80,000 Hours Podcast

In this episode from our second show, 80k After Hours, Luisa Rodriguez and Keiran Harris chat about the consequences of letting go of enduring guilt, shame, anger, and pride.Links to learn more, highlights, and full transcript.They cover:Keiran’s views on free will, and how he came to hold themWhat it’s like not experiencing sustained guilt, shame, and angerWhether Luisa would become a worse person if she felt less guilt and shame — specifically whether she’d work fewer hours, or donate less money, or become a worse friendWhether giving up guilt and shame also means giving up prideThe implications for loveThe neurological condition ‘Jerk Syndrome’And some practical advice on feeling less guilt, shame, and angerWho this episode is for:People sympathetic to the idea that free will is an illusionPeople who experience tons of guilt, shame, or angerPeople worried about what would happen if they stopped feeling tonnes of guilt, shame, or angerWho this episode isn’t for:People strongly in favour of retributive justicePhilosophers who can’t stand random non-philosophers talking about philosophyNon-philosophers who can’t stand random non-philosophers talking about philosophyChapters:Cold open (00:00:00)Luisa's intro (00:01:16)The chat begins (00:03:15)Keiran's origin story (00:06:30)Charles Whitman (00:11:00)Luisa's origin story (00:16:41)It's unlucky to be a bad person (00:19:57)Doubts about whether free will is an illusion (00:23:09)Acting this way just for other people (00:34:57)Feeling shame over not working enough (00:37:26)First person / third person distinction (00:39:42)Would Luisa become a worse person if she felt less guilt? (00:44:09)Feeling bad about not being a different person (00:48:18)Would Luisa donate less money? (00:55:14)Would Luisa become a worse friend? (01:01:07)Pride (01:08:02)Love (01:15:35)Bears and hurricanes (01:19:53)Jerk Syndrome (01:24:24)Keiran's outro (01:34:47)Get more episodes like this by subscribing to our more experimental podcast on the world’s most pressing problems and how to solve them: type "80k After Hours" into your podcasting app. Producer: Keiran HarrisAudio mastering: Milo McGuireTranscriptions: Katy Moore

"For every far-out idea that turns out to be true, there were probably hundreds that were simply crackpot ideas. In general, [science] advances building on the knowledge we have, and seeing what the next questions are, and then getting to the next stage and the next stage and so on. And occasionally there’ll be revolutionary ideas which will really completely change your view of science. And it is possible that some revolutionary breakthrough in our understanding will come about and we might crack this problem, but there’s no evidence for that. It doesn’t mean that there isn’t a lot of promising work going on. There are many legitimate areas which could lead to real improvements in health in old age. So I’m fairly balanced: I think there are promising areas, but there’s a lot of work to be done to see which area is going to be promising, and what the risks are, and how to make them work." —Venki RamakrishnanIn today’s episode, host Luisa Rodriguez speaks to Venki Ramakrishnan — molecular biologist and Nobel Prize winner — about his new book, Why We Die: The New Science of Aging and the Quest for Immortality.Links to learn more, highlights, and full transcript.They cover:What we can learn about extending human lifespan — if anything — from “immortal” aquatic animal species, cloned sheep, and the oldest people to have ever lived.Which areas of anti-ageing research seem most promising to Venki — including caloric restriction, removing senescent cells, cellular reprogramming, and Yamanaka factors — and which Venki thinks are overhyped.Why eliminating major age-related diseases might only extend average lifespan by 15 years.The social impacts of extending healthspan or lifespan in an ageing population — including the potential danger of massively increasing inequality if some people can access life-extension interventions while others can’t.And plenty more.Chapters:Cold open (00:00:00)Luisa's intro (00:01:04)The interview begins (00:02:21)Reasons to explore why we age and die (00:02:35)Evolutionary pressures and animals that don't biologically age (00:06:55)Why does ageing cause us to die? (00:12:24)Is there a hard limit to the human lifespan? (00:17:11)Evolutionary tradeoffs between fitness and longevity (00:21:01)How ageing resets with every generation, and what we can learn from clones (00:23:48)Younger blood (00:31:20)Freezing cells, organs, and bodies (00:36:47)Are the goals of anti-ageing research even realistic? (00:43:44)Dementia (00:49:52)Senescence (01:01:58)Caloric restriction and metabolic pathways (01:11:45)Yamanaka factors (01:34:07)Cancer (01:47:44)Mitochondrial dysfunction (01:58:40)Population effects of extended lifespan (02:06:12)Could increased longevity increase inequality? (02:11:48)What’s surprised Venki about this research (02:16:06)Luisa's outro (02:19:26)Producer: Keiran HarrisAudio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongContent editing: Luisa Rodriguez, Katy Moore, and Keiran HarrisTranscriptions: Katy Moore

"Perception is quite difficult with cameras: even if you have a stereo camera, you still can’t really build a map of where everything is in space. It’s just very difficult. And I know that sounds surprising, because humans are very good at this. In fact, even with one eye, we can navigate and we can clear the dinner table. But it seems that we’re building in a lot of understanding and intuition about what’s happening in the world and where objects are and how they behave. For robots, it’s very difficult to get a perfectly accurate model of the world and where things are. So if you’re going to go manipulate or grasp an object, a small error in that position will maybe have your robot crash into the object, a delicate wine glass, and probably break it. So the perception and the control are both problems." —Ken GoldbergIn today’s episode, host Luisa Rodriguez speaks to Ken Goldberg — robotics professor at UC Berkeley — about the major research challenges still ahead before robots become broadly integrated into our homes and societies.Links to learn more, highlights, and full transcript.They cover:Why training robots is harder than training large language models like ChatGPT.The biggest engineering challenges that still remain before robots can be widely useful in the real world.The sectors where Ken thinks robots will be most useful in the coming decades — like homecare, agriculture, and medicine.Whether we should be worried about robot labour affecting human employment.Recent breakthroughs in robotics, and what cutting-edge robots can do today.Ken’s work as an artist, where he explores the complex relationship between humans and technology.And plenty more.Chapters:Cold open (00:00:00)Luisa's intro (00:01:19)General purpose robots and the “robotics bubble” (00:03:11)How training robots is different than training large language models (00:14:01)What can robots do today? (00:34:35)Challenges for progress: fault tolerance, multidimensionality, and perception (00:41:00)Recent breakthroughs in robotics (00:52:32)Barriers to making better robots: hardware, software, and physics (01:03:13)Future robots in home care, logistics, food production, and medicine (01:16:35)How might robot labour affect the job market? (01:44:27)Robotics and art (01:51:28)Luisa's outro (02:00:55)Producer: Keiran HarrisAudio engineering: Dominic Armstrong, Ben Cordell, Milo McGuire, and Simon MonsourContent editing: Luisa Rodriguez, Katy Moore, and Keiran HarrisTranscriptions: Katy Moore

"It’s very hard to find examples where people say, 'I’m starting from this point. I’m starting from this belief.' So we wanted to make that very legible to people. We wanted to say, 'Experts think this; accurate forecasters think this.' They might both be wrong, but we can at least start from here and figure out where we’re coming into a discussion and say, 'I am much less concerned than the people in this report; or I am much more concerned, and I think people in this report were missing major things.' But if you don’t have a reference set of probabilities, I think it becomes much harder to talk about disagreement in policy debates in a space that’s so complicated like this." —Ezra KargerIn today’s episode, host Luisa Rodriguez speaks to Ezra Karger — research director at the Forecasting Research Institute — about FRI’s recent Existential Risk Persuasion Tournament to come up with estimates of a range of catastrophic risks.Links to learn more, highlights, and full transcript.They cover:How forecasting can improve our understanding of long-term catastrophic risks from things like AI, nuclear war, pandemics, and climate change.What the Existential Risk Persuasion Tournament (XPT) is, how it was set up, and the results.The challenges of predicting low-probability, high-impact events.Why superforecasters’ estimates of catastrophic risks seem so much lower than experts’, and which group Ezra puts the most weight on.The specific underlying disagreements that superforecasters and experts had about how likely catastrophic risks from AI are.Why Ezra thinks forecasting tournaments can help build consensus on complex topics, and what he wants to do differently in future tournaments and studies.Recent advances in the science of forecasting and the areas Ezra is most excited about exploring next.Whether large language models could help or outperform human forecasters.How people can improve their calibration and start making better forecasts personally.Why Ezra thinks high-quality forecasts are relevant to policymakers, and whether they can really improve decision-making.And plenty more.Chapters:Cold open (00:00:00)Luisa’s intro (00:01:07)The interview begins (00:02:54)The Existential Risk Persuasion Tournament (00:05:13)Why is this project important? (00:12:34)How was the tournament set up? (00:17:54)Results from the tournament (00:22:38)Risk from artificial intelligence (00:30:59)How to think about these numbers (00:46:50)Should we trust experts or superforecasters more? (00:49:16)The effect of debate and persuasion (01:02:10)Forecasts from the general public (01:08:33)How can we improve people’s forecasts? (01:18:59)Incentives and recruitment (01:26:30)Criticisms of the tournament (01:33:51)AI adversarial collaboration (01:46:20)Hypotheses about stark differences in views of AI risk (01:51:41)Cruxes and different worldviews (02:17:15)Ezra’s experience as a superforecaster (02:28:57)Forecasting as a research field (02:31:00)Can large language models help or outperform human forecasters? (02:35:01)Is forecasting valuable in the real world? (02:39:11)Ezra’s book recommendations (02:45:29)Luisa's outro (02:47:54)Producer: Keiran HarrisAudio engineering: Dominic Armstrong, Ben Cordell, Milo McGuire, and Simon MonsourContent editing: Luisa Rodriguez, Katy Moore, and Keiran HarrisTranscriptions: Katy Moore

"I do think that there is a really significant sentiment among parts of the opposition that it’s not really just that this bill itself is that bad or extreme — when you really drill into it, it feels like one of those things where you read it and it’s like, 'This is the thing that everyone is screaming about?' I think it’s a pretty modest bill in a lot of ways, but I think part of what they are thinking is that this is the first step to shutting down AI development. Or that if California does this, then lots of other states are going to do it, and we need to really slam the door shut on model-level regulation or else they’re just going to keep going. "I think that is like a lot of what the sentiment here is: it’s less about, in some ways, the details of this specific bill, and more about the sense that they want this to stop here, and they’re worried that if they give an inch that there will continue to be other things in the future. And I don’t think that is going to be tolerable to the public in the long run. I think it’s a bad choice, but I think that is the calculus that they are making." —Nathan CalvinIn today’s episode, host Luisa Rodriguez speaks to Nathan Calvin — senior policy counsel at the Center for AI Safety Action Fund — about the new AI safety bill in California, SB 1047, which he’s helped shape as it’s moved through the state legislature.Links to learn more, highlights, and full transcript.They cover:What’s actually in SB 1047, and which AI models it would apply to.The most common objections to the bill — including how it could affect competition, startups, open source models, and US national security — and which of these objections Nathan thinks hold water.What Nathan sees as the biggest misunderstandings about the bill that get in the way of good public discourse about it.Why some AI companies are opposed to SB 1047, despite claiming that they want the industry to be regulated.How the bill is different from Biden’s executive order on AI and voluntary commitments made by AI companies.Why California is taking state-level action rather than waiting for federal regulation.How state-level regulations can be hugely impactful at national and global scales, and how listeners could get involved in state-level work to make a real difference on lots of pressing problems.And plenty more.Chapters:Cold open (00:00:00)Luisa's intro (00:00:57)The interview begins (00:02:30)What risks from AI does SB 1047 try to address? (00:03:10)Supporters and critics of the bill (00:11:03)Misunderstandings about the bill (00:24:07)Competition, open source, and liability concerns (00:30:56)Model size thresholds (00:46:24)How is SB 1047 different from the executive order? (00:55:36)Objections Nathan is sympathetic to (00:58:31)Current status of the bill (01:02:57)How can listeners get involved in work like this? (01:05:00)Luisa's outro (01:11:52)Producer and editor: Keiran HarrisAudio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"This is a group of animals I think people are particularly unfamiliar with. They are especially poorly covered in our science curriculum; they are especially poorly understood, because people don’t spend as much time learning about them at museums; and they’re just harder to spend time with in a lot of ways, I think, for people. So people have pets that are vertebrates that they take care of across the taxonomic groups, and people get familiar with those from going to zoos and watching their behaviours there, and watching nature documentaries and more. But I think the insects are still really underappreciated, and that means that our intuitions are probably more likely to be wrong than with those other groups." —Meghan BarrettIn today’s episode, host Luisa Rodriguez speaks to Meghan Barrett — insect neurobiologist and physiologist at Indiana University Indianapolis and founding director of the Insect Welfare Research Society — about her work to understand insects’ potential capacity for suffering, and what that might mean for how humans currently farm and use insects. If you're interested in getting involved with this work, check out Meghan's recent blog post: I’m into insect welfare! What’s next?Links to learn more, highlights, and full transcript.They cover:The scale of potential insect suffering in the wild, on farms, and in labs.Examples from cutting-edge insect research, like how depression- and anxiety-like states can be induced in fruit flies and successfully treated with human antidepressants.How size bias might help explain why many people assume insects can’t feel pain.Practical solutions that Meghan’s team is working on to improve farmed insect welfare, such as standard operating procedures for more humane slaughter methods.Challenges facing the nascent field of insect welfare research, and where the main research gaps are.Meghan’s personal story of how she went from being sceptical of insect pain to working as an insect welfare scientist, and her advice for others who want to improve the lives of insects.And much more.Chapters:Cold open (00:00:00)Luisa's intro (00:01:02)The interview begins (00:03:06)What is an insect? (00:03:22)Size diversity (00:07:24)How important is brain size for sentience? (00:11:27)Offspring, parental investment, and lifespan (00:19:00)Cognition and behaviour (00:23:23)The scale of insect suffering (00:27:01)Capacity to suffer (00:35:56)The empirical evidence for whether insects can feel pain (00:47:18)Nociceptors (01:00:02)Integrated nociception (01:08:39)Response to analgesia (01:16:17)Analgesia preference (01:25:57)Flexible self-protective behaviour (01:31:19)Motivational tradeoffs and associative learning (01:38:45)Results (01:43:31)Reasons to be sceptical (01:47:18)Meghan’s probability of sentience in insects (02:10:20)Views of the broader entomologist community (02:18:18)Insect farming (02:26:52)How much to worry about insect farming (02:40:56)Inhumane slaughter and disease in insect farms (02:44:45)Inadequate nutrition, density, and photophobia (02:53:50)Most humane ways to kill insects at home (03:01:33)Challenges in researching this (03:07:53)Most promising reforms (03:18:44)Why Meghan is hopeful about working with the industry (03:22:17)Careers (03:34:08)Insect Welfare Research Society (03:37:16)Luisa's outro (03:47:01)Producer and editor: Keiran HarrisAudio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

