Chris Olah has had a fascinating and unconventional career path. 

Most people who want to pursue a research career feel they need a degree to get taken seriously. But Chris not only doesn't have a PhD, but doesn’t even have an undergraduate degree. After dropping out of university to help defend an acquaintance who was facing bogus criminal charges, Chris started independently working on machine learning research, and eventually got an internship at Google Brain, a leading AI research group. 

In this interview — a follow-up to <a href="https://80000hours.org/podcast/episodes/chris-olah-interpretability-research/">our episode on his technical work</a> — we discuss what, if anything, can be learned from his unusual career path. Should more people pass on university and just throw themselves at solving a problem they care about? Or would it be foolhardy for others to try to copy a unique case like Chris’? 

<a href="https://80000hours.org/podcast/episodes/chris-olah-unconventional-career-path/?utm_campaign=podcast__chris-olah&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

We also cover some of Chris' personal passions over the years, including his attempts to reduce what he calls 'research debt' by starting a new academic journal called Distill, focused just on explaining existing results unusually clearly. 

As Chris explains, as fields develop they accumulate huge bodies of knowledge that researchers are meant to be familiar with before they start contributing themselves. But the weight of that existing knowledge — and the need to keep up with what everyone else is doing — can become crushing. It can take someone until their 30s or later to earn their stripes, and sometimes a field will split in two just to make it possible for anyone to stay on top of it. 

If that were unavoidable it would be one thing, but Chris thinks we're nowhere near communicating existing knowledge as well as we could. Incrementally improving an explanation of a technical idea might take a single author weeks to do, but could go on to save a day for thousands, tens of thousands, or hundreds of thousands of students, if it becomes the best option available. 

Despite that, academics have little incentive to produce outstanding explanations of complex ideas that can speed up the education of everyone coming up in their field. And some even see the process of deciphering bad explanations as a desirable right of passage all should pass through, just as they did. 

So Chris tried his hand at chipping away at this problem — but concluded the nature of the problem wasn't quite what he originally thought. In this conversation we talk about that, as well as: 

• Why highly thoughtful cold emails can be surprisingly effective, but average cold emails do little 
• Strategies for growing as a researcher 
• Thinking about research as a market 
• How Chris thinks about writing outstanding explanations 
• The concept of 'micromarriages' and ‘microbestfriendships’ 
• 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 
Transcriptions: Sofia Davis-Fogel

80,000 Hours Podcast

#108 – Chris Olah on working at top AI labs without an undergrad degree

One of 80,000 Hours' main services is our <a href="https://80000hours.org/speak-with-us/?int_campaign=podcast__michelle-habiba">free one-on-one careers advising</a>, 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. 

<a href="https://80000hours.org/podcast/episodes/michelle-hutchinson-habiba-islam-themes-from-careers-advising/?utm_campaign=podcast__michelle-habiba&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

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 <a href="https://80000hours.org/after-hours-podcast/">80k After Hours</a>. This is a shameless attempt to encourage listeners to our first show to subscribe to our second feed. 

That <a href="https://80000hours.org/podcast/episodes/80k-after-hours-michelle-habiba-advice-for-younger-selves">second part</a> 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 

Get this second part 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. 

Want to get free one-on-one 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. 

<a href="https://80000hours.org/speak-with-us/?int_campaign=podcast__michelle-habiba">Learn about and apply for advising.</a> 

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

#122 – Michelle Hutchinson & Habiba Islam on themes from our 1-on-1 careers advising

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, <a href="https://chrisblattman.com/why-we-fight/">Why We Fight: The Roots of War and the Paths to Peace</a>, which summarises what they think they've learned. 

<a href="https://80k.link/CB">Links to learn more, summary and full transcript.</a> 

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 

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

#128 – Chris Blattman on the five reasons wars happen

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. 

<a href="https://80k.link/JT">Links to learn more, summary and full transcript.</a> 

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 
• 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: Ryan Kessler 
Transcriptions: Katy Moore

#129 – James Tibenderana on the state of the art in malaria control and elimination

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 <a href="https://www.whatweowethefuture.com/">What We Owe The Future</a>, and founding figure in the effective altruism movement. 

<a href="https://80k.link/WM3">Links to learn more, summary and full transcript.</a> 

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. 

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

#130 – Will MacAskill on balancing frugality with ambition, and whether you need longtermism

“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 <a href="http://the-knowledge.org/en-gb/the-book/">The Knowledge: How to Rebuild Civilization in the Aftermath of a Cataclysm.</a> 

<a href="https://80k.link/LD">Links to learn more, summary and full transcript.</a> 

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. 

They cover: 

• The biggest impediments to bouncing back 
• The reality of humans trying to actually do this 
• The most valuable pro-resilience adjustments we can make today 
• How to recover without much coal or oil 
• How to feed the Earth in disasters 

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

#131 – Lewis Dartnell on getting humanity to bounce back faster in a post-apocalyptic world

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. 

<a href="https://80k.link/NDS">Links to learn more, summary and full transcript.</a> 

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 
• 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

#132 – Nova DasSarma on why information security may be critical to the safe development of AI

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. 

<a href="https://80k.link/MT">Links to learn more, summary and full transcript.</a> 

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 would be able to 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 

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

#133 – Max Tegmark on how a 'put-up-or-shut-up' resolution led him to work on AI

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. 

<a href="https://80k.link/IM">Links to learn more, summary and full transcript.</a> 

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 macrohistory 

Producer: Keiran Harris 
Audio mastering: Ben Cordell 
Transcriptions: Katy Moore

#134 – Ian Morris on what big picture history teaches us

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 <a href="https://80000hours.org/podcast/episodes/samuel-charap-why-putin-invaded-ukraine/">second time</a> with Samuel Charap — one of the US’s foremost experts on Russia’s relationship with former Soviet states, and coauthor of the 2017 book <a href="https://www.amazon.com/Everyone-Loses-Ukraine-Ruinous-Post-Soviet/dp/1138633089">Everyone Loses: The Ukraine Crisis and the Ruinous Contest for Post-Soviet Eurasia.</a> 

<a href="https://80k.link/SC2">Links to learn more, summary and full transcript.</a> 

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. 

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

#135 – Samuel Charap on key lessons from five months of war in Ukraine

1. People who exist in the future deserve some degree of moral consideration. 
2. The future could be very big, very long, and/or very good. 
3. We can reasonably hope to influence whether people in the future exist, and how good or bad their lives are. 
4. 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 <a href="https://www.whatweowethefuture.com/">What We Owe The Future</a>, the latest book from today's guest — University of Oxford philosopher and cofounder of the effective altruism community, Will MacAskill. 

<a href="https://80k.link/WM4">Links to learn more, summary and full transcript.</a> 

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 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 
Transcriptions: Katy Moore

#136 – Will MacAskill on what we owe the future

Hey SoundCloud listeners. I'm afraid I have some bad news. After 5 years manually posting our episodes on SoundCloud we're going to stop doing so, so if you'd like to keep listening to the show you'll need to subscribe somewhere else.

Soundcloud was actually where we launched the show back in 2017 because it was the easiest place to get started.

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So if you want to keep getting notified about new episodes of the show you'll need to subscribe on some other app — such as Apple Podcasts, Google Podcasts, Spotify, Podcast Addict, Pocket Casts, or whatever you'd prefer! Just search for 80,000 Hours in there and we'll come straight up.

Those apps also have better features for listening to long podcasts anyway.

So go add us in a podcast app now and we'll see you over there for some new conversations soon! 

Thanks so much.

