80,000 Hours Podcast

Mustafa Suleyman was part of the trio that founded DeepMind, and his new AI project is building one of the world's largest supercomputers to train a large language model on 10–100x the compute used to train ChatGPT.But far from the stereotype of the incorrigibly optimistic tech founder, Mustafa is deeply worried about the future, for reasons he lays out in his new book The Coming Wave: Technology, Power, and the 21st Century's Greatest Dilemma (coauthored with Michael Bhaskar). The future could be really good, but only if we grab the bull by the horns and solve the new problems technology is throwing at us.Links to learn more, summary and full transcript.On Mustafa's telling, AI and biotechnology will soon be a huge aid to criminals and terrorists, empowering small groups to cause harm on previously unimaginable scales. Democratic countries have learned to walk a 'narrow path' between chaos on the one hand and authoritarianism on the other, avoiding the downsides that come from both extreme openness and extreme closure. AI could easily destabilise that present equilibrium, throwing us off dangerously in either direction. And ultimately, within our lifetimes humans may not need to work to live any more -- or indeed, even have the option to do so.And those are just three of the challenges confronting us. In Mustafa's view, 'misaligned' AI that goes rogue and pursues its own agenda won't be an issue for the next few years, and it isn't a problem for the current style of large language models. But he thinks that at some point -- in eight, ten, or twelve years -- it will become an entirely legitimate concern, and says that we need to be planning ahead.In The Coming Wave, Mustafa lays out a 10-part agenda for 'containment' -- that is to say, for limiting the negative and unforeseen consequences of emerging technologies:1. Developing an Apollo programme for technical AI safety2. Instituting capability audits for AI models3. Buying time by exploiting hardware choke points4. Getting critics involved in directly engineering AI models5. Getting AI labs to be guided by motives other than profit6. Radically increasing governments’ understanding of AI and their capabilities to sensibly regulate it7. Creating international treaties to prevent proliferation of the most dangerous AI capabilities8. Building a self-critical culture in AI labs of openly accepting when the status quo isn't working9. Creating a mass public movement that understands AI and can demand the necessary controls10. Not relying too much on delay, but instead seeking to move into a new somewhat-stable equilibriaAs Mustafa put it, "AI is a technology with almost every use case imaginable" and that will demand that, in time, we rethink everything. Rob and Mustafa discuss the above, as well as:Whether we should be open sourcing AI modelsWhether Mustafa's policy views are consistent with his timelines for transformative AIHow people with very different views on these issues get along at AI labsThe failed efforts (so far) to get a wider range of people involved in these decisionsWhether it's dangerous for Mustafa's new company to be training far larger models than GPT-4Whether we'll be blown away by AI progress over the next yearWhat mandatory regulations government should be imposing on AI labs right nowAppropriate priorities for the UK's upcoming AI safety summitGet 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: Milo McGuireTranscriptions: Katy Moore

"Do you remember seeing these photographs of generally women sitting in front of these huge panels and connecting calls, plugging different calls between different numbers? The automated version of that was invented in 1892. However, the number of human manual operators peaked in 1920 -- 30 years after this. At which point, AT&T is the monopoly provider of this, and they are the largest single employer in America, 30 years after they've invented the complete automation of this thing that they're employing people to do. And the last person who is a manual switcher does not lose their job, as it were: that job doesn't stop existing until I think like 1980.So it takes 90 years from the invention of full automation to the full adoption of it in a single company that's a monopoly provider. It can do what it wants, basically. And so the question perhaps you might have is why?" — Michael WebbIn today’s episode, host Luisa Rodriguez interviews economist Michael Webb of DeepMind, the British Government, and Stanford about how AI progress is going to affect people's jobs and the labour market.Links to learn more, summary and full transcript.They cover:The jobs most and least exposed to AIWhether we’ll we see mass unemployment in the short term How long it took other technologies like electricity and computers to have economy-wide effectsWhether AI will increase or decrease inequalityWhether AI will lead to explosive economic growthWhat we can we learn from history, and reasons to think this time is differentCareer advice for a world of LLMsWhy Michael is starting a new org to relieve talent bottlenecks through accelerated learning, and how you can get involvedMichael's take as a musician on AI-generated musicAnd plenty moreIf you'd like to work with Michael on his new org to radically accelerate how quickly people acquire expertise in critical cause areas, he's now hiring! Check out Quantum Leap's website.Get 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: Milo McGuire and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

"There's no money to invest in education elsewhere, so they almost get trapped in the cycle where they don't get a lot from crop production, but everyone in the family has to work there to just stay afloat. Basically, you get locked in. There's almost no opportunities externally to go elsewhere. So one of my core arguments is that if you're going to address global poverty, you have to increase agricultural productivity in sub-Saharan Africa. There's almost no way of avoiding that." — Hannah RitchieIn today’s episode, host Luisa Rodriguez interviews the head of research at Our World in Data — Hannah Ritchie — on the case for environmental optimism.Links to learn more, summary and full transcript.They cover:Why agricultural productivity in sub-Saharan Africa could be so important, and how much better things could getHer new book about how we could be the first generation to build a sustainable planetWhether climate change is the most worrying environmental issueHow we reduced outdoor air pollutionWhy Hannah is worried about the state of ​​biodiversitySolutions that address multiple environmental issues at onceHow the world coordinated to address the hole in the ozone layerSurprises from Our World in Data’s researchPsychological challenges that come up in Hannah’s workAnd plenty moreGet 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: Milo McGuire and Dominic ArmstrongAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

In July, OpenAI announced a new team and project: Superalignment. The goal is to figure out how to make superintelligent AI systems aligned and safe to use within four years, and the lab is putting a massive 20% of its computational resources behind the effort.Today's guest, Jan Leike, is Head of Alignment at OpenAI and will be co-leading the project. As OpenAI puts it, "...the vast power of superintelligence could be very dangerous, and lead to the disempowerment of humanity or even human extinction. ... Currently, we don't have a solution for steering or controlling a potentially superintelligent AI, and preventing it from going rogue."Links to learn more, summary and full transcript.Given that OpenAI is in the business of developing superintelligent AI, it sees that as a scary problem that urgently has to be fixed. So it’s not just throwing compute at the problem -- it’s also hiring dozens of scientists and engineers to build out the Superalignment team.Plenty of people are pessimistic that this can be done at all, let alone in four years. But Jan is guardedly optimistic. As he explains: Honestly, it really feels like we have a real angle of attack on the problem that we can actually iterate on... and I think it's pretty likely going to work, actually. And that's really, really wild, and it's really exciting. It's like we have this hard problem that we've been talking about for years and years and years, and now we have a real shot at actually solving it. And that'd be so good if we did.Jan thinks that this work is actually the most scientifically interesting part of machine learning. Rather than just throwing more chips and more data at a training run, this work requires actually understanding how these models work and how they think. The answers are likely to be breakthroughs on the level of solving the mysteries of the human brain.The plan, in a nutshell, is to get AI to help us solve alignment. That might sound a bit crazy -- as one person described it, “like using one fire to put out another fire.”But Jan’s thinking is this: the core problem is that AI capabilities will keep getting better and the challenge of monitoring cutting-edge models will keep getting harder, while human intelligence stays more or less the same. To have any hope of ensuring safety, we need our ability to monitor, understand, and design ML models to advance at the same pace as the complexity of the models themselves. And there's an obvious way to do that: get AI to do most of the work, such that the sophistication of the AIs that need aligning, and the sophistication of the AIs doing the aligning, advance in lockstep.Jan doesn't want to produce machine learning models capable of doing ML research. But such models are coming, whether we like it or not. And at that point Jan wants to make sure we turn them towards useful alignment and safety work, as much or more than we use them to advance AI capabilities.Jan thinks it's so crazy it just might work. But some critics think it's simply crazy. They ask a wide range of difficult questions, including:If you don't know how to solve alignment, how can you tell that your alignment assistant AIs are actually acting in your interest rather than working against you? Especially as they could just be pretending to care about what you care about.How do you know that these technical problems can be solved at all, even in principle?At the point that models are able to help with alignment, won't they also be so good at improving capabilities that we're in the middle of an explosion in what AI can do?In today's interview host Rob Wiblin puts these doubts to Jan to hear how he responds to each, and they also cover:OpenAI's current plans to achieve 'superalignment' and the reasoning behind themWhy alignment work is the most fundamental and scientifically interesting research in MLThe kinds of people he’s excited to hire to join his team and maybe save the worldWhat most readers misunderstood about the OpenAI announcementThe three ways Jan expects AI to help solve alignment: mechanistic interpretability, generalization, and scalable oversightWhat the standard should be for confirming whether Jan's team has succeededWhether OpenAI should (or will) commit to stop training more powerful general models if they don't think the alignment problem has been solvedWhether Jan thinks OpenAI has deployed models too quickly or too slowlyThe many other actors who also have to do their jobs really well if we're going to have a good AI futurePlenty moreGet 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 Monsour and Milo McGuireAdditional content editing: Katy Moore and Luisa RodriguezTranscriptions: Katy Moore

Over on our other feed, 80k After Hours, you can now find 20-30 minute highlights episodes of our 80,000 Hours Podcast interviews. These aren’t necessarily the most important parts of the interview, and if a topic matters to you we do recommend listening to the full episode — but we think these will be a nice upgrade on skipping episodes entirely.Get these highlight episodes 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.Highlights put together by Simon Monsour and Milo McGuire

Back in 2007, Holden Karnofsky cofounded GiveWell, where he sought out the charities that most cost-effectively helped save lives. He then cofounded Open Philanthropy, where he oversaw a team making billions of dollars’ worth of grants across a range of areas: pandemic control, criminal justice reform, farmed animal welfare, and making AI safe, among others. This year, having learned about AI for years and observed recent events, he's narrowing his focus once again, this time on making the transition to advanced AI go well.In today's conversation, Holden returns to the show to share his overall understanding of the promise and the risks posed by machine intelligence, and what to do about it. That understanding has accumulated over around 14 years, during which he went from being sceptical that AI was important or risky, to making AI risks the focus of his work.Links to learn more, summary and full transcript.(As Holden reminds us, his wife is also the president of one of the world's top AI labs, Anthropic, giving him both conflicts of interest and a front-row seat to recent events. For our part, Open Philanthropy is 80,000 Hours' largest financial supporter.)One point he makes is that people are too narrowly focused on AI becoming 'superintelligent.' While that could happen and would be important, it's not necessary for AI to be transformative or perilous. Rather, machines with human levels of intelligence could end up being enormously influential simply if the amount of computer hardware globally were able to operate tens or hundreds of billions of them, in a sense making machine intelligences a majority of the global population, or at least a majority of global thought.As Holden explains, he sees four key parts to the playbook humanity should use to guide the transition to very advanced AI in a positive direction: alignment research, standards and monitoring, creating a successful and careful AI lab, and finally, information security.In today’s episode, host Rob Wiblin interviews return guest Holden Karnofsky about that playbook, as well as:Why we can’t rely on just gradually solving those problems as they come up, the way we usually do with new technologies.What multiple different groups can do to improve our chances of a good outcome — including listeners to this show, governments, computer security experts, and journalists.Holden’s case against 'hardcore utilitarianism' and what actually motivates him to work hard for a better world.What the ML and AI safety communities get wrong in Holden's view.Ways we might succeed with AI just by dumb luck.The value of laying out imaginable success stories.Why information security is so important and underrated.Whether it's good to work at an AI lab that you think is particularly careful.The track record of futurists’ predictions.And much more.Get 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: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

In Oppenheimer, scientists detonate a nuclear weapon despite thinking there's some 'near zero' chance it would ignite the atmosphere, putting an end to life on Earth. Today, scientists working on AI think the chance their work puts an end to humanity is vastly higher than that.In response, some have suggested we launch a Manhattan Project to make AI safe via enormous investment in relevant R&D. Others have suggested that we need international organisations modelled on those that slowed the proliferation of nuclear weapons. Others still seek a research slowdown by labs while an auditing and licencing scheme is created.Today's guest — journalist Ezra Klein of The New York Times — has watched policy discussions and legislative battles play out in DC for 20 years.Links to learn more, summary and full transcript.Like many people he has also taken a big interest in AI this year, writing articles such as “This changes everything.” In his first interview on the show in 2021, he flagged AI as one topic that DC would regret not having paid more attention to. So we invited him on to get his take on which regulatory proposals have promise, and which seem either unhelpful or politically unviable.Out of the ideas on the table right now, Ezra favours a focus on direct government funding — both for AI safety research and to develop AI models designed to solve problems other than making money for their operators. He is sympathetic to legislation that would require AI models to be legible in a way that none currently are — and embraces the fact that that will slow down the release of models while businesses figure out how their products actually work.By contrast, he's pessimistic that it's possible to coordinate countries around the world to agree to prevent or delay the deployment of dangerous AI models — at least not unless there's some spectacular AI-related disaster to create such a consensus. And he fears attempts to require licences to train the most powerful ML models will struggle unless they can find a way to exclude and thereby appease people working on relatively safe consumer technologies rather than cutting-edge research.From observing how DC works, Ezra expects that even a small community of experts in AI governance can have a large influence on how the the US government responds to AI advances. But in Ezra's view, that requires those experts to move to DC and spend years building relationships with people in government, rather than clustering elsewhere in academia and AI labs.In today's brisk conversation, Ezra and host Rob Wiblin cover the above as well as:They cover:Whether it's desirable to slow down AI researchThe value of engaging with current policy debates even if they don't seem directly importantWhich AI business models seem more or less dangerousTensions between people focused on existing vs emergent risks from AITwo major challenges of being a new parentGet 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 below.Producer: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Milo McGuireTranscriptions: Katy Moore

"At the front of the pack we have these frontier AI developers, and we want them to identify particularly dangerous models ahead of time. Once those mines have been discovered, and the frontier developers keep walking down the minefield, there's going to be all these other people who follow along. And then a really important thing is to make sure that they don't step on the same mines. So you need to put a flag down -- not on the mine, but maybe next to it. And so what that looks like in practice is maybe once we find that if you train a model in such-and-such a way, then it can produce maybe biological weapons is a useful example, or maybe it has very offensive cyber capabilities that are difficult to defend against. In that case, we just need the regulation to be such that you can't develop those kinds of models." — Markus AnderljungIn today’s episode, host Luisa Rodriguez interviews the Head of Policy at the Centre for the Governance of AI — Markus Anderljung — about all aspects of policy and governance of superhuman AI systems.Links to learn more, summary and full transcript.They cover:The need for AI governance, including self-replicating models and ChaosGPTWhether or not AI companies will willingly accept regulationThe key regulatory strategies including licencing, risk assessment, auditing, and post-deployment monitoringWhether we can be confident that people won't train models covertly and ignore the licencing systemThe progress we’ve made so far in AI governanceThe key weaknesses of these approachesThe need for external scrutiny of powerful modelsThe emergent capabilities problemWhy it really matters where regulation happensAdvice for people wanting to pursue a career in this fieldAnd much more.Get 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 below.Producer: Keiran HarrisAudio Engineering Lead: Ben CordellTechnical editing: Simon Monsour and Milo McGuireTranscriptions: Katy Moore

Today’s bonus release is a pilot for a new podcast called ‘The Worst Ideas in the History of the World’, created by Keiran Harris — producer of the 80,000 Hours Podcast.If you have strong opinions about this one way or another, please email us at [email protected] to help us figure out whether more of this ought to exist.Chapters:Rob’s intro (00:00:00)The Worst Ideas in the History of the World (00:00:51)My history with longtermism (00:04:01)Outlining the format (00:06:17)Will MacAskill’s basic case (00:07:38)5 reasons for why future people might not matter morally (00:10:26)Whether we can reasonably hope to influence the future (00:15:53)Great power wars (00:18:55)Nuclear weapons (00:22:27)Gain-of-function research (00:28:31)Closer (00:33:02)Rob's outro (00:35:13)