The three biggest AI companies — Anthropic, OpenAI, and DeepMind — have now all released policies designed to make their AI models less likely to go rogue or cause catastrophic damage as they approach, and eventually exceed, human capabilities. Are they good enough?That’s what host Rob Wiblin tries to hash out in this interview (recorded May 30) with Nick Joseph — one of the original cofounders of Anthropic, its current head of training, and a big fan of Anthropic’s “responsible scaling policy” (or “RSP”). Anthropic is the most safety focused of the AI companies, known for a culture that treats the risks of its work as deadly serious.Links to learn more, highlights, video, and full transcript.As Nick explains, these scaling policies commit companies to dig into what new dangerous things a model can do — after it’s trained, but before it’s in wide use. The companies then promise to put in place safeguards they think are sufficient to tackle those capabilities before availability is extended further. For instance, if a model could significantly help design a deadly bioweapon, then its weights need to be properly secured so they can’t be stolen by terrorists interested in using it that way.As capabilities grow further — for example, if testing shows that a model could exfiltrate itself and spread autonomously in the wild — then new measures would need to be put in place to make that impossible, or demonstrate that such a goal can never arise.Nick points out what he sees as the biggest virtues of the RSP approach, and then Rob pushes him on some of the best objections he’s found to RSPs being up to the task of keeping AI safe and beneficial. The two also discuss whether it's essential to eventually hand over operation of responsible scaling policies to external auditors or regulatory bodies, if those policies are going to be able to hold up against the intense commercial pressures that might end up arrayed against them.In addition to all of that, Nick and Rob talk about:What Nick thinks are the current bottlenecks in AI progress: people and time (rather than data or compute).What it’s like working in AI safety research at the leading edge, and whether pushing forward capabilities (even in the name of safety) is a good idea.What it’s like working at Anthropic, and how to get the skills needed to help with the safe development of AI.And as a reminder, if you want to let us know your reaction to this interview, or send any other feedback, our inbox is always open at [email protected]:Cold open (00:00:00)Rob’s intro (00:01:00)The interview begins (00:03:44)Scaling laws (00:04:12)Bottlenecks to further progress in making AIs helpful (00:08:36)Anthropic’s responsible scaling policies (00:14:21)Pros and cons of the RSP approach for AI safety (00:34:09)Alternatives to RSPs (00:46:44)Is an internal audit really the best approach? (00:51:56)Making promises about things that are currently technically impossible (01:07:54)Nick’s biggest reservations about the RSP approach (01:16:05)Communicating “acceptable” risk (01:19:27)Should Anthropic’s RSP have wider safety buffers? (01:26:13)Other impacts on society and future work on RSPs (01:34:01)Working at Anthropic (01:36:28)Engineering vs research (01:41:04)AI safety roles at Anthropic (01:48:31)Should concerned people be willing to take capabilities roles? (01:58:20)Recent safety work at Anthropic (02:10:05)Anthropic culture (02:14:35)Overrated and underrated AI applications (02:22:06)Rob’s outro (02:26:36)Producer and editor: Keiran HarrisAudio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongVideo engineering: Simon MonsourTranscriptions: Katy Moore

"In the 1980s, it was still apparently common to perform surgery on newborn babies without anaesthetic on both sides of the Atlantic. This led to appalling cases, and to public outcry, and to campaigns to change clinical practice. And as soon as [some courageous scientists] looked for evidence, it showed that this practice was completely indefensible and then the clinical practice was changed. People don’t need convincing anymore that we should take newborn human babies seriously as sentience candidates. But the tale is a useful cautionary tale, because it shows you how deep that overconfidence can run and how problematic it can be. It just underlines this point that overconfidence about sentience is everywhere and is dangerous." —Jonathan BirchIn today’s episode, host Luisa Rodriguez speaks to Dr Jonathan Birch — philosophy professor at the London School of Economics — about his new book, The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI. (Check out the free PDF version!)Links to learn more, highlights, and full transcript.They cover:Candidates for sentience, such as humans with consciousness disorders, foetuses, neural organoids, invertebrates, and AIsHumanity’s history of acting as if we’re sure that such beings are incapable of having subjective experiences — and why Jonathan thinks that that certainty is completely unjustified.Chilling tales about overconfident policies that probably caused significant suffering for decades.How policymakers can act ethically given real uncertainty.Whether simulating the brain of the roundworm C. elegans or Drosophila (aka fruit flies) would create minds equally sentient to the biological versions.How new technologies like brain organoids could replace animal testing, and how big the risk is that they could be sentient too.Why Jonathan is so excited about citizens’ assemblies.Jonathan’s conversation with the Dalai Lama about whether insects are sentient.And plenty more.Chapters:Cold open (00:00:00)Luisa’s intro (00:01:20)The interview begins (00:03:04)Why does sentience matter? (00:03:31)Inescapable uncertainty about other minds (00:05:43)The “zone of reasonable disagreement” in sentience research (00:10:31)Disorders of consciousness: comas and minimally conscious states (00:17:06)Foetuses and the cautionary tale of newborn pain (00:43:23)Neural organoids (00:55:49)AI sentience and whole brain emulation (01:06:17)Policymaking at the edge of sentience (01:28:09)Citizens’ assemblies (01:31:13)The UK’s Sentience Act (01:39:45)Ways Jonathan has changed his mind (01:47:26)Careers (01:54:54)Discussing animal sentience with the Dalai Lama (01:59:08)Luisa’s outro (02:01:04)Producer and editor: Keiran HarrisAudio engineering by Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"Computational systems have literally millions of physical and conceptual components, and around 98% of them are embedded into your infrastructure without you ever having heard of them. And an inordinate amount of them can lead to a catastrophic failure of your security assumptions. And because of this, the Iranian secret nuclear programme failed to prevent a breach, most US agencies failed to prevent multiple breaches, most US national security agencies failed to prevent breaches. So ensuring your system is truly secure against highly resourced and dedicated attackers is really, really hard." —Sella NevoIn today’s episode, host Luisa Rodriguez speaks to Sella Nevo — director of the Meselson Center at RAND — about his team’s latest report on how to protect the model weights of frontier AI models from actors who might want to steal them.Links to learn more, highlights, and full transcript.They cover:Real-world examples of sophisticated security breaches, and what we can learn from them.Why AI model weights might be such a high-value target for adversaries like hackers, rogue states, and other bad actors.The many ways that model weights could be stolen, from using human insiders to sophisticated supply chain hacks.The current best practices in cybersecurity, and why they may not be enough to keep bad actors away.New security measures that Sella hopes can mitigate with the growing risks.Sella’s work using machine learning for flood forecasting, which has significantly reduced injuries and costs from floods across Africa and Asia.And plenty more.Also, RAND is currently hiring for roles in technical and policy information security — check them out if you're interested in this field! Chapters:Cold open (00:00:00)Luisa’s intro (00:00:56)The interview begins (00:02:30)The importance of securing the model weights of frontier AI models (00:03:01)The most sophisticated and surprising security breaches (00:10:22)AI models being leaked (00:25:52)Researching for the RAND report (00:30:11)Who tries to steal model weights? (00:32:21)Malicious code and exploiting zero-days (00:42:06)Human insiders (00:53:20)Side-channel attacks (01:04:11)Getting access to air-gapped networks (01:10:52)Model extraction (01:19:47)Reducing and hardening authorised access (01:38:52)Confidential computing (01:48:05)Red-teaming and security testing (01:53:42)Careers in information security (01:59:54)Sella’s work on flood forecasting systems (02:01:57)Luisa’s outro (02:04:51)Producer and editor: Keiran HarrisAudio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"If you’re a power that is an island and that goes by sea, then you’re more likely to do things like valuing freedom, being democratic, being pro-foreigner, being open-minded, being interested in trade. If you are on the Mongolian steppes, then your entire mindset is kill or be killed, conquer or be conquered … the breeding ground for basically everything that all of us consider to be dystopian governance. If you want more utopian governance and less dystopian governance, then find ways to basically change the landscape, to try to make the world look more like mountains and rivers and less like the Mongolian steppes." —Vitalik ButerinCan ‘effective accelerationists’ and AI ‘doomers’ agree on a common philosophy of technology? Common sense says no. But programmer and Ethereum cofounder Vitalik Buterin showed otherwise with his essay “My techno-optimism,” which both camps agreed was basically reasonable.Links to learn more, highlights, video, and full transcript.Seeing his social circle divided and fighting, Vitalik hoped to write a careful synthesis of the best ideas from both the optimists and the apprehensive.Accelerationists are right: most technologies leave us better off, the human cost of delaying further advances can be dreadful, and centralising control in government hands often ends disastrously.But the fearful are also right: some technologies are important exceptions, AGI has an unusually high chance of being one of those, and there are options to advance AI in safer directions.The upshot? Defensive acceleration: humanity should run boldly but also intelligently into the future — speeding up technology to get its benefits, but preferentially developing ‘defensive’ technologies that lower systemic risks, permit safe decentralisation of power, and help both individuals and countries defend themselves against aggression and domination.Entrepreneur First is running a defensive acceleration incubation programme with $250,000 of investment. If these ideas resonate with you, learn about the programme and apply by August 2, 2024. You don’t need a business idea yet — just the hustle to start a technology company.In addition to all of that, host Rob Wiblin and Vitalik discuss:AI regulation disagreements being less about AI in particular, and more whether you’re typically more scared of anarchy or totalitarianism.Vitalik’s updated p(doom).Whether the social impact of blockchain and crypto has been a disappointment.Whether humans can merge with AI, and if that’s even desirable.The most valuable defensive technologies to accelerate.How to trustlessly identify what everyone will agree is misinformationWhether AGI is offence-dominant or defence-dominant.Vitalik’s updated take on effective altruism.Plenty more.Chapters:Cold open (00:00:00)Rob’s intro (00:00:56)The interview begins (00:04:47)Three different views on technology (00:05:46)Vitalik’s updated probability of doom (00:09:25)Technology is amazing, and AI is fundamentally different from other tech (00:15:55)Fear of totalitarianism and finding middle ground (00:22:44)Should AI be more centralised or more decentralised? (00:42:20)Humans merging with AIs to remain relevant (01:06:59)Vitalik’s “d/acc” alternative (01:18:48)Biodefence (01:24:01)Pushback on Vitalik’s vision (01:37:09)How much do people actually disagree? (01:42:14)Cybersecurity (01:47:28)Information defence (02:01:44)Is AI more offence-dominant or defence-dominant? (02:21:00)How Vitalik communicates among different camps (02:25:44)Blockchain applications with social impact (02:34:37)Rob’s outro (03:01:00)Producer and editor: Keiran HarrisAudio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic ArmstrongTranscriptions: Katy Moore

"You don’t necessarily need world-leading compute to create highly risky AI systems. The biggest biological design tools right now, like AlphaFold’s, are orders of magnitude smaller in terms of compute requirements than the frontier large language models. And China has the compute to train these systems. And if you’re, for instance, building a cyber agent or something that conducts cyberattacks, perhaps you also don’t need the general reasoning or mathematical ability of a large language model. You train on a much smaller subset of data. You fine-tune it on a smaller subset of data. And those systems — one, if China intentionally misuses them, and two, if they get proliferated because China just releases them as open source, or China does not have as comprehensive AI regulations — this could cause a lot of harm in the world." —Sihao HuangIn today’s episode, host Luisa Rodriguez speaks to Sihao Huang about his work on AI governance and tech policy in China, what’s happening on the ground in China in AI development and regulation, and the importance of US–China cooperation on AI governance.Links to learn more, highlights, video, and full transcript.They cover:Whether the US and China are in an AI race, and the global implications if they are.The state of the art of AI in China.China’s response to American export controls, and whether China is on track to indigenise its semiconductor supply chain.How China’s current AI regulations try to maintain a delicate balance between fostering innovation and keeping strict information control over the Chinese people.Whether China’s extensive AI regulations signal real commitment to safety or just censorship — and how AI is already used in China for surveillance and authoritarian control.How advancements in AI could reshape global power dynamics, and Sihao’s vision of international cooperation to manage this responsibly.And plenty more.Chapters:Cold open (00:00:00)Luisa's intro (00:01:02)The interview begins (00:02:06)Is China in an AI race with the West? (00:03:20)How advanced is Chinese AI? (00:15:21)Bottlenecks in Chinese AI development (00:22:30)China and AI risks (00:27:41)Information control and censorship (00:31:32)AI safety research in China (00:36:31)Could China be a source of catastrophic AI risk? (00:41:58)AI enabling human rights abuses and undermining democracy (00:50:10)China’s semiconductor industry (00:59:47)China’s domestic AI governance landscape (01:29:22)China’s international AI governance strategy (01:49:56)Coordination (01:53:56)Track two dialogues (02:03:04)Misunderstandings Western actors have about Chinese approaches (02:07:34)Complexity thinking (02:14:40)Sihao’s pet bacteria hobby (02:20:34)Luisa's outro (02:22:47)Producer and editor: Keiran HarrisAudio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"Ring one: total annihilation; no cellular life remains. Ring two, another three-mile diameter out: everything is ablaze. Ring three, another three or five miles out on every side: third-degree burns among almost everyone. You are talking about people who may have gone down into the secret tunnels beneath Washington, DC, escaped from the Capitol and such: people are now broiling to death; people are dying from carbon monoxide poisoning; people who followed instructions and went into their basement are dying of suffocation. Everywhere there is death, everywhere there is fire."That iconic mushroom stem and cap that represents a nuclear blast — when a nuclear weapon has been exploded on a city — that stem and cap is made up of people. What is left over of people and of human civilisation." —Annie JacobsenIn today’s episode, host Luisa Rodriguez speaks to Pulitzer Prize finalist and New York Times bestselling author Annie Jacobsen about her latest book, Nuclear War: A Scenario.Links to learn more, highlights, and full transcript.They cover:The most harrowing findings from Annie’s hundreds of hours of interviews with nuclear experts.What happens during the window that the US president would have to decide about nuclear retaliation after hearing news of a possible nuclear attack.The horrific humanitarian impacts on millions of innocent civilians from nuclear strikes.The overlooked dangers of a nuclear-triggered electromagnetic pulse (EMP) attack crippling critical infrastructure within seconds.How we’re on the razor’s edge between the logic of nuclear deterrence and catastrophe, and urgently need reforms to move away from hair-trigger alert nuclear postures.And plenty more.Chapters:Cold open (00:00:00)Luisa’s intro (00:01:03)The interview begins (00:02:28)The first 24 minutes (00:02:59)The Black Book and presidential advisors (00:13:35)False alarms (00:40:43)Russian misperception of US counterattack (00:44:50)A narcissistic madman with a nuclear arsenal (01:00:13)Is escalation inevitable? (01:02:53)Firestorms and rings of annihilation (01:12:56)Nuclear electromagnetic pulses (01:27:34)Continuity of government (01:36:35)Rays of hope (01:41:07)Where we’re headed (01:43:52)Avoiding politics (01:50:34)Luisa’s outro (01:52:29)Producer and editor: Keiran HarrisAudio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