Announcement for Soundcloud listeners

Note that this interview was recorded before we were running a professional podcast. Summary and discussion here: <a href="https://80000hours.org/2015/12/interview-with-ben-who-raised-eight-figures-for-charity-through-tech-entrepreneurship/?utm_campaign=podcast__ben_west&utm_source=80000+Hours+Podcast&utm_medium=podcast">https://80000hours.org/2015/12/interview-with-ben-who-raised-eight-figures-for-charity-through-tech-entrepreneurship/</a>

[Archive] Ben West and Ben Todd on donating most of your income from entrepreneurship (Dec 2015)

Note that this interview was recorded before we were running a professional podcast. Summary and discussion: <a href="https://80000hours.org/2016/02/plan-change-story-interview-with-dillon-bowen-leader-of-effective-altruism-at-tufts-university/?utm_campaign=podcast__dillon_bowen&utm_source=80000+Hours+Podcast&utm_medium=podcast">https://80000hours.org/2016/02/plan-change-story-interview-with-dillon-bowen-leader-of-effective-altruism-at-tufts-university/</a>

[Archive] Dillon Bowen and Roman Duda, on why to do an economics PhD (Jan 2016)

Note that this interview was recorded before we were running a professional podcast. Summary and discussion: <a href="https://80000hours.org/2016/06/interview-with-maria-gutierrez-about-doing-good-through-art/?utm_campaign=podcast__maria_gutierrez&utm_source=80000+Hours+Podcast&utm_medium=podcast">https://80000hours.org/2016/06/interview-with-maria-gutierrez-about-doing-good-through-art/</a>

[Archive] Maria Gutierrez and Robert Wiblin on doing good through art (May 2016)

Just two years ago OpenAI didn’t exist. It’s now among the most elite groups of machine learning researchers. They’re trying to make an AI that’s smarter than humans and have $1b at their disposal. 
 
Even stranger for a Silicon Valley start-up, it’s not a business, but rather a non-profit founded by Elon Musk and Sam Altman among others, to ensure the benefits of AI are distributed broadly to all of society. 
 
I did a long interview with one of its first machine learning researchers, Dr Dario Amodei, to learn about: 
 
* OpenAI’s latest plans and research progress. 
* His paper *Concrete Problems in AI Safety*, which outlines five specific ways machine learning algorithms can act in dangerous ways their designers don’t intend - something OpenAI has to work to avoid. 
* How listeners can best go about pursuing a career in machine learning and AI development themselves. 

<a href="https://80000hours.org/2017/07/podcast-the-world-needs-ai-researchers-heres-how-to-become-one/?utm_campaign=podcast__amodei&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, apply for personalised coaching to work on AI safety, see what questions are asked when, and read extra resources to learn more.</a> 
 
1m33s - What OpenAI is doing, Dario’s research and why AI is important 
13m - Why OpenAI scaled back its Universe project 
15m50s - Why AI could be dangerous 
24m20s - Would smarter than human AI solve most of the world’s problems? 
29m - Paper on five concrete problems in AI safety 
43m48s - Has OpenAI made progress? 
49m30s - What this back flipping noodle can teach you about AI safety 
55m30s - How someone can pursue a career in AI safety and get a job at OpenAI 
1h02m30s - Where and what should people study? 
1h4m15s - What other paradigms for AI are there? 
1h7m55s - How do you go from studying to getting a job? What places are there to work? 
1h13m30s - If there's a 17-year-old listening here what should they start reading first? 
1h19m - Is this a good way to develop your broader career options? Is it a safe move? 
1h21m10s - What if you’re older and haven’t studied machine learning? How do you break in? 
1h24m - What about doing this work in academia? 
1h26m50s - Is the work frustrating because solutions may not exist? 
1h31m35s - How do we prevent a dangerous arms race? 
1h36m30s - Final remarks on how to get into doing useful work in machine learning

#3 - Dr Dario Amodei on OpenAI and how AI will change the world for good and ill

Of all the people whose well-being we should care about, only a small fraction are alive today. The rest are members of future generations who are yet to exist. Whether they’ll be born into a world that is flourishing or disintegrating – and indeed, whether they will ever be born at all – is in large part up to us. As such, the welfare of future generations should be our number one moral concern. 

This conclusion holds true regardless of whether your moral framework is based on common sense, consequences, rules of ethical conduct, cooperating with others, virtuousness, keeping options open – or just a sense of wonder about the universe we find ourselves in. 

That’s the view of Dr Toby Ord, a philosophy Fellow at the University of Oxford and co-founder of the effective altruism community. In this episode of the 80,000 Hours Podcast Dr Ord makes the case that aiming for a positive long-term future is likely the best way to improve the world. 

<a href="https://80000hours.org/articles/why-the-long-run-future-matters-more-than-anything-else-and-what-we-should-do-about-it/?utm_campaign=podcast__toby-ord&utm_source=80000+Hours+Podcast&utm_medium=podcast">Apply for personalised coaching, see what questions are asked when, and read extra resources to learn more.</a> 

We then discuss common objections to long-termism, such as the idea that benefits to future generations are less valuable than those to people alive now, or that we can’t meaningfully benefit future generations beyond taking the usual steps to improve the present. 

Later the conversation turns to how individuals can and have changed the course of history, what could go wrong and why, and whether plans to colonise Mars would actually put humanity in a safer position than it is today. 

This episode goes deep into the most distinctive features of our advice. It’s likely the most in-depth discussion of how 80,000 Hours and the effective altruism community think about the long term future and why - and why we so often give it top priority. 

It’s best to subscribe, so you can listen at leisure on your phone, speed up the conversation if you like, and get notified about future episodes. You can do so by searching ‘80,000 Hours’ wherever you get your podcasts. 

Want to help ensure humanity has a positive future instead of destroying itself? We want to help. 

We’ve helped 100s of people compare between their options, get introductions, and jobs important for the the long-run future. If you want to work on any of the problems discussed in this episode, such as artificial intelligence or biosecurity, <a href="https://80000hours.org/coaching/?utm_campaign=podcast__toby-ord&utm_source=80000+Hours+Podcast&utm_medium=podcast" title="" class="btn btn-primary">find out if our coaching can help you.</a> 

Overview of the discussion 

3m30s - Why is the long-term future of humanity such a big deal, and perhaps the most important issue for us to be thinking about? 
9m05s - Five arguments that future generations matter 
21m50s - How bad would it be if humanity went extinct or civilization collapses? 
26m40s - Why do people start saying such strange things when this topic comes up? 
30m30s - Are there any other reasons to prioritize thinking about the long-term future of humanity that you wanted to raise before we move to objections? 
36m10s - What is this school of thought called? 
<a href=" https://80000hours.org/articles/why-the-long-run-future-matters-more-than-anything-else-and-what-we-should-do-about-it/?utm_campaign=podcast__toby-ord&utm_source=80000+Hours+Podcast&utm_medium=podcast">Read more...</a>

#6 - Dr Toby Ord on why the long-term future matters more than anything else & what to do about it

The scientific revolution in the 16th century was one of the biggest societal shifts in human history, driven by the discovery of new and better methods of figuring out who was right and who was wrong. 

Julia Galef - a well-known writer and researcher focused on improving human judgment, especially about high stakes questions - believes that if we could again develop new techniques to predict the future, resolve disagreements and make sound decisions together, it could dramatically improve the world across the board. We brought her in to talk about her ideas. 

This interview complements a <a href="https://80000hours.org/problem-profiles/improving-institutional-decision-making/?utm_campaign=podcast__julia-galef&utm_source=80000+Hours+Podcast&utm_medium=podcast">new detailed review</a> of whether and how to follow Julia’s career path.

<a href="https://80000hours.org/2017/09/is-it-time-for-a-new-scientific-revolution-julia-galef-on-how-to-make-humans-smarter/?utm_campaign=podcast__julia-galef&utm_source=80000+Hours+Podcast&utm_medium=podcast">Apply for personalised coaching, see what questions are asked when, and read extra resources to learn more.</a> 

Julia has been host of the Rationally Speaking podcast since 2010, co-founder of the Center for Applied Rationality in 2012, and is currently working for the Open Philanthropy Project on an investigation of expert disagreements. 

In our conversation we ended up speaking about a wide range of topics, including: 

* Her research on how people can have productive intellectual disagreements. 
* Why she once planned to become an urban designer. 
* Why she doubts people are more rational than 200 years ago. 
* What makes her a fan of Twitter (while I think it’s dystopian). 
* Whether people should write more books. 
* Whether it’s a good idea to run a podcast, and how she grew her audience. 
* Why saying you don’t believe X often won’t convince people you don’t. 
* Why she started a PhD in economics but then stopped. 
* Whether she would recommend an unconventional career like her own. 
* Whether the incentives in the intelligence community actually support sound thinking. 
* Whether big institutions will actually pick up new tools for improving decision-making if they are developed. 
* How to start out pursuing a career in which you enhance human judgement and foresight. 