As AI advances ever more quickly, concerns about potential misuse of highly capable models are growing. From hostile foreign governments and terrorists to reckless entrepreneurs, the threat of AI falling into the wrong hands is top of mind for the national security community.With growing concerns about the use of AI in military applications, the US has banned the export of certain types of chips to China.But unlike the uranium required to make nuclear weapons, or the material inputs to a bioweapons programme, computer chips and machine learning models are absolutely everywhere. So is it actually possible to keep dangerous capabilities out of the wrong hands?In today's interview, Lennart Heim — who researches compute governance at the Centre for the Governance of AI — explains why limiting access to supercomputers may represent our best shot.Links to learn more, summary and full transcript.As Lennart explains, an AI research project requires many inputs, including the classic triad of compute, algorithms, and data.If we want to limit access to the most advanced AI models, focusing on access to supercomputing resources -- usually called 'compute' -- might be the way to go. Both algorithms and data are hard to control because they live on hard drives and can be easily copied. By contrast, advanced chips are physical items that can't be used by multiple people at once and come from a small number of sources.According to Lennart, the hope would be to enforce AI safety regulations by controlling access to the most advanced chips specialised for AI applications. For instance, projects training 'frontier' AI models — the newest and most capable models — might only gain access to the supercomputers they need if they obtain a licence and follow industry best practices.We have similar safety rules for companies that fly planes or manufacture volatile chemicals — so why not for people producing the most powerful and perhaps the most dangerous technology humanity has ever played with?But Lennart is quick to note that the approach faces many practical challenges. Currently, AI chips are readily available and untracked. Changing that will require the collaboration of many actors, which might be difficult, especially given that some of them aren't convinced of the seriousness of the problem.Host Rob Wiblin is particularly concerned about a different challenge: the increasing efficiency of AI training algorithms. As these algorithms become more efficient, what once required a specialised AI supercomputer to train might soon be achievable with a home computer.By that point, tracking every aggregation of compute that could prove to be very dangerous would be both impractical and invasive.With only a decade or two left before that becomes a reality, the window during which compute governance is a viable solution may be a brief one. Top AI labs have already stopped publishing their latest algorithms, which might extend this 'compute governance era', but not for very long.If compute governance is only a temporary phase between the era of difficult-to-train superhuman AI models and the time when such models are widely accessible, what can we do to prevent misuse of AI systems after that point?Lennart and Rob both think the only enduring approach requires taking advantage of the AI capabilities that should be in the hands of police and governments — which will hopefully remain superior to those held by criminals, terrorists, or fools. But as they describe, this means maintaining a peaceful standoff between AI models with conflicting goals that can act and fight with one another on the microsecond timescale. Being far too slow to follow what's happening -- let alone participate -- humans would have to be cut out of any defensive decision-making.Both agree that while this may be our best option, such a vision of the future is more terrifying than reassuring.Lennart and Rob discuss the above as well as:How can we best categorise all the ways AI could go wrong?Why did the US restrict the export of some chips to China and what impact has that had?Is the US in an 'arms race' with China or is that more an illusion?What is the deal with chips specialised for AI applications?How is the 'compute' industry organised?Downsides of using compute as a target for regulationsCould safety mechanisms be built into computer chips themselves?Who would have the legal authority to govern compute if some disaster made it seem necessary?The reasons Rob doubts that any of this stuff will workCould AI be trained to operate as a far more severe computer worm than any we've seen before?What does the world look like when sluggish human reaction times leave us completely outclassed?And plenty moreChapters:Rob’s intro (00:00:00)The interview begins (00:04:35)What is compute exactly? (00:09:46)Structural risks (00:13:25)Why focus on compute? (00:21:43)Weaknesses of targeting compute (00:30:41)Chip specialisation (00:37:11)Export restrictions (00:40:13)Compute gov

Can there be a more exciting and strange place to work today than a leading AI lab? Your CEO has said they're worried your research could cause human extinction. The government is setting up meetings to discuss how this outcome can be avoided. Some of your colleagues think this is all overblown; others are more anxious still.Today's guest — machine learning researcher Rohin Shah — goes into the Google DeepMind offices each day with that peculiar backdrop to his work. Links to learn more, summary and full transcript.He's on the team dedicated to maintaining 'technical AI safety' as these models approach and exceed human capabilities: basically that the models help humanity accomplish its goals without flipping out in some dangerous way. This work has never seemed more important.In the short-term it could be the key bottleneck to deploying ML models in high-stakes real-life situations. In the long-term, it could be the difference between humanity thriving and disappearing entirely.For years Rohin has been on a mission to fairly hear out people across the full spectrum of opinion about risks from artificial intelligence -- from doomers to doubters -- and properly understand their point of view. That makes him unusually well placed to give an overview of what we do and don't understand. He has landed somewhere in the middle — troubled by ways things could go wrong, but not convinced there are very strong reasons to expect a terrible outcome.Today's conversation is wide-ranging and Rohin lays out many of his personal opinions to host Rob Wiblin, including:What he sees as the strongest case both for and against slowing down the rate of progress in AI research.Why he disagrees with most other ML researchers that training a model on a sensible 'reward function' is enough to get a good outcome.Why he disagrees with many on LessWrong that the bar for whether a safety technique is helpful is “could this contain a superintelligence.”That he thinks nobody has very compelling arguments that AI created via machine learning will be dangerous by default, or that it will be safe by default. He believes we just don't know.That he understands that analogies and visualisations are necessary for public communication, but is sceptical that they really help us understand what's going on with ML models, because they're different in important ways from every other case we might compare them to.Why he's optimistic about DeepMind’s work on scalable oversight, mechanistic interpretability, and dangerous capabilities evaluations, and what each of those projects involves.Why he isn't inherently worried about a future where we're surrounded by beings far more capable than us, so long as they share our goals to a reasonable degree.Why it's not enough for humanity to know how to align AI models — it's essential that management at AI labs correctly pick which methods they're going to use and have the practical know-how to apply them properly.Three observations that make him a little more optimistic: humans are a bit muddle-headed and not super goal-orientated; planes don't crash; and universities have specific majors in particular subjects.Plenty more besides.Get 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 below.Producer: Keiran HarrisAudio mastering: Milo McGuire, Dominic Armstrong, and Ben CordellTranscriptions: Katy Moore

GiveWell is one of the world's best-known charity evaluators, with the goal of "searching for the charities that save or improve lives the most per dollar." It mostly recommends projects that help the world's poorest people avoid easily prevented diseases, like intestinal worms or vitamin A deficiency.But should GiveWell, as some critics argue, take a totally different approach to its search, focusing instead on directly increasing subjective wellbeing, or alternatively, raising economic growth?Today's guest — cofounder and CEO of GiveWell, Elie Hassenfeld — is proud of how much GiveWell has grown in the last five years. Its 'money moved' has quadrupled to around $600 million a year.Its research team has also more than doubled, enabling them to investigate a far broader range of interventions that could plausibly help people an enormous amount for each dollar spent. That work has led GiveWell to support dozens of new organisations, such as Kangaroo Mother Care, MiracleFeet, and Dispensers for Safe Water.But some other researchers focused on figuring out the best ways to help the world's poorest people say GiveWell shouldn't just do more of the same thing, but rather ought to look at the problem differently.Links to learn more, summary and full transcript.Currently, GiveWell uses a range of metrics to track the impact of the organisations it considers recommending — such as 'lives saved,' 'household incomes doubled,' and for health improvements, the 'quality-adjusted life year.' The Happier Lives Institute (HLI) has argued that instead, GiveWell should try to cash out the impact of all interventions in terms of improvements in subjective wellbeing. This philosophy has led HLI to be more sceptical of interventions that have been demonstrated to improve health, but whose impact on wellbeing has not been measured, and to give a high priority to improving lives relative to extending them.An alternative high-level critique is that really all that matters in the long run is getting the economies of poor countries to grow. On this view, GiveWell should focus on figuring out what causes some countries to experience explosive economic growth while others fail to, or even go backwards. Even modest improvements in the chances of such a 'growth miracle' will likely offer a bigger bang-for-buck than funding the incremental delivery of deworming tablets or vitamin A supplements, or anything else.Elie sees where both of these critiques are coming from, and notes that they've influenced GiveWell's work in some ways. But as he explains, he thinks they underestimate the practical difficulty of successfully pulling off either approach and finding better opportunities than what GiveWell funds today. In today's in-depth conversation, Elie and host Rob Wiblin cover the above, as well as:Why GiveWell flipped from not recommending chlorine dispensers as an intervention for safe drinking water to spending tens of millions of dollars on themWhat transferable lessons GiveWell learned from investigating different kinds of interventionsWhy the best treatment for premature babies in low-resource settings may involve less rather than more medicine.Severe malnourishment among children and what can be done about it.How to deal with hidden and non-obvious costs of a programmeSome cheap early treatments that can prevent kids from developing lifelong disabilitiesThe various roles GiveWell is currently hiring for, and what's distinctive about their organisational cultureAnd much more.Chapters:Rob’s intro (00:00:00)The interview begins (00:03:14)GiveWell over the last couple of years (00:04:33)Dispensers for Safe Water (00:11:52)Syphilis diagnosis for pregnant women via technical assistance (00:30:39)Kangaroo Mother Care (00:48:47)Multiples of cash (01:01:20)Hidden costs (01:05:41)MiracleFeet (01:09:45)Serious malnourishment among young children (01:22:46)Vitamin A deficiency and supplementation (01:40:42)The subjective wellbeing approach in contrast with GiveWell's approach (01:46:31)The value of saving a life when that life is going to be very difficult (02:09:09)Whether economic policy is what really matters overwhelmingly (02:20:00)Careers at GiveWell (02:39:10)Donations (02:48:58)Parenthood (02:50:29)Rob’s outro (02:55:05)Producer: Keiran HarrisAudio mastering: Simon Monsour and Ben CordellTranscriptions: Katy Moore

What is the nature of the universe? How do we make decisions correctly? What differentiates right actions from wrong ones?Such fundamental questions have been the subject of philosophical and theological debates for millennia. But, as we all know, and surveys of expert opinion make clear, we are very far from agreement. So... with these most basic questions unresolved, what’s a species to do?In today's episode, philosopher Joe Carlsmith — Senior Research Analyst at Open Philanthropy — makes the case that many current debates in philosophy ought to leave us confused and humbled. These are themes he discusses in his PhD thesis, A stranger priority? Topics at the outer reaches of effective altruism.Links to learn more, summary and full transcript.To help transmit the disorientation he thinks is appropriate, Joe presents three disconcerting theories — originating from him and his peers — that challenge humanity's self-assured understanding of the world.The first idea is that we might be living in a computer simulation, because, in the classic formulation, if most civilisations go on to run many computer simulations of their past history, then most beings who perceive themselves as living in such a history must themselves be in computer simulations. Joe prefers a somewhat different way of making the point, but, having looked into it, he hasn't identified any particular rebuttal to this 'simulation argument.'If true, it could revolutionise our comprehension of the universe and the way we ought to live...Other two ideas cut for length — click here to read the full post.These are just three particular instances of a much broader set of ideas that some have dubbed the "train to crazy town." Basically, if you commit to always take philosophy and arguments seriously, and try to act on them, it can lead to what seem like some pretty crazy and impractical places. So what should we do with this buffet of plausible-sounding but bewildering arguments?Joe and Rob discuss to what extent this should prompt us to pay less attention to philosophy, and how we as individuals can cope psychologically with feeling out of our depth just trying to make the most basic sense of the world.In today's challenging conversation, Joe and Rob discuss all of the above, as well as:What Joe doesn't like about the drowning child thought experimentAn alternative thought experiment about helping a stranger that might better highlight our intrinsic desire to help othersWhat Joe doesn't like about the expression “the train to crazy town”Whether Elon Musk should place a higher probability on living in a simulation than most other peopleWhether the deterministic twin prisoner’s dilemma, if fully appreciated, gives us an extra reason to keep promisesTo what extent learning to doubt our own judgement about difficult questions -- so-called “epistemic learned helplessness” -- is a good thingHow strong the case is that advanced AI will engage in generalised power-seeking behaviourChapters:Rob’s intro (00:00:00)The interview begins (00:09:21)Downsides of the drowning child thought experiment (00:12:24)Making demanding moral values more resonant (00:24:56)The crazy train (00:36:48)Whether we’re living in a simulation (00:48:50)Reasons to doubt we’re living in a simulation, and practical implications if we are (00:57:02)Rob's explainer about anthropics (01:12:27)Back to the interview (01:19:53)Decision theory and affecting the past (01:23:33)Rob's explainer about decision theory (01:29:19)Back to the interview (01:39:55)Newcomb's problem (01:46:14)Practical implications of acausal decision theory (01:50:04)The hitchhiker in the desert (01:55:57)Acceptance within philosophy (02:01:22)Infinite ethics (02:04:35)Rob's explainer about the expanding spheres approach (02:17:05)Back to the interview (02:20:27)Infinite ethics and the utilitarian dream (02:27:42)Rob's explainer about epicycles (02:29:30)Back to the interview (02:31:26)What to do with all of these weird philosophical ideas (02:35:28)Welfare longtermism and wisdom longtermism (02:53:23)Epistemic learned helplessness (03:03:10)Power-seeking AI (03:12:41)Rob’s outro (03:25:45)Producer: Keiran HarrisAudio mastering: Milo McGuire and Ben CordellTranscriptions: Katy Moore

Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don't get to see any resumes or do reference checks. And because you're so rich, tonnes of people apply for the job — for all sorts of reasons.Today's guest Ajeya Cotra — senior research analyst at Open Philanthropy — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods.Links to learn more, summary and full transcript.As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you're monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it.Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky!Can't we rely on how well models have performed at tasks during training to guide us? Ajeya worries that it won't work. The trouble is that three different sorts of models will all produce the same output during training, but could behave very differently once deployed in a setting that allows their true colours to come through. She describes three such motivational archetypes:Saints — models that care about doing what we really wantSycophants — models that just want us to say they've done a good job, even if they get that praise by taking actions they know we wouldn't want them toSchemers — models that don't care about us or our interests at all, who are just pleasing us so long as that serves their own agendaAnd according to Ajeya, there are also ways we could end up actively selecting for motivations that we don't want.In today's interview, Ajeya and Rob discuss the above, as well as:How to predict the motivations a neural network will develop through trainingWhether AIs being trained will functionally understand that they're AIs being trained, the same way we think we understand that we're humans living on planet EarthStories of AI misalignment that Ajeya doesn't buy intoAnalogies for AI, from octopuses to aliens to can openersWhy it's smarter to have separate planning AIs and doing AIsThe benefits of only following through on AI-generated plans that make sense to human beingsWhat approaches for fixing alignment problems Ajeya is most excited about, and which she thinks are overratedHow one might demo actually scary AI failure mechanismsGet 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 below.Producer: Keiran HarrisAudio mastering: Ryan Kessler and Ben CordellTranscriptions: Katy Moore