This is the second part of our marathon interview with Carl Shulman. The first episode is on the economy and national security after AGI. You can listen to them in either order!If we develop artificial general intelligence that's reasonably aligned with human goals, it could put a fast and near-free superhuman advisor in everyone's pocket. How would that affect culture, government, and our ability to act sensibly and coordinate together?It's common to worry that AI advances will lead to a proliferation of misinformation and further disconnect us from reality. But in today's conversation, AI expert Carl Shulman argues that this underrates the powerful positive applications the technology could have in the public sphere.Links to learn more, highlights, and full transcript.As Carl explains, today the most important questions we face as a society remain in the "realm of subjective judgement" -- without any "robust, well-founded scientific consensus on how to answer them." But if AI 'evals' and interpretability advance to the point that it's possible to demonstrate which AI models have truly superhuman judgement and give consistently trustworthy advice, society could converge on firm or 'best-guess' answers to far more cases.If the answers are publicly visible and confirmable by all, the pressure on officials to act on that advice could be great. That's because when it's hard to assess if a line has been crossed or not, we usually give people much more discretion. For instance, a journalist inventing an interview that never happened will get fired because it's an unambiguous violation of honesty norms — but so long as there's no universally agreed-upon standard for selective reporting, that same journalist will have substantial discretion to report information that favours their preferred view more often than that which contradicts it.Similarly, today we have no generally agreed-upon way to tell when a decision-maker has behaved irresponsibly. But if experience clearly shows that following AI advice is the wise move, not seeking or ignoring such advice could become more like crossing a red line — less like making an understandable mistake and more like fabricating your balance sheet.To illustrate the possible impact, Carl imagines how the COVID pandemic could have played out in the presence of AI advisors that everyone agrees are exceedingly insightful and reliable. But in practice, a significantly superhuman AI might suggest novel approaches better than any we can suggest.In the past we've usually found it easier to predict how hard technologies like planes or factories will change than to imagine the social shifts that those technologies will create — and the same is likely happening for AI.Carl Shulman and host Rob Wiblin discuss the above, as well as:The risk of society using AI to lock in its values.The difficulty of preventing coups once AI is key to the military and police.What international treaties we need to make this go well.How to make AI superhuman at forecasting the future.Whether AI will be able to help us with intractable philosophical questions.Whether we need dedicated projects to make wise AI advisors, or if it will happen automatically as models scale.Why Carl doesn't support AI companies voluntarily pausing AI research, but sees a stronger case for binding international controls once we're closer to 'crunch time.'Opportunities for listeners to contribute to making the future go well.Chapters:Cold open (00:00:00)Rob’s intro (00:01:16)The interview begins (00:03:24)COVID-19 concrete example (00:11:18)Sceptical arguments against the effect of AI advisors (00:24:16)Value lock-in (00:33:59)How democracies avoid coups (00:48:08)Where AI could most easily help (01:00:25)AI forecasting (01:04:30)Application to the most challenging topics (01:24:03)How to make it happen (01:37:50)International negotiations and coordination and auditing (01:43:54)Opportunities for listeners (02:00:09)Why Carl doesn't support enforced pauses on AI research (02:03:58)How Carl is feeling about the future (02:15:47)Rob’s outro (02:17:37)Producer and editor: Keiran HarrisAudio engineering team: Ben Cordell, Simon Monsour, Milo McGuire, and Dominic ArmstrongTranscriptions: Katy Moore

This is the first part of our marathon interview with Carl Shulman. The second episode is on government and society after AGI. You can listen to them in either order!The human brain does what it does with a shockingly low energy supply: just 20 watts — a fraction of a cent worth of electricity per hour. What would happen if AI technology merely matched what evolution has already managed, and could accomplish the work of top human professionals given a 20-watt power supply?Many people sort of consider that hypothetical, but maybe nobody has followed through and considered all the implications as much as Carl Shulman. Behind the scenes, his work has greatly influenced how leaders in artificial general intelligence (AGI) picture the world they're creating.Links to learn more, highlights, and full transcript.Carl simply follows the logic to its natural conclusion. This is a world where 1 cent of electricity can be turned into medical advice, company management, or scientific research that would today cost $100s, resulting in a scramble to manufacture chips and apply them to the most lucrative forms of intellectual labour.It's a world where, given their incredible hourly salaries, the supply of outstanding AI researchers quickly goes from 10,000 to 10 million or more, enormously accelerating progress in the field.It's a world where companies operated entirely by AIs working together are much faster and more cost-effective than those that lean on humans for decision making, and the latter are progressively driven out of business. It's a world where the technical challenges around control of robots are rapidly overcome, leading to robots into strong, fast, precise, and tireless workers able to accomplish any physical work the economy requires, and a rush to build billions of them and cash in.As the economy grows, each person could effectively afford the practical equivalent of a team of hundreds of machine 'people' to help them with every aspect of their lives.And with growth rates this high, it doesn't take long to run up against Earth's physical limits — in this case, the toughest to engineer your way out of is the Earth's ability to release waste heat. If this machine economy and its insatiable demand for power generates more heat than the Earth radiates into space, then it will rapidly heat up and become uninhabitable for humans and other animals.This creates pressure to move economic activity off-planet. So you could develop effective populations of billions of scientific researchers operating on computer chips orbiting in space, sending the results of their work, such as drug designs, back to Earth for use.These are just some of the wild implications that could follow naturally from truly embracing the hypothetical: what if we develop AGI that could accomplish everything that the most productive humans can, using the same energy supply?In today's episode, Carl explains the above, and then host Rob Wiblin pushes back on whether that’s realistic or just a cool story, asking:If we're heading towards the above, how come economic growth is slow now and not really increasing?Why have computers and computer chips had so little effect on economic productivity so far?Are self-replicating biological systems a good comparison for self-replicating machine systems?Isn't this just too crazy and weird to be plausible?What bottlenecks would be encountered in supplying energy and natural resources to this growing economy?Might there not be severely declining returns to bigger brains and more training?Wouldn't humanity get scared and pull the brakes if such a transformation kicked off?If this is right, how come economists don't agree?Finally, Carl addresses the moral status of machine minds themselves. Would they be conscious or otherwise have a claim to moral or rights? And how might humans and machines coexist with neither side dominating or exploiting the other?Chapters:Cold open (00:00:00)Rob’s intro (00:01:00)Transitioning to a world where AI systems do almost all the work (00:05:21)Economics after an AI explosion (00:14:25)Objection: Shouldn’t we be seeing economic growth rates increasing today? (00:59:12)Objection: Speed of doubling time (01:07:33)Objection: Declining returns to increases in intelligence? (01:11:59)Objection: Physical transformation of the environment (01:17:39)Objection: Should we expect an increased demand for safety and security? (01:29:14)Objection: “This sounds completely whack” (01:36:10)Income and wealth distribution (01:48:02)Economists and the intelligence explosion (02:13:31)Baumol effect arguments (02:19:12)Denying that robots can exist (02:27:18)Classic economic growth models (02:36:12)Robot nannies (02:48:27)Slow integration of decision-making and authority power (02:57:39)Economists’ mistaken heuristics (03:01:07)Moral status of AIs (03:11:45)Rob’s outro (04:11:47)Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Domin