Get free, one-on-one career advice to help you improve judgement and decision-making 

We’ve helped dozens of people compare between their options, get introductions, and jobs important for the the long-run future. **If you want to work on any of the problems discussed in this episode, find out if our coaching can help you:** 

<a href="https://80000hours.org/coaching/?utm_campaign=podcast__julia-galef&utm_source=80000+Hours+Podcast&utm_medium=podcast" title="" class="btn btn-primary">APPLY FOR COACHING</a> 

Overview of the conversation 

**1m30s** So what projects are you working on at the moment? 
**3m50s** How are you working on the problem of expert disagreement? 
**6m0s** Is this the same method as the double crux process that was developed at the Center for Applied Rationality? 
**10m** Why did the Open Philanthropy Project decide this was a very valuable project to fund? 
**13m** Is the double crux process actually that effective? 
**14m50s** Is Facebook dangerous? 
**17m** What makes for a good life? Can you be mistaken about having a good life? 
**19m** Should more people write books? 
<a href="https://80000hours.org/2017/09/is-it-time-for-a-new-scientific-revolution-julia-galef-on-how-to-make-humans-smarter/?utm_campaign=podcast__julia-galef&utm_source=80000+Hours+Podcast&utm_medium=podcast">Read more...</a>

#7 - Julia Galef on making humanity more rational, what EA does wrong, and why Twitter isn’t all bad

Every year tens of billions of animals are raised in terrible conditions in factory farms before being killed for human consumption. Over the last two years Lewis Bollard – Project Officer for Farm Animal Welfare at the Open Philanthropy Project – has conducted extensive research into the best ways to eliminate animal suffering in farms as soon as possible. This has resulted in $30 million in grants to farm animal advocacy. 

<a href="https://80000hours.org/2017/09/lewis-bollard-end-factory-farming/?utm_campaign=podcast__lewis-bollard&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, coaching application form, overview of the conversation, and extra resources to learn more:</a> 

We covered almost every approach being taken, which ones work, and how individuals can best contribute through their careers. 

We also had time to venture into a wide range of issues that are less often discussed, including: 

* Why Lewis thinks insect farming would be worse than the status quo, and whether we should look for ‘humane’ insecticides; 
* How young people can set themselves up to contribute to scientific research into meat alternatives; 
* How genetic manipulation of chickens has caused them to suffer much more than their ancestors, but could also be used to make them better off; 
* Why Lewis is skeptical of vegan advocacy; 
* Why he doubts that much can be done to tackle factory farming through legal advocacy or electoral politics; 
* Which species of farm animals is best to focus on first; 
* Whether fish and crustaceans are conscious, and if so what can be done for them; 
* Many other issues listed below in the Overview of the discussion. 

Get free, one-on-one career advice 

We’ve helped dozens of people compare between their options, get introductions, and jobs important for the the long-run future. <a href="https://80000hours.org/coaching/?int_campaign=lewis-bollard-podcast" title="" class="btn btn-primary">If you want to work on any of the problems discussed in this episode, find out if our coaching can help you.</a> 

Overview of the discussion 

**2m40s** What originally drew you to dedicate your career to helping animals and why did Open Philanthropy end up focusing on it? 
**5m40s** Do you have any concrete way of assessing the severity of animal suffering? 
**7m10s** Do you think the environmental gains are large compared to those that we might hope to get from animal welfare improvement? 
**7m55s** What grants have you made at Open Phil? How did you go about deciding which groups to fund and which ones not to fund? 
**9m50s** Why does Open Phil focus on chickens and fish? Is this the right call? 
<a href="https://80000hours.org/2017/09/lewis-bollard-end-factory-farming?utm_campaign=podcast__lewis-bollard&utm_source=80000+Hours+Podcast&utm_medium=podcast">More...</a>

#8 - Lewis Bollard on how to end factory farming in our lifetimes

Do most meat eaters think it’s wrong to hurt animals? Do Americans think climate change is likely to cause human extinction? What is the best, state-of-the-art therapy for depression? How can we make academics more intellectually honest, so we can actually trust their findings? How can we speed up social science research ten-fold? Do most startups improve the world, or make it worse? 

If you’re interested in these question, this interview is for you. 

<a href="https://80000hours.org/2017/10/spencer-greenberg-social-science/?utm_campaign=podcast__spencer-greenberg&utm_source=80000+Hours+Podcast&utm_medium=podcast">Click for a full transcript, links discussed in the show, etc.</a> 

A scientist, entrepreneur, writer and mathematician, Spencer Greenberg is constantly working to create tools to speed up and improve research and critical thinking. These include: 

* Rapid public opinion surveys to find out what most people actually think about animal consciousness, farm animal welfare, the impact of developing world charities and the likelihood of extinction by various different means; 
* Tools to enable social science research to be run en masse very cheaply; 
* ClearerThinking.org, a highly popular site for improving people’s judgement and decision-making; 
* Ways to transform data analysis methods to ensure that papers only show true findings; 
* Innovative research methods; 
* Ways to decide which research projects are actually worth pursuing. 

In this interview, Spencer discusses all of these and more. If you don’t feel like listening, that just shows that you have poor judgement and need to benefit from his wisdom even more! 

<a href="https://80000hours.org/coaching/?int_campaign=podcast__spencer-greenberg" title="" class="btn btn-primary">Get free, one-on-one career advice</a> 

We’ve helped hundreds of people compare their options, get introductions, and find high impact jobs. If you want to work on any of the problems discussed in this episode, find out if our coaching can help you.

#11 - Dr Spencer Greenberg on speeding up social science 10-fold & why plenty of startups cause harm

Immanuel Kant is a profoundly influential figure in modern philosophy, and was one of the earliest proponents for universal democracy and international cooperation. He <a href="https://80000hours.org/2018/01/will-macaskill-moral-philosophy/?utm_campaign=podcast__will-macaskill&utm_source=80000+Hours+Podcast&utm_medium=podcast">also thought</a> that women have no place in civil society, that it was okay to kill illegitimate children, and that there was a ranking in the moral worth of different races. 
 
Throughout history we’ve consistently believed, as common sense, truly horrifying things by today’s standards. According to University of Oxford Professor Will MacAskill, it’s extremely likely that we’re in the same boat today. If we accept that we’re probably making major moral errors, how should we proceed? 

<a href="https://80000hours.org/2018/01/will-macaskill-moral-philosophy/?utm_campaign=podcast__will-macaskill&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, key points and links to articles and career guides discussed in the show.</a> 
 
If our morality is tied to common sense intuitions, we’re probably just preserving these biases and moral errors. Instead we need to develop a moral view that criticises common sense intuitions, and gives us a chance to move beyond them. And if humanity is going to spread to the stars it could be worth dedicating hundreds or thousands of years to moral reflection, lest we spread our errors far and wide. 
 
Will is an Associate Professor in Philosophy at Oxford University, author of Doing Good Better, and one of the co-founders of the effective altruism community. In this interview we discuss a wide range of topics: 
 
* How would we go about a ‘long reflection’ to fix our moral errors? 
* Will’s forthcoming book on how one should reason and act if you don't know which moral theory is correct. What are the practical implications of so-called ‘moral uncertainty’? 
* If we basically solve existential risks, what does humanity do next? 
* What are some of Will’s most unusual philosophical positions? 
* What are the best arguments for and against utilitarianism? 
* Given disagreements among philosophers, how much should we believe the findings of philosophy as a field? 
* What are some the biases we should be aware of within academia? 
* What are some of the downsides of becoming a professor? 
* What are the merits of becoming a philosopher? 
* How does the media image of EA differ to the actual goals of the community? 
* What kinds of things would you like to see the EA community do differently? 
* How much should we explore potentially controversial ideas? 
* How focused should we be on diversity? 
* What are the best arguments against effective altruism? 