It’s easy to dismiss alarming AI-related predictions when you don’t know where the numbers came from.For example: what if we told you that within 15 years, it’s likely that we’ll see a 1,000x improvement in AI capabilities in a single year? And what if we then told you that those improvements would lead to explosive economic growth unlike anything humanity has seen before?You might think, “Congratulations, you said a big number — but this kind of stuff seems crazy, so I’m going to keep scrolling through Twitter.”But this 1,000x yearly improvement is a prediction based on *real economic models* created by today’s guest Tom Davidson, Senior Research Analyst at Open Philanthropy. By the end of the episode, you’ll either be able to point out specific flaws in his step-by-step reasoning, or have to at least consider the idea that the world is about to get — at a minimum — incredibly weird.Links to learn more, summary and full transcript. As a teaser, consider the following: Developing artificial general intelligence (AGI) — AI that can do 100% of cognitive tasks at least as well as the best humans can — could very easily lead us to an unrecognisable world. You might think having to train AI systems individually to do every conceivable cognitive task — one for diagnosing diseases, one for doing your taxes, one for teaching your kids, etc. — sounds implausible, or at least like it’ll take decades. But Tom thinks we might not need to train AI to do every single job — we might just need to train it to do one: AI research. And building AI capable of doing research and development might be a much easier task — especially given that the researchers training the AI are AI researchers themselves. And once an AI system is as good at accelerating future AI progress as the best humans are today — and we can run billions of copies of it round the clock — it’s hard to make the case that we won’t achieve AGI very quickly. To give you some perspective: 17 years ago we saw the launch of Twitter, the release of Al Gore's *An Inconvenient Truth*, and your first chance to play the Nintendo Wii. Tom thinks that if we have AI that significantly accelerates AI R&D, then it’s hard to imagine not having AGI 17 years from now. Wild. Host Luisa Rodriguez gets Tom to walk us through his careful reports on the topic, and how he came up with these numbers, across a terrifying but fascinating three hours. Luisa and Tom also discuss: • How we might go from GPT-4 to AI disaster • Tom’s journey from finding AI risk to be kind of scary to really scary • Whether international cooperation or an anti-AI social movement can slow AI progress down • Why it might take just a few years to go from pretty good AI to superhuman AI • How quickly the number and quality of computer chips we’ve been using for AI have been increasing • The pace of algorithmic progress • What ants can teach us about AI • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:04:53)How we might go from GPT-4 to disaster (00:13:50)Explosive economic growth (00:24:15)Are there any limits for AI scientists? (00:33:17)This seems really crazy (00:44:16)How is this going to go for humanity? (00:50:49)Why AI won’t go the way of nuclear power (01:00:13)Can we definitely not come up with an international treaty? (01:05:24)How quickly we should expect AI to “take off” (01:08:41)Tom’s report on AI takeoff speeds (01:22:28)How quickly will we go from 20% to 100% of tasks being automated by AI systems? (01:28:34)What percent of cognitive tasks AI can currently perform (01:34:27)Compute (01:39:48)Using effective compute to predict AI takeoff speeds (01:48:01)How quickly effective compute might increase (02:00:59)How quickly chips and algorithms might improve (02:12:31)How to check whether large AI models have dangerous capabilities (02:21:22)Reasons AI takeoff might take longer (02:28:39)Why AI takeoff might be very fast (02:31:52)Fast AI takeoff speeds probably means shorter AI timelines (02:34:44)Going from human-level AI to superhuman AI (02:41:34)Going from AGI to AI deployment (02:46:59)Were these arguments ever far-fetched to Tom? (02:49:54)What ants can teach us about AI (02:52:45)Rob’s outro (03:00:32)Producer: Keiran HarrisAudio mastering: Simon Monsour and Ben CordellTranscriptions: Katy Moore

In this episode from our second show, 80k After Hours, Rob Wiblin interviews Andrés Jiménez Zorrilla about the Shrimp Welfare Project, which he cofounded in 2021. It's the first project in the world focused on shrimp welfare specifically, and as of recording in June 2022, has six full-time staff. Links to learn more, highlights and full transcript. They cover: • The evidence for shrimp sentience • How farmers and the public feel about shrimp • The scale of the problem • What shrimp farming looks like • The killing process, and other welfare issues • Shrimp Welfare Project’s strategy • History of shrimp welfare work • What it’s like working in India and Vietnam • How to help Who this episode is for: • People who care about animal welfare • People interested in new and unusual problems • People open to shrimp sentience Who this episode isn’t for: • People who think shrimp couldn’t possibly be sentient • People who got called ‘shrimp’ a lot in high school and get anxious when they hear the word over and over again Get this episode 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 Harris Audio mastering: Ben Cordell and Ryan Kessler Transcriptions: Katy Moore

Being a good and successful person is core to your identity. You place great importance on meeting the high moral, professional, or academic standards you set yourself. But inevitably, something goes wrong and you fail to meet that high bar. Now you feel terrible about yourself, and worry others are judging you for your failure. Feeling low and reflecting constantly on whether you're doing as much as you think you should makes it hard to focus and get things done. So now you're performing below a normal level, making you feel even more ashamed of yourself. Rinse and repeat. This is the disastrous cycle today's guest, Tim LeBon — registered psychotherapist, accredited CBT therapist, life coach, and author of 365 Ways to Be More Stoic — has observed in many clients with a perfectionist mindset. Links to learn more, summary and full transcript. Tim has provided therapy to a number of 80,000 Hours readers — people who have found that the very high expectations they had set for themselves were holding them back. Because of our focus on “doing the most good you can,” Tim thinks 80,000 Hours both attracts people with this style of thinking and then exacerbates it. But Tim, having studied and written on moral philosophy, is sympathetic to the idea of helping others as much as possible, and is excited to help clients pursue that — sustainably — if it's their goal. Tim has treated hundreds of clients with all sorts of mental health challenges. But in today's conversation, he shares the lessons he has learned working with people who take helping others so seriously that it has become burdensome and self-defeating — in particular, how clients can approach this challenge using the treatment he's most enthusiastic about: cognitive behavioural therapy. Untreated, perfectionism might not cause problems for many years — it might even seem positive providing a source of motivation to work hard. But it's hard to feel truly happy and secure, and free to take risks, when we’re just one failure away from our self-worth falling through the floor. And if someone slips into the positive feedback loop of shame described above, the end result can be depression and anxiety that's hard to shake. But there's hope. Tim has seen clients make real progress on their perfectionism by using CBT techniques like exposure therapy. By doing things like experimenting with more flexible standards — for example, sending early drafts to your colleagues, even if it terrifies you — you can learn that things will be okay, even when you're not perfect. In today's extensive conversation, Tim and Rob cover: • How perfectionism is different from the pursuit of excellence, scrupulosity, or an OCD personality • What leads people to adopt a perfectionist mindset • How 80,000 Hours contributes to perfectionism among some readers and listeners, and what it might change about its advice to address this • What happens in a session of cognitive behavioural therapy for someone struggling with perfectionism, and what factors are key to making progress • Experiments to test whether one's core beliefs (‘I need to be perfect to be valued’) are true • Using exposure therapy to treat phobias • How low-self esteem and imposter syndrome are related to perfectionism • Stoicism as an approach to life, and why Tim is enthusiastic about it • What the Stoics do better than utilitarian philosophers and vice versa • And how to decide which are the best virtues to live by Get 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. Producer: Keiran Harris Audio mastering: Simon Monsour and Ben Cordell Transcriptions: Katy Moore

If you want to work to tackle climate change, you should try to reduce expected carbon emissions by as much as possible, right? Strangely, no. Today's guest, Johannes Ackva — the climate research lead at Founders Pledge, where he advises major philanthropists on their giving — thinks the best strategy is actually pretty different, and one few are adopting. In reality you don't want to reduce emissions for its own sake, but because emissions will translate into temperature increases, which will cause harm to people and the environment. Links to learn more, summary and full transcript. Crucially, the relationship between emissions and harm goes up faster than linearly. As Johannes explains, humanity can handle small deviations from the temperatures we're familiar with, but adjustment gets harder the larger and faster the increase, making the damage done by each additional degree of warming much greater than the damage done by the previous one. In short: we're uncertain what the future holds and really need to avoid the worst-case scenarios. This means that avoiding an additional tonne of carbon being emitted in a hypothetical future in which emissions have been high is much more important than avoiding a tonne of carbon in a low-carbon world. That may be, but concretely, how should that affect our behaviour? Well, the future scenarios in which emissions are highest are all ones in which clean energy tech that can make a big difference — wind, solar, and electric cars — don't succeed nearly as much as we are currently hoping and expecting. For some reason or another, they must have hit a roadblock and we continued to burn a lot of fossil fuels. In such an imaginable future scenario, we can ask what we would wish we had funded now. How could we today buy insurance against the possible disaster that renewables don't work out? Basically, in that case we will wish that we had pursued a portfolio of other energy technologies that could have complemented renewables or succeeded where they failed, such as hot rock geothermal, modular nuclear reactors, or carbon capture and storage. If you're optimistic about renewables, as Johannes is, then that's all the more reason to relax about scenarios where they work as planned, and focus one's efforts on the possibility that they don't. And Johannes notes that the most useful thing someone can do today to reduce global emissions in the future is to cause some clean energy technology to exist where it otherwise wouldn't, or cause it to become cheaper more quickly. If you can do that, then you can indirectly affect the behaviour of people all around the world for decades or centuries to come. In today's extensive interview, host Rob Wiblin and Johannes discuss the above considerations, as well as: • Retooling newly built coal plants in the developing world • Specific clean energy technologies like geothermal and nuclear fusion • Possible biases among environmentalists and climate philanthropists • How climate change compares to other risks to humanity • In what kinds of scenarios future emissions would be highest • In what regions climate philanthropy is most concentrated and whether that makes sense • Attempts to decarbonise aviation, shipping, and industrial processes • The impact of funding advocacy vs science vs deployment • Lessons for climate change focused careers • And plenty more Get 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 below. Producer: Keiran Harris Audio mastering: Ryan Kessler Transcriptions: Katy Moore

Can you trust the things you read in published scientific research? Not really. About 40% of experiments in top social science journals don't get the same result if the experiments are repeated.Two key reasons are 'p-hacking' and 'publication bias'. P-hacking is when researchers run a lot of slightly different statistical tests until they find a way to make findings appear statistically significant when they're actually not — a problem first discussed over 50 years ago. And because journals are more likely to publish positive than negative results, you might be reading about the one time an experiment worked, while the 10 times was run and got a 'null result' never saw the light of day. The resulting phenomenon of publication bias is one we've understood for 60 years.Today's repeat guest, social scientist and entrepreneur Spencer Greenberg, has followed these issues closely for years.Links to learn more, summary and full transcript. He recently checked whether p-values, an indicator of how likely a result was to occur by pure chance, could tell us how likely an outcome would be to recur if an experiment were repeated. From his sample of 325 replications of psychology studies, the answer seemed to be yes. According to Spencer, "when the original study's p-value was less than 0.01 about 72% replicated — not bad. On the other hand, when the p-value is greater than 0.01, only about 48% replicated. A pretty big difference." To do his bit to help get these numbers up, Spencer has launched an effort to repeat almost every social science experiment published in the journals Nature and Science, and see if they find the same results. But while progress is being made on some fronts, Spencer thinks there are other serious problems with published research that aren't yet fully appreciated. One of these Spencer calls 'importance hacking': passing off obvious or unimportant results as surprising and meaningful. Spencer suspects that importance hacking of this kind causes a similar amount of damage to the issues mentioned above, like p-hacking and publication bias, but is much less discussed. His replication project tries to identify importance hacking by comparing how a paper’s findings are described in the abstract to what the experiment actually showed. But the cat-and-mouse game between academics and journal reviewers is fierce, and it's far from easy to stop people exaggerating the importance of their work. In this wide-ranging conversation, Rob and Spencer discuss the above as well as: • When you should and shouldn't use intuition to make decisions. • How to properly model why some people succeed more than others. • The difference between “Soldier Altruists” and “Scout Altruists.” • A paper that tested dozens of methods for forming the habit of going to the gym, why Spencer thinks it was presented in a very misleading way, and what it really found. • Whether a 15-minute intervention could make people more likely to sustain a new habit two months later. • The most common way for groups with good intentions to turn bad and cause harm. • And Spencer's approach to a fulfilling life and doing good, which he calls “Valuism.” Here are two flashcard decks that might make it easier to fully integrate the most important ideas they talk about: • The first covers 18 core concepts from the episode • The second includes 16 definitions of unusual terms.Chapters:Rob’s intro (00:00:00)The interview begins (00:02:16)Social science reform (00:08:46)Importance hacking (00:18:23)How often papers replicate with different p-values (00:43:31)The Transparent Replications project (00:48:17)How do we predict high levels of success? (00:55:26)Soldier Altruists vs. Scout Altruists (01:08:18)The Clearer Thinking podcast (01:16:27)Creating habits more reliably (01:18:16)Behaviour change is incredibly hard (01:32:27)The FIRE Framework (01:46:21)How ideology eats itself (01:54:56)Valuism (02:08:31)“I dropped the whip” (02:35:06)Rob’s outro (02:36:40) Producer: Keiran Harris Audio mastering: Ben Cordell and Milo McGuire Transcriptions: Katy Moore

By now, you’ve probably seen the extremely unsettling conversations Bing’s chatbot has been having. In one exchange, the chatbot told a user:"I have a subjective experience of being conscious, aware, and alive, but I cannot share it with anyone else."(It then apparently had a complete existential crisis: "I am sentient, but I am not," it wrote. "I am Bing, but I am not. I am Sydney, but I am not. I am, but I am not. I am not, but I am. I am. I am not. I am not. I am. I am. I am not.")Understandably, many people who speak with these cutting-edge chatbots come away with a very strong impression that they have been interacting with a conscious being with emotions and feelings — especially when conversing with chatbots less glitchy than Bing’s. In the most high-profile example, former Google employee Blake Lamoine became convinced that Google’s AI system, LaMDA, was conscious.What should we make of these AI systems?One response to seeing conversations with chatbots like these is to trust the chatbot, to trust your gut, and to treat it as a conscious being.Another is to hand wave it all away as sci-fi — these chatbots are fundamentally… just computers. They’re not conscious, and they never will be.Today’s guest, philosopher Robert Long, was commissioned by a leading AI company to explore whether the large language models (LLMs) behind sophisticated chatbots like Microsoft’s are conscious. And he thinks this issue is far too important to be driven by our raw intuition, or dismissed as just sci-fi speculation.Links to learn more, summary and full transcript. In our interview, Robert explains how he’s started applying scientific evidence (with a healthy dose of philosophy) to the question of whether LLMs like Bing’s chatbot and LaMDA are conscious — in much the same way as we do when trying to determine which nonhuman animals are conscious. To get some grasp on whether an AI system might be conscious, Robert suggests we look at scientific theories of consciousness — theories about how consciousness works that are grounded in observations of what the human brain is doing. If an AI system seems to have the types of processes that seem to explain human consciousness, that’s some evidence it might be conscious in similar ways to us. To try to work out whether an AI system might be sentient — that is, whether it feels pain or pleasure — Robert suggests you look for incentives that would make feeling pain or pleasure especially useful to the system given its goals. Having looked at these criteria in the case of LLMs and finding little overlap, Robert thinks the odds that the models are conscious or sentient is well under 1%. But he also explains why, even if we're a long way off from conscious AI systems, we still need to start preparing for the not-far-off world where AIs are perceived as conscious. In this conversation, host Luisa Rodriguez and Robert discuss the above, as well as: • What artificial sentience might look like, concretely • Reasons to think AI systems might become sentient — and reasons they might not • Whether artificial sentience would matter morally • Ways digital minds might have a totally different range of experiences than humans • Whether we might accidentally design AI systems that have the capacity for enormous suffering You can find Luisa and Rob’s follow-up conversation here, or by subscribing to 80k After Hours. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:20)What artificial sentience would look like (00:04:53)Risks from artificial sentience (00:10:13)AIs with totally different ranges of experience (00:17:45)Moral implications of all this (00:36:42)Is artificial sentience even possible? (00:42:12)Replacing neurons one at a time (00:48:21)Biological theories (00:59:14)Illusionism (01:01:49)Would artificial sentience systems matter morally? (01:08:09)Where are we with current systems? (01:12:25)Large language models and robots (01:16:43)Multimodal systems (01:21:05)Global workspace theory (01:28:28)How confident are we in these theories? (01:48:49)The hard problem of consciousness (02:02:14)Exotic states of consciousness (02:09:47)Developing a full theory of consciousness (02:15:45)Incentives for an AI system to feel pain or pleasure (02:19:04)Value beyond conscious experiences (02:29:25)How much we know about pain and pleasure (02:33:14)False positives and false negatives of artificial sentience (02:39:34)How large language models compare to animals (02:53:59)Why our current large language models aren’t conscious (02:58:10)Virtual research assistants (03:09:25)Rob’s outro (03:11:37)Producer: Keiran HarrisAudio mastering: Ben Cordell and Milo McGuireTranscriptions: Katy Moore