"One of the most amazing things about planet Earth is that there are complex bags of mostly water — you and me – and we can look up at the stars, and look into our brains, and try to grapple with the most complex, difficult questions that there are. And even if we can’t make great progress on them and don’t come to completely satisfying solutions, just the fact of trying to grapple with these things is kind of the universe looking at itself and trying to understand itself. So we’re kind of this bright spot of reflectiveness in the cosmos, and I think we should celebrate that fact for its own intrinsic value and interestingness." —Eric SchwitzgebelIn today’s episode, host Luisa Rodriguez speaks to Eric Schwitzgebel — professor of philosophy at UC Riverside — about some of the most bizarre and unintuitive claims from his recent book, The Weirdness of the World.Links to learn more, highlights, and full transcript.They cover:Why our intuitions seem so unreliable for answering fundamental questions about reality.What the materialist view of consciousness is, and how it might imply some very weird things — like that the United States could be a conscious entity.Thought experiments that challenge our intuitions — like supersquids that think and act through detachable tentacles, and intelligent species whose brains are made up of a million bugs.Eric’s claim that consciousness and cosmology are universally bizarre and dubious.How to think about borderline states of consciousness, and whether consciousness is more like a spectrum or more like a light flicking on.The nontrivial possibility that we could be dreaming right now, and the ethical implications if that’s true.Why it’s worth it to grapple with the universe’s most complex questions, even if we can’t find completely satisfying solutions.And much more.Chapters:Cold open |00:00:00|Luisa’s intro |00:01:10|Bizarre and dubious philosophical theories |00:03:13|The materialist view of consciousness |00:13:55|What would it mean for the US to be conscious? |00:19:46|Supersquids and antheads thought experiments |00:22:37|Alternatives to the materialist perspective |00:35:19|Are our intuitions useless for thinking about these things? |00:42:55|Key ingredients for consciousness |00:46:46|Reasons to think the US isn’t conscious |01:01:15|Overlapping consciousnesses [01:09:32]Borderline cases of consciousness |01:13:22|Are we dreaming right now? |01:40:29|Will we ever have answers to these dubious and bizarre questions? |01:56:16|Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"You can’t charge what something is worth during a pandemic. So we estimated that the value of one course of COVID vaccine in January 2021 was over $5,000. They were selling for between $6 and $40. So nothing like their social value. Now, don’t get me wrong. I don’t think that they should have charged $5,000 or $6,000. That’s not ethical. It’s also not economically efficient, because they didn’t cost $5,000 at the marginal cost. So you actually want low price, getting out to lots of people."But it shows you that the market is not going to reward people who do the investment in preparation for a pandemic — because when a pandemic hits, they’re not going to get the reward in line with the social value. They may even have to charge less than they would in a non-pandemic time. So prepping for a pandemic is not an efficient market strategy if I’m a firm, but it’s a very efficient strategy for society, and so we’ve got to bridge that gap." —Rachel GlennersterIn today’s episode, host Luisa Rodriguez speaks to Rachel Glennerster — associate professor of economics at the University of Chicago and a pioneer in the field of development economics — about how her team’s new Market Shaping Accelerator aims to leverage market forces to drive innovations that can solve pressing world problems.Links to learn more, highlights, and full transcript.They cover:How market failures and misaligned incentives stifle critical innovations for social goods like pandemic preparedness, climate change interventions, and vaccine development.How “pull mechanisms” like advance market commitments (AMCs) can help overcome these challenges — including concrete examples like how one AMC led to speeding up the development of three vaccines which saved around 700,000 lives in low-income countries.The challenges in designing effective pull mechanisms, from design to implementation.Why it’s important to tie innovation incentives to real-world impact and uptake, not just the invention of a new technology.The massive benefits of accelerating vaccine development, in some cases, even if it’s only by a few days or weeks.The case for a $6 billion advance market commitment to spur work on a universal COVID-19 vaccine.The shortlist of ideas from the Market Shaping Accelerator’s recent Innovation Challenge that use pull mechanisms to address market failures around improving indoor air quality, repurposing generic drugs for alternative uses, and developing eco-friendly air conditioners for a warming planet.“Best Buys” and “Bad Buys” for improving education systems in low- and middle-income countries, based on evidence from over 400 studies.Lessons from Rachel’s career at the forefront of global development, and how insights from economics can drive transformative change.And much more.Chapters:The Market Shaping Accelerator (00:03:33)Pull mechanisms for innovation (00:13:10)Accelerating the pneumococcal and COVID vaccines (00:19:05)Advance market commitments (00:41:46)Is this uncertainty hard for funders to plan around? (00:49:17)The story of the malaria vaccine that wasn’t (00:57:15)Challenges with designing and implementing AMCs and other pull mechanisms (01:01:40)Universal COVID vaccine (01:18:14)Climate-resilient crops (01:34:09)The Market Shaping Accelerator’s Innovation Challenge (01:45:40)Indoor air quality to reduce respiratory infections (01:49:09)Repurposing generic drugs (01:55:50)Clean air conditioning units (02:02:41)Broad-spectrum antivirals for pandemic prevention (02:09:11)Improving education in low- and middle-income countries (02:15:53)What’s still weird for Rachel about living in the US? (02:45:06)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"Suppose we make these grants, we do some of those experiments I talk about. We discover, for example — I’m just making this up — but we give people superforecasting tests when they’re doing peer review, and we find that you can identify people who are super good at picking science. And then we have this much better targeted science, and we’re making progress at a 10% faster rate than we normally would have. Over time, that aggregates up, and maybe after 10 years, we’re a year ahead of where we would have been if we hadn’t done this kind of stuff."Now, suppose in 10 years we’re going to discover a cheap new genetic engineering technology that anyone can use in the world if they order the right parts off of Amazon. That could be great, but could also allow bad actors to genetically engineer pandemics and basically try to do terrible things with this technology. And if we’ve brought that forward, and that happens at year nine instead of year 10 because of some of these interventions we did, now we start to think that if that’s really bad, if these people using this technology causes huge problems for humanity, it begins to sort of wash out the benefits of getting the science a little bit faster." —Matt ClancyIn today’s episode, host Luisa Rodriguez speaks to Matt Clancy — who oversees Open Philanthropy’s Innovation Policy programme — about his recent work modelling the risks and benefits of the increasing speed of scientific progress.Links to learn more, highlights, and full transcript.They cover:Whether scientific progress is actually net positive for humanity.Scenarios where accelerating science could lead to existential risks, such as advanced biotechnology being used by bad actors.Why Matt thinks metascience research and targeted funding could improve the scientific process and better incentivise outcomes that are good for humanity.Whether Matt trusts domain experts or superforecasters more when estimating how the future will turn out.Why Matt is sceptical that AGI could really cause explosive economic growth.And much more.Chapters:Is scientific progress net positive for humanity? (00:03:00)The time of biological perils (00:17:50)Modelling the benefits of science (00:25:48)Income and health gains from scientific progress (00:32:49)Discount rates (00:42:14)How big are the returns to science? (00:51:08)Forecasting global catastrophic biological risks from scientific progress (01:05:20)What’s the value of scientific progress, given the risks? (01:15:09)Factoring in extinction risk (01:21:56)How science could reduce extinction risk (01:30:18)Are we already too late to delay the time of perils? (01:42:38)Domain experts vs superforecasters (01:46:03)What Open Philanthropy’s Innovation Policy programme settled on (01:53:47)Explosive economic growth (02:06:28)Matt’s favourite thought experiment (02:34:57)Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"Earth economists, when they measure how bad the potential for exploitation is, they look at things like, how is labour mobility? How much possibility do labourers have otherwise to go somewhere else? Well, if you are on the one company town on Mars, your labour mobility is zero, which has never existed on Earth. Even in your stereotypical West Virginian company town run by immigrant labour, there’s still, by definition, a train out. On Mars, you might not even be in the launch window. And even if there are five other company towns or five other settlements, they’re not necessarily rated to take more humans. They have their own oxygen budget, right? "And so economists use numbers like these, like labour mobility, as a way to put an equation and estimate the ability of a company to set noncompetitive wages or to set noncompetitive work conditions. And essentially, on Mars you’re setting it to infinity." — Zach WeinersmithIn today’s episode, host Luisa Rodriguez speaks to Zach Weinersmith — the cartoonist behind Saturday Morning Breakfast Cereal — about the latest book he wrote with his wife Kelly: A City on Mars: Can We Settle Space, Should We Settle Space, and Have We Really Thought This Through?Links to learn more, highlights, and full transcript.They cover:Why space travel is suddenly getting a lot cheaper and re-igniting enthusiasm around space settlement.What Zach thinks are the best and worst arguments for settling space.Zach’s journey from optimistic about space settlement to a self-proclaimed “space bastard” (pessimist).How little we know about how microgravity and radiation affects even adults, much less the children potentially born in a space settlement.A rundown of where we could settle in the solar system, and the major drawbacks of even the most promising candidates.Why digging bunkers or underwater cities on Earth would beat fleeing to Mars in a catastrophe.How new space settlements could look a lot like old company towns — and whether or not that’s a bad thing.The current state of space law and how it might set us up for international conflict.How space cannibalism legal loopholes might work on the International Space Station.And much more.Chapters:Space optimism and space bastards (00:03:04)Bad arguments for why we should settle space (00:14:01)Superficially plausible arguments for why we should settle space (00:28:54)Is settling space even biologically feasible? (00:32:43)Sex, pregnancy, and child development in space (00:41:41)Where’s the best space place to settle? (00:55:02)Creating self-sustaining habitats (01:15:32)What about AI advances? (01:26:23)A roadmap for settling space (01:33:45)Space law (01:37:22)Space signalling and propaganda (01:51:28) Space war (02:00:40)Mining asteroids (02:06:29)Company towns and communes in space (02:10:55)Sending digital minds into space (02:26:37)The most promising space governance models (02:29:07)The tragedy of the commons (02:35:02)The tampon bandolier and other bodily functions in space (02:40:14)Is space cannibalism legal? (02:47:09)The pregnadrome and other bizarre proposals (02:50:02)Space sexism (02:58:38)What excites Zach about the future (03:02:57)Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"I work in a place called Uttar Pradesh, which is a state in India with 240 million people. One in every 33 people in the whole world lives in Uttar Pradesh. It would be the fifth largest country if it were its own country. And if it were its own country, you’d probably know about its human development challenges, because it would have the highest neonatal mortality rate of any country except for South Sudan and Pakistan. Forty percent of children there are stunted. Only two-thirds of women are literate. So Uttar Pradesh is a place where there are lots of health challenges."And then even within that, we’re working in a district called Bahraich, where about 4 million people live. So even that district of Uttar Pradesh is the size of a country, and if it were its own country, it would have a higher neonatal mortality rate than any other country. In other words, babies born in Bahraich district are more likely to die in their first month of life than babies born in any country around the world." — Dean SpearsIn today’s episode, host Luisa Rodriguez speaks to Dean Spears — associate professor of economics at the University of Texas at Austin and founding director of r.i.c.e. — about his experience implementing a surprisingly low-tech but highly cost-effective kangaroo mother care programme in Uttar Pradesh, India to save the lives of vulnerable newborn infants.Links to learn more, highlights, and full transcript.They cover:The shockingly high neonatal mortality rates in Uttar Pradesh, India, and how social inequality and gender dynamics contribute to poor health outcomes for both mothers and babies.The remarkable benefits for vulnerable newborns that come from skin-to-skin contact and breastfeeding support.The challenges and opportunities that come with working with a government hospital to implement new, evidence-based programmes.How the currently small programme might be scaled up to save more newborns’ lives in other regions of Uttar Pradesh and beyond.How targeted health interventions stack up against direct cash transfers.Plus, a sneak peak into Dean’s new book, which explores the looming global population peak that’s expected around 2080, and the consequences of global depopulation.And much more.Chapters:Why is low birthweight a major problem in Uttar Pradesh? (00:02:45)Neonatal mortality and maternal health in Uttar Pradesh (00:06:10)Kangaroo mother care (00:12:08)What would happen without this intervention? (00:16:07)Evidence of KMC’s effectiveness (00:18:15)Longer-term outcomes (00:32:14)GiveWell’s support and implementation challenges (00:41:13)How can KMC be so cost effective? (00:52:38)Programme evaluation (00:57:21)Is KMC is better than direct cash transfers? (00:59:12)Expanding the programme and what skills are needed (01:01:29)Fertility and population decline (01:07:28)What advice Dean would give his younger self (01:16:09)Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"The constraint right now on factory farming is how far can you push the biology of these animals? But AI could remove that constraint. It could say, 'Actually, we can push them further in these ways and these ways, and they still stay alive. And we’ve modelled out every possibility and we’ve found that it works.' I think another possibility, which I don’t understand as well, is that AI could lock in current moral values. And I think in particular there’s a risk that if AI is learning from what we do as humans today, the lesson it’s going to learn is that it’s OK to tolerate mass cruelty, so long as it occurs behind closed doors. I think there’s a risk that if it learns that, then it perpetuates that value, and perhaps slows human moral progress on this issue." —Lewis BollardIn today’s episode, host Luisa Rodriguez speaks to Lewis Bollard — director of the Farm Animal Welfare programme at Open Philanthropy — about the promising progress and future interventions to end the worst factory farming practices still around today.Links to learn more, highlights, and full transcript.They cover:The staggering scale of animal suffering in factory farms, and how it will only get worse without intervention.Work to improve farmed animal welfare that Open Philanthropy is excited about funding.The amazing recent progress made in farm animal welfare — including regulatory attention in the EU and a big win at the US Supreme Court — and the work that still needs to be done.The occasional tension between ending factory farming and curbing climate changeHow AI could transform factory farming for better or worse — and Lewis’s fears that the technology will just help us maximise cruelty in the name of profit.How Lewis has updated his opinions or grantmaking as a result of new research on the “moral weights” of different species.Lewis’s personal journey working on farm animal welfare, and how he copes with the emotional toll of confronting the scale of animal suffering.How listeners can get involved in the growing movement to end factory farming — from career and volunteer opportunities to impactful donations.And much more.Chapters:Common objections to ending factory farming (00:13:21)Potential solutions (00:30:55)Cage-free reforms (00:34:25)Broiler chicken welfare (00:46:48)Do companies follow through on these commitments? (01:00:21)Fish welfare (01:05:02)Alternatives to animal proteins (01:16:36)Farm animal welfare in Asia (01:26:00)Farm animal welfare in Europe (01:30:45)Animal welfare science (01:42:09)Approaches Lewis is less excited about (01:52:10)Will we end factory farming in our lifetimes? (01:56:36)Effect of AI (01:57:59)Recent big wins for farm animals (02:07:38)How animal advocacy has changed since Lewis first got involved (02:15:57)Response to the Moral Weight Project (02:19:52)How to help (02:28:14)Producer and editor: Keiran HarrisAudio engineering lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Many of you will have heard of Zvi Mowshowitz as a superhuman information-absorbing-and-processing machine — which he definitely is. As the author of the Substack Don’t Worry About the Vase, Zvi has spent as much time as literally anyone in the world over the last two years tracking in detail how the explosion of AI has been playing out — and he has strong opinions about almost every aspect of it. Links to learn more, summary, and full transcript.In today’s episode, host Rob Wiblin asks Zvi for his takes on:US-China negotiationsWhether AI progress has stalledThe biggest wins and losses for alignment in 2023EU and White House AI regulationsWhich major AI lab has the best safety strategyThe pros and cons of the Pause AI movementRecent breakthroughs in capabilitiesIn what situations it’s morally acceptable to work at AI labsWhether you agree or disagree with his views, Zvi is super informed and brimming with concrete details.Zvi and Rob also talk about:The risk of AI labs fooling themselves into believing their alignment plans are working when they may not be.The “sleeper agent” issue uncovered in a recent Anthropic paper, and how it shows us how hard alignment actually is.Why Zvi disagrees with 80,000 Hours’ advice about gaining career capital to have a positive impact.Zvi’s project to identify the most strikingly horrible and neglected policy failures in the US, and how Zvi founded a new think tank (Balsa Research) to identify innovative solutions to overthrow the horrible status quo in areas like domestic shipping, environmental reviews, and housing supply.Why Zvi thinks that improving people’s prosperity and housing can make them care more about existential risks like AI.An idea from the online rationality community that Zvi thinks is really underrated and more people should have heard of: simulacra levels.And plenty more.Chapters:Zvi’s AI-related worldview (00:03:41)Sleeper agents (00:05:55)Safety plans of the three major labs (00:21:47)Misalignment vs misuse vs structural issues (00:50:00)Should concerned people work at AI labs? (00:55:45)Pause AI campaign (01:30:16)Has progress on useful AI products stalled? (01:38:03)White House executive order and US politics (01:42:09)Reasons for AI policy optimism (01:56:38)Zvi’s day-to-day (02:09:47)Big wins and losses on safety and alignment in 2023 (02:12:29)Other unappreciated technical breakthroughs (02:17:54)Concrete things we can do to mitigate risks (02:31:19)Balsa Research and the Jones Act (02:34:40)The National Environmental Policy Act (02:50:36)Housing policy (02:59:59)Underrated rationalist worldviews (03:16:22)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongTranscriptions and additional content editing: Katy Moore

Today’s release is a reading of our career review of AI governance and policy, written and narrated by Cody Fenwick.Advanced AI systems could have massive impacts on humanity and potentially pose global catastrophic risks, and there are opportunities in the broad field of AI governance to positively shape how society responds to and prepares for the challenges posed by the technology.Given the high stakes, pursuing this career path could be many people’s highest-impact option. But they should be very careful not to accidentally exacerbate the threats rather than mitigate them.If you want to check out the links, footnotes and figures in today’s article, you can find those here.Editing and audio proofing: Ben Cordell and Simon MonsourNarration: Cody Fenwick

"When a friend comes to me with a decision, and they want my thoughts on it, very rarely am I trying to give them a really specific answer, like, 'I solved your problem.' What I’m trying to do often is give them other ways of thinking about what they’re doing, or giving different framings. A classic example of this would be someone who’s been working on a project for a long time and they feel really trapped by it. And someone says, 'Let’s suppose you currently weren’t working on the project, but you could join it. And if you joined, it would be exactly the state it is now. Would you join?' And they’d be like, 'Hell no!' It’s a reframe. It doesn’t mean you definitely shouldn’t join, but it’s a reframe that gives you a new way of looking at it." —Spencer GreenbergIn today’s episode, host Rob Wiblin speaks for a fourth time with listener favourite Spencer Greenberg — serial entrepreneur and host of the Clearer Thinking podcast — about a grab-bag of topics that Spencer has explored since his last appearance on the show a year ago.Links to learn more, summary, and full transcript.They cover:How much money makes you happy — and the tricky methodological issues that come up trying to answer that question.The importance of hype in making valuable things happen.How to recognise warning signs that someone is untrustworthy or likely to hurt you.Whether Registered Reports are successfully solving reproducibility issues in science.The personal principles Spencer lives by, and whether or not we should all establish our own list of life principles.The biggest and most harmful systemic mistakes we commit when making decisions, both individually and as groups.The potential harms of lightgassing, which is the opposite of gaslighting.How Spencer’s team used non-statistical methods to test whether astrology works.Whether there’s any social value in retaliation.And much more.Chapters:Does money make you happy? (00:05:54)Hype vs value (00:31:27)Warning signs that someone is bad news (00:41:25)Integrity and reproducibility in social science research (00:57:54)Personal principles (01:16:22)Decision-making errors (01:25:56)Lightgassing (01:49:23)Astrology (02:02:26)Game theory, tit for tat, and retaliation (02:20:51)Parenting (02:30:00)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour, Milo McGuire, and Dominic ArmstrongTranscriptions: Katy Moore

