powers, how it’s impacting the government, if DOGE is actually working, building a startup in the Midwest (specifically Ann Arbor, MI), and how to disagree with your team. We also get into Mike’s prior company Wisely, and how they went from $11 in the bank account and unable to run payroll for six months, to over $10 million ARR and a $187 million exit to public company Olo a few years later. Thanks to Jack Altman and Blake Robbins for their help brainstorming good topics for Mike! Full episode here on X, or grab a link in the replies. Timestamps: 3:21 How AI changes Helmer’s 7 Powers 17:06 What becomes important in AI-first economy 21:02 How AI interfaces with the government 24:02 “The rules intended to save taxpayer money ironically cause taxpayer money to be wasted” 29:34 How change orders impact public sector costs 33:20 Why DOGE has not impacted US government spending yet 38:15 Three pieces of wisdom from 2nd-time founders 41:44 Starting Pursuit to make selling to the public sector as easy as the private sector 45:35 Why cities grow expenses 5x faster than tax revenue 51:42 Pros + Cons of building startups in Ann Arbor, MI 57:43 Hiring talent density in the Midwest 59:30 Starting his first company to fix consumer credit cards 1:08:50 Pivoting Wisely to restaurant loyalty 1:12:49 $11 in the bank, missing payroll for six months 1:15:21 Embarrassing demo at an Ann Arbor tech meetup 1:18:18 Why CEOs don’t always have to be right 1:20:54 How to disagree 1:25:48 Hiring at Pursuit 1:28:30 “A bad day with customers is better than the best day in the office” 1:31:33 Crashing their first customer’s PoS on Labor Day Weekend 1:35:55 Using “The Cadence” to hit $10M ARR 1:41:55 Selling Wisely to Olo for $187M

Episode #92: How AI Changes Governments & Business Models Mike Vichich is the Co-founder and CEO of Pursuit, which helps companies drive more revenue from the public sector. We talk about how AI is changing Helmer’s 7 Powers, how it’s impacting the government, if DOGE is actually working, building a startup in the Midwest (specifically Ann Arbor, MI), and how to disagree with your team. We also get into Mike’s prior company Wisely, and how they went from $11 in the bank account and unable to run payroll for six months, to over $10 million ARR and a $187 million exit to public company Olo a few years later. Thanks to Jack Altman and Blake Robbins for their help brainstorming good topics for Mike! Full episode here on X, or grab a link in the replies. Timestamps: 3:21 How AI changes Helmer’s 7 Powers 17:06 What becomes important in AI-first economy 21:02 How AI interfaces with the government 24:02 “The rules intended to save taxpayer money ironically cause taxpayer money to be wasted” 29:34 How change orders impact public sector costs 33:20 Why DOGE has not impacted US government spending yet 38:15 Three pieces of wisdom from 2nd-time founders 41:44 Starting Pursuit to make selling to the public sector as easy as the private sector 45:35 Why cities grow expenses 5x faster than tax revenue 51:42 Pros + Cons of building startups in Ann Arbor, MI 57:43 Hiring talent density in the Midwest 59:30 Starting his first company to fix consumer credit cards 1:08:50 Pivoting Wisely to restaurant loyalty 1:12:49 $11 in the bank, missing payroll for six months 1:15:21 Embarrassing demo at an Ann Arbor tech meetup 1:18:18 Why CEOs don’t always have to be right 1:20:54 How to disagree 1:25:48 Hiring at Pursuit 1:28:30 “A bad day with customers is better than the best day in the office” 1:31:33 Crashing their first customer’s PoS on Labor Day Weekend 1:35:55 Using “The Cadence” to hit $10M ARR 1:41:55 Selling Wisely to Olo for $187M

The Peel

27,474 Aufrufe • vor 1 Jahr