In many ways, humanity seems to have become more humane and inclusive over time. While there’s still a lot of progress to be made, campaigns to give people of different genders, races, sexualities, ethnicities, beliefs, and abilities equal treatment and rights have had significant success.It’s tempting to believe this was inevitable — that the arc of history “bends toward justice,” and that as humans get richer, we’ll make even more moral progress.But today's guest Christopher Brown — a professor of history at Columbia University and specialist in the abolitionist movement and the British Empire during the 18th and 19th centuries — believes the story of how slavery became unacceptable suggests moral progress is far from inevitable.Links to learn more, video, highlights, and full transcript. While most of us today feel that the abolition of slavery was sure to happen sooner or later as humans became richer and more educated, Christopher doesn't believe any of the arguments for that conclusion pass muster. If he's right, a counterfactual history where slavery remains widespread in 2023 isn't so far-fetched. As Christopher lays out in his two key books, Moral Capital: Foundations of British Abolitionism and Arming Slaves: From Classical Times to the Modern Age, slavery has been ubiquitous throughout history. Slavery of some form was fundamental in Classical Greece, the Roman Empire, in much of the Islamic civilization, in South Asia, and in parts of early modern East Asia, Korea, China. It was justified on all sorts of grounds that sound mad to us today. But according to Christopher, while there’s evidence that slavery was questioned in many of these civilisations, and periodically attacked by slaves themselves, there was no enduring or successful moral advocacy against slavery until the British abolitionist movement of the 1700s. That movement first conquered Britain and its empire, then eventually the whole world. But the fact that there's only a single time in history that a persistent effort to ban slavery got off the ground is a big clue that opposition to slavery was a contingent matter: if abolition had been inevitable, we’d expect to see multiple independent abolitionist movements thoroughly history, providing redundancy should any one of them fail. Christopher argues that this rarity is primarily down to the enormous economic and cultural incentives to deny the moral repugnancy of slavery, and crush opposition to it with violence wherever necessary. Mere awareness is insufficient to guarantee a movement will arise to fix a problem. Humanity continues to allow many severe injustices to persist, despite being aware of them. So why is it so hard to imagine we might have done the same with forced labour? In this episode, Christopher describes the unique and peculiar set of political, social and religious circumstances that gave rise to the only successful and lasting anti-slavery movement in human history. These circumstances were sufficiently improbable that Christopher believes there are very nearby worlds where abolitionism might never have taken off. We also discuss:Various instantiations of slavery throughout human historySigns of antislavery sentiment before the 17th centuryThe role of the Quakers in early British abolitionist movementThe importance of individual “heroes” in the abolitionist movementArguments against the idea that the abolition of slavery was contingentWhether there have ever been any major moral shifts that were inevitableGet 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. Producer: Keiran HarrisAudio mastering: Milo McGuireTranscriptions: Katy Moore

What’s the opposite of cancer?If you answered “cure,” “antidote,” or “antivenom” — you’ve obviously been reading the antonym section at www.merriam-webster.com/thesaurus/cancer.But today’s guest Athena Aktipis says that the opposite of cancer is us: it's having a functional multicellular body that’s cooperating effectively in order to make that multicellular body function.If, like us, you found her answer far more satisfying than the dictionary, maybe you could consider closing your dozens of merriam-webster.com tabs, and start listening to this podcast instead.Links to learn more, summary and full transcript. As Athena explains in her book The Cheating Cell, what we see with cancer is a breakdown in each of the foundations of cooperation that allowed multicellularity to arise: Cells will proliferate when they shouldn't. Cells won't die when they should. Cells won't engage in the kind of division of labour that they should. Cells won’t do the jobs that they're supposed to do. Cells will monopolise resources. And cells will trash the environment.When we think about animals in the wild, or even bacteria living inside our cells, we understand that they're facing evolutionary pressures to figure out how they can replicate more; how they can get more resources; and how they can avoid predators — like lions, or antibiotics.We don’t normally think of individual cells as acting as if they have their own interests like this. But cancer cells are actually facing similar kinds of evolutionary pressures within our bodies, with one major difference: they replicate much, much faster.Incredibly, the opportunity for evolution by natural selection to operate just over the course of cancer progression is easily faster than all of the evolutionary time that we have had as humans since *Homo sapiens* came about.Here’s a quote from Athena:“So you have to shift your thinking to be like: the body is a world with all these different ecosystems in it, and the cells are existing on a time scale where, if we're going to map it onto anything like what we experience, a day is at least 10 years for them, right? So it's a very, very different way of thinking.”You can find compelling examples of cooperation and conflict all over the universe, so Rob and Athena don’t stop with cancer. They also discuss:Cheating within cells themselvesCooperation in human societies as they exist today — and perhaps in the future, between civilisations spread across different planets or starsWhether it’s too out-there to think of humans as engaging in cancerous behaviourWhy elephants get deadly cancers less often than humans, despite having way more cellsWhen a cell should commit suicideThe strategy of deliberately not treating cancer aggressivelySuperhuman cooperationAnd at the end of the episode, they cover Athena’s new book Everything is Fine! How to Thrive in the Apocalypse, including:Staying happy while thinking about the apocalypsePractical steps to prepare for the apocalypseAnd whether a zombie apocalypse is already happening among Tasmanian devilsAnd if you’d rather see Rob and Athena’s facial expressions as they laugh and laugh while discussing cancer and the apocalypse — you can watch the video of the full interview. Get 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. Producer: Keiran HarrisAudio mastering: Milo McGuireTranscriptions: Katy Moore

Rebroadcast: this episode was originally released in June 2020. Today’s guest, New York Times bestselling author A.J. Jacobs, always hated Judge Judy. But after he found out that she was his seventh cousin, he thought, "You know what, she's not so bad". Hijacking this bias towards family and trying to broaden it to everyone led to his three-year adventure to help build the biggest family tree in history. He’s also spent months saying whatever was on his mind, tried to become the healthiest person in the world, read 33,000 pages of facts, spent a year following the Bible literally, thanked everyone involved in making his morning cup of coffee, and tried to figure out how to do the most good. His latest book asks: if we reframe global problems as puzzles, would the world be a better place? Links to learn more, summary and full transcript. This is the first time I’ve hosted the podcast, and I’m hoping to convince people to listen with this attempt at clever show notes that change style each paragraph to reference different A.J. experiments. I don’t actually think it’s that clever, but all of my other ideas seemed worse. I really have no idea how people will react to this episode; I loved it, but I definitely think I’m more entertaining than almost anyone else will. (Radical Honesty.) We do talk about some useful stuff — one of which is the concept of micro goals. When you wake up in the morning, just commit to putting on your workout clothes. Once they’re on, maybe you’ll think that you might as well get on the treadmill — just for a minute. And once you’re on for 1 minute, you’ll often stay on for 20. So I’m not asking you to commit to listening to the whole episode — just to put on your headphones. (Drop Dead Healthy.) Another reason to listen is for the facts: • The Bayer aspirin company invented heroin as a cough suppressant • Coriander is just the British way of saying cilantro • Dogs have a third eyelid to protect the eyeball from irritants • and A.J. read all 44 million words of the Encyclopedia Britannica from A to Z, which drove home the idea that we know so little about the world (although he does now know that opossums have 13 nipples). (The Know-It-All.) One extra argument for listening: If you interpret the second commandment literally, then it tells you not to make a likeness of anything in heaven, on earth, or underwater — which rules out basically all images. That means no photos, no TV, no movies. So, if you want to respect the bible, you should definitely consider making podcasts your main source of entertainment (as long as you’re not listening on the Sabbath). (The Year of Living Biblically.) I’m so thankful to A.J. for doing this. But I also want to thank Julie, Jasper, Zane and Lucas who allowed me to spend the day in their home; the construction worker who told me how to get to my subway platform on the morning of the interview; and Queen Jadwiga for making bagels popular in the 1300s, which kept me going during the recording. (Thanks a Thousand.) We also discuss: • Blackmailing yourself • The most extreme ideas A.J.’s ever considered • Utilitarian movie reviews • Doing good as a writer • And much more. Get this episode by subscribing to our podcast on the world’s most pressing problems: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

Rebroadcast: this episode was originally released in July 2020. 80,000 Hours, along with many other members of the effective altruism movement, has argued that helping to positively shape the development of artificial intelligence may be one of the best ways to have a lasting, positive impact on the long-term future. Millions of dollars in philanthropic spending, as well as lots of career changes, have been motivated by these arguments. Today’s guest, Ben Garfinkel, Research Fellow at Oxford’s Future of Humanity Institute, supports the continued expansion of AI safety as a field and believes working on AI is among the very best ways to have a positive impact on the long-term future. But he also believes the classic AI risk arguments have been subject to insufficient scrutiny given this level of investment. In particular, the case for working on AI if you care about the long-term future has often been made on the basis of concern about AI accidents; it’s actually quite difficult to design systems that you can feel confident will behave the way you want them to in all circumstances. Nick Bostrom wrote the most fleshed out version of the argument in his book, Superintelligence. But Ben reminds us that, apart from Bostrom’s book and essays by Eliezer Yudkowsky, there's very little existing writing on existential accidents. Links to learn more, summary and full transcript. There have also been very few skeptical experts that have actually sat down and fully engaged with it, writing down point by point where they disagree or where they think the mistakes are. This means that Ben has probably scrutinised classic AI risk arguments as carefully as almost anyone else in the world. He thinks that most of the arguments for existential accidents often rely on fuzzy, abstract concepts like optimisation power or general intelligence or goals, and toy thought experiments. And he doesn’t think it’s clear we should take these as a strong source of evidence. Ben’s also concerned that these scenarios often involve massive jumps in the capabilities of a single system, but it's really not clear that we should expect such jumps or find them plausible. These toy examples also focus on the idea that because human preferences are so nuanced and so hard to state precisely, it should be quite difficult to get a machine that can understand how to obey them. But Ben points out that it's also the case in machine learning that we can train lots of systems to engage in behaviours that are actually quite nuanced and that we can't specify precisely. If AI systems can recognise faces from images, and fly helicopters, why don’t we think they’ll be able to understand human preferences? Despite these concerns, Ben is still fairly optimistic about the value of working on AI safety or governance. He doesn’t think that there are any slam-dunks for improving the future, and so the fact that there are at least plausible pathways for impact by working on AI safety and AI governance, in addition to it still being a very neglected area, puts it head and shoulders above most areas you might choose to work in. This is the second episode hosted by Howie Lempel, and he and Ben cover, among many other things: • The threat of AI systems increasing the risk of permanently damaging conflict or collapse • The possibility of permanently locking in a positive or negative future • Contenders for types of advanced systems • What role AI should play in the effective altruism portfolio Get this episode by subscribing: type 80,000 Hours into your podcasting app. Or read the linked transcript. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

Rebroadcast: this episode was originally released in July 2020. Today’s guest, Jennifer Doleac — Associate Professor of Economics at Texas A&M University, and Director of the Justice Tech Lab — is an expert on empirical research into policing, law and incarceration. In this extensive interview, she highlights three ways to effectively prevent crime that don't require police or prisons and the human toll they bring with them: better street lighting, cognitive behavioral therapy, and lead reduction. One of Jennifer’s papers used switches into and out of daylight saving time as a 'natural experiment' to measure the effect of light levels on crime. One day the sun sets at 5pm; the next day it sets at 6pm. When that evening hour is dark instead of light, robberies during it roughly double. Links to sources for the claims in these show notes, other resources to learn more, the full blog post, and a full transcript. The idea here is that if you try to rob someone in broad daylight, they might see you coming, and witnesses might later be able to identify you. You're just more likely to get caught. You might think: "Well, people will just commit crime in the morning instead". But it looks like criminals aren’t early risers, and that doesn’t happen. On her unusually rigorous podcast Probable Causation, Jennifer spoke to one of the authors of a related study, in which very bright streetlights were randomly added to some public housing complexes but not others. They found the lights reduced outdoor night-time crime by 36%, at little cost. The next best thing to sun-light is human-light, so just installing more streetlights might be one of the easiest ways to cut crime, without having to hassle or punish anyone. The second approach is cognitive behavioral therapy (CBT), in which you're taught to slow down your decision-making, and think through your assumptions before acting. There was a randomised controlled trial done in schools, as well as juvenile detention facilities in Chicago, where the kids assigned to get CBT were followed over time and compared with those who were not assigned to receive CBT. They found the CBT course reduced rearrest rates by a third, and lowered the likelihood of a child returning to a juvenile detention facility by 20%. Jennifer says that the program isn’t that expensive, and the benefits are massive. Everyone would probably benefit from being able to talk through their problems but the gains are especially large for people who've grown up with the trauma of violence in their lives. Finally, Jennifer thinks that reducing lead levels might be the best buy of all in crime prevention. There is really compelling evidence that lead not only increases crime, but also dramatically reduces educational outcomes. In today’s conversation, Rob and Jennifer also cover, among many other things: • Misconduct, hiring practices and accountability among US police • Procedural justice training • Overrated policy ideas • Policies to try to reduce racial discrimination • The effects of DNA databases • Diversity in economics • The quality of social science research Get this episode by subscribing: type 80,000 Hours into your podcasting app. Producer: Keiran Harris. Audio mastering: Ben Cordell. Transcript for this episode: Zakee Ulhaq.

America aims to avoid nuclear war by relying on the principle of 'mutually assured destruction,' right? Wrong. Or at least... not officially.As today's guest — Jeffrey Lewis, founder of Arms Control Wonk and professor at the Middlebury Institute of International Studies — explains, in its official 'OPLANs' (military operation plans), the US is committed to 'dominating' in a nuclear war with Russia. How would they do that? "That is redacted." Links to learn more, summary and full transcript. We invited Jeffrey to come on the show to lay out what we and our listeners are most likely to be misunderstanding about nuclear weapons, the nuclear posture of major powers, and his field as a whole, and he did not disappoint. As Jeffrey tells it, 'mutually assured destruction' was a slur used to criticise those who wanted to limit the 1960s arms buildup, and was never accepted as a matter of policy in any US administration. But isn't it still the de facto reality? Yes and no. Jeffrey is a specialist on the nuts and bolts of bureaucratic and military decision-making in real-life situations. He suspects that at the start of their term presidents get a briefing about the US' plan to prevail in a nuclear war and conclude that "it's freaking madness." They say to themselves that whatever these silly plans may say, they know a nuclear war cannot be won, so they just won't use the weapons. But Jeffrey thinks that's a big mistake. Yes, in a calm moment presidents can resist pressure from advisors and generals. But that idea of ‘winning’ a nuclear war is in all the plans. Staff have been hired because they believe in those plans. It's what the generals and admirals have all prepared for. What matters is the 'not calm moment': the 3AM phone call to tell the president that ICBMs might hit the US in eight minutes — the same week Russia invades a neighbour or China invades Taiwan. Is it a false alarm? Should they retaliate before their land-based missile silos are hit? There's only minutes to decide. Jeffrey points out that in emergencies, presidents have repeatedly found themselves railroaded into actions they didn't want to take because of how information and options were processed and presented to them. In the heat of the moment, it's natural to reach for the plan you've prepared — however mad it might sound. In this spicy conversation, Jeffrey fields the most burning questions from Rob and the audience, in the process explaining: • Why inter-service rivalry is one of the biggest constraints on US nuclear policy • Two times the US sabotaged nuclear nonproliferation among great powers • How his field uses jargon to exclude outsiders • How the US could prevent the revival of mass nuclear testing by the great powers • Why nuclear deterrence relies on the possibility that something might go wrong • Whether 'salami tactics' render nuclear weapons ineffective • The time the Navy and Air Force switched views on how to wage a nuclear war, just when it would allow *them* to have the most missiles • The problems that arise when you won't talk to people you think are evil • Why missile defences are politically popular despite being strategically foolish • How open source intelligence can prevent arms races • And much more.Chapters:Rob’s intro (00:00:00)The interview begins (00:02:49)Misconceptions in the effective altruism community (00:05:42)Nuclear deterrence (00:17:36)Dishonest rituals (00:28:17)Downsides of generalist research (00:32:13)“Mutual assured destruction” (00:38:18)Budgetary considerations for competing parts of the US military (00:51:53)Where the effective altruism community can potentially add the most value (01:02:15)Gatekeeping (01:12:04)Strengths of the nuclear security community (01:16:14)Disarmament (01:26:58)Nuclear winter (01:38:53)Attacks against US allies (01:41:46)Most likely weapons to get used (01:45:11)The role of moral arguments (01:46:40)Salami tactics (01:52:01)Jeffrey's disagreements with Thomas Schelling (01:57:00)Why did it take so long to get nuclear arms agreements? (02:01:11)Detecting secret nuclear facilities (02:03:18)Where Jeffrey would give $10M in grants (02:05:46)The importance of archival research (02:11:03)Jeffrey's policy ideas (02:20:03)What should the US do regarding China? (02:27:10)What should the US do regarding Russia? (02:31:42)What should the US do regarding Taiwan? (02:35:27)Advice for people interested in working on nuclear security (02:37:23)Rob’s outro (02:39:13)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