"[One] thing is just to spend time thinking about the kinds of things animals can do and what their lives are like. Just how hard a chicken will work to get to a nest box before she lays an egg, the amount of labour she’s willing to go through to do that, to think about how important that is to her. And to realise that we can quantify that, and see how much they care, or to see that they get stressed out when fellow chickens are threatened and that they seem to have some sympathy for conspecifics."Those kinds of things make me say there is something in there that is recognisable to me as another individual, with desires and preferences and a vantage point on the world, who wants things to go a certain way and is frustrated and upset when they don’t. And recognising the individuality, the perspective of nonhuman animals, for me, really challenges my tendency to not take them as seriously as I think I ought to, all things considered." — Bob FischerIn today’s episode, host Luisa Rodriguez speaks to Bob Fischer — senior research manager at Rethink Priorities and the director of the Society for the Study of Ethics and Animals — about Rethink Priorities’s Moral Weight Project.Links to learn more, summary, and full transcript.They cover:The methods used to assess the welfare ranges and capacities for pleasure and pain of chickens, pigs, octopuses, bees, and other animals — and the limitations of that approach.Concrete examples of how someone might use the estimated moral weights to compare the benefits of animal vs human interventions.The results that most surprised Bob.Why the team used a hedonic theory of welfare to inform the project, and what non-hedonic theories of welfare might bring to the table.Thought experiments like Tortured Tim that test different philosophical assumptions about welfare.Confronting our own biases when estimating animal mental capacities and moral worth.The limitations of using neuron counts as a proxy for moral weights.How different types of risk aversion, like avoiding worst-case scenarios, could impact cause prioritisation.And plenty more.Chapters:Welfare ranges (00:10:19)Historical assessments (00:16:47)Method (00:24:02)The present / absent approach (00:27:39)Results (00:31:42)Chickens (00:32:42)Bees (00:50:00)Salmon and limits of methodology (00:56:18)Octopuses (01:00:31)Pigs (01:27:50)Surprises about the project (01:30:19)Objections to the project (01:34:25)Alternative decision theories and risk aversion (01:39:14)Hedonism assumption (02:00:54)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"The question I care about is: What do I want to do? Like, when I'm 80, how strong do I want to be? OK, and then if I want to be that strong, how well do my muscles have to work? OK, and then if that's true, what would they have to look like at the cellular level for that to be true? Then what do we have to do to make that happen? In my head, it's much more about agency and what choice do I have over my health. And even if I live the same number of years, can I live as an 80-year-old running every day happily with my grandkids?" — Laura DemingIn today’s episode, host Luisa Rodriguez speaks to Laura Deming — founder of The Longevity Fund — about the challenge of ending ageing.Links to learn more, summary, and full transcript.They cover:How lifespan is surprisingly easy to manipulate in animals, which suggests human longevity could be increased too.Why we irrationally accept age-related health decline as inevitable.The engineering mindset Laura takes to solving the problem of ageing.Laura’s thoughts on how ending ageing is primarily a social challenge, not a scientific one.The recent exciting regulatory breakthrough for an anti-ageing drug for dogs.Laura’s vision for how increased longevity could positively transform society by giving humans agency over when and how they age.Why this decade may be the most important decade ever for making progress on anti-ageing research.The beauty and fascination of biology, which makes it such a compelling field to work in.And plenty more.Chapters:The case for ending ageing (00:04:00)What might the world look like if this all goes well? (00:21:57)Reasons not to work on ageing research (00:27:25)Things that make mice live longer (00:44:12)Parabiosis, changing the brain, and organ replacement can increase lifespan (00:54:25)Big wins the field of ageing research (01:11:40)Talent shortages and other bottlenecks for ageing research (01:17:36)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

The World Economic Forum’s global risks survey of 1,400 experts, policymakers, and industry leaders ranked misinformation and disinformation as the number one global risk over the next two years — ranking it ahead of war, environmental problems, and other threats from AI.And the discussion around misinformation and disinformation has shifted to focus on how generative AI or a future super-persuasive AI might change the game and make it extremely hard to figure out what was going on in the world — or alternatively, extremely easy to mislead people into believing convenient lies.But this week’s guest, cognitive scientist Hugo Mercier, has a very different view on how people form beliefs and figure out who to trust — one in which misinformation really is barely a problem today, and is unlikely to be a problem anytime soon. As he explains in his book Not Born Yesterday, Hugo believes we seriously underrate the perceptiveness and judgement of ordinary people.Links to learn more, summary, and full transcript.In this interview, host Rob Wiblin and Hugo discuss:How our reasoning mechanisms evolved to facilitate beneficial communication, not blind gullibility.How Hugo makes sense of our apparent gullibility in many cases — like falling for financial scams, astrology, or bogus medical treatments, and voting for policies that aren’t actually beneficial for us.Rob and Hugo’s ideas about whether AI might make misinformation radically worse, and which mass persuasion approaches we should be most worried about.Why Hugo thinks our intuitions about who to trust are generally quite sound, even in today’s complex information environment.The distinction between intuitive beliefs that guide our actions versus reflective beliefs that don’t.Why fake news and conspiracy theories actually have less impact than most people assume.False beliefs that have persisted across cultures and generations — like bloodletting and vaccine hesitancy — and theories about why.And plenty more.Chapters:The view that humans are really gullible (00:04:26)The evolutionary argument against humans being gullible (00:07:46) Open vigilance (00:18:56)Intuitive and reflective beliefs (00:32:25)How people decide who to trust (00:41:15)Redefining beliefs (00:51:57)Bloodletting (01:00:38)Vaccine hesitancy and creationism (01:06:38)False beliefs without skin in the game (01:12:36)One consistent weakness in human judgement (01:22:57)Trying to explain harmful financial decisions (01:27:15)Astrology (01:40:40)Medical treatments that don’t work (01:45:47)Generative AI, LLMs, and persuasion (01:54:50)Ways AI could improve the information environment (02:29:59)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

Mental health problems like depression and anxiety affect enormous numbers of people and severely interfere with their lives. By contrast, we don’t see similar levels of physical ill health in young people. At any point in time, something like 20% of young people are working through anxiety or depression that’s seriously interfering with their lives — but nowhere near 20% of people in their 20s have severe heart disease or cancer or a similar failure in a key organ of the body other than the brain.From an evolutionary perspective, that’s to be expected, right? If your heart or lungs or legs or skin stop working properly while you’re a teenager, you’re less likely to reproduce, and the genes that cause that malfunction get weeded out of the gene pool.So why is it that these evolutionary selective pressures seemingly fixed our bodies so that they work pretty smoothly for young people most of the time, but it feels like evolution fell asleep on the job when it comes to the brain? Why did evolution never get around to patching the most basic problems, like social anxiety, panic attacks, debilitating pessimism, or inappropriate mood swings? For that matter, why did evolution go out of its way to give us the capacity for low mood or chronic anxiety or extreme mood swings at all?Today’s guest, Randy Nesse — a leader in the field of evolutionary psychiatry — wrote the book Good Reasons for Bad Feelings, in which he sets out to try to resolve this paradox.Links to learn more, video, highlights, and full transcript.In the interview, host Rob Wiblin and Randy discuss the key points of the book, as well as:How the evolutionary psychiatry perspective can help people appreciate that their mental health problems are often the result of a useful and important system.How evolutionary pressures and dynamics lead to a wide range of different personalities, behaviours, strategies, and tradeoffs.The missing intellectual foundations of psychiatry, and how an evolutionary lens could revolutionise the field.How working as both an academic and a practicing psychiatrist shaped Randy’s understanding of treating mental health problems.The “smoke detector principle” of why we experience so many false alarms along with true threats.The origins of morality and capacity for genuine love, and why Randy thinks it’s a mistake to try to explain these from a selfish gene perspective.Evolutionary theories on why we age and die.And much more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic ArmstrongTranscriptions: Katy Moore

"I think at various times — before you have the kid, after you have the kid — it's useful to sit down and think about: What do I want the shape of this to look like? What time do I want to be spending? Which hours? How do I want the weekends to look? The things that are going to shape the way your day-to-day goes, and the time you spend with your kids, and what you're doing in that time with your kids, and all of those things: you have an opportunity to deliberately plan them. And you can then feel like, 'I've thought about this, and this is a life that I want. This is a life that we're trying to craft for our family, for our kids.' And that is distinct from thinking you're doing a good job in every moment — which you can't achieve. But you can achieve, 'I'm doing this the way that I think works for my family.'" — Emily OsterIn today’s episode, host Luisa Rodriguez speaks to Emily Oster — economist at Brown University, host of the ParentData podcast, and the author of three hugely popular books that provide evidence-based insights into pregnancy and early childhood.Links to learn more, summary, and full transcript.They cover:Common pregnancy myths and advice that Emily disagrees with — and why you should probably get a doula.Whether it’s fine to continue with antidepressants and coffee during pregnancy.What the data says — and doesn’t say — about outcomes from parenting decisions around breastfeeding, sleep training, childcare, and more.Which factors really matter for kids to thrive — and why that means parents shouldn’t sweat the small stuff.How to reduce parental guilt and anxiety with facts, and reject judgemental “Mommy Wars” attitudes when making decisions that are best for your family.The effects of having kids on career ambitions, pay, and productivity — and how the effects are different for men and women.Practical advice around managing the tradeoffs between career and family.What to consider when deciding whether and when to have kids.Relationship challenges after having kids, and the protective factors that help.And plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Back in December we spoke with Nathan Labenz — AI entrepreneur and host of The Cognitive Revolution Podcast — about the speed of progress towards AGI and OpenAI's leadership drama, drawing on Nathan's alarming experience red-teaming an early version of GPT-4 and resulting conversations with OpenAI staff and board members.Links to learn more, video, highlights, and full transcript.Today we go deeper, diving into:What AI now actually can and can’t do, across language and visual models, medicine, scientific research, self-driving cars, robotics, weapons — and what the next big breakthrough might be.Why most people, including most listeners, probably don’t know and can’t keep up with the new capabilities and wild results coming out across so many AI applications — and what we should do about that.How we need to learn to talk about AI more productively, particularly addressing the growing chasm between those concerned about AI risks and those who want to see progress accelerate, which may be counterproductive for everyone.Where Nathan agrees with and departs from the views of ‘AI scaling accelerationists.’The chances that anti-regulation rhetoric from some AI entrepreneurs backfires.How governments could (and already do) abuse AI tools like facial recognition, and how militarisation of AI is progressing.Preparing for coming societal impacts and potential disruption from AI.Practical ways that curious listeners can try to stay abreast of everything that’s going on.And plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

You wake up in a mysterious box, and hear the booming voice of God: “I just flipped a coin. If it came up heads, I made ten boxes, labeled 1 through 10 — each of which has a human in it. If it came up tails, I made ten billion boxes, labeled 1 through 10 billion — also with one human in each box. To get into heaven, you have to answer this correctly: Which way did the coin land?”You think briefly, and decide you should bet your eternal soul on tails. The fact that you woke up at all seems like pretty good evidence that you’re in the big world — if the coin landed tails, way more people should be having an experience just like yours.But then you get up, walk outside, and look at the number on your box.‘3’. Huh. Now you don’t know what to believe.If God made 10 billion boxes, surely it’s much more likely that you would have seen a number like 7,346,678,928?In today’s interview, Ajeya Cotra — a senior research analyst at Open Philanthropy — explains why this thought experiment from the niche of philosophy known as ‘anthropic reasoning’ could be relevant for figuring out where we should direct our charitable giving.Rebroadcast: this episode was originally released in January 2021.Links to learn more, summary, and full transcript.Some thinkers both inside and outside Open Philanthropy believe that philanthropic giving should be guided by ‘longtermism’ — the idea that we can do the most good if we focus primarily on the impact our actions will have on the long-term future.Ajeya thinks that for that notion to make sense, there needs to be a good chance we can settle other planets and solar systems and build a society that’s both very large relative to what’s possible on Earth and, by virtue of being so spread out, able to protect itself from extinction for a very long time.But imagine that humanity has two possible futures ahead of it: Either we’re going to have a huge future like that, in which trillions of people ultimately exist, or we’re going to wipe ourselves out quite soon, thereby ensuring that only around 100 billion people ever get to live.If there are eventually going to be 1,000 trillion humans, what should we think of the fact that we seemingly find ourselves so early in history? Being among the first 100 billion humans, as we are, is equivalent to walking outside and seeing a three on your box. Suspicious! If the future will have many trillions of people, the odds of us appearing so strangely early are very low indeed.If we accept the analogy, maybe we can be confident that humanity is at a high risk of extinction based on this so-called ‘doomsday argument‘ alone.If that’s true, maybe we should put more of our resources into avoiding apparent extinction threats like nuclear war and pandemics. But on the other hand, maybe the argument shows we’re incredibly unlikely to achieve a long and stable future no matter what we do, and we should forget the long term and just focus on the here and now instead.There are many critics of this theoretical ‘doomsday argument’, and it may be the case that it logically doesn’t work. This is why Ajeya spent time investigating it, with the goal of ultimately making better philanthropic grants.In this conversation, Ajeya and Rob discuss both the doomsday argument and the challenge Open Phil faces striking a balance between taking big ideas seriously, and not going all in on philosophical arguments that may turn out to be barking up the wrong tree entirely.They also discuss:Which worldviews Open Phil finds most plausible, and how it balances themWhich worldviews Ajeya doesn’t embrace but almost doesHow hard it is to get to other solar systemsThe famous ‘simulation argument’When transformative AI might actually arriveThe biggest challenges involved in working on big research reportsWhat it’s like working at Open PhilAnd much moreProducer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Sofia Davis-Fogel