Get free, one-on-one career advice 

We’ve helped hundreds of people compare their options, get introductions, and find high impact jobs. If you want to work on global priorities research or other important questions in academia, <a href="https://80000hours.org/coaching/?int_campaign=podcast__will-macaskill">find out if our coaching can help you.</a>

#17 - Prof Will MacAskill on moral uncertainty, utilitarianism & how to avoid being a moral monster

Ofir Reich started out doing math in the military, before spending 8 years in tech startups - but then made a sharp turn to become a data scientist focussed on helping the global poor. 

At UC Berkeley’s Center for Effective Global Action he helps prevent tax evasion by identifying fake companies in India, enable Afghanistan to pay its teachers electronically, and raise yields for Ethiopian farmers by messaging them when local conditions make it ideal to apply fertiliser. Or at least that’s the hope - he’s also working on ways to test whether those interventions actually work.
 

<a href="https://80000hours.org/2018/01/ofir-reich-data-science/?utm_campaign=podcast__ofir-reich&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full post about this episode, including a transcript and relevant links to learn more.</a> 

Why dedicate his life to helping the global poor? 

Ofir sees little moral difference between harming people and failing to help them. After all, if you had to press a button to keep all of your money from going to charity, and you pressed that button, would that be an action, or an inaction? Is there even an answer? 

After reflecting on cases like this, he decided that to not engage with a problem is an active choice, one whose consequences he is just as morally responsible for as if he were directly involved. On top of his life philosophy we also discuss:
 

* The benefits of working in a top academic environment 
* How best to start a career in global development 
* Are RCTs worth the money? Should we focus on big picture policy change instead? Or more economic theory? 
* How the delivery standards of nonprofits compare to top universities 
* Why he doesn’t enjoy living in the San Francisco bay area 
* How can we fix the problem of most published research being false? 
* How good a career path is data science? 
* How important is experience in development versus technical skills? 
* How he learned much of what he needed to know in the army 
* How concerned should effective altruists be about burnout? 

Keiran Harris helped produce today’s episode.

#18 - Ofir Reich on using data science to end poverty & the spurious action-inaction distinction

First, decide on the type of animal. Next, pick the cell type. Then take a small, painless biopsy, and put the cells in a solution that makes them feel like they’re still in the body. Once the cells are in this comfortable state, they'll proliferate. One cell becomes two, two becomes four, four becomes eight, and so on. Continue until you have enough cells to make a burger, a nugget, a sausage, or a piece of bacon, then concentrate them until they bind into solid meat. 

It's all surprisingly straightforward in principle according to Marie Gibbons​, a research fellow with The Good Food Institute, who has been researching how to improve this process at Harvard Medical School. We might even see clean meat sold commercially within a year. 

The real technical challenge is developing large bioreactors and cheap solutions so that we can make huge volumes and drive down costs. 

This interview covers the science and technology involved at each stage of clean meat production, the challenges and opportunities that face cutting-edge researchers like Marie, and how you could become one of them. 

<a href="https://80000hours.org/podcast/episodes/marie-gibbons-clean-meat/?utm_campaign=podcast__marie-gibbons&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, key points, and links to learn more.</a> 

Marie’s research focuses on turkey cells. But as she explains, with clean meat the possibilities extend well beyond those of traditional meat. Chicken, cow, pig, but also panda - and even dinosaurs could be on the menus of the future. 

Today’s episode is hosted by Natalie Cargill, a barrister in London with a background in animal advocacy. Natalie and Marie also discuss: 

* Why Marie switched from being a vet to developing clean meat 
* For people who want to dedicate themselves to animal welfare, how does working in clean meat fare compared to other career options? How can people get jobs in the area? 
* How did this become an established field? 
* How important is the choice of animal species and cell type in this process? 
* What are the biggest problems with current production methods? 
* Is this kind of research best done in an academic setting, a commercial setting, or a balance between the two? 
* How easy will it be to get consumer acceptance? 
* How valuable would extra funding be for cellular agriculture? 
* Can we use genetic modification to speed up the process? 
* Is it reasonable to be sceptical of the possibility of clean meat becoming financially competitive with traditional meat any time in the near future? 

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#26 - Marie Gibbons on how exactly clean meat is made & what's needed to get it in every supermarket

Part 2 out now: #33 - Dr Anders Sandberg on what if we ended ageing, solar flares & the annual risk of nuclear war 

The universe is so vast, yet we don’t see any alien civilizations. If they exist, where are they? Oxford University’s Anders Sandberg has an original answer: they’re ‘sleeping’, and for a very compelling reason. 

Because of the thermodynamics of computation, the colder it gets, the more computations you can do. The universe is getting exponentially colder as it expands, and as the universe cools, one Joule of energy gets worth more and more. If they wait long enough this can become a 10,000,000,000,000,000,000,000,000,000,000x gain. So, if a civilization wanted to maximize its ability to perform computations – its best option might be to lie in wait for trillions of years. 

Why would a civilization want to maximise the number of computations they can do? Because conscious minds are probably generated by computation, so doing twice as many computations is like living twice as long, in subjective time. Waiting will allow them to generate vastly more science, art, pleasure, or almost anything else they are likely to care about. 

<a href="https://80000hours.org/podcast/episodes/anders-sandberg-fermi-paradox/?utm_campaign=podcast__anders-sandberg&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, related links, and key quotes.</a> 

But there’s no point waking up to find another civilization has taken over and used up the universe’s energy. So they’ll need some sort of monitoring to protect their resources from potential competitors like us. 

It’s plausible that this civilization would want to keep the universe’s matter concentrated, so that each part would be in reach of the other parts, even after the universe’s expansion. But that would mean changing the trajectory of galaxies during this dormant period. That we don’t see anything like that makes it more likely that these aliens have local outposts throughout the universe, and we wouldn’t notice them until we broke their rules. But breaking their rules might be our last action as a species. 

This ‘aestivation hypothesis’ is the invention of Dr Sandberg, a Senior Research Fellow at the Future of Humanity Institute at Oxford University, where he looks at low-probability, high-impact risks, predicting the capabilities of future technologies and very long-range futures for humanity. 

In this incredibly fun conversation we cover this and other possible explanations to the Fermi paradox, as well as questions like: 

* Should we want optimists or pessimists working on our most important problems? 
* How should we reason about low probability, high impact risks? 
* Would a galactic civilization want to stop the stars from burning? 
* What would be the best strategy for exploring and colonising the universe? 
* How can you stay coordinated when you’re spread across different galaxies? 
* What should humanity decide to do with its future? 

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#29 - Dr Anders Sandberg on 3 new resolutions for the Fermi paradox & how to colonise the universe

The debate around the impacts of artificial intelligence often centres on ‘superintelligence’ - a general intellect that is much smarter than the best humans, in practically every field. 

But according to Allan Dafoe - Assistant Professor of Political Science at Yale University - even if we stopped at today's AI technology and simply collected more data, built more sensors, and added more computing capacity, extreme systemic risks could emerge, including: 

* Mass labor displacement, unemployment, and inequality; 
* The rise of a more oligopolistic global market structure, potentially moving us away from our liberal economic world order; 
* Imagery intelligence and other mechanisms for revealing most of the ballistic missile-carrying submarines that countries rely on to be able to respond to nuclear attack; 
* Ubiquitous sensors and algorithms that can identify individuals through face recognition, leading to universal surveillance; 
* Autonomous weapons with an independent chain of command, making it easier for authoritarian regimes to violently suppress their citizens. 

Allan is Co-Director of the Governance of AI Program, at the Future of Humanity Institute within Oxford University. His goals have been to understand the causes of world peace and stability, which in the past has meant studying why war has declined, the role of reputation and honor as drivers of war, and the motivations behind provocation in crisis escalation. 

<a href="https://80000hours.org/podcast/episodes/allan-dafoe-politics-of-ai/?utm_campaign=podcast__allan-dafoe&utm_source=80000+Hours+Podcast&utm_medium=podcast">Full transcript, links to learn more, and summary of key points.</a> 

His current focus is helping humanity safely navigate the invention of advanced artificial intelligence. 