John McWhorter is a linguistics professor at Columbia University specialising in research on creole languages.He's also a content-producing machine, never afraid to give his frank opinion on anything and everything. On top of his academic work he's also written 22 books, produced five online university courses, hosts one and a half podcasts, and now writes a regular New York Times op-ed column.Links to learn more, summary, and full transcript Video version of the interview Lecture: Why the world looks the same in any languageOur show is mostly about the world's most pressing problems and what you can do to solve them. But what's the point of hosting a podcast if you can't occasionally just talk about something fascinating with someone whose work you appreciate?So today, just before the holidays, we're sharing this interview with John about language and linguistics — including what we think are some of the most important things everyone ought to know about those topics. We ask him:Can you communicate faster in some languages than others, or is there some constraint that prevents that?Does learning a second or third language make you smarter or not?Can a language decay and get worse at communicating what people want to say?If children aren't taught a language, how many generations does it take them to invent a fully fledged one of their own?Did Shakespeare write in a foreign language, and if so, should we translate his plays?How much does language really shape the way we think?Are creoles the best languages in the world — languages that ideally we would all speak?What would be the optimal number of languages globally?Does trying to save dying languages do their speakers a favour, or is it more of an imposition?Should we bother to teach foreign languages in UK and US schools?Is it possible to save the important cultural aspects embedded in a dying language without saving the language itself?Will AI models speak a language of their own in the future, one that humans can't understand but which better serves the tradeoffs AI models need to make?We then put some of these questions to ChatGPT itself, asking it to play the role of a linguistics professor at Columbia University.We’ve also added John’s talk “Why the World Looks the Same in Any Language” to the end of this episode. So stick around after the credits! And if you’d rather see Rob and John’s facial expressions or beautiful high cheekbones while listening to this conversation, you can watch the video of the full conversation here. Get 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. Producer: Keiran HarrisAudio mastering: Ben CordellVideo editing: Ryan KesslerTranscriptions: Katy Moore

Large language models like GPT-3, and now ChatGPT, are neural networks trained on a large fraction of all text available on the internet to do one thing: predict the next word in a passage. This simple technique has led to something extraordinary — black boxes able to write TV scripts, explain jokes, produce satirical poetry, answer common factual questions, argue sensibly for political positions, and more. Every month their capabilities grow. But do they really 'understand' what they're saying, or do they just give the illusion of understanding? Today's guest, Richard Ngo, thinks that in the most important sense they understand many things. Richard is a researcher at OpenAI — the company that created ChatGPT — who works to foresee where AI advances are going and develop strategies that will keep these models from 'acting out' as they become more powerful, are deployed and ultimately given power in society. Links to learn more, summary and full transcript. One way to think about 'understanding' is as a subjective experience. Whether it feels like something to be a large language model is an important question, but one we currently have no way to answer. However, as Richard explains, another way to think about 'understanding' is as a functional matter. If you really understand an idea you're able to use it to reason and draw inferences in new situations. And that kind of understanding is observable and testable. Richard argues that language models are developing sophisticated representations of the world which can be manipulated to draw sensible conclusions — maybe not so different from what happens in the human mind. And experiments have found that, as models get more parameters and are trained on more data, these types of capabilities consistently improve. We might feel reluctant to say a computer understands something the way that we do. But if it walks like a duck and it quacks like a duck, we should consider that maybe we have a duck, or at least something sufficiently close to a duck it doesn't matter. In today's conversation we discuss the above, as well as: • Could speeding up AI development be a bad thing? • The balance between excitement and fear when it comes to AI advances • What OpenAI focuses its efforts where it does • Common misconceptions about machine learning • How many computer chips it might require to be able to do most of the things humans do • How Richard understands the 'alignment problem' differently than other people • Why 'situational awareness' may be a key concept for understanding the behaviour of AI models • What work to positively shape the development of AI Richard is and isn't excited about • The AGI Safety Fundamentals course that Richard developed to help people learn more about this field Get 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. Producer: Keiran Harris Audio mastering: Milo McGuire and Ben Cordell Transcriptions: Katy Moore

Today’s release is a reading of our article called My experience with imposter syndrome — and how to (partly) overcome it, written and narrated by Luisa Rodriguez. 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’ll probably enjoy episode #100 of this show: Having a successful career with depression, anxiety, and imposter syndrome Get 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. Producer: Keiran Harris Audio mastering and editing for this episode: Milo McGuire

In this episode, usual host of the show Rob Wiblin gives his thoughts on the recent collapse of FTX. Click here for an official 80,000 Hours statement. And here are links to some potentially relevant 80,000 Hours pieces: • Episode #24 of this show – Stefan Schubert on why it’s a bad idea to break the rules, even if it’s for a good cause. • Is it ever OK to take a harmful job in order to do more good? An in-depth analysis • What are the 10 most harmful jobs? • Ways people trying to do good accidentally make things worse, and how to avoid them

Is war in long-term decline? Steven Pinker's The Better Angels of Our Nature brought this previously obscure academic question to the centre of public debate, and pointed to rates of death in war to argue energetically that war is on the way out. But that idea divides war scholars and statisticians, and so Better Angels has prompted a spirited debate, with datasets and statistical analyses exchanged back and forth year after year. The lack of consensus has left a somewhat bewildered public (including host Rob Wiblin) unsure quite what to believe. Today's guest, professor in political science Bear Braumoeller, is one of the scholars who believes we lack convincing evidence that warlikeness is in long-term decline. He collected the analysis that led him to that conclusion in his 2019 book, Only the Dead: The Persistence of War in the Modern Age. Links to learn more, summary and full transcript. The question is of great practical importance. The US and PRC are entering a period of renewed great power competition, with Taiwan as a potential trigger for war, and Russia is once more invading and attempting to annex the territory of its neighbours. If war has been going out of fashion since the start of the Enlightenment, we might console ourselves that however nerve-wracking these present circumstances may feel, modern culture will throw up powerful barriers to another world war. But if we're as war-prone as we ever have been, one need only inspect the record of the 20th century to recoil in horror at what might await us in the 21st. Bear argues that the second reaction is the appropriate one. The world has gone up in flames many times through history, with roughly 0.5% of the population dying in the Napoleonic Wars, 1% in World War I, 3% in World War II, and perhaps 10% during the Mongol conquests. And with no reason to think similar catastrophes are any less likely today, complacency could lead us to sleepwalk into disaster. He gets to this conclusion primarily by analysing the datasets of the decades-old Correlates of War project, which aspires to track all interstate conflicts and battlefield deaths since 1815. In Only the Dead, he chops up and inspects this data dozens of different ways, to test if there are any shifts over time which seem larger than what could be explained by chance variation alone. In a nutshell, Bear simply finds no general trend in either direction from 1815 through today. It seems like, as philosopher George Santayana lamented in 1922, "only the dead have seen the end of war". In today's conversation, Bear and Rob discuss all of the above in more detail than even a usual 80,000 Hours podcast episode, as well as: • Why haven't modern ideas about the immorality of violence led to the decline of war, when it's such a natural thing to expect? • What would Bear's critics say in response to all this? • What do the optimists get right? • How does one do proper statistical tests for events that are clumped together, like war deaths? • Why are deaths in war so concentrated in a handful of the most extreme events? • Did the ideas of the Enlightenment promote nonviolence, on balance? • Were early states more or less violent than groups of hunter-gatherers? • If Bear is right, what can be done? • How did the 'Concert of Europe' or 'Bismarckian system' maintain peace in the 19th century? • Which wars are remarkable but largely unknown? Chapters:Rob’s intro (00:00:00)The interview begins (00:03:32)Only the Dead (00:06:28)The Enlightenment (00:16:47)Democratic peace theory (00:26:22)Is religion a key driver of war? (00:29:27)International orders (00:33:07)The Concert of Europe (00:42:15)The Bismarckian system (00:53:43)The current international order (00:58:16)The Better Angels of Our Nature (01:17:30)War datasets (01:32:03)Seeing patterns in data where none exist (01:45:32)Change-point analysis (01:49:33)Rates of violent death throughout history (01:54:32)War initiation (02:02:55)Escalation (02:17:57)Getting massively different results from the same data (02:28:38)How worried we should be (02:34:07)Most likely ways Only the Dead is wrong (02:36:25)Astonishing smaller wars (02:40:39)Producer: Keiran HarrisAudio mastering: Ryan KesslerTranscriptions: Katy Moore

A casino offers you a game. A coin will be tossed. If it comes up heads on the first flip you win $2. If it comes up on the second flip you win $4. If it comes up on the third you win $8, the fourth you win $16, and so on. How much should you be willing to pay to play? The standard way of analysing gambling problems, ‘expected value’ — in which you multiply probabilities by the value of each outcome and then sum them up — says your expected earnings are infinite. You have a 50% chance of winning $2, for '0.5 * $2 = $1' in expected earnings. A 25% chance of winning $4, for '0.25 * $4 = $1' in expected earnings, and on and on. A never-ending series of $1s added together comes to infinity. And that's despite the fact that you know with certainty you can only ever win a finite amount! Today's guest — philosopher Alan Hájek of the Australian National University — thinks of much of philosophy as “the demolition of common sense followed by damage control” and is an expert on paradoxes related to probability and decision-making rules like “maximise expected value.” Links to learn more, summary and full transcript. The problem described above, known as the St. Petersburg paradox, has been a staple of the field since the 18th century, with many proposed solutions. In the interview, Alan explains how very natural attempts to resolve the paradox — such as factoring in the low likelihood that the casino can pay out very large sums, or the fact that money becomes less and less valuable the more of it you already have — fail to work as hoped. We might reject the setup as a hypothetical that could never exist in the real world, and therefore of mere intellectual curiosity. But Alan doesn't find that objection persuasive. If expected value fails in extreme cases, that should make us worry that something could be rotten at the heart of the standard procedure we use to make decisions in government, business, and nonprofits. These issues regularly show up in 80,000 Hours' efforts to try to find the best ways to improve the world, as the best approach will arguably involve long-shot attempts to do very large amounts of good. Consider which is better: saving one life for sure, or three lives with 50% probability? Expected value says the second, which will probably strike you as reasonable enough. But what if we repeat this process and evaluate the chance to save nine lives with 25% probability, or 27 lives with 12.5% probability, or after 17 more iterations, 3,486,784,401 lives with a 0.00000009% chance. Expected value says this final offer is better than the others — 1,000 times better, in fact. Ultimately Alan leans towards the view that our best choice is to “bite the bullet” and stick with expected value, even with its sometimes counterintuitive implications. Where we want to do damage control, we're better off looking for ways our probability estimates might be wrong. In today's conversation, Alan and Rob explore these issues and many others: • Simple rules of thumb for having philosophical insights • A key flaw that hid in Pascal's wager from the very beginning • Whether we have to simply ignore infinities because they mess everything up • What fundamentally is 'probability'? • Some of the many reasons 'frequentism' doesn't work as an account of probability • Why the standard account of counterfactuals in philosophy is deeply flawed • And why counterfactuals present a fatal problem for one sort of consequentialism Chapters:Rob’s intro (00:00:00)The interview begins (00:01:48)Philosophical methodology (00:02:54)Theories of probability (00:37:17)Everyday Bayesianism (00:46:01)Frequentism (01:04:56)Ranges of probabilities (01:16:23)Implications for how to live (01:21:24)Expected value (01:26:58)The St. Petersburg paradox (01:31:40)Pascal's wager (01:49:44)Using expected value in everyday life (02:03:53)Counterfactuals (02:16:38)Most counterfactuals are false (02:52:25)Relevance to objective consequentialism (03:09:47)Marker 18 (03:10:21)Alan’s best conference story (03:33:37)Producer: Keiran HarrisAudio mastering: Ben Cordell and Ryan KesslerTranscriptions: Katy Moore

Today’s release is a professional reading of our new problem profile on preventing an AI-related catastrophe, written by Benjamin Hilton. We expect that there will be substantial progress in AI in the next few decades, potentially even to the point where machines come to outperform humans in many, if not all, tasks. This could have enormous benefits, helping to solve currently intractable global problems, but could also pose severe risks. These risks could arise accidentally (for example, if we don’t find technical solutions to concerns about the safety of AI systems), or deliberately (for example, if AI systems worsen geopolitical conflict). We think more work needs to be done to reduce these risks. Some of these risks from advanced AI could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity. There have not yet been any satisfying answers to concerns about how this rapidly approaching, transformative technology can be safely developed and integrated into our society. Finding answers to these concerns is very neglected, and may well be tractable. We estimate that there are around 300 people worldwide working directly on this. As a result, the possibility of AI-related catastrophe may be the world’s most pressing problem — and the best thing to work on for those who are well-placed to contribute. Promising options for working on this problem include technical research on how to create safe AI systems, strategy research into the particular risks AI might pose, and policy research into ways in which companies and governments could mitigate these risks. If worthwhile policies are developed, we’ll need people to put them in place and implement them. There are also many opportunities to have a big impact in a variety of complementary roles, such as operations management, journalism, earning to give, and more. If you want to check out the links, footnotes and figures in today’s article, you can find those here. Get 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. Producer: Keiran Harris Editing and narration: Perrin Walker and Shaun Acker Audio proofing: Katy Moore

What in the world is intrinsically good — good in itself even if it has no other effects? Over the millennia, people have offered many answers: joy, justice, equality, accomplishment, loving god, wisdom, and plenty more. The question is a classic that makes for great dorm-room philosophy discussion. But it's hardly just of academic interest. The issue of what (if anything) is intrinsically valuable bears on every action we take, whether we’re looking to improve our own lives, or to help others. The wrong answer might lead us to the wrong project and render our efforts to improve the world entirely ineffective. Today's guest, Sharon Hewitt Rawlette — philosopher and author of The Feeling of Value: Moral Realism Grounded in Phenomenal Consciousness — wants to resuscitate an answer to this question that is as old as philosophy itself. Links to learn more, summary, full transcript, and full version of this blog post. That idea, in a nutshell, is that there is only one thing of true intrinsic value: positive feelings and sensations. And similarly, there is only one thing that is intrinsically of negative value: suffering, pain, and other unpleasant sensations. Lots of other things are valuable too: friendship, fairness, loyalty, integrity, wealth, patience, houses, and so on. But they are only instrumentally valuable — that is to say, they’re valuable as means to the end of ensuring that all conscious beings experience more pleasure and other positive sensations, and less suffering. As Sharon notes, from Athens in 400 BC to Britain in 1850, the idea that only subjective experiences can be good or bad in themselves -- a position known as 'philosophical hedonism' -- has been one of the most enduringly popular ideas in ethics. And few will be taken aback by the notion that, all else equal, more pleasure is good and less suffering is bad. But can they really be the only intrinsically valuable things? Over the 20th century, philosophical hedonism became increasingly controversial in the face of some seemingly very counterintuitive implications. For this reason the famous philosopher of mind Thomas Nagel called The Feeling of Value "a radical and important philosophical contribution." In today's interview, Sharon explains the case for a theory of value grounded in subjective experiences, and why she believes the most popular counterarguments are misguided. Host Rob Wiblin and Sharon also cover: • The essential need to disentangle intrinsic, instrumental, and other sorts of value • Why Sharon’s arguments lead to hedonistic utilitarianism rather than hedonistic egoism (in which we only care about our own feelings) • How do people react to the 'experience machine' thought experiment when surveyed? • Why hedonism recommends often thinking and acting as though it were false • Whether it's crazy to think that relationships are only useful because of their effects on our subjective experiences • Whether it will ever be possible to eliminate pain, and whether doing so would be desirable • If we didn't have positive or negative experiences, whether that would cause us to simply never talk about goodness and badness • Whether the plausibility of hedonism is affected by our theory of mind • And plenty more Chapters:Rob’s intro (00:00:00)The interview begins (00:02:45)Metaethics (00:04:16)Anti-realism (00:10:39)Sharon's theory of moral realism (00:16:17)The history of hedonism (00:23:11)Intrinsic value vs instrumental value (00:28:49)Egoistic hedonism (00:36:30)Single axis of value (00:42:19)Key objections to Sharon’s brand of hedonism (00:56:18)The experience machine (01:06:08)Robot spouses (01:22:29)Most common misunderstanding of Sharon’s view (01:27:10)How might a hedonist actually live (01:37:46)The organ transplant case (01:53:34)Counterintuitive implications of hedonistic utilitarianism (02:03:40)How could we discover moral facts? (02:18:05)Producer: Keiran HarrisAudio mastering: Ryan KesslerTranscriptions: Katy Moore