Preventing the apocalypse may sound like an idiosyncratic activity, and it sometimes is justified on exotic grounds, such as the potential for humanity to become a galaxy-spanning civilisation.But the policy of US government agencies is already to spend up to $4 million to save the life of a citizen, making the death of all Americans a $1,300,000,000,000,000 disaster.According to Carl Shulman, research associate at Oxford University’s Future of Humanity Institute, that means you don’t need any fancy philosophical arguments about the value or size of the future to justify working to reduce existential risk — it passes a mundane cost-benefit analysis whether or not you place any value on the long-term future.Rebroadcast: this episode was originally released in October 2021.Links to learn more, summary, and full transcript.The key reason to make it a top priority is factual, not philosophical. That is, the risk of a disaster that kills billions of people alive today is alarmingly high, and it can be reduced at a reasonable cost. A back-of-the-envelope version of the argument runs:The US government is willing to pay up to $4 million (depending on the agency) to save the life of an American.So saving all US citizens at any given point in time would be worth $1,300 trillion.If you believe that the risk of human extinction over the next century is something like one in six (as Toby Ord suggests is a reasonable figure in his book The Precipice), then it would be worth the US government spending up to $2.2 trillion to reduce that risk by just 1%, in terms of American lives saved alone.Carl thinks it would cost a lot less than that to achieve a 1% risk reduction if the money were spent intelligently. So it easily passes a government cost-benefit test, with a very big benefit-to-cost ratio — likely over 1000:1 today.This argument helped NASA get funding to scan the sky for any asteroids that might be on a collision course with Earth, and it was directly promoted by famous economists like Richard Posner, Larry Summers, and Cass Sunstein.If the case is clear enough, why hasn’t it already motivated a lot more spending or regulations to limit existential risks — enough to drive down what any additional efforts would achieve?Carl thinks that one key barrier is that infrequent disasters are rarely politically salient. Research indicates that extra money is spent on flood defences in the years immediately following a massive flood — but as memories fade, that spending quickly dries up. Of course the annual probability of a disaster was the same the whole time; all that changed is what voters had on their minds.Carl suspects another reason is that it’s difficult for the average voter to estimate and understand how large these respective risks are, and what responses would be appropriate rather than self-serving. If the public doesn’t know what good performance looks like, politicians can’t be given incentives to do the right thing.It’s reasonable to assume that if we found out a giant asteroid were going to crash into the Earth one year from now, most of our resources would be quickly diverted into figuring out how to avert catastrophe.But even in the case of COVID-19, an event that massively disrupted the lives of everyone on Earth, we’ve still seen a substantial lack of investment in vaccine manufacturing capacity and other ways of controlling the spread of the virus, relative to what economists recommended.Carl expects that all the reasons we didn’t adequately prepare for or respond to COVID-19 — with excess mortality over 15 million and costs well over $10 trillion — bite even harder when it comes to threats we’ve never faced before, such as engineered pandemics, risks from advanced artificial intelligence, and so on.Today’s episode is in part our way of trying to improve this situation. In today’s wide-ranging conversation, Carl and Rob also cover:A few reasons Carl isn’t excited by ‘strong longtermism’How x-risk reduction compares to GiveWell recommendationsSolutions for asteroids, comets, supervolcanoes, nuclear war, pandemics, and climate changeThe history of bioweaponsWhether gain-of-function research is justifiableSuccesses and failures around COVID-19The history of existential riskAnd much moreProducer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

If you’re living in the Niger Delta in Nigeria, your best bet at a high-paying career is probably ‘artisanal refining’ — or, in plain language, stealing oil from pipelines.The resulting oil spills damage the environment and cause severe health problems, but the Nigerian government has continually failed in their attempts to stop this theft.They send in the army, and the army gets corrupted. They send in enforcement agencies, and the enforcement agencies get corrupted. What’s happening here?According to Mushtaq Khan, economics professor at SOAS University of London, this is a classic example of ‘networked corruption’. Everyone in the community is benefiting from the criminal enterprise — so much so that the locals would prefer civil war to following the law. It pays vastly better than other local jobs, hotels and restaurants have formed around it, and houses are even powered by the electricity generated from the oil.Rebroadcast: this episode was originally released in September 2021.Links to learn more, summary, and full transcript.In today’s episode, Mushtaq elaborates on the models he uses to understand these problems and make predictions he can test in the real world.Some of the most important factors shaping the fate of nations are their structures of power: who is powerful, how they are organized, which interest groups can pull in favours with the government, and the constant push and pull between the country’s rulers and its ruled. While traditional economic theory has relatively little to say about these topics, institutional economists like Mushtaq have a lot to say, and participate in lively debates about which of their competing ideas best explain the world around us.The issues at stake are nothing less than why some countries are rich and others are poor, why some countries are mostly law abiding while others are not, and why some government programmes improve public welfare while others just enrich the well connected.Mushtaq’s specialties are anti-corruption and industrial policy, where he believes mainstream theory and practice are largely misguided. To root out fraud, aid agencies try to impose institutions and laws that work in countries like the U.K. today. Everyone nods their heads and appears to go along, but years later they find nothing has changed, or worse — the new anti-corruption laws are mostly just used to persecute anyone who challenges the country’s rulers.As Mushtaq explains, to people who specialise in understanding why corruption is ubiquitous in some countries but not others, this is entirely predictable. Western agencies imagine a situation where most people are law abiding, but a handful of selfish fat cats are engaging in large-scale graft. In fact in the countries they’re trying to change everyone is breaking some rule or other, or participating in so-called ‘corruption’, because it’s the only way to get things done and always has been.Mushtaq’s rule of thumb is that when the locals most concerned with a specific issue are invested in preserving a status quo they’re participating in, they almost always win out.To actually reduce corruption, countries like his native Bangladesh have to follow the same gradual path the U.K. once did: find organizations that benefit from rule-abiding behaviour and are selfishly motivated to promote it, and help them police their peers.Trying to impose a new way of doing things from the top down wasn’t how Europe modernised, and it won’t work elsewhere either.In cases like oil theft in Nigeria, where no one wants to follow the rules, Mushtaq says corruption may be impossible to solve directly. Instead you have to play a long game, bringing in other employment opportunities, improving health services, and deploying alternative forms of energy — in the hope that one day this will give people a viable alternative to corruption.In this extensive interview Rob and Mushtaq cover this and much more, including:How does one test theories like this?Why are companies in some poor countries so much less productive than their peers in rich countries?Have rich countries just legalized the corruption in their societies?What are the big live debates in institutional economics?Should poor countries protect their industries from foreign competition?Where has industrial policy worked, and why?How can listeners use these theories to predict which policies will work in their own countries?Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Sofia Davis-Fogel

Happy new year! We've got a different kind of holiday release for you today. Rather than a 'classic episode,' we've put together one of our favourite highlights from each episode of the show that came out in 2023. That's 32 of our favourite ideas packed into one episode that's so bursting with substance it might be more than the human mind can safely handle.There's something for everyone here:Ezra Klein on punctuated equilibriumTom Davidson on why AI takeoff might be shockingly fastJohannes Ackva on political action versus lifestyle changesHannah Ritchie on how buying environmentally friendly technology helps low-income countries Bryan Caplan on rational irrationality on the part of votersJan Leike on whether the release of ChatGPT increased or reduced AI extinction risksAthena Aktipis on why elephants get deadly cancers less often than humansAnders Sandberg on the lifespan of civilisationsNita Farahany on hacking neural interfaces...plus another 23 such gems. And they're in an order that our audio engineer Simon Monsour described as having an "eight-dimensional-tetris-like rationale."I don't know what the hell that means either, but I'm curious to find out.And remember: if you like these highlights, note that we release 20-minute highlights reels for every new episode over on our sister feed, which is called 80k After Hours. So even if you're struggling to make time to listen to every single one, you can always get some of the best bits of our episodes.We hope for all the best things to happen for you in 2024, and we'll be back with a traditional classic episode soon.This Mega-highlights Extravaganza was brought to you by Ben Cordell, Simon Monsour, Milo McGuire, and Dominic Armstrong

Today’s episode is one of the most remarkable and really, unique, pieces of content we’ve ever produced (and I can say that because I had almost nothing to do with making it!).The producer of this show, Keiran Harris, interviewed our mutual colleague Howie about the major ways that mental illness has affected his life and career. While depression, anxiety, ADHD and other problems are extremely common, it’s rare for people to offer detailed insight into their thoughts and struggles — and even rarer for someone as perceptive as Howie to do so.Rebroadcast: this episode was originally released in May 2021.Links to learn more, summary, and full transcript.The first half of this conversation is a searingly honest account of Howie’s story, including losing a job he loved due to a depressed episode, what it was like to be basically out of commission for over a year, how he got back on his feet, and the things he still finds difficult today.The second half covers Howie’s advice. Conventional wisdom on mental health can be really focused on cultivating willpower — telling depressed people that the virtuous thing to do is to start exercising, improve their diet, get their sleep in check, and generally fix all their problems before turning to therapy and medication as some sort of last resort.Howie tries his best to be a corrective to this misguided attitude and pragmatically focus on what actually matters — doing whatever will help you get better.Mental illness is one of the things that most often trips up people who could otherwise enjoy flourishing careers and have a large social impact, so we think this could plausibly be one of our more valuable episodes. If you’re in a hurry, we’ve extracted the key advice that Howie has to share in a section below.Howie and Keiran basically treated it like a private conversation, with the understanding that it may be too sensitive to release. But, after getting some really positive feedback, they’ve decided to share it with the world.Here are a few quotes from early reviewers:"I think there’s a big difference between admitting you have depression/seeing a psych and giving a warts-and-all account of a major depressive episode like Howie does in this episode… His description was relatable and really inspiring."Someone who works on mental health issues said:"This episode is perhaps the most vivid and tangible example of what it is like to experience psychological distress that I’ve ever encountered. Even though the content of Howie and Keiran’s discussion was serious, I thought they both managed to converse about it in an approachable and not-overly-somber way."And another reviewer said:"I found Howie’s reflections on what is actually going on in his head when he engages in negative self-talk to be considerably more illuminating than anything I’ve heard from my therapist."We also hope that the episode will:Help people realise that they have a shot at making a difference in the future, even if they’re experiencing (or have experienced in the past) mental illness, self doubt, imposter syndrome, or other personal obstacles.Give insight into what it’s like in the head of one person with depression, anxiety, and imposter syndrome, including the specific thought patterns they experience on typical days and more extreme days. In addition to being interesting for its own sake, this might make it easier for people to understand the experiences of family members, friends, and colleagues — and know how to react more helpfully.Several early listeners have even made specific behavioral changes due to listening to the episode — including people who generally have good mental health but were convinced it’s well worth the low cost of setting up a plan in case they have problems in the future.So we think this episode will be valuable for:People who have experienced mental health problems or might in future;People who have had troubles with stress, anxiety, low mood, low self esteem, imposter syndrome and similar issues, even if their experience isn’t well described as ‘mental illness’;People who have never experienced these problems but want to learn about what it’s like, so they can better relate to and assist family, friends or colleagues who do.In other words, we think this episode could be worthwhile for almost everybody.Just a heads up that this conversation gets pretty intense at times, and includes references to self-harm and suicidal thoughts.If you don’t want to hear or read the most intense section, you can skip the chapter called ‘Disaster’. And if you’d rather avoid almost all of these references, you could skip straight to the chapter called ‘80,000 Hours’.We’ve collected a large list of high quality resources for overcoming mental health problems in our links section.If you’re feeling suicidal or have thoughts of harming yourself right now, there are suicide hotlines at National Suicide Prevention Lifeline in the US (800-273-8255) and Samaritans in the UK (116 123). You may also want

OpenAI says its mission is to build AGI — an AI system that is better than human beings at everything. Should the world trust them to do that safely?That’s the central theme of today’s episode with Nathan Labenz — entrepreneur, AI scout, and host of The Cognitive Revolution podcast.Links to learn more, video, highlights, and full transcript. Nathan saw the AI revolution coming years ago, and, astonished by the research he was seeing, set aside his role as CEO of Waymark and made it his full-time job to understand AI capabilities across every domain. He has been obsessively tracking the AI world since — including joining OpenAI’s “red team” that probed GPT-4 to find ways it could be abused, long before it was public.Whether OpenAI was taking AI safety seriously enough became a topic of dinner table conversation around the world after the shocking firing and reinstatement of Sam Altman as CEO last month.Nathan’s view: it’s complicated. Discussion of this topic has often been heated, polarising, and personal. But Nathan wants to avoid that and simply lay out, in a way that is impartial and fair to everyone involved, what OpenAI has done right and how it could do better in his view.When he started on the GPT-4 red team, the model would do anything from diagnose a skin condition to plan a terrorist attack without the slightest reservation or objection. When later shown a “Safety” version of GPT-4 that was almost the same, he approached a member of OpenAI’s board to share his concerns and tell them they really needed to try out GPT-4 for themselves and form an opinion.In today’s episode, we share this story as Nathan told it on his own show, The Cognitive Revolution, which he did in the hope that it would provide useful background to understanding the OpenAI board’s reservations about Sam Altman, which to this day have not been laid out in any detail.But while he feared throughout 2022 that OpenAI and Sam Altman didn’t understand the power and risk of their own system, he has since been repeatedly impressed, and came to think of OpenAI as among the better companies that could hypothetically be working to build AGI.Their efforts to make GPT-4 safe turned out to be much larger and more successful than Nathan was seeing. Sam Altman and other leaders at OpenAI seem to sincerely believe they’re playing with fire, and take the threat posed by their work very seriously. With the benefit of hindsight, Nathan suspects OpenAI’s decision to release GPT-4 when it did was for the best.On top of that, OpenAI has been among the most sane and sophisticated voices advocating for AI regulations that would target just the most powerful AI systems — the type they themselves are building — and that could make a real difference. They’ve also invested major resources into new ‘Superalignment’ and ‘Preparedness’ teams, while avoiding using competition with China as an excuse for recklessness.At the same time, it’s very hard to know whether it’s all enough. The challenge of making an AGI safe and beneficial may require much more than they hope or have bargained for. Given that, Nathan poses the question of whether it makes sense to try to build a fully general AGI that can outclass humans in every domain at the first opportunity. Maybe in the short term, we should focus on harvesting the enormous possible economic and humanitarian benefits of narrow applied AI models, and wait until we not only have a way to build AGI, but a good way to build AGI — an AGI that we’re confident we want, which we can prove will remain safe as its capabilities get ever greater.By threatening to follow Sam Altman to Microsoft before his reinstatement as OpenAI CEO, OpenAI’s research team has proven they have enormous influence over the direction of the company. If they put their minds to it, they’re also better placed than maybe anyone in the world to assess if the company’s strategy is on the right track and serving the interests of humanity as a whole. Nathan concludes that this power and insight only adds to the enormous weight of responsibility already resting on their shoulders.In today’s extensive conversation, Nathan and host Rob Wiblin discuss not only all of the above, but also:Speculation about the OpenAI boardroom drama with Sam Altman, given Nathan’s interactions with the board when he raised concerns from his red teaming efforts.Which AI applications we should be urgently rolling out, with less worry about safety.Whether governance issues at OpenAI demonstrate AI research can only be slowed by governments.Whether AI capabilities are advancing faster than safety efforts and controls.The costs and benefits of releasing powerful models like GPT-4.Nathan’s view on the game theory of AI arms races and China.Whether it’s worth taking some risk with AI for huge potential upside.The need for more “AI scouts” to understand and communicate AI progress.And plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Mi