I ask Allan: 

* What are the distinctive characteristics of artificial intelligence from a political or international governance point of view? 
* Is Allan’s work just a continuation of previous research on transformative technologies, like nuclear weapons? 
* How can AI be well-governed? 
* How should we think about the idea of arms races between companies or countries? 
* What would you say to people skeptical about the importance of this topic? 
* How urgently do we need to figure out solutions to these problems? When can we expect artificial intelligence to be dramatically better than today? 
* What’s the most urgent questions to deal with in this field? 
* What can people do if they want to get into the field? 
* Is there anything unusual that people can look for in themselves to tell if they're a good fit to do this kind of research? 

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#31 - Prof Dafoe on defusing the political & economic risks posed by existing AI capabilities

Today's episode is a cross-post of an interview I did with The Jolly Swagmen Podcast which came out this week. I recommend regular listeners skip to 24 minutes in to avoid hearing things they already know. Later in the episode I talk about my contrarian views, utilitarianism, how 80,000 Hours has changed and will change in the future, where I think EA is performing worst, how to use social media most effectively, and whether or not effective altruism is any sacrifice. 

Subscribe and get the episode by searching for '80,000 Hours' in your podcasting app. 

<a href="https://thejollyswagmen.com/new-blog/robwiblin">Blog post of the episode to share, including a list of topics and links to learn more.</a> 

"Most people want to help others with their career, but what’s the best way to do that? Become a doctor? A politician? Work at a non-profit? How can any of us figure out the best way to use our skills to improve the world? 

Rob Wiblin is the Director of Research at 80,000 Hours, an organisation founded in Oxford in 2011, which aims to answer just this question and help talented people find their highest-impact career path. He hosts a popular podcast on ‘the world’s most pressing problems and how you can use your career to solve them’. 

After seven years of research, the 80,000 Hours team recommends against becoming a teacher, or a doctor, or working at most non-profits. And they claim their research shows some common careers do 10 or 100x as much good as others. 

80,000 Hours was one of the organisations that kicked off the effective altruism movement, was a Y Combinator-backed non-profit, and has already shifted over 80 million career hours through its advice. 

Joe caught up with Rob in Berkeley, California, to discuss how 80,000 Hours assesses which of the world’s problems are most pressing, how you can build career capital and succeed in any role, and why you could easily save more lives than a doctor - if you think carefully about your impact." 

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: search for '80,000 Hours' in your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

Rob Wiblin on the art/science of a high impact career

Experts believe that artificial intelligence will be better than humans at driving trucks by 2027, working in retail by 2031, writing bestselling books by 2049, and working as surgeons by 2053. But how seriously should we take these predictions? 

Katja Grace, lead author of <a href="https://arxiv.org/pdf/1705.08807.pdf?_sp=c803ec8d-9f8f-4843-a81e-3284733403a0.1500631875031" rel="nofollow" target="_blank">‘When Will AI Exceed Human Performance?’</a>, thinks we should treat such guesses as only weak evidence. But she also says there might be much better ways to forecast transformative technology, and that anticipating such advances could be one of our most important projects. 

Note: Katja's organisation AI Impacts <a href="https://aiimpacts.org/jobs/">is currently hiring part- and full-time researchers.</a> 

There’s often pessimism around making accurate predictions in general, and some areas of artificial intelligence might be particularly difficult to forecast. 

But there are also many things we’re able to predict confidently today -- like the climate of Oxford in five years -- that we no longer give ourselves much credit for. 

Some aspects of transformative technologies could fall into this category. And these easier predictions could give us some structure on which to base the more complicated ones. 

<a href="https://80000hours.org/podcast/episodes/katja-grace-forecasting-technology/?utm_campaign=podcast__katja-grace&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

One controversial debate surrounds the idea of an intelligence explosion; how likely is it that there will be a sudden jump in AI capability? 

And one way to tackle this is to investigate a more concrete question: what’s the base rate of any technology having a big discontinuity? 

A significant historical example was the development of nuclear weapons. Over thousands of years, the efficacy of explosives didn’t increase by much. Then within a few years, it got thousands of times better. Discovering what leads to such anomalies may allow us to better predict the possibility of a similar jump in AI capabilities. 

In today’s interview we also discuss: 

* Why is AI impacts one of the most important projects in the world? 
* How do you structure important surveys? Why do you get such different answers when asking what seem to be very similar questions? 
* How does writing an academic paper differ from posting a summary online? 
* When will unguided machines be able to produce better and cheaper work than humans for every possible task? 
* What’s one of the most likely jobs to be automated soon? 
* Are people always just predicting the same timelines for new technologies? 
* How do AGI researchers different from other AI researchers in their predictions? 
* What are attitudes to safety research like within ML? Are there regional differences? 
* How much should we believe experts generally? 
* How does the human brain compare to our best supercomputers? How many human brains are worth all the hardware in the world? 
* How quickly has the processing capacity for machine learning problems been increasing? 
* What can we learn from the development of previous technologies in figuring out how fast transformative AI will arrive? 
* What should we expect from a post AI dominated economy? 
* How much influence can people ever have on things that will happen in 20 years? Are there any examples of people really trying to do this? 

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. 

The 80,000 Hours podcast is produced by Keiran Harris.

#40 - Katja Grace on forecasting future technology & how much we should trust expert predictions

Consider two familiar moments at a family reunion. 

Our host, Uncle Bill, takes pride in his barbecuing skills. But his niece Becky says that she now refuses to eat meat. A groan goes round the table; the family mostly think of this as an annoying picky preference. But if seriously considered as a moral position, as they might if instead Becky were avoiding meat on religious grounds, it would usually receive a very different reaction. 

An hour later Bill expresses a strong objection to abortion. Again, a groan goes round the table; the family mostly think that he has no business in trying to foist his regressive preference on anyone. But if considered not as a matter of personal taste, but rather as a moral position - that Bill genuinely believes he’s opposing mass-murder - his comment might start a serious conversation. 

Amanda Askell, who recently completed a PhD in philosophy at NYU focused on the ethics of infinity, thinks that we often betray a complete lack of moral empathy. All sides of the political spectrum struggle to get inside the mind of people we disagree with and see issues from their point of view. 

<a href="https://80k.link/amanda">Links to learn more, summary and full transcript.</a> 

This often happens because of confusion between preferences and moral positions. 

Assuming good faith on the part of the person you disagree with, and actually engaging with the beliefs they claim to hold, is perhaps the best remedy for our inability to make progress on controversial issues. 

One potential path for progress surrounds contraception; a lot of people who are anti-abortion are also anti-contraception. But they’ll usually think that abortion is much worse than contraception, so why can’t we compromise and agree to have much more contraception available? 

<a href="http://www.rationalreflection.net/vegetarianism-abortion-and-moral-empathy/">According to Amanda</a>, a charitable explanation for this is that people who are anti-abortion and anti-contraception engage in moral reasoning and advocacy based on what, in their minds, is the best of all possible worlds: one where people neither use contraception nor get abortions. 

So instead of arguing about abortion and contraception, we could discuss the underlying principle that one should advocate for the best possible world, rather than the best probable world. 

Successfully break down such ethical beliefs, absent political toxicity, and it might be possible to actually converge on a key point of agreement. 

Today’s episode blends such everyday topics with in-depth philosophy, including: 

* What is 'moral cluelessness' and how can we work around it? 
* Amanda's biggest criticisms of social justice activists, and of critics of social justice activists 
* Is there an ethical difference between prison and corporal punishment? 
* How to resolve 'infinitarian paralysis' - the inability to make decisions when infinities are involved. 
* What’s effective altruism doing wrong? 
* How should we think about jargon? Are a lot of people who don’t communicate clearly just scamming us? 
* How can people be more successful within the cocoon of school and university? 
* How did Amanda find doing a philosophy PhD, and how will she decide what to do now? 

Links: 
<a href="https://80k.link/congressional-staffer-amanda">* Career review: Congressional staffer</a> 
<a href="https://80k.link/quitting">* Randomised experiment on quitting</a> 
<a href="https://80k.link/psychology-replication-quiz-amanda">* Psychology replication quiz</a> 
<a href="https://80k.link/comparative-advantage-amanda">* Should you focus on your comparative advantage.</a> 

Get this episode by subscribing: type 80,000 Hours into your podcasting app. 