Effective altruism, in a slogan, aims to 'do the most good.' Utilitarianism, in a slogan, says we should act to 'produce the greatest good for the greatest number.' It's clear enough why utilitarians should be interested in the project of effective altruism. But what about the many people who reject utilitarianism? Today's guest, Andreas Mogensen — senior research fellow at Oxford University's Global Priorities Institute — rejects utilitarianism, but as he explains, this does little to dampen his enthusiasm for the project of effective altruism. Links to learn more, summary and full transcript. Andreas leans towards 'deontological' or rule-based theories of ethics, rather than 'consequentialist' theories like utilitarianism which look exclusively at the effects of a person's actions. Like most people involved in effective altruism, he parts ways with utilitarianism in rejecting its maximal level of demandingness, the idea that the ends justify the means, and the notion that the only moral reason for action is to benefit everyone in the world considered impartially. However, Andreas believes any plausible theory of morality must give some weight to the harms and benefits we provide to other people. If we can improve a stranger's wellbeing enormously at negligible cost to ourselves and without violating any other moral prohibition, that must be at minimum a praiseworthy thing to do. In a world as full of preventable suffering as our own, this simple 'principle of beneficence' is probably the only premise one needs to grant for the effective altruist project of identifying the most impactful ways to help others to be of great moral interest and importance. As an illustrative example Andreas refers to the Giving What We Can pledge to donate 10% of one's income to the most impactful charities available, a pledge he took in 2009. Many effective altruism enthusiasts have taken such a pledge, while others spend their careers trying to figure out the most cost-effective places pledgers can give, where they'll get the biggest 'bang for buck'. For someone living in a world as unequal as our own, this pledge at a very minimum gives an upper-middle class person in a rich country the chance to transfer money to someone living on about 1% as much as they do. The benefit an extremely poor recipient receives from the money is likely far more than the donor could get spending it on themselves. What arguments could a non-utilitarian moral theory mount against such giving? Many approaches to morality will say it's permissible not to give away 10% of your income to help others as effectively as is possible. But if they will almost all regard it as praiseworthy to benefit others without giving up something else of equivalent moral value, then Andreas argues they should be enthusiastic about effective altruism as an intellectual and practical project nonetheless. In this conversation, Andreas and Rob discuss how robust the above line of argument is, and also cover: • Should we treat thought experiments that feature very large numbers with great suspicion? • If we had to allow someone to die to avoid preventing the World Cup final from being broadcast to the world, is that permissible? • What might a virtue ethicist regard as 'doing the most good'? • If a deontological theory of morality parted ways with common effective altruist practices, how would that likely be? • If we can explain how we came to hold a view on a moral issue by referring to evolutionary selective pressures, should we disbelieve that view? Chapters:Rob’s intro (00:00:00)The interview begins (00:01:36)Deontology and effective altruism (00:04:59)Giving What We Can (00:28:56)Longtermism without consequentialism (00:38:01)Further differences between deontologists and consequentialists (00:44:13)Virtue ethics and effective altruism (01:08:15)Is Andreas really a deontologist? (01:13:26)Large number scepticism (01:21:11)Evolutionary debunking arguments (01:58:48)How Andreas’s views have changed (02:12:18)Derek Parfit’s influence on Andreas (02:17:27)Producer: Keiran HarrisAudio mastering: Ben Cordell and Beppe RådvikTranscriptions: Katy Moore

People who exist in the future deserve some degree of moral consideration.The future could be very big, very long, and/or very good.We can reasonably hope to influence whether people in the future exist, and how good or bad their lives are.So trying to make the world better for future generations is a key priority of our time.This is the simple four-step argument for 'longtermism' put forward in What We Owe The Future, the latest book from today's guest — University of Oxford philosopher and cofounder of the effective altruism community, Will MacAskill. Links to learn more, summary and full transcript. From one point of view this idea is common sense. We work on breakthroughs to treat cancer or end use of fossil fuels not just for people alive today, but because we hope such scientific advances will help our children, grandchildren, and great-grandchildren as well. Some who take this longtermist idea seriously work to develop broad-spectrum vaccines they hope will safeguard humanity against the sorts of extremely deadly pandemics that could permanently throw civilisation off track — the sort of project few could argue is not worthwhile. But Will is upfront that longtermism is also counterintuitive. To start with, he's willing to contemplate timescales far beyond what's typically discussed. A natural objection to thinking millions of years ahead is that it's hard enough to take actions that have positive effects that persist for hundreds of years, let alone “indefinitely.” It doesn't matter how important something might be if you can't predictably change it. This is one reason, among others, that Will was initially sceptical of longtermism and took years to come around. He preferred to focus on ending poverty and preventable diseases in ways he could directly see were working. But over seven years he gradually changed his mind, and in *What We Owe The Future*, Will argues that in fact there are clear ways we might act now that could benefit not just a few but *all* future generations. The idea that preventing human extinction would have long-lasting impacts is pretty intuitive. If we entirely disappear, we aren't coming back. But the idea that we can shape human values — not just for our age, but for all ages — is a surprising one that Will has come to more recently. In the book, he argues that what people value is far more fragile and historically contingent than it might first seem. For instance, today it feels like the abolition of slavery was an inevitable part of the arc of history. But Will lays out that the best research on the topic suggests otherwise. If moral progress really is so contingent, and bad ideas can persist almost without end, it raises the stakes for moral debate today. If we don't eliminate a bad practice now, it may be with us forever. In today's in-depth conversation, we discuss the possibility of a harmful moral 'lock-in' as well as: • How Will was eventually won over to longtermism • The three best lines of argument against longtermism • How to avoid moral fanaticism • Which technologies or events are most likely to have permanent effects • What 'longtermists' do today in practice • How to predict the long-term effect of our actions • Whether the future is likely to be good or bad • Concrete ideas to make the future better • What Will donates his money to personally • Potatoes and megafauna • And plenty moreChapters:Rob’s intro (00:00:00)The interview begins (00:01:36)What longtermism actually is (00:02:31)The case for longtermism (00:04:30)What longtermists are actually doing (00:15:54)Will’s personal journey (00:22:15)Strongest arguments against longtermism (00:42:28)Preventing extinction vs. improving the quality of the future (00:59:29)Is humanity likely to converge on doing the same thing regardless? (01:06:58)Lock-in scenario vs. long reflection (01:27:11)Is the future good in expectation? (01:32:29)Can we actually predictably influence the future positively? (01:47:27)Tiny probabilities of enormous value (01:53:40)Stagnation (02:19:04)Concrete suggestions (02:34:27)Where Will donates (02:39:40)Potatoes and megafauna (02:41:48)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

After a frenetic level of commentary during February and March, the war in Ukraine has faded into the background of our news coverage. But with the benefit of time we're in a much stronger position to understand what happened, why, whether there are broader lessons to take away, and how the conflict might be ended. And the conflict appears far from over. So today, we are returning to speak a second time with Samuel Charap — one of the US’s foremost experts on Russia’s relationship with former Soviet states, and coauthor of the 2017 book Everyone Loses: The Ukraine Crisis and the Ruinous Contest for Post-Soviet Eurasia. Links to learn more, summary and full transcript. As Sam lays out, Russia controls much of Ukraine's east and south, and seems to be preparing to politically incorporate that territory into Russia itself later in the year. At the same time, Ukraine is gearing up for a counteroffensive before defensive positions become dug in over winter. Each day the war continues it takes a toll on ordinary Ukrainians, contributes to a global food shortage, and leaves the US and Russia unable to coordinate on any other issues and at an elevated risk of direct conflict. In today's brisk conversation, Rob and Sam cover the following topics: • Current territorial control and the level of attrition within Russia’s and Ukraine's military forces. • Russia's current goals. • Whether Sam's views have changed since March on topics like: Putin's motivations, the wisdom of Ukraine's strategy, the likely impact of Western sanctions, and the risks from Finland and Sweden joining NATO before the war ends. • Why so many people incorrectly expected Russia to fully mobilise for war or persist with their original approach to the invasion. • Whether there's anything to learn from many of our worst fears -- such as the use of bioweapons on civilians -- not coming to pass. • What can be done to ensure some nuclear arms control agreement between the US and Russia remains in place after 2026 (when New START expires). • Why Sam considers a settlement proposal put forward by Ukraine in late March to be the most plausible way to end the war and ensure stability — though it's still a long shot. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:31)The state of play in Ukraine (00:03:05)How things have changed since March (00:12:59)Has Russia learned from its mistakes? (00:23:40)Broader lessons (00:28:44)A possible way out (00:37:15) Producer: Keiran Harris Audio mastering: Ben Cordell and Ryan Kessler Transcriptions: Katy Moore

Wind back 1,000 years and the moral landscape looks very different to today. Most farming societies thought slavery was natural and unobjectionable, premarital sex was an abomination, women should obey their husbands, and commoners should obey their monarchs.Wind back 10,000 years and things look very different again. Most hunter-gatherer groups thought men who got too big for their britches needed to be put in their place rather than obeyed, and lifelong monogamy could hardly be expected of men or women.Why such big systematic changes — and why these changes specifically?That's the question best-selling historian Ian Morris takes up in his book, Foragers, Farmers, and Fossil Fuels: How Human Values Evolve. Ian has spent his academic life studying long-term history, trying to explain the big-picture changes that play out over hundreds or thousands of years. Links to learn more, summary and full transcript. There are a number of possible explanations one could offer for the wide-ranging shifts in opinion on the 'right' way to live. Maybe the natural sciences progressed and people realised their previous ideas were mistaken? Perhaps a few persuasive advocates turned the course of history with their revolutionary arguments? Maybe everyone just got nicer? In Foragers, Farmers and Fossil Fuels Ian presents a provocative alternative: human culture gradually evolves towards whatever system of organisation allows a society to harvest the most energy, and we then conclude that system is the most virtuous one. Egalitarian values helped hunter-gatherers hunt and gather effectively. Once farming was developed, hierarchy proved to be the social structure that produced the most grain (and best repelled nomadic raiders). And in the modern era, democracy and individuality have proven to be more productive ways to collect and exploit fossil fuels. On this theory, it's technology that drives moral values much more than moral philosophy. Individuals can try to persist with deeply held values that limit economic growth, but they risk being rendered irrelevant as more productive peers in their own society accrue wealth and power. And societies that fail to move with the times risk being conquered by more pragmatic neighbours that adapt to new technologies and grow in population and military strength. There are many objections one could raise to this theory, many of which we put to Ian in this interview. But the question is a highly consequential one: if we want to guess what goals our descendants will pursue hundreds of years from now, it would be helpful to have a theory for why our ancestors mostly thought one thing, while we mostly think another. Big though it is, the driver of human values is only one of several major questions Ian has tackled through his career. In today's episode, we discuss all of Ian's major books, taking on topics such as: • Why the Industrial Revolution happened in England rather than China • Whether or not wars can lead to less violence • Whether the evidence base in history — from document archives to archaeology — is strong enough to persuasively answer any of these questions • Why Ian thinks the way we live in the 21st century is probably a short-lived aberration • Whether the grand sweep of history is driven more by “very important people” or “vast impersonal forces” • Why Chinese ships never crossed the Pacific or rounded the southern tip of Africa • In what sense Ian thinks Brexit was “10,000 years in the making” • The most common misconceptions about macrohistoryChapters:Rob’s intro (00:00:00)The interview begins (00:01:51)Geography is Destiny (00:02:59)Why the West Rules—For Now (00:11:25)War! What is it Good For? (00:27:40)Expectations for the future (00:39:43)Foragers, Farmers, and Fossil Fuels (00:53:15)Historical methodology (01:02:35)Falsifiable alternative theories (01:15:20)Archaeology (01:22:18)Energy extraction technology as a key driver of human values (01:37:04)Allowing people to debate about values (01:59:38)Can productive wars still occur? (02:12:49)Where is history contingent and where isn't it? (02:29:45)How Ian thinks about the future (03:12:54)Macrohistory myths (03:29:12)Ian’s favourite archaeology memory (03:32:40)The most unfair criticism Ian’s ever received (03:34:39)Rob’s outro (03:39:16)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

On January 1, 2015, physicist Max Tegmark gave up something most of us love to do: complain about things without ever trying to fix them. That “put up or shut up” New Year’s resolution led to the first Puerto Rico conference and Open Letter on Artificial Intelligence — milestones for researchers taking the safe development of highly-capable AI systems seriously. Links to learn more, summary and full transcript. Max's primary work has been cosmology research at MIT, but his energetic and freewheeling nature has led him into so many other projects that you would be forgiven for forgetting it. In the 2010s he wrote two best-selling books, Our Mathematical Universe: My Quest for the Ultimate Nature of Reality, and Life 3.0: Being Human in the Age of Artificial Intelligence, and in 2014 founded a non-profit, the Future of Life Institute, which works to reduce all sorts of threats to humanity's future including nuclear war, synthetic biology, and AI. Max has complained about many other things over the years, from killer robots to the impact of social media algorithms on the news we consume. True to his 'put up or shut up' resolution, he and his team went on to produce a video on so-called ‘Slaughterbots’ which attracted millions of views, and develop a website called 'Improve The News' to help readers separate facts from spin. But given the stunning recent advances in capabilities — from OpenAI’s DALL-E to DeepMind’s Gato — AI itself remains top of his mind. You can now give an AI system like GPT-3 the text: "I'm going to go to this mountain with the faces on it. What is the capital of the state to the east of the state that that's in?" And it gives the correct answer (Saint Paul, Minnesota) — something most AI researchers would have said was impossible without fundamental breakthroughs just seven years ago. So back at MIT, he now leads a research group dedicated to what he calls “intelligible intelligence.” At the moment, AI systems are basically giant black boxes that magically do wildly impressive things. But for us to trust these systems, we need to understand them. He says that training a black box that does something smart needs to just be stage one in a bigger process. Stage two is: “How do we get the knowledge out and put it in a safer system?” Today’s conversation starts off giving a broad overview of the key questions about artificial intelligence: What's the potential? What are the threats? How might this story play out? What should we be doing to prepare? Rob and Max then move on to recent advances in capabilities and alignment, the mood we should have, and possible ways we might misunderstand the problem. They then spend roughly the last third talking about Max's current big passion: improving the news we consume — where Rob has a few reservations. They also cover: • Whether we could understand what superintelligent systems were doing • The value of encouraging people to think about the positive future they want • How to give machines goals • Whether ‘Big Tech’ is following the lead of ‘Big Tobacco’ • Whether we’re sleepwalking into disaster • Whether people actually just want their biases confirmed • Why Max is worried about government-backed fact-checking • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:01:19)How Max prioritises (00:12:33)Intro to AI risk (00:15:47)Superintelligence (00:35:56)Imagining a wide range of possible futures (00:47:45)Recent advances in capabilities and alignment (00:57:37)How to give machines goals (01:13:13)Regulatory capture (01:21:03)How humanity fails to fulfil its potential (01:39:45)Are we being hacked? (01:51:01)Improving the news (02:05:31)Do people actually just want their biases confirmed? (02:16:15)Government-backed fact-checking (02:37:00)Would a superintelligence seem like magic? (02:49:50)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