Lead is one of the most poisonous things going. A single sugar sachet of lead, spread over a park the size of an American football field, is enough to give a child that regularly plays there lead poisoning. For life they’ll be condemned to a ~3-point-lower IQ; a 50% higher risk of heart attacks; and elevated risk of kidney disease, anaemia, and ADHD, among other effects.We’ve known lead is a health nightmare for at least 50 years, and that got lead out of car fuel everywhere. So is the situation under control? Not even close.Around half the kids in poor and middle-income countries have blood lead levels above 5 micrograms per decilitre; the US declared a national emergency when just 5% of the children in Flint, Michigan exceeded that level. The collective damage this is doing to children’s intellectual potential, health, and life expectancy is vast — the health damage involved is around that caused by malaria, tuberculosis, and HIV combined.This week’s guest, Lucia Coulter — cofounder of the incredibly successful Lead Exposure Elimination Project (LEEP) — speaks about how LEEP has been reducing childhood lead exposure in poor countries by getting bans on lead in paint enforced.Links to learn more, summary, and full transcript.Various estimates suggest the work is absurdly cost effective. LEEP is in expectation preventing kids from getting lead poisoning for under $2 per child (explore the analysis here). Or, looking at it differently, LEEP is saving a year of healthy life for $14, and in the long run is increasing people’s lifetime income anywhere from $300–1,200 for each $1 it spends, by preventing intellectual stunting.Which raises the question: why hasn’t this happened already? How is lead still in paint in most poor countries, even when that’s oftentimes already illegal? And how is LEEP able to get bans on leaded paint enforced in a country while spending barely tens of thousands of dollars? When leaded paint is gone, what should they target next?With host Robert Wiblin, Lucia answers all those questions and more:Why LEEP isn’t fully funded, and what it would do with extra money (you can donate here).How bad lead poisoning is in rich countries.Why lead is still in aeroplane fuel.How lead got put straight in food in Bangladesh, and a handful of people got it removed.Why the enormous damage done by lead mostly goes unnoticed.The other major sources of lead exposure aside from paint.Lucia’s story of founding a highly effective nonprofit, despite having no prior entrepreneurship experience, through Charity Entrepreneurship’s Incubation Program.Why Lucia pledges 10% of her income to cost-effective charities.Lucia’s take on why GiveWell didn’t support LEEP earlier on.How the invention of cheap, accessible lead testing for blood and consumer products would be a game changer.Generalisable lessons LEEP has learned from coordinating with governments in poor countries.And plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuire and Dominic ArmstrongTranscriptions: Katy Moore

"It will change everything: it will change our workplaces, it will change our interactions with the government, it will change our interactions with each other. It will make all of us unwitting neuromarketing subjects at all times, because at every moment in time, when you’re interacting on any platform that also has issued you a multifunctional device where they’re looking at your brainwave activity, they are marketing to you, they’re cognitively shaping you."So I wrote the book as both a wake-up call, but also as an agenda-setting: to say, what do we need to do, given that this is coming? And there’s a lot of hope, and we should be able to reap the benefits of the technology, but how do we do that without actually ending up in this world of like, 'Oh my god, mind reading is here. Now what?'" — Nita FarahanyIn today’s episode, host Luisa Rodriguez speaks to Nita Farahany — professor of law and philosophy at Duke Law School — about applications of cutting-edge neurotechnology.Links to learn more, summary, and full transcript.They cover:How close we are to actual mind reading.How hacking neural interfaces could cure depression.How companies might use neural data in the workplace — like tracking how productive you are, or using your emotional states against you in negotiations.How close we are to being able to unlock our phones by singing a song in our heads.How neurodata has been used for interrogations, and even criminal prosecutions.The possibility of linking brains to the point where you could experience exactly the same thing as another person.Military applications of this tech, including the possibility of one soldier controlling swarms of drones with their mind.And plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"We do have a tendency to anthropomorphise nonhumans — which means attributing human characteristics to them, even when they lack those characteristics. But we also have a tendency towards anthropodenial — which involves denying that nonhumans have human characteristics, even when they have them. And those tendencies are both strong, and they can both be triggered by different types of systems. So which one is stronger, which one is more probable, is again going to be contextual. "But when we then consider that we, right now, are building societies and governments and economies that depend on the objectification, exploitation, and extermination of nonhumans, that — plus our speciesism, plus a lot of other biases and forms of ignorance that we have — gives us a strong incentive to err on the side of anthropodenial instead of anthropomorphism." — Jeff SeboIn today’s episode, host Luisa Rodriguez interviews Jeff Sebo — director of the Mind, Ethics, and Policy Program at NYU — about preparing for a world with digital minds.Links to learn more, highlights, and full transcript.They cover:The non-negligible chance that AI systems will be sentient by 2030What AI systems might want and need, and how that might affect our moral conceptsWhat happens when beings can copy themselves? Are they one person or multiple people? Does the original own the copy or does the copy have its own rights? Do copies get the right to vote?What kind of legal and political status should AI systems have? Legal personhood? Political citizenship?What happens when minds can be connected? If two minds are connected, and one does something illegal, is it possible to punish one but not the other?The repugnant conclusion and the rebugnant conclusionThe experience of trying to build the field of AI welfareWhat improv comedy can teach us about doing good in the worldAnd plenty more.Chapters:Cold open (00:00:00)Luisa's intro (00:01:00)The interview begins (00:02:45)We should extend moral consideration to some AI systems by 2030 (00:06:41)A one-in-1,000 threshold (00:15:23)What does moral consideration mean? (00:24:36)Hitting the threshold by 2030 (00:27:38)Is the threshold too permissive? (00:38:24)The Rebugnant Conclusion (00:41:00)A world where AI experiences could matter more than human experiences (00:52:33)Should we just accept this argument? (00:55:13)Searching for positive-sum solutions (01:05:41)Are we going to sleepwalk into causing massive amounts of harm to AI systems? (01:13:48)Discourse and messaging (01:27:17)What will AI systems want and need? (01:31:17)Copies of digital minds (01:33:20)Connected minds (01:40:26)Psychological connectedness and continuity (01:49:58)Assigning responsibility to connected minds (01:58:41)Counting the wellbeing of connected minds (02:02:36)Legal personhood and political citizenship (02:09:49)Building the field of AI welfare (02:24:03)What we can learn from improv comedy (02:29:29)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic Armstrong and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Is following important political and international news a civic duty — or is it our civic duty to avoid it?It's common to think that 'staying informed' and checking the headlines every day is just what responsible adults do. But in today's episode, host Rob Wiblin is joined by economist Bryan Caplan to discuss the book Stop Reading the News: A Manifesto for a Happier, Calmer and Wiser Life — which argues that reading the news both makes us miserable and distorts our understanding of the world. Far from informing us and enabling us to improve the world, consuming the news distracts us, confuses us, and leaves us feeling powerless.Links to learn more, summary, and full transcript.In the first half of the episode, Bryan and Rob discuss various alleged problems with the news, including:That it overwhelmingly provides us with information we can't usefully act on.That it's very non-representative in what it covers, in particular favouring the negative over the positive and the new over the significant.That it obscures the big picture, falling into the trap of thinking 'something important happens every day.'That it's highly addictive, for many people chewing up 10% or more of their waking hours.That regularly checking the news leaves us in a state of constant distraction and less able to engage in deep thought.And plenty more.Bryan and Rob conclude that if you want to understand the world, you're better off blocking news websites and spending your time on Wikipedia, Our World in Data, or reading a textbook. And if you want to generate political change, stop reading about problems you already know exist and instead write your political representative a physical letter — or better yet, go meet them in person.In the second half of the episode, Bryan and Rob cover: Why Bryan is pretty sceptical that AI is going to lead to extreme, rapid changes, or that there's a meaningful chance of it going terribly.Bryan’s case that rational irrationality on the part of voters leads to many very harmful policy decisions.How to allocate resources in space.Bryan's experience homeschooling his kids.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"Rare events can still cause catastrophic accidents. The concern that has been raised by experts going back over time, is that really, the more of these experiments, the more labs, the more opportunities there are for a rare event to occur — that the right pathogen is involved and infects somebody in one of these labs, or is released in some way from these labs. And what I chronicle in Pandora's Gamble is that there have been these previous outbreaks that have been associated with various kinds of lab accidents. So this is not a theoretical thing that can happen: it has happened in the past." — Alison YoungIn today’s episode, host Luisa Rodriguez interviews award-winning investigative journalist Alison Young on the surprising frequency of lab leaks and what needs to be done to prevent them in the future.Links to learn more, summary, and full transcript.They cover:The most egregious biosafety mistakes made by the CDC, and how Alison uncovered them through her investigative reportingThe Dugway life science test facility case, where live anthrax was accidentally sent to labs across the US and several other countries over a period of many yearsThe time the Soviets had a major anthrax leak, and then hid it for over a decadeThe 1977 influenza pandemic caused by vaccine trial gone wrong in ChinaThe last death from smallpox, caused not by the virus spreading in the wild, but by a lab leak in the UK Ways we could get more reliable oversight and accountability for these labsAnd the investigative work Alison’s most proud ofChapters:Cold open (00:00:00)Luisa's intro (00:01:13)Investigating leaks at the CDC (00:05:16)High-profile CDC accidents (00:16:13)Dugway live anthrax accidents (00:32:08)Soviet anthrax leak (00:44:41)The 1977 influenza pandemic (00:53:43)The last death from smallpox (00:59:27)How common are lab leaks? (01:09:05)Improving the regulation of dangerous biological research (01:18:36)Potential solutions (01:34:55)The investigative work Alison’s most proud of (01:40:33)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"One [outrageous example of air pollution] is municipal waste burning that happens in many cities in the Global South. Basically, this is waste that gets collected from people's homes, and instead of being transported to a waste management facility or a landfill or something, gets burned at some point, because that's the fastest way to dispose of it — which really points to poor delivery of public services. But this is ubiquitous in virtually every small- or even medium-sized city. It happens in larger cities too, in this part of the world. "That's something that truly annoys me, because it feels like the kind of thing that ought to be fairly easily managed, but it happens a lot. It happens because people presumably don't think that it's particularly harmful. I don't think it saves a tonne of money for the municipal corporations and other local government that are meant to manage it. I find it particularly annoying simply because it happens so often; it's something that you're able to smell in so many different parts of these cities." — Santosh HarishIn today’s episode, host Rob Wiblin interviews Santosh Harish — leader of Open Philanthropy’s grantmaking in South Asian air quality — about the scale of the harm caused by air pollution.Links to learn more, summary, and full transcript.They cover:How bad air pollution is for our health and life expectancyThe different kinds of harm that particulate pollution causesThe strength of the evidence that it damages our brain function and reduces our productivityWhether it was a mistake to switch our attention to climate change and away from air pollutionWhether most listeners to this show should have an air purifier running in their house right nowWhere air pollution in India is worst and why, and whether it's going up or downWhere most air pollution comes fromThe policy blunders that led to many sources of air pollution in India being effectively unregulatedWhy indoor air pollution packs an enormous punchThe politics of air pollution in IndiaHow India ended up spending a lot of money on outdoor air purifiersThe challenges faced by foreign philanthropists in IndiaWhy Santosh has made the grants he has so farAnd plenty moreChapters:Cold open (00:00:00)Rob's intro (00:01:07)How bad is air pollution? (00:03:41)Quantifying the scale of the damage (00:15:47)Effects on cognitive performance and mood (00:24:19)How do we really know the harms are as big as is claimed? (00:27:05)Misconceptions about air pollution (00:36:56)Why don’t environmental advocacy groups focus on air pollution? (00:42:22)How listeners should approach air pollution in their own lives (00:46:58)How bad is air pollution in India in particular (00:54:23)The trend in India over the last few decades (01:12:33)Why aren’t people able to fix these problems? (01:24:17)Household waste burning (01:35:06)Vehicle emissions (01:42:10)The role that courts have played in air pollution regulation in India (01:50:09)Industrial emissions (01:57:10)The political economy of air pollution in northern India (02:02:14)Can philanthropists drive policy change? (02:13:42)Santosh’s grants (02:29:45)Examples of other countries that have managed to greatly reduce air pollution (02:45:44)Career advice for listeners in India (02:51:11)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"One of our earliest supporters and a dear friend of mine, Mark Lampert, once said to me, “The way I think about it is, imagine that this money were already in the hands of people living in poverty. If I could, would I want to tax it and then use it to finance other projects that I think would benefit them?” I think that's an interesting thought experiment -- and a good one -- to say, “Are there cases in which I think that's justifiable?” — Paul NiehausIn today’s episode, host Luisa Rodriguez interviews Paul Niehaus — co-founder of GiveDirectly — on the case for giving unconditional cash to the world's poorest households.Links to learn more, summary and full transcript.They cover:The empirical evidence on whether giving cash directly can drive meaningful economic growthHow the impacts of GiveDirectly compare to USAID employment programmesGiveDirectly vs GiveWell’s top-recommended charitiesHow long-term guaranteed income affects people's risk-taking and investmentsWhether recipients prefer getting lump sums or monthly instalmentsHow GiveDirectly tackles cases of fraud and theftThe case for universal basic income, and GiveDirectly’s UBI studies in Kenya, Malawi, and LiberiaThe political viability of UBIPlenty moreChapters:Cold open (00:00:00)Luisa’s intro (00:00:58)The basic case for giving cash directly to the poor (00:03:28)Comparing GiveDirectly to USAID programmes (00:15:42)GiveDirectly vs GiveWell’s top-recommended charities (00:35:16)Cash might be able to drive economic growth (00:41:59)Fraud and theft of GiveDirectly funds (01:09:48)Universal basic income studies (01:22:33)Skyjo (01:44:43)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic Armstrong and Milo McGuireAdditional content editing: Luisa Rodriguez and Katy MooreTranscriptions: Katy Moore