The 80,000 Hours podcast is produced by Keiran Harris.

#42 - Dr Amanda Askell on moral empathy, the value of information & the ethics of infinity

Paul Christiano is one of the smartest people I know. After our first session produced such great material, we decided to do a second recording, resulting in our longest interview so far. While challenging at times I can strongly recommend listening - Paul works on AI himself and has a very unusually thought through view of how it will change the world. This is now the top resource I'm going to refer people to if they're interested in positively shaping the development of AI, and want to understand the problem better. Even though I'm familiar with Paul's writing I felt I was learning a great deal and am now in a better position to make a difference to the world. 

A few of the topics we cover are: 

* Why Paul expects AI to transform the world gradually rather than explosively and what that would look like 
* Several concrete methods OpenAI is trying to develop to ensure AI systems do what we want even if they become more competent than us 
* Why AI systems will probably be granted legal and property rights 
* How an advanced AI that doesn't share human goals could still have moral value 
* Why machine learning might take over science research from humans before it can do most other tasks 
* Which decade we should expect human labour to become obsolete, and how this should affect your savings plan. 

<a href="https://80000hours.org/podcast/episodes/paul-christiano-ai-alignment-solutions/?utm_campaign=podcast__paul-christiano&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

Important new article: <a href="https://80k.link/high-impact-careers-paul">These are the world’s highest impact career paths according to our research</a> 

Here's a situation we all regularly confront: you want to answer a difficult question, but aren't quite smart or informed enough to figure it out for yourself. The good news is you have access to experts who *are* smart enough to figure it out. The bad news is that they disagree. 

If given plenty of time - and enough arguments, counterarguments and counter-counter-arguments between all the experts - should you eventually be able to figure out which is correct? What if one expert were deliberately trying to mislead you? And should the expert with the correct view just tell the whole truth, or will competition force them to throw in persuasive lies in order to have a chance of winning you over? 

In other words: does 'debate', in principle, lead to truth? 

According to Paul Christiano - researcher at the machine learning research lab OpenAI and legendary thinker in the effective altruism and rationality communities - this question is of more than mere philosophical interest. That's because 'debate' is a promising method of keeping artificial intelligence aligned with human goals, even if it becomes much more intelligent and sophisticated than we are. 

It's a method OpenAI is <a href="https://blog.openai.com/debate/">actively trying to develop</a>, because in the long-term it wants to train AI systems to make decisions that are too complex for any human to grasp, but without the risks that arise from a complete loss of human oversight. 

If AI-1 is free to choose any line of argument in order to attack the ideas of AI-2, and AI-2 always seems to successfully defend them, it suggests that every possible line of argument would have been unsuccessful. 

But does that mean that the ideas of AI-2 were actually right? It would be nice if the optimal strategy in debate were to be completely honest, provide good arguments, and respond to counterarguments in a valid way. But we don't know that's the case. 

Get this episode by subscribing: type '80,000 Hours' into your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#44 - Dr Paul Christiano on how we'll hand the future off to AI, & solving the alignment problem

After dropping out of a machine learning PhD at Stanford, Daniel Ziegler needed to decide what to do next. He’d always enjoyed building stuff and wanted to shape the development of AI, so he thought a research engineering position at an org dedicated to aligning AI with human interests could be his best option. 

He decided to apply to OpenAI, and spent about 6 weeks preparing for the interview before landing the job. His PhD, by contrast, might have taken 6 years. Daniel thinks this highly accelerated career path may be possible for many others. 

On today’s episode Daniel is joined by Catherine Olsson, who has also worked at OpenAI, and left her computational neuroscience PhD to become a research engineer at Google Brain. She and Daniel share this piece of advice for those curious about this career path: just dive in. If you're trying to get good at something, just start doing that thing, and figure out that way what's necessary to be able to do it well. 

Catherine has even <a href="https://80k.link/ml-engineering-career-transition-guide" rel="nofollow" target="_blank">created a simple step-by-step guide</a> for 80,000 Hours, to make it as easy as possible for others to copy her and Daniel's success. 

Please let us know how we've helped you: fill out <a href="https://80k.link/survey-c-and-z">our 2018 annual impact survey</a> so that 80,000 Hours can continue to operate and grow. 

<a href="https://80k.link/olsson-and-ziegler-ml-engineering-and-safety" rel="nofollow" target="_blank">Blog post with links to learn more, a summary & full transcript.</a> 

Daniel thinks the key for him was nailing the job interview. 

OpenAI needed him to be able to demonstrate the ability to do the kind of stuff he'd be working on day-to-day. So his approach was to take a list of 50 key deep reinforcement learning papers, read one or two a day, and pick a handful to actually reproduce. He spent a bunch of time coding in Python and TensorFlow, sometimes 12 hours a day, trying to debug and tune things until they were actually working. 

Daniel emphasizes that the most important thing was to practice *exactly* those things that he knew he needed to be able to do. His dedicated preparation also led to an offer from the Machine Intelligence Research Institute, and so he had the opportunity to decide between two organisations focused on the global problem that most concerns him. 

Daniel’s path might seem unusual, but both he and Catherine expect it can be replicated by others. If they're right, it could greatly increase our ability to get new people into important ML roles in which they can make a difference, as quickly as possible. 

Catherine says that her move from OpenAI to an ML research team at Google now allows her to bring a different set of skills to the table. Technical AI safety is a multifaceted area of research, and the many sub-questions in areas such as reward learning, robustness, and interpretability all need to be answered to maximize the probability that AI development goes well for humanity. 

Today’s episode combines the expertise of two pioneers and is a key resource for anyone wanting to follow in their footsteps. We cover: 

* What are OpenAI and Google Brain doing? 
* Why work on AI? 
* Do you learn more on the job, or while doing a PhD? 
* Controversial issues within ML 
* Is replicating papers a good way of determining suitability? 
* What % of software developers could make similar transitions? 
* How in-demand are research engineers? 
* The development of Dota 2 bots 
* Do research scientists have more influence on the vision of an org? 
* Has learning more made you more or less worried about the future? 

Get this episode by subscribing: type '80,000 Hours' into your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#47 - Catherine Olsson & Daniel Ziegler on the fast path into high-impact ML engineering roles

Please let us know if we've helped you: <a href="https://80k.link/survey-christian">Fill out our annual impact survey</a> 

Ever felt that you were so busy you spent all your time paralysed trying to figure out where to start, and couldn't get much done? Computer scientists have a term for this - thrashing - and it's a common reason our computers freeze up. The solution, for people as well as laptops, is to 'work dumber': pick something at random and finish it, without wasting time thinking about the bigger picture. 

Bestselling author Brian Christian studied computer science, and in the book Algorithms to Live By he's out to find the lessons it can offer for a better life. He investigates into when to quit your job, when to marry, the best way to sell your house, how long to spend on a difficult decision, and how much randomness to inject into your life. In each case computer science gives us a theoretically optimal solution, and in this episode we think hard about whether its models match our reality. 

<a href="https://80k.link/brian-podcast" rel="nofollow" target="_blank">Links to learn more, summary and full transcript.</a> 

One genre of problems Brian explores in his book are 'optimal stopping problems', the canonical example of which is ‘the secretary problem’. Imagine you're hiring a secretary, you receive *n* applicants, they show up in a random order, and you interview them one after another. You either have to hire that person on the spot and dismiss everybody else, or send them away and lose the option to hire them in future. 

It turns out most of life can be viewed this way - a series of unique opportunities you pass by that will never be available in exactly the same way again. 

So how do you attempt to hire the very best candidate in the pool? There's a risk that you stop before finding the best, and a risk that you set your standards too high and let the best candidate pass you by. 

Mathematicians of the mid-twentieth century produced an elegant optimal approach: spend exactly one over *e*, or approximately 37% of your search, just establishing a baseline without hiring anyone, no matter how promising they seem. Then immediately hire the next person who's better than anyone you've seen so far. 

It turns out that your odds of success in this scenario are also 37%. And the optimal strategy and the odds of success are identical regardless of the size of the pool. So as *n* goes to infinity you still want to follow this 37% rule, and you still have a 37% chance of success. Even if you interview a million people. 