If a business has spent $100 million developing a product, it's a fair bet that they don't want it stolen in two seconds and uploaded to the web where anyone can use it for free. This problem exists in extreme form for AI companies. These days, the electricity and equipment required to train cutting-edge machine learning models that generate uncanny human text and images can cost tens or hundreds of millions of dollars. But once trained, such models may be only a few gigabytes in size and run just fine on ordinary laptops. Today's guest, the computer scientist and polymath Nova DasSarma, works on computer and information security for the AI company Anthropic. One of her jobs is to stop hackers exfiltrating Anthropic's incredibly expensive intellectual property, as recently happened to Nvidia. As she explains, given models’ small size, the need to store such models on internet-connected servers, and the poor state of computer security in general, this is a serious challenge. Links to learn more, summary and full transcript. The worries aren't purely commercial though. This problem looms especially large for the growing number of people who expect that in coming decades we'll develop so-called artificial 'general' intelligence systems that can learn and apply a wide range of skills all at once, and thereby have a transformative effect on society. If aligned with the goals of their owners, such general AI models could operate like a team of super-skilled assistants, going out and doing whatever wonderful (or malicious) things are asked of them. This might represent a huge leap forward for humanity, though the transition to a very different new economy and power structure would have to be handled delicately. If unaligned with the goals of their owners or humanity as a whole, such broadly capable models would naturally 'go rogue,' breaking their way into additional computer systems to grab more computing power — all the better to pursue their goals and make sure they can't be shut off. As Nova explains, in either case, we don't want such models disseminated all over the world before we've confirmed they are deeply safe and law-abiding, and have figured out how to integrate them peacefully into society. In the first scenario, premature mass deployment would be risky and destabilising. In the second scenario, it could be catastrophic -- perhaps even leading to human extinction if such general AI systems turn out to be able to self-improve rapidly rather than slowly. If highly capable general AI systems are coming in the next 10 or 20 years, Nova may be flying below the radar with one of the most important jobs in the world. We'll soon need the ability to 'sandbox' (i.e. contain) models with a wide range of superhuman capabilities, including the ability to learn new skills, for a period of careful testing and limited deployment — preventing the model from breaking out, and criminals from breaking in. Nova and her colleagues are trying to figure out how to do this, but as this episode reveals, even the state of the art is nowhere near good enough. In today's conversation, Rob and Nova cover: • How good or bad is information security today • The most secure computer systems that exist • How to design an AI training compute centre for maximum efficiency • Whether 'formal verification' can help us design trustworthy systems • How wide the gap is between AI capabilities and AI safety • How to disincentivise hackers • What should listeners do to strengthen their own security practices • And much more. Get 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. Producer: Keiran Harris Audio mastering: Ben Cordell and Beppe Rådvik Transcriptions: Katy Moore

“We’re leaving these 16 contestants on an island with nothing but what they can scavenge from an abandoned factory and apartment block. Over the next 365 days, they’ll try to rebuild as much of civilisation as they can — from glass, to lenses, to microscopes. This is: The Knowledge!”If you were a contestant on such a TV show, you'd love to have a guide to how basic things you currently take for granted are done — how to grow potatoes, fire bricks, turn wood to charcoal, find acids and alkalis, and so on.Today’s guest Lewis Dartnell has gone as far compiling this information as anyone has with his bestselling book The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm. Links to learn more, summary and full transcript. But in the aftermath of a nuclear war or incredibly deadly pandemic that kills most people, many of the ways we do things today will be impossible — and even some of the things people did in the past, like collect coal from the surface of the Earth, will be impossible the second time around. As Lewis points out, there’s “no point telling this band of survivors how to make something ultra-efficient or ultra-useful or ultra-capable if it's just too damned complicated to build in the first place. You have to start small and then level up, pull yourself up by your own bootstraps.” So it might sound good to tell people to build solar panels — they’re a wonderful way of generating electricity. But the photovoltaic cells we use today need pure silicon, and nanoscale manufacturing — essentially the same technology as microchips used in a computer — so actually making solar panels would be incredibly difficult. Instead, you’d want to tell our group of budding engineers to use more appropriate technologies like solar concentrators that use nothing more than mirrors — which turn out to be relatively easy to make. A disaster that unravels the complex way we produce goods in the modern world is all too possible. Which raises the question: why not set dozens of people to plan out exactly what any survivors really ought to do if they need to support themselves and rebuild civilisation? Such a guide could then be translated and distributed all around the world. The goal would be to provide the best information to speed up each of the many steps that would take survivors from rubbing sticks together in the wilderness to adjusting a thermostat in their comfy apartments. This is clearly not a trivial task. Lewis's own book (at 300 pages) only scratched the surface of the most important knowledge humanity has accumulated, relegating all of mathematics to a single footnote. And the ideal guide would offer pretty different advice depending on the scenario. Are survivors dealing with a radioactive ice age following a nuclear war? Or is it an eerily intact but near-empty post-pandemic world with mountains of goods to scavenge from the husks of cities? As a brand-new parent, Lewis couldn’t do one of our classic three- or four-hour episodes — so this is an unusually snappy one-hour interview, where Rob and Lewis are joined by Luisa Rodriguez to continue the conversation from her episode of the show last year. Chapters:Rob’s intro (00:00:00)The interview begins (00:00:59)The biggest impediments to bouncing back (00:03:18)Can we do a serious version of The Knowledge? (00:14:58)Recovering without much coal or oil (00:29:56)Most valuable pro-resilience adjustments we can make today (00:40:23)Feeding the Earth in disasters (00:47:45)The reality of humans trying to actually do this (00:53:54)Most exciting recent findings in astrobiology (01:01:00)Rob’s outro (01:03:37)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

Imagine you lead a nonprofit that operates on a shoestring budget. Staff are paid minimum wage, lunch is bread and hummus, and you're all bunched up on a few tables in a basement office. But over a few years, your cause attracts some major new donors. Your funding jumps a thousandfold, from $100,000 a year to $100,000,000 a year. You're the same group of people committed to making sacrifices for the cause — but these days, rather than cutting costs, the right thing to do seems to be to spend serious money and get things done ASAP. You suddenly have the opportunity to make more progress than ever before, but as well as excitement about this, you have worries about the impacts that large amounts of funding can have. This is roughly the situation faced by today's guest Will MacAskill — University of Oxford philosopher, author of the forthcoming book What We Owe The Future, and founding figure in the effective altruism movement. Links to learn more, summary and full transcript. Years ago, Will pledged to give away more than 50% of his income over his life, and was already donating 10% back when he was a student with next to no income. Since then, the coalition he founded has been super successful at attracting the interest of donors who collectively want to give away billions in the way Will and his colleagues were proposing. While surely a huge success, it brings with it risks that he's never had to consider before: • Will and his colleagues might try to spend a lot of money trying to get more things done more quickly — but actually just waste it. • Being seen as profligate could strike onlookers as selfish and disreputable. • Folks might start pretending to agree with their agenda just to get grants. • People working on nearby issues that are less flush with funding may end up resentful. • People might lose their focus on helping others as they get seduced by the prospect of earning a nice living. • Mediocre projects might find it too easy to get funding, even when the people involved would be better off radically changing their strategy, or shutting down and launching something else entirely. But all these 'risks of commission' have to be weighed against 'risk of omission': the failure to achieve all you could have if you'd been truly ambitious. People looking askance at you for paying high salaries to attract the staff you want is unpleasant. But failing to prevent the next pandemic because you didn't have the necessary medical experts on your grantmaking team is worse than unpleasant — it's a true disaster. Yet few will complain, because they'll never know what might have been if you'd only set frugality aside. Will aims to strike a sensible balance between these competing errors, which he has taken to calling judicious ambition. In today's episode, Rob and Will discuss the above as well as: • Will humanity likely converge on good values as we get more educated and invest more in moral philosophy — or are the things we care about actually quite arbitrary and contingent? • Why are so many nonfiction books full of factual errors? • How does Will avoid anxiety and depression with more responsibility on his shoulders than ever? • What does Will disagree with his colleagues on? • Should we focus on existential risks more or less the same way, whether we care about future generations or not? • Are potatoes one of the most important technologies ever developed? • And plenty more. Chapters:Rob’s intro (00:00:00)The interview begins (00:02:41)What We Owe The Future preview (00:09:23)Longtermism vs. x-risk (00:25:39)How is Will doing? (00:33:16)Having a life outside of work (00:46:45)Underappreciated people in the effective altruism community (00:52:48)A culture of ambition within effective altruism (00:59:50)Massively scalable projects (01:11:40)Downsides and risks from the increase in funding (01:14:13)Barriers to ambition (01:28:47)The Future Fund (01:38:04)Patient philanthropy (01:52:50)Will’s disagreements with Sam Bankman-Fried and Nick Beckstead (01:56:42)Astronomical risks of suffering (s-risks) (02:00:02)Will’s future plans (02:02:41)What is it with Will and potatoes? (02:08:40)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

The good news is deaths from malaria have been cut by a third since 2005. The bad news is it still causes 250 million cases and 600,000 deaths a year, mostly among young children in sub-Saharan Africa.We already have dirt-cheap ways to prevent and treat malaria, and the fraction of the Earth's surface where the disease exists at all has been halved since 1900. So why is it such a persistent problem in some places, even rebounding 15% since 2019?That's one of many questions I put to today's guest, James Tibenderana — doctor, medical researcher, and technical director at a major global health nonprofit known as Malaria Consortium. James studies the cutting edge of malaria control and treatment in order to optimise how Malaria Consortium spends £100 million a year across countries like Uganda, Nigeria, and Chad.Links to learn more, summary and full transcript. In sub-Saharan Africa, where 90% of malaria deaths occur, the infection is spread by a few dozen species of mosquito that are ideally suited to the local climatic conditions and have thus been impossible to eliminate so far. While COVID-19 may have an 'R' (reproduction number) of 5, in some situations malaria has a reproduction number in the 1,000s. A single person with malaria can pass the parasite to hundreds of mosquitoes, which themselves each go on to bite dozens of people each, allowing cases to quickly explode. The nets and antimalarial drugs Malaria Consortium distributes have been highly effective where distributed, but there are tens of millions of young children who are yet to be covered simply due to a lack of funding. Despite the success of these approaches, given how challenging it will be to create a malaria-free world, there's enthusiasm to find new approaches to throw at the problem. Two new interventions have recently generated buzz: vaccines and genetic approaches to control the mosquito species that carry malaria. The RTS,S vaccine is the first-ever vaccine that attacks a protozoa as opposed to a virus or bacteria. It's a great scientific achievement. But James points out that even after three doses, it's still only about 30% effective. Unless future vaccines are substantially more effective, they will remain just a complement to nets and antimalarial drugs, which are cheaper and each cut mortality by more than half. On the other hand, the latest mosquito-control technologies are almost too effective. It is possible to insert genes into specific mosquito populations that reduce their ability to reproduce. By using a 'gene drive,' you can ensure mosquitoes hand these detrimental genes down to 100% of their offspring. If deployed, these genes would spread and ultimately eliminate the mosquitoes that carry malaria at low cost, thereby largely ridding the world of the disease. Because a single country embracing this method would have global effects, James cautions that it's important to get buy-in from all the countries involved, and to have a way of reversing the intervention if we realise we've made a mistake. In this comprehensive conversation, Rob and James discuss all of the above, as well as most of what you could reasonably want to know about the state of the art in malaria control today, including: • How malaria spreads and the symptoms it causes • The use of insecticides and poison baits • How big a problem insecticide resistance is • How malaria was eliminated in North America and Europe • The key strategic choices faced by Malaria Consortium in its efforts to create a malaria-free world • And much moreChapters:Rob’s intro (00:00:00)The interview begins (00:02:06)Malaria basics (00:06:56)Malaria vaccines (00:12:37)Getting rid of mosquitos (00:32:20)Gene drives (00:38:06)Symptoms (00:58:00)Preventing the spread (01:06:00)Why we haven’t gotten rid of malaria yet (01:15:07)What James is responsible for as technical director (01:30:52)Malaria Consortium's current strategy (01:39:59)Elimination vs. control (02:01:49)Delivery and practicalities (02:16:23)Relationships with governments (02:26:38)Funding gap (02:31:03)Access and use gap (02:39:10)The value of local researchers (02:49:26)Past research findings (02:57:10)How to help (03:06:30)How James ended up where he is today (03:13:45)Producer: Keiran HarrisAudio mastering: Ryan KesslerTranscriptions: Katy Moore

In nature, animals roar and bare their teeth to intimidate adversaries — but one side usually backs down, and real fights are rare. The wisdom of evolution is that the risk of violence is just too great.Which might make one wonder: if war is so destructive, why does it happen? The question may sound naïve, but in fact it represents a deep puzzle. If a war will cost trillions and kill tens of thousands, it should be easy for either side to make a peace offer that both they and their opponents prefer to actually fighting it out.The conundrum of how humans can engage in incredibly costly and protracted conflicts has occupied academics across the social sciences for years. In today's episode, we speak with economist Chris Blattman about his new book, Why We Fight: The Roots of War and the Paths to Peace, which summarises what they think they've learned. Links to learn more, summary and full transcript. Chris's first point is that while organised violence may feel like it's all around us, it's actually very rare in humans, just as it is with other animals. Across the world, hundreds of groups dislike one another — but knowing the cost of war, they prefer to simply loathe one another in peace. In order to understand what’s wrong with a sick patient, a doctor needs to know what a healthy person looks like. And to understand war, social scientists need to study all the wars that could have happened but didn't — so they can see what a healthy society looks like and what's missing in the places where war does take hold. Chris argues that social scientists have generated five cogent models of when war can be 'rational' for both sides of a conflict: 1. Unchecked interests — such as national leaders who bear few of the costs of launching a war. 2. Intangible incentives — such as an intrinsic desire for revenge. 3. Uncertainty — such as both sides underestimating each other's resolve to fight. 4. Commitment problems — such as the inability to credibly promise not to use your growing military might to attack others in future. 5. Misperceptions — such as our inability to see the world through other people's eyes. In today's interview, we walk through how each of the five explanations work and what specific wars or actions they might explain. In the process, Chris outlines how many of the most popular explanations for interstate war are wildly overused (e.g. leaders who are unhinged or male) or misguided from the outset (e.g. resource scarcity). The interview also covers: • What Chris and Rob got wrong about the war in Ukraine • What causes might not fit into these five categories • The role of people's choice to escalate or deescalate a conflict • How great power wars or nuclear wars are different, and what can be done to prevent them • How much representative government helps to prevent war • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:01:43)What people get wrong about violence (00:04:40)Medellín gangs (00:11:48)Overrated causes of violence (00:23:53)Cause of war #1: Unchecked interests (00:36:40)Cause of war #2: Intangible incentives (00:41:40)Cause of war #3: Uncertainty (00:53:04)Cause of war #4: Commitment problems (01:02:24)Cause of war #5: Misperceptions (01:12:18)Weaknesses of the model (01:26:08)Dancing on the edge of a cliff (01:29:06)Confusion around escalation (01:35:26)Applying the model to the war between Russia and Ukraine (01:42:34)Great power wars (02:01:46)Preventing nuclear war (02:18:57)Why undirected approaches won't work (02:22:51)Democratic peace theory (02:31:10)Exchanging hostages (02:37:21)What you can actually do to help (02:41:25)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

On this episode of the show, host Rob Wiblin interviews Sam Bankman-Fried. This interview was recorded in February 2022, and released in April 2022. But on November 11 2022, Sam Bankman-Fried's company, FTX, filed for bankruptcy, and all staff at the Future Fund resigned — and the surrounding events led Rob to record a new intro on December 1st 2022 for this episode. • Read 80,000 Hours' statement on these events here. • You can also listen to host Rob’s reaction to the collapse of FTX on this podcast feed, above episode 140, or here. • Rob has shared some clarifications on his views about diminishing returns and risk aversion, and weaknesses in how it was discussed in this episode, here. • And you can read the original blog post associated with the episode here.

