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 history Signs of antislavery sentiment before the 17th century The role of the Quakers in early British abolitionist movement The importance of individual “heroes” in the abolitionist movement Arguments against the idea that the abolition of slavery was contingent Whether 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 themselves • Cooperation in human societies as they exist today — and perhaps in the future, between civilisations spread across different planets or stars • Whether it’s too out-there to think of humans as engaging in cancerous behaviour • Why elephants get deadly cancers less often than humans, despite having way more cells • When a cell should commit suicide • The strategy of deliberately not treating cancer aggressively • Superhuman cooperation And 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 apocalypse • Practical steps to prepare for the apocalypse • And whether a zombie apocalypse is already happening among Tasmanian devils And 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 Harris Audio mastering: Milo McGuire Transcriptions: Katy Moore