But if you have the option to go back, say by apologising to the first applicant and begging them to come work with you, and you have a 50% chance of your apology being accepted, then the optimal explore percentage rises all the way to 61%. 

Today’s episode focuses on Brian’s book-length exploration of how insights from computer algorithms can and can't be applied to our everyday lives. We cover: 

* Computational kindness, and the best way to schedule meetings 
* How can we characterize a computational model of what people are actually doing, and is there a rigorous way to analyse just how good their instincts actually are? 
* What’s it like being a human confederate in the Turing test competition? 
* Is trying to detect fake social media accounts a losing battle? 
* The canonical explore/exploit problem in computer science: the multi-armed bandit 
* What’s the optimal way to buy or sell a house? 
* Why is information economics so important? 
* What kind of decisions should people randomize more in life? 
* How much time should we spend on prioritisation? 

Get this episode by subscribing: type '80,000 Hours' into your podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#48 - Brian Christian on better living through the wisdom of computer science

If an asteroid impact or nuclear winter blocked the sun for years, our inability to grow food would result in billions dying of starvation, right? According to Dr David Denkenberger, co-author of <a href="https://en.wikipedia.org/wiki/Feeding_Everyone_No_Matter_What">Feeding Everyone No Matter What</a>: no. If he's to be believed, nobody need starve at all. 

Even without the sun, David sees the Earth as a bountiful food source. Mushrooms farmed on decaying wood. Bacteria fed with natural gas. Fish and mussels supported by sudden upwelling of ocean nutrients - and more. 

Dr Denkenberger is an Assistant Professor at the University of Alaska Fairbanks, and he's out to spread the word that while a nuclear winter might be horrible, experts have been mistaken to assume that mass starvation is an inevitability. In fact, the only thing that would prevent us from feeding the world is insufficient preparation. 

∙ <a href="https://80000hours.org/podcast/episodes/david-denkenberger-allfed-and-feeding-everyone-no-matter-what/?utm_campaign=podcast__david-denkenberger&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript</a> 

Not content to just write a book pointing this out, David has gone on to found a growing non-profit - the <a href="http://allfed.info/">Alliance to Feed the Earth in Disasters (ALLFED)</a> - to prepare the world to feed everyone come what may. He expects that today 10% of people would find enough food to survive a massive disaster. In principle, if we did everything right, nobody need go hungry. But being more realistic about how much we're likely to invest, David thinks a plan to inform people ahead of time could save 30%, and a decent research and development scheme 80%. 

∙ 80,000 Hours' updated article on <a href="https://80k.link/best-charity-david">How to find the best charity to give to</a> 
∙ A potential donor <a href="https://forum.effectivealtruism.org/posts/SYeJnv9vYzq9oQMbQ/2017-donor-lottery-report">evaluates ALLFED</a> 

According to David's <a href="https://link.springer.com/article/10.1007/s13753-016-0097-2">published cost-benefit analyses</a>, work on this problem may be able to save lives, in expectation, for under $100 each, making it an incredible investment. 

These preparations could also help make humanity more resilient to global catastrophic risks, by forestalling an ‘everyone for themselves' mentality, which then causes trade and civilization to unravel. 

But some worry that David's cost-effectiveness estimates are exaggerations, so I challenge him on the practicality of his approach, and how much his non-profit's work would actually matter in a post-apocalyptic world. In our extensive conversation, we cover: 

* How could the sun end up getting blocked, or agriculture otherwise be decimated? 
* What are all the ways we could we eat nonetheless? What kind of life would this be? 
* Can these methods be scaled up fast? 
* What is his organisation, ALLFED, actually working on? 
* How does he estimate the cost-effectiveness of this work, and what are the biggest weaknesses of the approach? 
* How would more food affect the post-apocalyptic world? Won't people figure it out at that point anyway? 
* Why not just leave guidebooks with this information in every city? 
* Would these preparations make nuclear war more likely? 
* What kind of people is ALLFED trying to hire? 
* What would ALLFED do with more money? 
* How he ended up doing this work. And his other engineering proposals for improving the world, including ideas to prevent a supervolcano explosion. 

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. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#50 - Dr David Denkenberger on how to feed all 8b people through an asteroid/nuclear winter

This is a cross-post of an interview Rob did with Julia Galef on her podcast <a href="http://rationallyspeakingpodcast.org/" target="_blank">Rationally Speaking</a>. Rob and Julia discuss how the career advice 80,000 Hours gives has changed over the years, and the biggest misconceptions about our views. 

The topics will be familiar to the most fervent fans of this show&nbsp;— but we think that if you’ve listened to less than about half of the episodes we've released so far, you’ll find something new to enjoy here. Julia may be familiar to you as the guest on episode 7 of the show, way back in September 2017. 

The conversation also covers topics like: 

• How many people should try to get a job in finance and donate their income? 
• The case for working to reduce global catastrophic risks in targeted ways, and historical precedents for this kind of work 
• Why reducing risk is a better way to help the future than increasing economic growth 
• What percentage of the world should ideally follow 80,000 Hours advice? 

<a href="https://80k.link/julia-galef-rationally-speaking">Links to learn more, summary and full transcript.</a> 

If you’re interested in the cooling and expansion of the universe, which comes up on the show, you should definitely check out our <a href="https://80000hours.org/podcast/episodes/anders-sandberg-fermi-paradox/?utm_campaign=podcast__julia-galef-2&amp;utm_source=80000+Hours+Podcast&amp;utm_medium=podcast">29th episode with Dr Anders Sandberg.</a> 

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 any podcasting app. 

The 80,000 Hours Podcast is produced by Keiran Harris.

Julia Galef and Rob Wiblin on an updated view of the best ways to help humanity

Have you ever been infuriated by a doctor's unwillingness to give you an honest, probabilistic estimate about what to expect? Or a lawyer who won't tell you the chances you'll win your case? 

Their behaviour is so frustrating because accurately predicting the future is central to every action we take. If we can't assess the likelihood of different outcomes we're in a complete bind, whether the decision concerns war and peace, work and study, or Black Mirror and RuPaul's Drag Race. 

Which is why the research of Professor Philip Tetlock is relevant for all of us each and every day. 

He has spent 40 years as a meticulous social scientist, collecting millions of predictions from tens of thousands of people, in order to figure out how good humans really are at foreseeing the future, and what habits of thought allow us to do better. 

Along with other psychologists, he identified that many ordinary people are attracted to a 'folk probability' that draws just three distinctions — 'impossible', 'possible' and 'certain' — and which leads to major systemic mistakes. But with the right mindset and training we can become capable of accurately discriminating between differences as fine as 56% as against 57% likely. 

• <a href="https://80k.link/tetlock-2">Links to learn more, summary and full transcript</a> 
• <a href="https://80k.link/tetlock-calibration">The calibration training app</a> 
• <a href="http://80k.link/civ">Sign up for the Civ-5 counterfactual forecasting tournament</a> 
• <a href="https://80k.link/ai-impacts-summary">A review of the evidence on good forecasting practices</a> 
• <a href="https://eaglobal.org">Learn more about Effective Altruism Global</a> 

In the aftermath of Iraq and WMDs the US intelligence community hired him to prevent the same ever happening again, and his guide — Superforecasting: The Art and Science of Prediction — became a bestseller back in 2014. 

That was five years ago. In today's interview, Tetlock explains how his research agenda continues to advance, today using the game Civilization 5 to see how well we can predict what would have happened in elusive counterfactual worlds we never get to see, and discovering how simple algorithms can complement or substitute for human judgement. 

We discuss how his work can be applied to your personal life to answer high-stakes questions, like how likely you are to thrive in a given career path, or whether your business idea will be a billion-dollar unicorn — or fall apart catastrophically. (To help you get better at figuring those things out, our site now has a <a href="https://80k.link/tetlock-calibration">training app</a> developed by the Open Philanthropy Project and Clearer Thinking that teaches you to distinguish your '70 percents' from your '80 percents'.) 