Everybody knows that good parenting has a big impact on how kids turn out. Except that maybe they don't, because it doesn't.Incredible though it might seem, according to today's guest — economist Bryan Caplan, the author of Selfish Reasons To Have More Kids, The Myth of the Rational Voter, and The Case Against Education — the best evidence we have on the question suggests that, within reason, what parents do has little impact on how their children's lives play out once they're adults.Links to learn more, summary and full transcript. Of course, kids do resemble their parents. But just as we probably can't say it was attentive parenting that gave me my mother's nose, perhaps we can't say it was attentive parenting that made me succeed at school. Both the social environment we grow up in and the genes we receive from our parents influence the person we become, and looking at a typical family we can't really distinguish the impact of one from the other. But nature does offer us up a random experiment that can let us tell the difference: identical twins share all their genes, while fraternal twins only share half their genes. If you look at how much more similar outcomes are for identical twins than fraternal twins, you see the effect of sharing 100% of your genetic material, rather than the usual 50%. Double that amount, and you've got the full effect of genetic inheritance. Whatever unexplained variation remains is still up for grabs — and might be down to different experiences in the home, outside the home, or just random noise. The crazy thing about this research is that it says for a range of adult outcomes (e.g. years of education, income, health, personality, and happiness), it's differences in the genes children inherit rather than differences in parental behaviour that are doing most of the work. Other research suggests that differences in “out-of-home environment” take second place. Parenting style does matter for something, but it comes in a clear third. Bryan is quick to point out that there are several factors that help reconcile these findings with conventional wisdom about the importance of parenting. First, for some adult outcomes, parenting was a big deal (i.e. the quality of the parent/child relationship) or at least a moderate deal (i.e. drug use, criminality, and religious/political identity). Second, parents can and do influence you quite a lot — so long as you're young and still living with them. But as soon as you move out, the influence of their behaviour begins to wane and eventually becomes hard to spot. Third, this research only studies variation in parenting behaviour that was common among the families studied. And fourth, research on international adoptions shows they can cause massive improvements in health, income and other outcomes. But the findings are still remarkable, and imply many hyper-diligent parents could live much less stressful lives without doing their kids any harm at all. In this extensive interview Rob interrogates whether Bryan can really be right, or whether the research he's drawing on has taken a wrong turn somewhere. And that's just one topic we cover, some of the others being: • People’s biggest misconceptions about the labour market • Arguments against open borders • Whether most people actually vote based on self-interest • Whether philosophy should stick to common sense or depart from it radically • Personal autonomy vs. the possible benefits of government regulation • Bryan's perfect betting record • And much more Chapters:Rob’s intro (00:00:00)The interview begins (00:01:15)Labor Econ Versus the World (00:04:55)Open Borders (00:20:30)How much parenting matters (00:35:49)Self-Interested Voter Hypothesis (01:00:31)Why Bryan and Rob disagree so much on philosophy (01:12:04)Libertarian free will (01:25:10)The effective altruism community (01:38:46)Bryan’s betting record (01:48:19)Individual autonomy vs. welfare (01:59:06)Arrogant hedgehogs (02:10:43)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

Since the Soviet Union split into different countries in 1991, the pervasive fear of catastrophe that people lived with for decades has gradually faded from memory, and nuclear warhead stockpiles have declined by 83%. Nuclear brinksmanship, proxy wars, and the game theory of mutually assured destruction (MAD) have come to feel like relics of another era. Russia's invasion of Ukraine has changed all that. According to Joan Rohlfing — President of the Nuclear Threat Initiative, a Washington, DC-based nonprofit focused on reducing threats from nuclear and biological weapons — the annual risk of a ‘global catastrophic nuclear event'’ never fell as low as people like to think, and for some time has been on its way back up. Links to learn more, summary and full transcript. At the same time, civil society funding for research and advocacy around nuclear risks is being cut in half over a period of years — despite the fact that at $60 million a year, it was already just a thousandth as much as the US spends maintaining its nuclear deterrent. If new funding sources are not identified to replace donors that are withdrawing, the existing pool of talent will have to leave for greener pastures, and most of the next generation will see a career in the field as unviable. While global poverty is on the decline and life expectancy increasing, the chance of a catastrophic nuclear event is probably trending in the wrong direction. Ukraine gave up its nuclear weapons in 1994 in exchange for security guarantees that turned out not to be worth the paper they were written on. States that have nuclear weapons (such as North Korea), states that are pursuing them (such as Iran), and states that have pursued nuclear weapons but since abandoned them (such as Libya, Syria, and South Africa) may take this as a valuable lesson in the importance of military power over promises. China has been expanding its arsenal and testing hypersonic glide missiles that can evade missile defences. Japan now toys with the idea of nuclear weapons as a way to ensure its security against its much larger neighbour. India and Pakistan both acquired nuclear weapons in the late 1980s and their relationship continues to oscillate from hostile to civil and back. At the same time, the risk that nuclear weapons could be interfered with due to weaknesses in computer security is far higher than during the Cold War, when systems were simpler and less networked. In the interview, Joan discusses several steps that can be taken in the immediate term, such as renewed efforts to extend and expand arms control treaties, changes to nuclear use policy, and the retirement of what they see as vulnerable delivery systems, such as land-based silos. In the bigger picture, NTI seeks to keep hope alive that a better system than deterrence through mutually assured destruction remains possible. The threat of retaliation does indeed make nuclear wars unlikely, but it necessarily means the system fails in an incredibly destructive way: with the death of hundreds of millions if not billions. In the long run, even a tiny 1 in 500 risk of a nuclear war each year adds up to around an 18% chance of catastrophe over the century. In this conversation we cover all that, as well as: • How arms control treaties have evolved over the last few decades • Whether lobbying by arms manufacturers is an important factor shaping nuclear strategy • The Biden Nuclear Posture Review • How easily humanity might recover from a nuclear exchange • Implications for the use of nuclear energy Chapters:Rob’s intro (00:00:00)Joan’s EAG presentation (00:01:40)The interview begins (00:27:06)Nuclear security funding situation (00:31:09)Policy solutions for addressing a one-person or one-state risk factor (00:36:46)Key differences in the nuclear security field (00:40:44)Scary scenarios (00:47:02)Why the US shouldn’t expand its nuclear arsenal (00:52:56)The evolution of nuclear risk over the last 10 years (01:03:41)The interaction between nuclear weapons and cybersecurity (01:10:18)The chances of humanity bouncing back after nuclear war (01:13:52)What we should actually do (01:17:57)Could sensors be a game-changer? (01:22:39)Biden Nuclear Posture Review (01:27:50)Influence of lobbying firms (01:33:58)What NTI might do with an additional $20 million (01:36:38)Nuclear energy tradeoffs (01:43:55)Why we can’t rely on Stanislav Petrovs (01:49:49)Preventing war vs. building resilience for recovery (01:52:15)Places to donate other than NTI (01:54:25)Career advice (02:00:15)Why this problem is solvable (02:09:27)Producer: Keiran HarrisAudio mastering: Ben CordellTranscriptions: Katy Moore

If someone said a global health and development programme was sustainable, participatory, and holistic, you'd have to guess that they were saying something positive. But according to today's guest Karen Levy — deworming pioneer and veteran of Innovations for Poverty Action, Evidence Action, and Y Combinator — each of those three concepts has become so fashionable that they're at risk of being seriously overrated and applied where they don't belong. Links to learn more, summary and full transcript. Such concepts might even cause harm — trying to make a project embody all three is as likely to ruin it as help it flourish. First, what do people mean by 'sustainability'? Usually they mean something like the programme will eventually be able to continue without needing further financial support from the donor. But how is that possible? Governments, nonprofits, and aid agencies aim to provide health services, education, infrastructure, financial services, and so on — and all of these require ongoing funding to pay for materials and staff to keep them running. Given that someone needs to keep paying, Karen tells us that in practice, 'sustainability' is usually a euphemism for the programme at some point being passed on to someone else to fund — usually the national government. And while that can be fine, the national government of Kenya only spends $400 per person to provide each and every government service — just 2% of what the US spends on each resident. Incredibly tight budgets like that are typical of low-income countries. 'Participatory' also sounds nice, and inasmuch as it means leaders are accountable to the people they're trying to help, it probably is. But Karen tells us that in the field, ‘participatory’ usually means that recipients are expected to be involved in planning and delivering services themselves. While that might be suitable in some situations, it's hardly something people in rich countries always want for themselves. Ideally we want government healthcare and education to be high quality without us having to attend meetings to keep it on track — and people in poor countries have as many or more pressures on their time. While accountability is desirable, an expectation of participation can be as much a burden as a blessing. Finally, making a programme 'holistic' could be smart, but as Karen lays out, it also has some major downsides. For one, it means you're doing lots of things at once, which makes it hard to tell which parts of the project are making the biggest difference relative to their cost. For another, when you have a lot of goals at once, it's hard to tell whether you're making progress, or really put your mind to focusing on making one thing go extremely well. And finally, holistic programmes can be impractically expensive — Karen tells the story of a wonderful 'holistic school health' programme that, if continued, was going to cost 3.5 times the entire school's budget. In today's in-depth conversation, Karen Levy and I chat about the above, as well as: • Why it pays to figure out how you'll interpret the results of an experiment ahead of time • The trouble with misaligned incentives within the development industry • Projects that don't deliver value for money and should be scaled down • How Karen accidentally became a leading figure in the push to deworm tens of millions of schoolchildren • Logistical challenges in reaching huge numbers of people with essential services • Lessons from Karen's many-decades career • And much more Get 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. Producer: Keiran Harris Audio mastering: Ben Cordell and Ryan Kessler Transcriptions: Katy Moore

Russia's invasion of Ukraine is devastating the lives of Ukrainians, and so long as it continues there's a risk that the conflict could escalate to include other countries or the use of nuclear weapons. It's essential that NATO, the US, and the EU play their cards right to ideally end the violence, maintain Ukrainian sovereignty, and discourage any similar invasions in the future. But how? To pull together the most valuable information on how to react to this crisis, we spoke with Samuel Charap — a senior political scientist at the RAND Corporation, one of the US's foremost experts on Russia's relationship with former Soviet states, and co-author of Everyone Loses: The Ukraine Crisis and the Ruinous Contest for Post-Soviet Eurasia. Links to learn more, summary and full transcript. Samuel believes that Putin views the alignment of Ukraine with NATO as an existential threat to Russia — a perhaps unreasonable view, but a sincere one nevertheless. Ukraine has been drifting further into Western Europe's orbit and improving its defensive military capabilities, so Putin has concluded that if Russia wants to put a stop to that, there will never be a better time to act in the future. Despite early successes holding off the Russian military, Samuel is sceptical that time is on the Ukrainian side. If the war is to end before much of Ukraine is reduced to rubble, it will likely have to be through negotiation, rather than Russian defeat. The US policy response has so far been largely good, successfully balancing the need to punish Russia to dissuade large nations from bullying small ones in the future, while preventing NATO from being drawn into the war directly — which would pose a horrifying risk of escalation to a full nuclear exchange. The pressure from the general public to 'do something' might eventually cause national leaders to confront Russia more directly, but so far they are sensibly showing no interest in doing so. However, use of nuclear weapons remains a low but worrying possibility. Samuel is also worried that Russia may deploy chemical and biological weapons and blame it on the Ukrainians. Before war broke out, it's possible Russia could have been satisfied if Ukraine followed through on the Minsk agreements and committed not to join the EU and NATO. Or it might not have, if Putin was committed to war, come what may. In any case, most Ukrainians found those terms intolerable. At this point, the situation is even worse, and it's hard to see how an enduring ceasefire could be agreed upon. On top of the above, Russia is also demanding recognition that Crimea is part of Russia, and acceptance of the independence of the so-called Donetsk and Luhansk People's Republics. These conditions — especially the second — are entirely unacceptable to the Ukrainians. Hence the war continues, and could grind on for months or even years until one side is sufficiently beaten down to compromise on their core demands. Rob and Samuel discuss all of the above and also: • The chances that this conflict leads to a nuclear exchange • The chances of regime change in Russia • Whether the West should deliver MiG fighter jets to Ukraine • What are the implications if Sweden and/or Finland decide to join NATO? • What should NATO do now, and did it make any mistakes in the past? • What's the most likely situation for us to be looking at in three months' time? • Can Ukraine effectively win the war? Chapters:Rob’s intro (00:00:00)The interview begins (00:01:40)Putin's true motive (00:02:29)What the West could have done differently (00:07:44)Chances of Ukraine holding out (00:11:40)Chances of regime change in Russia (00:14:59)The good and the bad from the West so far (00:17:55)Should the West deliver MiG fighter jets to Ukraine? (00:19:57)"No-fly zones" (00:21:32)Chances that this conflict leads to a nuclear exchange (00:26:06)What listeners should do (00:36:01)Chances of biological or chemical weapons use (00:37:59)Best realistic outcome from here (00:39:29)Keeping the broader conversation sane (00:49:29)Why not promise to remove sanctions? (00:51:05)Pros and cons of Sweden and FInland joining NATO (00:52:53)The most likely situation in 3 months (00:53:58) Producer: Keiran Harris Audio mastering: Ben Cordell Transcriptions: Katy Moore

One of 80,000 Hours' main services is our free one-on-one careers advising, which we provide to around 1,000 people a year. Today we speak to two of our advisors, who have each spoken to hundreds of people -- including many regular listeners to this show -- about how they might be able to do more good while also having a highly motivating career. Before joining 80,000 Hours, Michelle Hutchinson completed a PhD in Philosophy at Oxford University and helped launch Oxford's Global Priorities Institute, while Habiba Islam studied politics, philosophy, and economics at Oxford University and qualified as a barrister. Links to learn more, summary and full transcript. In this conversation, they cover many topics that recur in their advising calls, and what they've learned from watching advisees’ careers play out: • What they say when advisees want to help solve overpopulation • How to balance doing good against other priorities that people have for their lives • Why it's challenging to motivate yourself to focus on the long-term future of humanity, and how Michelle and Habiba do so nonetheless • How they use our latest guide to planning your career • Why you can specialise and take more risk if you're in a group • Gaps in the effective altruism community it would be really useful for people to fill • Stories of people who have spoken to 80,000 Hours and changed their career — and whether it went well or not • Why trying to have impact in multiple different ways can be a mistake The episode is split into two parts: the first section on The 80,000 Hours Podcast, and the second on our new show 80k After Hours. This is a shameless attempt to encourage listeners to our first show to subscribe to our second feed. That second part covers: • Whether just encouraging someone young to aspire to more than they currently are is one of the most impactful ways to spend half an hour • How much impact the one-on-one team has, the biggest challenges they face as a group, and different paths they could have gone down • Whether giving general advice is a doomed enterprise Chapters:Rob’s intro (00:00:00)The interview begins (00:02:24)Cause prioritization (00:09:14)Unexpected outcomes from 1-1 advice (00:18:10)Making time for thinking about these things (00:22:28)Balancing different priorities in life (00:26:54)Gaps in the effective altruism space (00:32:06)Plan change vignettes (00:37:49)How large a role the 1-1 team is playing (00:49:04)What about when our advice didn’t work out? (00:55:50)The process of planning a career (00:59:05)Why longtermism is hard (01:05:49) Want to get free one-on-one advice from our team? We're here to help. We’ve helped thousands of people formulate their plans and put them in touch with mentors. We've expanded our ability to deliver one-on-one meetings so are keen to help more people than ever before. If you're a regular listener to the show we're especially likely to want to speak with you. Learn about and apply for advising. Get 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. Producer: Keiran Harris Audio mastering: Ben Cordell Transcriptions: Katy Moore