"If we carry on looking at these industrialised economies, not thinking about what it is they're actually doing and what the potential of this is, you can make an argument that, yes, rates of growth are slowing, the rate of innovation is slowing. But it isn't. What we're doing is creating wildly new technologies: basically producing what is nothing less than an evolutionary change in what it means to be a human being. But this has not yet spilled over into the kind of growth that we have accustomed ourselves to in the fossil-fuel industrial era. That is about to hit us in a big way." — Ian MorrisIn today’s episode, host Rob Wiblin speaks with repeat guest Ian Morris about what big-picture history says about the likely impact of machine intelligence. Links to learn more, summary and full transcript.They cover:Some crazy anomalies in the historical record of civilisational progressWhether we should think about technology from an evolutionary perspectiveWhether we ought to expect war to make a resurgence or continue dying outWhy we can't end up living like The JetsonsWhether stagnation or cyclical recurring futures seem very plausibleWhat it means that the rate of increase in the economy has been increasingWhether violence is likely between humans and powerful AI systemsThe most likely reasons for Rob and Ian to be really wrong about all of thisHow professional historians react to this sort of talkThe future of Ian’s workPlenty moreChapters:Cold open (00:00:00)Rob’s intro (00:01:27)Why we should expect the future to be wild (00:04:08)How historians have reacted to the idea of radically different futures (00:21:20)Why we won’t end up in The Jetsons (00:26:20)The rise of machine intelligence (00:31:28)AI from an evolutionary point of view (00:46:32)Is violence likely between humans and powerful AI systems? (00:59:53)Most troubling objections to this approach in Ian’s view (01:28:20)Confronting anomalies in the historical record (01:33:10)The cyclical view of history (01:56:11)Is stagnation plausible? (02:01:38)The limit on how long this growth trend can continue (02:20:57)The future of Ian’s work (02:37:17)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuireTranscriptions: Katy Moore

"There have been literally thousands of years of breeding and living with animals to optimise these kinds of problems. But because we're just so early on with alternative proteins and there's so much white space, it's actually just really exciting to know that we can keep on innovating and being far more efficient than this existing technology — which, fundamentally, is just quite inefficient. You're feeding animals a bunch of food to then extract a small fraction of their biomass to then eat that.Animal agriculture takes up 83% of farmland, but produces just 18% of food calories. So the current system just is so wasteful. And the limiting factor is that you're just growing a bunch of food to then feed a third of the world's crops directly to animals, where the vast majority of those calories going in are lost to animals existing." — Seren KellLinks to learn more, summary and full transcript.In today’s episode, host Luisa Rodriguez interviews Seren Kell — Senior Science and Technology Manager at the Good Food Institute Europe — about making alternative proteins as tasty, cheap, and convenient as traditional meat, dairy, and egg products.They cover:The basic case for alternative proteins, and why they’re so hard to makeWhy fermentation is a surprisingly promising technology for creating delicious alternative proteins The main scientific challenges that need to be solved to make fermentation even more usefulThe progress that’s been made on the cultivated meat front, and what it will take to make cultivated meat affordableHow GFI Europe is helping with some of these challengesHow people can use their careers to contribute to replacing factory farming with alternative proteinsThe best part of Seren’s jobPlenty moreChapters:Cold open (00:00:00)Luisa’s intro (00:01:08)The interview begins (00:02:22)Why alternative proteins? (00:02:36)What makes alternative proteins so hard to make? (00:11:30)Why fermentation is so exciting (00:24:23)The technical challenges involved in scaling fermentation (00:44:38)Progress in cultivated meat (01:06:04)GFI Europe’s work (01:32:47)Careers (01:45:10)The best part of Seren’s job (01:50:07)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Dominic Armstrong and Milo McGuireAdditional content editing: Luisa Rodriguez and Katy MooreTranscriptions: Katy Moore

"If you and I and 100 other people were on the first ship that was going to go settle Mars, and were going to build a human civilisation, and we have to decide what that government looks like, and we have all of the technology available today, how do we think about choosing a subset of that design space? That space is huge and it includes absolutely awful things, and mixed-bag things, and maybe some things that almost everyone would agree are really wonderful, or at least an improvement on the way that things work today. But that raises all kinds of tricky questions. My concern is that if we don't approach the evolution of collective decision making and government in a deliberate way, we may end up inadvertently backing ourselves into a corner, where we have ended up on some slippery slope -- and all of a sudden we have, let's say, autocracies on the global stage are strengthened relative to democracies." — Tantum CollinsIn today’s episode, host Rob Wiblin gets the rare chance to interview someone with insider AI policy experience at the White House and DeepMind who’s willing to speak openly — Tantum Collins.Links to learn more, highlights, and full transcript.They cover:How AI could strengthen government capacity, and how that's a double-edged swordHow new technologies force us to confront tradeoffs in political philosophy that we were previously able to pretend weren't thereTo what extent policymakers take different threats from AI seriouslyWhether the US and China are in an AI arms race or notWhether it's OK to transform the world without much of the world agreeing to itThe tyranny of small differences in AI policyDisagreements between different schools of thought in AI policy, and proposals that could unite themHow the US AI Bill of Rights could be improvedWhether AI will transform the labour market, and whether it will become a partisan political issueThe tensions between the cultures of San Francisco and DC, and how to bridge the divide between themWhat listeners might be able to do to help with this whole messPanpsychismPlenty moreChapters:Cold open (00:00:00)Rob's intro (00:01:00)The interview begins (00:04:01)The risk of autocratic lock-in due to AI (00:10:02)The state of play in AI policymaking (00:13:40)China and AI (00:32:12)The most promising regulatory approaches (00:57:51)Transforming the world without the world agreeing (01:04:44)AI Bill of Rights (01:17:32)Who’s ultimately responsible for the consequences of AI? (01:20:39)Policy ideas that could appeal to many different groups (01:29:08)Tension between those focused on x-risk and those focused on AI ethics (01:38:56)Communicating with policymakers (01:54:22)Is AI going to transform the labour market in the next few years? (01:58:51)Is AI policy going to become a partisan political issue? (02:08:10)The value of political philosophy (02:10:53)Tantum’s work at DeepMind (02:21:20)CSET (02:32:48)Career advice (02:35:21)Panpsychism (02:55:24)Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"Now, the really interesting question is: How much is there an attacker-versus-defender advantage in this kind of advanced future? Right now, if somebody's sitting on Mars and you're going to war against them, it's very hard to hit them. You don't have a weapon that can hit them very well. But in theory, if you fire a missile, after a few months, it's going to arrive and maybe hit them, but they have a few months to move away. Distance actually makes you safer: if you spread out in space, it's actually very hard to hit you. So it seems like you get a defence-dominant situation if you spread out sufficiently far. But if you're in Earth orbit, everything is close, and the lasers and missiles and the debris are a terrible danger, and everything is moving very fast. So my general conclusion has been that war looks unlikely on some size scales but not on others." — Anders SandbergIn today’s episode, host Rob Wiblin speaks with repeat guest and audience favourite Anders Sandberg about the most impressive things that could be achieved in our universe given the laws of physics.Links to learn more, summary and full transcript.They cover:The epic new book Anders is working on, and whether he’ll ever finish itWhether there's a best possible world or we can just keep improving foreverWhat wars might look like if the galaxy is mostly settledThe impediments to AI or humans making it to other starsHow the universe will end a million trillion years in the futureWhether it’s useful to wonder about whether we’re living in a simulationThe grabby aliens theoryWhether civilizations get more likely to fail the older they getThe best way to generate energy that could ever existBlack hole bombsWhether superintelligence is necessary to get a lot of valueThe likelihood that life from elsewhere has already visited EarthAnd plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

"Imagine a fast-spreading respiratory HIV. It sweeps around the world. Almost nobody has symptoms. Nobody notices until years later, when the first people who are infected begin to succumb. They might die, something else debilitating might happen to them, but by that point, just about everyone on the planet would have been infected already. And then it would be a race. Can we come up with some way of defusing the thing? Can we come up with the equivalent of HIV antiretrovirals before it's too late?" — Kevin EsveltIn today’s episode, host Luisa Rodriguez interviews Kevin Esvelt — a biologist at the MIT Media Lab and the inventor of CRISPR-based gene drive — about the threat posed by engineered bioweapons.Links to learn more, summary and full transcript.They cover:Why it makes sense to focus on deliberately released pandemicsCase studies of people who actually wanted to kill billions of humansHow many people have the technical ability to produce dangerous virusesThe different threats of stealth and wildfire pandemics that could crash civilisationThe potential for AI models to increase access to dangerous pathogensWhy scientists try to identify new pandemic-capable pathogens, and the case against that researchTechnological solutions, including UV lights and advanced PPEUsing CRISPR-based gene drive to fight diseases and reduce animal sufferingAnd plenty more.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon MonsourAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Today’s release is a reading of our Great power conflict problem profile, written and narrated by Stephen Clare.If you want to check out the links, footnotes and figures in today’s article, you can find those here.And if you like this article, you might enjoy a couple of related episodes of this podcast:#128 – Chris Blattman on the five reasons wars happen#140 – Bear Braumoeller on the case that war isn’t in declineAudio mastering and editing for this episode: Dominic ArmstrongAudio Engineering Lead: Ben CordellProducer: Keiran Harris

Effective altruism is associated with the slogan "do the most good." On one level, this has to be unobjectionable: What could be bad about helping people more and more?But in today's interview, Toby Ord — moral philosopher at the University of Oxford and one of the founding figures of effective altruism — lays out three reasons to be cautious about the idea of maximising the good that you do. He suggests that rather than “doing the most good that we can,” perhaps we should be happy with a more modest and manageable goal: “doing most of the good that we can.”Links to learn more, summary and full transcript.Toby was inspired to revisit these ideas by the possibility that Sam Bankman-Fried, who stands accused of committing severe fraud as CEO of the cryptocurrency exchange FTX, was motivated to break the law by a desire to give away as much money as possible to worthy causes.Toby's top reason not to fully maximise is the following: if the goal you're aiming at is subtly wrong or incomplete, then going all the way towards maximising it will usually cause you to start doing some very harmful things.This result can be shown mathematically, but can also be made intuitive, and may explain why we feel instinctively wary of going “all-in” on any idea, or goal, or way of living — even something as benign as helping other people as much as possible.Toby gives the example of someone pursuing a career as a professional swimmer. Initially, as our swimmer takes their training and performance more seriously, they adjust their diet, hire a better trainer, and pay more attention to their technique. While swimming is the main focus of their life, they feel fit and healthy and also enjoy other aspects of their life as well — family, friends, and personal projects.But if they decide to increase their commitment further and really go all-in on their swimming career, holding back nothing back, then this picture can radically change. Their effort was already substantial, so how can they shave those final few seconds off their racing time? The only remaining options are those which were so costly they were loath to consider them before.To eke out those final gains — and go from 80% effort to 100% — our swimmer must sacrifice other hobbies, deprioritise their relationships, neglect their career, ignore food preferences, accept a higher risk of injury, and maybe even consider using steroids.Now, if maximising one's speed at swimming really were the only goal they ought to be pursuing, there'd be no problem with this. But if it's the wrong goal, or only one of many things they should be aiming for, then the outcome is disastrous. In going from 80% to 100% effort, their swimming speed was only increased by a tiny amount, while everything else they were accomplishing dropped off a cliff.The bottom line is simple: a dash of moderation makes you much more robust to uncertainty and error.As Toby notes, this is similar to the observation that a sufficiently capable superintelligent AI, given any one goal, would ruin the world if it maximised it to the exclusion of everything else. And it follows a similar pattern to performance falling off a cliff when a statistical model is 'overfit' to its data.In the full interview, Toby also explains the “moral trade” argument against pursuing narrow goals at the expense of everything else, and how consequentialism changes if you judge not just outcomes or acts, but everything according to its impacts on the world.Toby and Rob also discuss:The rise and fall of FTX and some of its impactsWhat Toby hoped effective altruism would and wouldn't become when he helped to get it off the groundWhat utilitarianism has going for it, and what's wrong with it in Toby's viewHow to mathematically model the importance of personal integrityWhich AI labs Toby thinks have been acting more responsibly than othersHow having a young child affects Toby’s feelings about AI riskWhether infinities present a fundamental problem for any theory of ethics that aspire to be fully impartialHow Toby ended up being the source of the highest quality images of the Earth from spaceGet this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.Producer and editor: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon MonsourTranscriptions: Katy Moore

An audio version of the 2023 80,000 Hours career guide, also available on our website, on Amazon, and on Audible.If you know someone who might find our career guide helpful, you can get a free copy sent to them by going to 80000hours.org/gift.Chapters:Rob's intro (00:00:00)Introduction (00:04:08)Chapter 1: What Makes for a Dream Job? (00:09:09)Chapter 2: Can One Person Make a Difference? What the Evidence Says. (00:33:02)Chapter 3: Three Ways Anyone Can Make a Difference, No Matter Their Job (00:43:33)Chapter 4: Want to Do Good? Here's How to Choose an Area to Focus on (00:58:50)Chapter 5: The World's Biggest Problems and Why They're Not What First Comes to Mind (01:12:03)Chapter 6: Which Jobs Help People the Most? (01:42:15)Chapter 7: Which Jobs Put You in the Best Long-Term Position? (02:19:11)Chapter 8: How to Find the Right Career for You (02:59:26)Chapter 9: How to Make Your Career Plan (03:32:30)Chapter 10: All the Best Advice We Could Find on How to Get a Job (03:55:34)Chapter 11: One of the Most Powerful Ways to Improve Your Career - Join a Community (04:24:21)The End: The Entire Guide, in One Minute (04:35:49)Rob's outro (04:40:05)