We also bring some tough methodological questions raised by the author of a recent <a href="https://aiimpacts.org/evidence-on-good-forecasting-practices-from-the-good-judgment-project-an-accompanying-blog-post/">review of the forecasting literature</a>. And we find out what jobs people can take to make improving the reasonableness of decision-making in major institutions that shape the world their profession, as it has been for Tetlock over many decades. 

We view Tetlock's work as so core to living well that we've brought him back for a second and longer appearance on the show — his first was back in <a href="https://80k.link/tetlock1">episode 15</a>. 

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. 

The 80,000 Hours Podcast is produced by Keiran Harris.

#60 - Prof Tetlock on why accurate forecasting matters for everything, and how you can do it better

Had World War 1 never happened, you might never have existed. 

It’s very unlikely that the exact chain of events that led to your conception would have happened otherwise — so perhaps you wouldn't have been born. 

Would that mean that it's better for you that World War 1 happened (regardless of whether it was better for the world overall)? 

On the one hand, if you're living a pretty good life, you might think the answer is yes – you get to live rather than not. 

On the other hand, it sounds strange to say that it's better for you to be alive, because if you'd never existed there'd be no you to be worse off. But if you wouldn't be worse off if you hadn't existed, can you be better off because you do? 

In this episode, philosophy professor Hilary Greaves – Director of Oxford University’s Global Priorities Institute – helps untangle this puzzle for us and walks me and Rob through the space of possible answers. She argues that philosophers have been too quick to conclude what she calls existence non-comparativism – i.e, that it can't be better for someone to exist vs. not. 

<a href="https://80000hours.org/podcast/episodes/hilary-greaves-comparing-existence-and-non-existence/?utm_campaign=podcast__hilary-greaves-2&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

Where we come down on this issue matters. If people are not made better off by existing and having good lives, you might conclude that bringing more people into existence isn't better for them, and thus, perhaps, that it's not better at all. 

This would imply that bringing about a world in which more people live happy lives might not actually be a good thing (if the people wouldn't otherwise have existed) — which would affect how we try to make the world a better place. 

Those wanting to have children in order to give them the pleasure of a good life would in some sense be mistaken. And if humanity stopped bothering to have kids and just gradually died out we would have no particular reason to be concerned. 

Furthermore it might mean we should deprioritise issues that primarily affect future generations, like climate change or the risk of humanity accidentally wiping itself out. 

This is our second episode with Professor Greaves. The <a href="https://80000hours.org/podcast/episodes/hilary-greaves-global-priorities-institute/?utm_campaign=podcast__hilary-greaves&utm_source=80000+Hours+Podcast&utm_medium=podcast">first one</a> was a big hit, so we thought we'd come back and dive into even more complex ethical issues. 

We discuss: 

• The case for different types of ‘strong longtermism’ — the idea that we ought morally to try to make the very long run future go as well as possible 
• What it means for us to be 'clueless' about the consequences of our actions 
• Moral uncertainty -- what we should do when we don't know which moral theory is correct 
• Whether we should take a bet on a really small probability of a really great outcome 
• The field of global priorities research at the Global Priorities Institute and beyond 

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. 
Transcriptions: Zakee Ulhaq.

#86 – Hilary Greaves on Pascal's mugging, longtermism, and whether existing can be good for us

In the <a href="https://80000hours.org/podcast/episodes/ben-todd-on-the-core-of-effective-altruism/">last '80k team chat'</a> with Ben Todd and Arden Koehler, we discussed what effective altruism is and isn't, and how to argue for it. In this episode we turn now to what the effective altruism community most needs. 

<a href="https://80000hours.org/podcast/episodes/ben-todd-on-what-effective-altruism-most-needs/?utm_campaign=podcast__ben-todd3&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

According to Ben, we can think of the effective altruism movement as having gone through several stages, categorised by what kind of resource has been most able to unlock more progress on important issues (i.e. by what's the 'bottleneck'). Plausibly, these stages are common for other social movements as well. 

• Needing money: In the first stage, when effective altruism was just getting going, more money (to do things like pay staff and put on events) was the main bottleneck to making progress. 
• Needing talent: In the second stage, we especially needed more talented people being willing to work on whatever seemed most pressing. 
• Needing specific skills and capacity: In the third stage, which Ben thinks we're in now, the main bottlenecks are organizational capacity, infrastructure, and management to help train people up, as well as specialist skills that people can put to work now. 

What's next? Perhaps needing coordination -- the ability to make sure people keep working efficiently and effectively together as the community grows. 

The <a href="https://www.surveymonkey.co.uk/r/EAS80K2">2020 Effective Altruism Survey</a> just opened. If you're involved with the effective altruism community, or sympathetic to its ideas, it's a great thing to fill out. 

Ben and I also cover the career implications of those stages, as well as the ability to save money and the possibility that someone else would do your job in your absence. 

If you’d like to learn more about these topics, you should check out a couple of articles on our site: 

• <a href="https://80000hours.org/2018/11/clarifying-talent-gaps/">Think twice before talking about ‘talent gaps’ – clarifying nine misconceptions</a> 
• <a href="https://80000hours.org/2019/08/how-replaceable-are-top-candidates-in-large-hiring-rounds/">How replaceable are the top candidates in large hiring rounds? Why the answer flips depending on the distribution of applicant ability</a> 

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. 
Transcriptions: Zakee Ulhaq.

Benjamin Todd on what the effective altruism community most needs (80k team chat #4)

From one point of view academia forms one big 'epistemic' system — a process which directs attention, generates ideas, and judges which are good. Traditional print media is another such system, and we can think of society as a whole as a huge epistemic system, made up of these and many other subsystems. 

How these systems absorb, process, combine and organise information will have a big impact on what humanity as a whole ends up doing with itself — in fact, at a broad level it basically entirely determines the direction of the future. 

With that in mind, today’s guest Owen Cotton-Barratt has founded the <a href="https://www.fhi.ox.ac.uk/rsp/#applying">Research Scholars Programme</a> (RSP) at the Future of Humanity Institute at Oxford University, which gives early-stage researchers leeway to try to understand how the world works. 

<a href="https://80000hours.org/podcast/episodes/owen-cotton-barratt-epistemic-systems/?utm_campaign=podcast__owen-cotton-barratt&utm_source=80000+Hours+Podcast&utm_medium=podcast">Links to learn more, summary and full transcript.</a> 

Instead of you having to pay for a masters degree, the RSP pays *you* to spend significant amounts of time thinking about high-level questions, like "What is important to do?” and “How can I usefully contribute?" 

Participants get to practice their research skills, while also thinking about research as a process and how research communities can function as epistemic systems that plug into the rest of society as productively as possible. 

The programme attracts people with several years of experience who are looking to take their existing knowledge — whether that’s in physics, medicine, policy work, or something else — and apply it to what they determine to be the most important topics. 

It also attracts people without much experience, but who have a lot of ideas. If you went directly into a PhD programme, you might have to narrow your focus quickly. But the RSP gives you time to explore the possibilities, and to figure out the answer to the question “What’s the topic that really matters, and that I’d be happy to spend several years of my life on?” 

Owen thinks one of the most useful things about the two-year programme is being around other people — other RSP participants, as well as other researchers at the Future of Humanity Institute — who are trying to think seriously about where our civilisation is headed and how to have a positive impact on this trajectory. 

Instead of being isolated in a PhD, you’re surrounded by folks with similar goals who can push back on your ideas and point out where you’re making mistakes. Saving years not pursuing an unproductive path could mean that you will ultimately have a much bigger impact with your career. 

<a href="https://www.fhi.ox.ac.uk/rsp/#applying">RSP applications</a> are set to open in the Spring of 2021 — but Owen thinks it’s helpful for people to think about it in advance. 

In today’s episode, Arden and Owen mostly talk about Owen’s own research. They cover: 


• Extinction risk classification and reduction strategies 
• Preventing small disasters from becoming large disasters 
• How likely we are to go from being in a collapsed state to going extinct 
• What most people should do if longtermism is true 
• Advice for mathematically-minded people 
• And much more 

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: Zakee Ulhaq

#89 – Owen Cotton-Barratt on epistemic systems & layers of defense against global catastrophes

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